department of exploration geophysics · 2020-03-18 · while the mathematical details of...

Don’t Bury Western Australia’s Geophysical Data: Uncovering Prospective Mineral Terrains with Regional Potential Field, Seismic and MT Transects through Cooperative Inversion

(Project M493)

Prof Brett Harris, Dr Ralf Schaa, Dr Andrew Pethick

C u r t i n Un i v e r s i t y

17 October 2019

Depar tment of Exp lo ra t ion Geophys i c s

Don’t Bury Western Australia’s Geophysical Data (MRIWA Project M493)

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ABSTRACT

Many of Australia’s prospective mineral terrains extend below expanses of deep cover. Examples are the vast

areas of the Yilgarn Craton and Mt Isa Inlier that remain unexplored as they descend beneath sedimentary basins.

As depth of investigation and or thickness of barren cover increases so does the cost and complexity of exploring

each cubic kilometre of rock. Discovery of tier one mineral deposits in these setting requires improvement in deep

geophysical exploration technologies.

This research presents regional scale strategies for seismic and magnetotelluric (MT) imaging with a focus on

cooperative inversion using Seismic and Magnetotelluric methods. These methods are intended to assist mineral

discovery below cover.

Seismic and MT datasets are typically processed independently and later integrated during interpretation. We

have developed, implemented and tested cooperative inversion workflows that provide alternative pathways to

reconstructing the subsurface. We link seismic imaging, unsupervised learning and inversion of MT data in a

unified process. The workflow is tested on the 450 km long Yilgan‐Officer Basin‐Musgraves (YOM) 2D seismic and

MT transects. We extend our MT analysis from the 2D YOM transect to recover a vast 3D conductivity distribution

with the aid of the AusLAMP 50 km by 50 km MT grid. We demonstrate how the AusLAMP MT grid can be

integrated with the relatively dense sampled 2D YOM transect MT data to generate coarse 3D conductivity imaging

that spans thousands of cubic kilometres. Elements of the research are published in two peer reviewed

documents:

Le C.V.A., Harris, B.D., Pethick, A.M., 2019, New perspectives on Solid Earth Geology from Seismic

Texture to Cooperative Inversion, Scientific Reports, DOI: 10.1038/s41598‐019‐50109‐z.

(https://rdcu.be/bUeKc)

Schaa R., Harris B., Pethick A., 2019, Magnetotelluric Inversion Strategies; AEGC conference Perth

Eastern Australia. (https://2019.aegc.com.au/programdirectory/docs/309.pdf )

During the course of the project software was developed for cooperative inversion of seismic and magnetotulluric

data. This includes: (i) a toolbox to create prior models based on unsupervised clustering of seismic attribute data,

(ii) an interactive graphical user interface to manually create prior models based on post stack seismic sections

and (iii) an interactive data cleaning tool for editing and exporting EDI data for use with MT inversion codes.

Our Cooperative Inversion of magnetotelluric and seismic data was driven through a suite of specialized software.

This includes an earth model software toolkit, JGeoEarthTools, whose functionality spans transformations

between earth model formats and between different coordinate projections to unsupervised statistical learning.

Overall, this package facilitates the recovery of otherwise hidden geological objects within geophysical datasets.

Quality control and MT data processing ancillary tools were developed within the project to ensure suitable inputs

to new inversion workflows. Note that JGeoEarthTools is a valuable software package in its own right for

unsupervised clustering of seismic sections.

This research leverages off the hundreds of line kilometres of regional Geoscience Australia/GSWA seismic and

MT transects located in Western Australia and the Pawsey Centre Cray XC40 supercomputer. Supercomputing

facilities were essential for regional 3D MT inversions that included recently acquired (2019) AusLAMP MT grid

stations. The techniques developed unlocked value in existing high‐quality seismic and MT data. In particular

objects hidden within seismic reflectivity images can be detected with unsupervised learning and seismic guided

cooperative inversion can significantly improve subsurface representation of conductivity distribution.


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Table of Contents Abstract ................................................................................................................................................................... 1

Table of Contents .................................................................................................................................................... 2

Major Outcomes ...................................................................................................................................................... 3

Report Structure ...................................................................................................................................................... 4

PART A .................................................................................................................................................................... 5

1 Introduction .................................................................................................................................................... 6

1.1 What is Cooperative Inversion? ........................................................................................................................ 7

1.2 What are Seismic Attributes? ............................................................................................................................ 8

1.3 What is k‐means clustering? ............................................................................................................................. 8

PART B .................................................................................................................................................................. 10

2 Custom‐made Software ................................................................................................................................. 11

2.1 Software for model preparation using unsupervised learning from seismic attributes ................................. 11

2.2 Software for model preparation using structural constraints ......................................................................... 13

2.3 Software for magnetotelluric data preparation .............................................................................................. 14

2.4 Software for model and data visualisation ..................................................................................................... 15

PART C ................................................................................................................................................................... 16

3 Data Description ............................................................................................................................................ 17

3.1 Seismic ............................................................................................................................................................. 17

3.2 Magnetotellurics ............................................................................................................................................. 17

4 Data Preparation for Cooperative Inversion ................................................................................................... 18

4.1 Seismic ............................................................................................................................................................. 18

4.2 Magnetotellurics ............................................................................................................................................. 19

4.3 Potential Field Framework .............................................................................................................................. 19

4.4 Supercomputer Configuration ........................................................................................................................ 20

PART D .................................................................................................................................................................. 22

5 Develop Cooperative Inversion Schemes ........................................................................................................ 23

5.1 CI using prior models based on structural constraints (interpreted geological models) ................................ 23

5.2 Cooperative Inversion using prior models based on unsupervised clustering of seismic attributes .............. 24

6 Cooperative Inversion outcomes.................................................................................................................... 24

6.1 Based on structural constraints ...................................................................................................................... 24

6.2 Based on unsupervised clustering of seismic attributes ................................................................................. 25

References ............................................................................................................................................................ 28

Published Papers ................................................................................................................................................... 29

APPENDICES .......................................................................................................................................................... 30

Appendix A ............................................................................................................................................................ 31

ASEG Extended Abstract ............................................................................................................................................... 31

Appendix B ............................................................................................................................................................ 32

JEarthGeoTools User Manual ....................................................................................................................................... 32

Appendix C ............................................................................................................................................................ 58

Cooperative Inversion Workflows ................................................................................................................................. 58


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MAJOR OUTCOMES

This research developed workflows and code for facilitating cooperative inversion of seismic and magnetotelluric

data to improve subsurface imaging. Major research outcomes include:

New software (also see Figure 1 approximately 37000 lines of code).

i. Software modules facilitating seismic and magnetotelluric (MT) cooperative inversion

ii. Unsupervised learning toolkit for geophysical input (i.e. seismic reflectivity attributes) was built

iii. Software was developed for automating data and model preparation for cooperative inversion

Comparison and analysis of MT inversion strategies.

i. A multi‐tiered 3D MT inversion strategy was developed and tested (i.e. this method facilitates

inclusion of sparse 3D MT stations)

ii. Potential fields driven geologically constrained MT inversion outcomes for the a plus 300 km

long transect through central Australia. transect

iii. Inversion outcomes and results from many thousands of CPU hours on the Pawsey Cray

Cascade supercomputer.

iv. Large‐scale conductivity distribution spanning approximately 16,000 km2 of central Australia

Figure 1: Infographic overviewing of the code development for cooperative inversion


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REPORT STRUCTURE

This report provides outcomes for Project M493. Significant outcomes include peer reviewed Journal Papers, new

methods, new code and practical examples of implementation of new subsurface imaging along the plus 400km

long YOM (Yilgarn Craton, Officer Basin, Musgrave Block) seismic and MT transect. It is provided in the following

parts:

Part A:

Preface describing our Cooperative Inversion toolbox.

Part B:

Description of the software developed for our Cooperative Inversion toolbox

Part C:

Description of the data that were used to trial our Cooperative Inversion toolbox

Part D:

Description of the Cooperative Inversion trial results from the YOM transect

Appendices:

Publications listing including AEGC abstract, Peer reviewed Journal paper (Nature – Scientific Reports) and

a software user manual

Publications:

Note: Some key research outcome from M493 are presented within peer reviewed research by Cuong V.A.L., Harris

B., Pethick A., 2019, “New perspectives on the Solid Earth Geology from Seismic Texture to Cooperative Inversion”,

Scientific Reports. This document is open source and complements this report.


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PART A Preface describing our Cooperative Inversion scheme


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1 INTRODUCTION

We have set out to develop a toolbox to address the challenges of practical applied inversion — linking seismic

and magnetotelluric datasets together with the aim of recovering subsurface electrical resistivity. With co‐located

seismic and magnetotelluric (MT) data becoming increasingly available it makes sense to develop practical

algorithms and software solutions to automatically combine the information contained from both datasets to

improve subsurface imaging. The newly developed software toolbox is intended to provide non‐specialists a

framework for cooperative inversion. Examples of application of regional cooperative inversion are applied along

the GA/GSWA Yilgarn Craton, Officer Basin, Musgrave Block (YOM) seismic and MT transect as shown in figure 2

(see Le C.V.A., Harris, B.D., and Pethick, A.M., 2019).

Large differences between the MT method and the seismic reflection method exist. While seismic data provides

high‐resolution structural and stratigraphic subsurface information, MT data yields relatively low‐resolution

information about subsurface electrical resistivity distributions (Cuong et al., 2016). Broadband MT data is rapidly

attenuated and resolution decreases with depth. Sharp geo‐electrical transitions become increasingly difficult to

resolve and are typically imaged as highly smoothed transitional zones within the MT derived conductivity imaging.

Seismic reflection methods are not subject to the same resolution limitations. The structural information derived

from seismic datasets can potentially complement magnetotelluric inversion — mitigating the limitations of the

poor boundary resolution of the MT method.

Our toolbox provides two different frameworks for cooperative inversion: (1) a more traditional method, where

the interpreted seismic boundaries are incorporated into the starting MT inversion model, and (2) a new

innovative approach, which adapts information from geo‐statistical clustering of a combination of seismic texture

attributes and incorporate these in a prior model ready for MT inversion. The first method requires user‐

interaction, whereas the second method has potential for automation. Below we provide baseline descriptions of

the seismic attribute analysis, cooperative inversion and the geo‐statistical methods that have been adapted and

deployed. We then present details of our cooperative inversion toolbox.


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Figure 2: Location of the YOM Transect. This figure shows the location of the Yilgarn Craton, Officer

Basin, Musgrave Block (YOM) seismic and MT transect. It is modified from;Le C.V.A., Harris, B.D., and

Pethick, A.M., 2019. The full Journal paper is open sources and can be accessed at

https://rdcu.be/bUeKc.

1.1 What is Cooperative Inversion?

While the mathematical details of geophysical inversion are complicated, the concept of inversion is relatively

simple. It entails iteratively matching data acquired in the field to model data. When the model data match the

field data the model is considered a possible subsurface parameter distribution. While matching field and model

data is a necessary condition for recovering the true subsurface parameter distribution, it is not sufficient. This is

because there are many models that will generate data that will fit the field data equally well (i.e. the solution is

non‐unique).

A desirable inversion outcome results in a rock property model that explains all observed geophysical data with

reasonable geological and petrophyiscal constraints.


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There exist different approaches to incorporate constraints in the inversion process. One common approach is to

establish a mathematical relationship between different rock properties. The mathematical relationship linking

the various rock properties can be explicitly included in the inversion process (e.g. the objective function that is

subject to optimisation). This is commonly known as joint inversion.

Another approach is to link the rock properties implicitly via the design of a starting or prior model derived from

other data. We call this approach cooperative inversion. For example MT inversion may be guided by manual

interpretation of seismic reflection imaging or based on geostatistical analysis of seismic attribute data. A detailed

analysis of inversion strategies can be found in the publication of Cuong et al. (2016).

1.2 What are Seismic Attributes?

Seismic attributes are derived from processed seismic reflectivity imaging. Attributes can be understood as a form

data enhancement. They are routinely uses as an aid to interpretation and sometime to assist in recovery of rock

properties. There are many different seismic attributes and we focus here on a specific subset which are known

as textural attributes.

Textural seismic attributes are most commonly derived from the Grey Level Co‐Occurrence Matrix (GLCM). The

GLCM is an array representing a number of levels of “pixel brightness” or grey‐levels within an image. The GLCM

seismic texture attributes are used as an input within our toolbox. These inputs are computed using the well‐

known Open‐Source software package, OpendTect. Three groups of attributes are supported as detailed in the

OpendTect User Documentation:

1. Contrast measures: Contrast, Dissimilarity, Homogeneity

2. Orderliness measures: Angular Second Moment (ASM), Energy, Entropy

3. Statistical measures: GLCM Mean, GLCM Variance, GLCM Standard Deviation, GLCM Correlation

Images of several attributes are presented for the YOM seismic Transect in Cuong V.A.L., et. al. 2019. It presents

three examples of textural attributes (GLCM Standard Deviation, GLCM Correlation and Energy) together with the

original seismic image for a 70 km long and 5 km deep window. The attributes shown in the paper reveal seismic

textures linked to weakly consolidated sediments, flood basalts and thick salt walls. Textural differences are

effective in summarizing the large scale distribution of these major rock types. There are significant differences in

contributions from GCLM seismic attributes in representation of different rock units and hence we use a

combination of attributes for isolating common rock mass volumes as a seed for the prior conductivity framework

within the cooperative inversion. This is implemented using k‐means clustering, a machine‐learning algorithm.

1.3 What is k‐means clustering?

K‐means clustering is an unsupervised learning algorithm developed by MacQueen (1967) and Hartigan and Wong

(1979). Unsupervised learning is used to find patterns often hidden in a complex image. The number of clusters

‘k’ must be specified. In our implementation we auto‐generate cluster images for different numbers of clusters

and different input seismic attributes.

The k‐means clustering algorithm initializes the centroids through randomly distributing them throughout the

dataspace and then by assigning each data point to a cluster. An iterative procedure is then followed until the

error is sufficiently reduced or a maximum number of iterations is reached. The iterative steps include (i)

computing the distances for each data point to each cluster centroid; and assigning datapoints to their closest

centroid (ii) the centroids are then updated by calculating the central position of the points within that cluster.

The iterative process stops when the within‐cluster variation cannot be reduced. The within cluster variation is

defined as the summation of Euclidean distances between the data points and their assigned cluster centroids.


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We deploy k‐means clustering with inputs being GLCM seismic texture attributes (examples for the YOM transect

are provided in Cuong V.A.L. et. al. 2019). Cuong V.A.L. et. al. 2019 show cluster distribution along the YOM

transect using texture attributes: dissimilarity, mean, contrast and correlation. The clusters identify the Lennis Fm

and Patterson Fm, salt walls, the Table Hill Volcanics, Neo‐Proterozoic sediments below the Table Hill volcanics

and basement rock.


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PART B Description of the developed software for our Cooperative Inversion toolbox


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2 CUSTOM‐MADE SOFTWARE Our software toolbox provides the non‐specialist a practical framework for cooperative inversion of

magnetotelluric data using seismic constraints. We pay attention to the new scheme that provides a near

automatic framework for cooperative inversion using k‐means clustering. Utilities for interactive model

preparation based on manual interpretation of seismic images are also created, as well as interactive data editing

tools. When planning and developing software modules, the following points have been taken into consideration:

The team set out to automate and encapsulate long and complex workflows within user‐friendly software

that would encourage uptake of cooperative inversion.

The software packages were designed to reduce data and model preparation time and to reduce the

number of steps requiring user input for cooperatively inverting co‐located seismic and MT datasets.

The packages developed should attempt to cover all aspects of the workflow, including: (i) data preparation,

(ii) model preparation with structural constraints or using unsupervised learning with seismic attributes to

model and (iii) data visualization.

2.1 Software for model preparation using unsupervised learning from seismic attributes

JGeoEarthTools: Written in Java (~15,000 lines of code).

Detailed methods and examples of applying unsupervised learning based on seismic textures are provided in

Coung, Harris and Pethick 2019.

Our software for unsupervised learning is called JGeoEarthTools and was written during the course of this project.

The output of JGeoEarthTools is a prior model based on categorised subvolumes of seismic attributes ready for

MT inversion. JGeoEarthTools has been developed for a wide range of users. This might include (i) beginner‐users

accessing functionality through a graphical user interface (cf. Figure 4), (ii) power‐users who want to run advanced

workflows through the command line, (iii) software developers who want to use JGeoEarthTools as a software

library. Overall this software facilitates semi‐automatic cooperative inversion in one workflow.

JGeoEarthTools addresses three problems.

(1) The first problem is in processing and converting between different geophysical earth model formats: a

seismic dataset cannot be directly input into an MT inversion package and MT inversion results are not

compatible with seismic visualization packages (e.g. OpendTect). These incompatibilities between different

geophysical earth model file formats is a major hurdle in the efficient application of cooperative inversion

and requires interconnections between various formats. This interconnectivity between formats is achieved

using a common earth model data structure (cf. Figure 3).

(2) Secondly, JGeoEarthTools addresses the ability to conduct unsupervised learning on any implemented

geophysical earth model format — mainly seismic attributes. JGeoEarthTools reads SEGY files of seismic

attributes and carries out k‐means clustering to categorise seismic facies based on the provided seismic

attributes. The clustering result is the basis for a starting model for magnetotelluric inversion.

(3) Thirdly, starting resistivity values can be assigned to the previous identified clusters via a flood‐filling

algorithm that assigns resistivity values from a previous inversion result (for example from a half‐space

inversion). Note that manual allocation of resistivity values is also possible using the interactive software

described further below in section 2.2. In addition JGeoEarthTools include other functionalities such as

down‐sampling of model files and most‐frequency filtering utilities.


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Figure 3: The functions within JGeoEarthTools, including KMeans clustering, down‐sampling and file format conversion are performed on an array of different geophysical earth model file standards.

The full functionality of JGeoEarthTools is described in the User Manual (cf. Appendix B); however a complete

workflow to generate a prior model for cooperative inversion consists of six core commands (cf. Appendix C):

(1) K‐means clustering of seismic attributes files. (the cluster information is stored in a specific JGeo format)

(2) Convert ModEM (magnetotelluric) model file to JGeo format (e.g. from a previous unconstrained inversion run)

(3) Map resistivities from the converted ModEM file to the k‐means clusters

(4) Downsample the k‐means cluster file to the same mesh as the ModEM file

(5) Apply a most frequency filter to provide a smoother prior model

(6) Convert the smoothed JGeo file back to ModEM format as prior model for MT inversion.

(Note that the commands can be carried out in different order, for example down‐sampling can be done before clustering, which speeds up the process).

Figure 4 Screenshot of the JGeoEarthTools graphical user interface.


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2.2 Software for model preparation using structural constraints

MTModelBuilder: Written in VB.NET (~12,000 lines of code).

MTModelBuilder facilitates creation of 2D models where structural constraints are provided as a high‐resolution

bitmap image (Figure 5). The image represents the subsurface that consist of several geological units and usually

reflects the interpretation of a seismic section. Each geological unit is depicted by a distinct colour code and the

user interactively assigns a resistivity value to each geological unit. In this approach, each colour pixel serves as a

proxy for resistivity. After allocating the resistivity values, the image can be exported to various formats for

magnetotelluric inversion: Occam1D and Occam2D (Constable et al., 1987), Rebocc2D (Siripunvaraporn and

Egbert, 2000) and ModEM2D (Egbert and Kelbert, 2012; Kelbert et al., 2014).

Note that the bitmap image has to be created by the user beforehand and as mentioned above, is usually based

on the structural interpretation of a seismic image, but can of course be based on other prior information as well.

One possible procedure is to use specialised image editing software. Free image editing software are for example

GIMP (raster graphics editor) or Inkscape (vector graphics editor); commercial variants are Adobe Photoshop® or

Adobe Illustrator®. In this workflow an image of the interpreted seismic section is loaded in the image editing

software and a new image layer is overlain. Using the seismic interpretation image as a template, in the new layer

the interpreted geological units of the seismic section are manually drawn using the tools available in the image

editing software; subsequently the units are filled with distinct colours. This image is saved as a high‐resolution

bitmap and used in MTModelBuilder.

Note that it is of importance that the relative image dimensions are linked to the extents of the survey. The

distance from the first station on the left, to the image edge on the left has to be provided in meters; likewise the

distance of the last station on the right, to the right image edge has to be given in meters. Furthermore the image

top and base have to be given in meters. For zero topography, the top is simply set to 0; whereas the image base

is the depth extent in meters of the displayed section starting from the top. The procedure is further detailed in

the software help files, which also describe the various options that this utility offers.

Figure 5 Screenshot of the MTModelBuilder user interface showing a conceptual geological 2D model of the Officer Basin.


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2.3 Software for magnetotelluric data preparation

MTSanitizer: Written in VB.NET (~7,500 lines of code).

MTSanitizer is a simple tool written for post‐processing and visualisation of standard EDI files (Figure 6). This tool

facilitates interactive editing of data points such as outliers and noise. Before inversion, data has to be free of

outliers in order to avoid introducing spurious biases in the recovered resistivity model. MTSanitizer displays EDI

data as apparent resistivity and phase, and as real/imaginary components of the impedance tensor and tipper

vector. Using the different display formats, the data can be visually inspected for any noise or outliers and

accordingly edited.

Upon starting MTSanitizer, the user selects a directory that contains the EDI files subject to interactive editing. EDI

files are identified by their filename ending, expected to be “*.edi”; all located EDI files of the directory are loaded

into MTSanitizer and shown with their filename in the left panel of the program. Upon clicking on a filename, the

EDI file is displayed in the main window in aforementioned formats. A displayed data point can be deleted by

clicking on it – this deletes the data point and all related components at that period. Deleted data points are

currently linearly interpolated. Edited data can then be exported to various MT data formats; merging of different

datasets (such as broadband and long‐period) is also possible. The following data formats are currently supported:

EDI, Rebocc2D, Mare2DEM, ModEM2D and ModEM3D. The various options that this utility offers is further

detailed in the software help files.

Note that the EDI file standard is quite flexible and supports many different formats. MTSanitizer only supports

EDI files where the MT responses are stored as the real and imaginary part of the impedance tensor as well as the

real and imaginary part of the tipper vector, including their respective error estimates. This is the most common

EDI data format. Other formats, such as the spectral format, are currently not supported.

Figure 6 MTSanitiser User Interface showing editable plot panels of magnetotelluric data components.


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2.4 Software for model and data visualisation

MTview: Written in Python (~2,500 lines of code).

MTview is a collection of Python modules for displaying data and models for ModEM2D and Rebocc2D. MTview

generates publication quality images. The modules are script‐driven and require user action such as providing

paths, files and various plot parameters. Examples of figures created with MTview are shown in Figure 7.

Model files from Rebocc2D and ModEM2D can be visualised as 2D sections, where the user can edit parameters

that control various aspects of the displayed image, such as: the horizontal and vertical extent; colour map and

stretch, font sizes, image size and resolution. Furthermore, MT data files from ModEM2D and Rebocc2D can be

displayed as data profiles, showing apparent resistivity and phase curves at each station for observed and

calculated data. Data misfits (RMS) are also provided for each station and component in this layout. The data files

can furthermore be displayed as data maps, which show the data as sections along selected stations and periods.

Of course, every detail can eventually be fine‐tuned as the source code of the scripts are available.

The choice of Python for MTview stems from its strength and flexibility of available visualisation libraries and it is

widely used in but also outside of academia. The provided Python scripts require only standard Python libraries,

which can be easily installed using the standard installation tools that Python provides (depending on the Python

distribution). The provided scripts are heavily commented and provide thorough user documentation.

At this place we would also like to mention the open source package MTPy, also written in Python and maintained

by Geoscience Australia (Kirkby et al., 2019) and previously started by University of Adelaide (Krieger at al., 2014).

MTPy also provides much functionality for processing and displaying MT data (but currently lacks capability for

Rebocc2D and ModEM2D).

Figure 7 Examples of figures produced by MTView. Top‐left: Rebocc2d model inversion result. Bottom‐left: apparent resistivity pseudo‐section for all stations and broadband periods. Left: curves of apparent resistivity and phase at one station showing observed and calculated data.


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PART C Description of the data that were used to trial our Cooperative Inversion toolbox


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3 DATA DESCRIPTION Our cooperative inversion framework offers a new approach to add value to the existing geophysical data. One

research goal was to leverage off the thousands of line kilometres of regional Geoscience Australia/GSWA seismic

and MT transects located in Western Australia; hence the project title “Don’t Bury Western Australia’s Geophysical

Data”.

A challenging regional‐scale focus area spanning the Yilgarn Craton–Officer Basin–Musgrave Province (YOM) was

selected to test the newly developed cooperative inversion workflows. Key data included a long seismic reflection

transect, a 2D MT transect and newly acquired MT data from the AusLAMP grid. Furthermore, auxiliary potential

field data in the form of a geological GOCAD model was utilised to provide the structural framework of a 3D prior

model.

3.1 Seismic

TWT Seismic Data

Deep seismic reflection data was acquired along a 484 km transect across the Yilgarn Craton – Officer

Basin – Musgrave Province in 2011 by Geoscience Australia in collaboration with the Geological Survey of

Western Australia (Holzschuh, 2013). The survey line follows the Great Central Road and hence was not

straight, but followed a crooked line geometry. No data was collected through the Warburton aboriginal

community from stations 6555 to 6669, which results in a gap in the seismic section. Final products in

SEGY format as provided by Geoscience Australia are post‐stack time migrated data with a maximum

record length of 22 seconds two‐way travel time (TWT). Detailed information on acquisition and

processing parameters can be found in Holzschuh (2013).

Velocity Data

Stacking velocities are provided as an ancillary data file and was used by us for depth‐conversion of the

TWT seismic data. For all further processing and analysis the depth‐converted seismic data was used. That

is, the various textural seismic attributes were all based on the depth‐converted seismic amplitude data.

Also, structural interpretations were based on the depth‐converted seismic section.

3.2 Magnetotellurics

YOM Data

Magnetotelluric impedance and tipper data were measured by Geoscience Australia in collaboration with

the Geological Survey of Western Australia in 2011 at 72 broadband (BB) MT sites and 31 long‐period (LP)

MT sites along the seismic line. The average site spacing was approximately 5–10 km for broadband data

and 10–15 km for long period data (Duan et al., 2013). Impedance tensor data contain the components

of the ratios of horizontal electric fields to horizontal magnetic fields; tipper data contain the ratios of

vertical to horizontal magnetic field components. The maximum period range for the BB data is ~0.002

sec to ~100 sec; whereas the maximum period range for the LP data is 2 sec to ~13000 sec.

AusLAMP Data

AuScope AusLAMP long‐period MT data are currently acquired on a ~50 km grid nationwide (Thiel et al.,

2018). Sixteen AusLAMP stations proximal to the YOM line were included to extend the dimensional scope

from 2D inversion to 3D inversion.


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4 DATA PREPARATION FOR COOPERATIVE INVERSION Seismic, MT and geological datasets are not readily linked and must be processed for compatibility and further

processing. The various datasets are in different domains and are unified for cooperative inversion: seismic is in

time, while MT earth models and geological models are in depth. The following points need consideration:

Seismic data stored as ‘wiggle traces’ are not compatible with MT inversion software

The MT data is provided in an uncleaned form and may suffer from outliers or various noise

contaminations such as static shift, dead‐bands, 2D/3D effects etc.

Inversion of data is slow and in order for Cooperative Inversion to be efficiently applied,

supercomputing resources are essential.

4.1 Seismic

Depth Conversion

Two‐way travel time (TWT) reflection seismic data was converted to depth using stacking velocities that

comes together with the TWT seismic data. Conversion was carried out in SeisSpace. The depth‐converted

seismic section was used for cooperative inversion workflows.

Seismic Interpretation

Lithostratigraphic boundaries have been identified from the depth‐converted seismic section, based on

published geological seismic interpretations of the TWT seismic section. Identified boundaries are used

directly as structural inversion constraints in some cooperative inversion strategies.

Seismic Attributes

Seismic attributes are derived quantities from the seismic data that may exhibit features previously not

directly apparent in the seismic amplitude data. Subvolumes with common characteristics are identified

using k‐means clustering. We have calculated several seismic attributes from the seismic data for cooperative

inversion. See also the introductory sections 1.2 (seismic attributes) and 1.3 (k‐means clustering).

Figure 8 Seismic attribute section (‘GLCM Entropy’) resampled on magnetotelluric grid (image created with MTview)


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4.2 Magnetotellurics

MT post‐processing

Industry standard EDI data are made available by Geoscience Australia without any further edit. For inversion

the data are post‐processed using custom‐made software MTSanitiser (see section 2.3). The EDI files were

edited for outliers as well as dead‐band noise. For some 3D inversions broadband and long‐period

magnetotelluric data was merged, which also can be done in MTSanitiser. The cleaned MT data can be

exported as EDI files and also in suitable formats for various inversion programs.

MT data analysis

Analysis of the cleaned MT can provide information about the geoelectric subsurface structure. In a

geoelectric context, analysis of MT data provides information about subsurface dimensionality (1D, 2D or

3D) and possible strike direction (in case of 2D). This information informs the model setup for inversion and

whether the data has to be rotated for 2D inversion. We used MTPy for MT data analysis (Kirkby et al., 2019).

Figure 9 Phase tensor pseudo section with colours indicating the skew angle of the ellipses. Angles larger than |3◦| suggest 3D; white and nearly circular ellipses indicate 1D; everything else is interpreted 2D.

4.3 Potential Field Framework

Potential field information was implicitly incorporated in 3D cooperative magnetotelluric inversion using the

3D geological GOCAD model by Goodwin et al. (2013). This 3D GOCAD model was constructed by extending

the seismic interpretation into 3D space derived from multiple seismic and drill‐hole datasets, surface

geology as well as gravity and magnetic datasets using forward modelling. The GOCAD model consists of

surfaces and faults representing the 3D architecture of the study area and was integrated as structural

constraints in 3D cooperative inversion. The model was matched to the depth converted seismic YOM

transect and the Table Hill Volcanics has been added as an additional geological unit.

Figure 10 Adapted GOCAD model for geologically constrained inversion of the YOM data.


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4.4 Supercomputer Configuration

Regional‐scale magnetotelluric inversion requires access to supercomputing facilities. Inversions were

carried out on the facilities of the Pawsey Supercomputing Centre; supercomputing facilities used on Pawsey

were the Magnus Cray XC40 supercomputer and the Zeus SGI Linux Cluster. While Magnus is able to run

massively parallel jobs on thousands of CPU cores, it is limited in memory with 64GB per node. Zeus has

double the memory per node and was more suited for inversion in that regard, however computations take

longer as there are only a limited number of nodes available.

The ModEM (2D & 3D) source code was compiled on both, Magnus and Zeus. While compilation on Magnus

was straightforward using the Cray Fortran compiler and available online help files, compilation on Zeus

required the help from the Pawsey Centre. For Zeus, the Intel Compiler was used, which initially failed due

to errors with regards to the LAPACK library (a standard software library for numerical linear algebra). Linking

against Intel’s Math Kernel Library (MKL), which contains an optimised version of the LAPACK library, also

failed. However, using the LAPACK library compiled with the GNU Compiler Collection (GCC) eventually was

successful when statically linking the GNU compiled LAPACK library. The header of the Makefile for Zeus for

compiling ModEM2D is shown below. Also shown is a job script for the Slurm Workload manager, which

requires special reference to the LAPACK library.

# Makefile suited for building the Mod2DMT program on Pawsey/Zeus

include Makefile.local

# -------------------------------------------------------------------

# INTEL FLAGS (MPI-version) linked with GCC-build LAPACK library

# -------------------------------------------------------------------

# F90

# mpif90 :: Generic name for Fortran Intel compiler

# FFLAGS

# -O3 :: highest optimisation

# -traceback :: feedback where error occurred

# -heap-arrays :: allocate memory on the heap

# -static-intel :: link statically

# MPIFLAG

# -fpp :: Intel Fortran preprocessor

# -DMPI :: Preprocessor variable definition

#

# MODULE

# -module :: directory to store module files

# OBJDIR

# -path :: directory to store module files

# LIBS

# -L<library> :: library to link with (static)

# -------------------------------------------------------------------

F90 = mpif90

FFLAGS = -O3 -traceback -heap-arrays -static-intel

MPIFLAGS = -DMPI -fpp

MODULE = -module $(OBJDIR)

OBJDIR = ./_objs/2D_MT

LIBS = -L/pawsey/sles12sp3/devel/broadwell/gcc/4.8.5/lapack/3.8.0/lib64 –llapack

Table 1 Makefile header for compiling ModEM2D on Zeus


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#!/bin/bash

# SLURM directives:

# -------------------------------------------------------------------

#SBATCH --job-name=clusters_v2

#SBATCH --nodes=8

#SBATCH --ntasks=8

#SBATCH --mem-per-cpu=120000

#SBATCH --account=pawsey0102

#SBATCH --time=23:00:00

# Load Intel modules:

# -------------------------------------------------------------------

module purge

module load broadwell intel openmpi

# Export the location of the LAPACK library:

# -------------------------------------------------------------------

export

LD_LIBRARY_PATH=/pawsey/sles12sp3/devel/broadwell/gcc/4.8.5/lapack/3.8.0/lib64:$LD_LIBRARY_PATH

# Actual command to run Mod2DMT on the Zeus cluster:

# -------------------------------------------------------------------

srun --export=all -n 8 -N 8 mod2dmt -I NLCG prior.mod data_3pc.modem2d

Table 2 Slurm script for running a ModEM2D MPI job on Zeus


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PART D Description of the Cooperative Inversion trial results


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5 DEVELOP COOPERATIVE INVERSION SCHEMES For cooperative inversion (CI), constraints are typically introduced implicitly via the prior model and/or by

manipulating the spatial distribution of inversion parameters such as “smoothness” or discretization. High

resolution seismic reflection imaging is ideally suited to constrain large scale geometries for inversion of

lower resolution potential field or electromagnetic data. This sets CI apart from joint inversion, where

constraints are usually introduced explicitly via the objective function (Harris et al., 2018), see also section

1.1 of the Introduction.

We trialled two cooperative inversion approaches: a manual‐driven scheme, where prior information is

carefully incorporated into a starting model; and a semi‐automatic scheme, where prior information is based

on an unsupervised learning algorithm of seismic attributes combined with initial half‐space inversion of MT

data. Both approaches are briefly outlined below.

5.1 CI using prior models based on structural constraints (interpreted geological models)

1D/2D Inversion

Incorporation of prior constraints are based on an interpreted seismic 2D section. The seismic section

provides structural constraints, such as horizons and faults, and it is assumed that these represent changes

in lithology in conjunction with changes in electrical resistivity. The interpreted seismic section is saved as a

high‐resolution bitmap that consist of several geological units. The user then interactively assigns resistivity

values to each geological unit and saves the image as a starting model for inversion for various programs.

This can be done with aforementioned software tool MTModelBuilder. We used the following free inversion

programs for 1D/2D inversion: Occam1D, ModEM2D and Rebocc2D.

3D Inversion

Incorporation of prior constraints are here based on a geological model. In particular we used the GOCAD

model as published by Goodwin et al. (2013). The existing surfaces were augmented with major structures

interpreted from the depth‐converted YOM seismic imaging and information from two nearby wells

(Empress‐1A and Yowalga‐3).

Surfaces include the expansive Table Hill Volcanics; note that these Cambrian Table Hill Volcanics (THV) are

made up of multiple basaltic flows, accumulating to a thicknesses up to 165 m (Korsch et al., 2013, pp. 37).

The basalt unit is intersected at Empress‐1A and has an average well log resistivity of approximately 220 ohm

m.. The Table Hill Volcanics is a distinct unit forming a high‐reflectivity layer within the 2D seismic imaging

(note that the THV have also been incorporated in the starting model for 2D MT inversion). The remainder

of the 3D GOCAD resistivity model was populated using averaged values from unconstrained inversions.

Resistivity were assigned to the various lithological units and 3D inversion was carried out using ModEM3D

(Kelbert et al., 2014)

Incorporation of AusLAMP data

Full, unconstrained 3D inversion was carried with broadband YOM Transect MT data and AusLAMP sparse

3D grid long period MT datda. AusLAMP is a national long‐period magnetotelluric survey, acquired on a 50km

grid Australia‐wide (Thiel et al.,2018). We used 18 AusLAMP stations, North and South of the YOM MT

transect. The additional data from just a few sparse stations significantly enhanced the outcome of 3D MT

inversion. A question that was addressed was; ‘how many additional off‐line AusLAMP stations are required


Page 24 of 60

to further improve characterisation of off‐line conductors?’. The workflow consisted of a multi‐tiered 3D MT

inversion approach which combined the broadband MT data from the 450 km long YOM transect with the

sparse 3D MT stations from the AusLAMP survey. In the initial phase the following three datasets were

created:

Merged dataset from Broadband and Low‐Period impedances from both YOM and from available AusLAMP stations (horizontal component data only).

Merged dataset as before but with vertical component data (Tipper) included.

Only YOM broadband impedances

The three datasets were then used for sequential inversions. Initial starting model for inversion was a half‐

space model. Starting models for subsequent inversions for each dataset was based on the inversion result

of the previous run. See Schaa R., Harris B., Pethick A., 2019, Magnetotelluric Inversion Strategies; AEGC

conference Perth.

5.2 Cooperative Inversion using prior models based on unsupervised clustering of seismic attributes

2D Inversion

The magnetotelluric and the seismic YOM datasets are both 2D and as such only 2D inversion was carried

out. To incorporate multiple seismic attributes in 2D magnetotelluric inversion, we employed a machine‐

learning approach, specifically we used the well‐known k‐means clustering algorithm. This algorithm is a type

of unsupervised learning, which finds clusters in unlabelled data – in this case seismic texture attributes. The

number of clusters is denoted by the variable ‘k’ and has to be provided beforehand by the user. The

algorithm then works iteratively to assign each data point to one of k clusters based on the features that are

provided in the seismic attribute data. Data points are clustered based on feature similarity. The clustering

algorithm automatically provides structural constraints for inversion based on similarities in seismic textures.

With the understanding that identified clusters represent different lithologies, representative electrical

resistivity values are assigned to each cluster. This can be handled in two ways:

a. Manual allocation of resistivity values (using the program MTModelBuilder, see above)

b. Automatically by mapping resistivities from ModEM models (e.g. from unconstrained inversions).

This is managed via program JGeoEarthTools, which handles the entire inversion workflow for

incorporating seismic attributes in cooperative inversion. See the JGeoEarthTools User Manual.

6 COOPERATIVE INVERSION OUTCOMES

6.1 Based on structural constraints The constrained inversions used starting models based on structural information from seismic imaging. This

framework was populated with prior resistivity from previous half‐space inversions except for the Table Hill

Volcanics which was populated with resistivity directly from wire‐line line logs (obtained in the well Empress‐1A).

Inversion results broadly honoured these soft constraints maintaining the relative sharp resistivity contrasts as

imposed in the starting model. In general, inversion results for 1D, 2D and 3D all exhibit improved RMS data fits

and preferable subsurface resistivity distributions consistent with the seismic information. An example of 3D

inversion of the 2D YOM profile data is shown in Figure 11.


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For the multitiered 3D unconstrained inversion we included data from the AusLAMP grid in the vicinity of the

YOM transect. This inversion furthermore included long‐period and tipper data and resulted in superior data

fits as compared with 2D/3D inversion of the broadband YOM transect alone. Given that the data from the

50 km AusLAMP grid will be available in the near future, existing or new 2D MT profiles can be improved by

inversion that incorporates a background model based on long‐period AusLAMP data. The results are

described in more detail in Schaa R., Harris B., Pethick A., 2019, Magnetotelluric Inversion Strategies; AEGC

conference Perth.

Figure 11 Prior 3D model (A) and (B) and section through model after inversion (C) of magnetotelluric data. The 2D profile data of the YOM line was subject to 3D inversion using the ModEM3D software. The prior model was based on an adapted 3D geological GOCAD model.

6.2 Based on unsupervised clustering of seismic attributes

The subsurface resistivity distribution after cooperative inversion is by design biased towards the prior resistivity

structure, which depends on the initial clustering results and the assigned cluster resistivity values – these can

come from previous unconstrained inversions, which are then automatically mapped onto the clusters, or these

can be assigned manually to each cluster using MTModelBuilder (cf. section 2.2).

The amount of bias depends on the specifics of the inversion algorithm, where Occam‐based inversion schemes

tend to provide smoother results (cf. Siripunvaraporn & Sarakorn, 2011; Patro et al., 2013; Tietze et al., 2015). For

our framework, we have so far only used ModEM2D for cooperative inversion based on prior models using

unsupervised clustering of seismic attributes. ModEM2D (and ModEM3D) is based on a non‐linear conjugate

gradient algorithm (NLCG), which penalizes smoothed deviations from a prior model and prefers solutions close

to the prior model. Hence, the resulting inversion outcomes show resemblance to their prior models.

Figure 12 (top) shows a prior model based on k‐means clustering of eight clusters and five textural attributes

(Entropy, Energy, Dissimilarity, Correlation and Angular Momentum). The clusters were manually assigned

resistivity values using MTModelBuilder. The inversion result after 43 iterations is shown in the lower panel, and

the large resistivity contrast near the centre depicts the change from the Officer Basin in the West to the Musgrave

(A)

(B) (C)


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Province in the East. Resistivity structures in the Officer Basin are complex and linked to the structure in the prior

model. The inversion resulted in excellent RMS fits for most stations and provides an alternative resistivity model

as compared with the inversion results using prior models based on interpreted seismic sections.

Figure 12 Top: prior model using k‐means clustered seismic attributes allocated with initial resistivity values. Bottom: inversion result after 43 iteration.

CONCLUDING REMARKS

Our objective in this project was to create and trial cooperative inversion frameworks linking seismic and MT data,

to recovering rock properties at depths that exceed that which is practical for routine minerals exploratory drilling.

Development focused on regional scale cooperative inversion methods spanning many hundreds of kilometres

with particular attention to applications that will assist explorers searching for Tier 1 Ore bodies in mineralized

terrains that descend below barren cover in Western Australia.

We have demonstrated cooperative inversion techniques for building geo‐electrical frameworks relevant to

shallower exploration depths. We considered several cooperative inversion approaches. One scheme is relatively

manual requiring prior information to be carefully incorporated into a starting model, A second scheme is semi‐

automatic.For this method prior information is based on unsupervised learning (i.e. clustering) from seismic

attributes combined with half‐space inversion of MT data. We tested the cooperative inversion strategies along

the regional scale Geoscience Australia/GSWA seismic and MT transect along the YOM transect..


Page 27 of 60

During the course of the project the main software programs developed, were:

JGeoEarthTools: A tool to create prior models based on unsupervised clustering of seismic attribute data.

MTModelBuilder:

An interactive tool to manually create prior models based on interpreted seismic sections.

MTSanitiser:

An interactive data cleaning tool for editing and exporting EDI data for use with MT inversion codes.

The project outcomes demonstrate new options, with practical examples from imaging the subsurface with

geophysical methods. These methods have been designed to assist in extending mineral exploration and discovery

to greater depths in deep covered often underexplored minerals provinces.


Page 28 of 60

REFERENCES

Constable S.C., Parker R.L. and Constable C.G., 1987, “Occam's inversion: A practical algorithm for generating

smooth models from electromagnetic sounding data”, Geophysics, 52, 289–300

Cuong V.A.L., Harris B., Pethick A., Takougang T.E. Brendan H., “Semiautomatic and Automatic Cooperative

Inversion of Seismic and Magnetotelluric Data”, Surveys in Geophysics, 37, 2016

Egbert G.D. and Kelbert A., 2012, “Computational recipes for electromagnetic inverse problems”, Geophysical

Journal International, 189, 251–267.

Goodwin J., Jones T., Brennan T. and Nicoll M., 2013, “Geophysical investigation and 3D geological model of the

Yilgarn Craton–Officer Basin–Musgrave Province region”, in Neumann, N.L., (editor) and Geoscience Australia

(issuing body), Yilgarn Craton‐Officer Basin‐Musgrave Province Seismic and MT Workshop, Canberra,

Geoscience Australia, 96‐129

Harris B., Pethick A., Schaa R., and Cuong V.A.L.., 2018, “Cooperative Inversion: A Review”, ASEG Extended

Abstracts, 1‐3

Korsch R., Blewett R., Pawley M., Carr L., Hocking R., Neumann N.L., Smithies R.H., Quentin de Gromard R., Howard

H., Kennett B., Aitken A., Holzschuh J., Duan J., Goodwin J.A., Jones T., Gessner K., Gorczyk W., 2013,

“Geological setting and interpretation of the southwest half of deep seismic reflection line 11GA‐YO1: Yamarna

Terrane of the Yilgarn Craton and the western Officer Basin”, in Neumann, N.L., (editor) and Geoscience

Australia (issuing body), Yilgarn Craton‐Officer Basin‐Musgrave Province Seismic and MT Workshop, Canberra,

Geoscience Australia, 24‐50

Hartigan J.A. and Wong, M.A., 1979, “A K‐means clustering algorithm”, Applied Statistics, 28, 100–108.

Kelbert A., Meqbel N., Egbert G.D., Tandon K., “ModEM: A modular system for inversion of electromagnetic

geophysical data”, 2014, Computers & Geosciences, 66, 40‐53

Kirkby, A.L., Zhang, F., Peacock, J., Hassan, R., Duan, J., 2019. The MTPy software package for magnetotelluric data

analysis and visualisation. Journal of Open Source Software, 37, pp. 1358

Krieger L., and Peacock J., 2014. “MTpy: A Python toolbox for magnetotellurics. Computers and Geosciences”, 72,

167‐175

MacQueen J., 1967, “Some methods for classification and analysis of multivariate observations”, in Proceedings

of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman,

281‐297, University of California Press.

OpendTect 6.2, User Documentation, dGB Earth Sciences

Patro P.K., Uyeshima M., and Siripunvaraporn W., 2013, “Three‐dimensional inversion of magnetotelluric phase

tensor data”, Geophysical Journal International, 192, 58‐66

Schaa R., Harris B., Pethick A., 2019, “Magnetotelluric Inversion Strategies”, ASEG Extended Abstracts (in press)

Siripunvaraporn W., and Egbert G., 2000, An efficient data‐subspace inversion method for 2‐D magnetotelluric

data, Geophysics, 65, 791–803

Siripunvaraporn W. and Sarakorn W., 2011, “An efficient data space conjugate gradient Occam's method for three‐

dimensional magnetotelluric inversion”, Geophysical Journal International, 186, 567‐579

Thiel S., Goleby B.R.and Heinson G., 2018, “Magnetotelluric imaging of intracontinental deformation zones:

example of the Musgraves Province in Central Australia”, Abstract, Australian Geoscience Council Convention,

Adelaide, Australia

Tietze K., Ritter O., and Egbert G.D., 2015, “3‐D joint inversion of the magnetotelluric phase tensor and vertical

magnetic transfer functions”, Geophysical Journal International, 203, 1128‐1148


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PUBLISHED PAPERS

Cuong V.A.L., Harris B., Pethick A., 2019, “New perspectives on the Solid Earth Geology from Seismic Texture to Cooperative Inversion”, Scientific Reports 9 (1):14737. doi: 10.1038/s41598-019-50109-z. (Published: https://rdcu.be/bUeKc).

Schaa R., Harris B., Pethick A., 2019, Magnetotelluric Inversion Strategies; AEGC conference Perth Eastern Australia (paper published and has been presented: https://2019.aegc.com.au/programdirectory/docs/309.pdf )


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APPENDICES AEGC abstract, software user manual and flowcharts


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APPENDIX A

ASEG Extended Abstract The abstract “Magnetotelluric inversion strategies” can be obtained from the link below:

Schaa R., Harris B., Pethick A., 2019, Magnetotelluric Inversion Strategies; AEGC conference Perth

Eastern Australia. (https://2019.aegc.com.au/programdirectory/docs/309.pdf )

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APPENDIX B

JEarthGeoTools User Manual

Introduction

What is JGeoEarthTools?

JGeoEarthTools enables the conversion between earth model standards of each of the different geophysical rock properties. This is done through the use of a common earth model on a rectilinear grid or volume.

This means that earth models from, but not limited to, potential field, passive and active source electromagnetic and seismic methods can be transformed into other formats for use in inversion, interpretation

and forward modelling. This software's intended use is in cooperative inversion workflows that use information from multiple datasets to help constrain or initialize earth models.

Project Overview

Project: MRIWA Project M493

Applicants: Curtin University

Deep Exploration Technologies CRC

Researchers: Brett Harris

Andrew Pethick

Ralf Schaa

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Getting Started

JGeoTools is written in Java for operating system portability. Testing is performed on Windows 10 x86 64bit, however no non‐java third party libraries are used so running on OSX and Linux should be simple. If

you have any problems with this software, please contact us.

To run JGeoEarth Tools install Java JDK/JRE (we use SE 12) must be installed. Visit Oracle to download Java (link)

Open the command window

Windows: Open Command Prompt or write a batch script

OSX: Open Terminal

Linux: Open Terminal

Enter the command java ‐jar JGeoTools.jar

This should run the program and print the commands available.

If you require more primary memory than the default 8 GB than as long as you have a 64 bit Java installation you can use the following command (replace the number with the desired memory requirements):

16GB: java ‐jar ‐Xmx16G JGeoTools.jar

32GB: java ‐jar ‐Xmx32G JGeoTools.jar

The flood fill function (map conductivities) is a recursive function and may result in a stack overflow. If this occurs you will need to increase the stack using the ‐Xss tag:

java ‐jar ‐Xss16m ‐Xmx32G JGeoTools.jar

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Commands

Data Input

Rectilinear Section/Volume ‐i %filename This is the default format for this program

ModEM file format ‐im %filename This will read in a ModEM 2d or 3d mesh into the corresponding 2D/3D rectilinear grid. If read in 2D coordinates, local coordinates (easting only) will be set by initialized by the offset from origin in the ModEM file and a coordinate transformation file must be used.

WSINV 3D volume file format(3D) ‐iw %filename This is the WSINV3D earth model description file as outlined by (Siripunvaraporn W.) This is largely untested but may work.

VTK (Currently Not Implemented) ‐iv %filename This is currently not implemented, but is a placeholders for possible future versions.

Seismic ASCII (Currently untested) ‐is %filename Input Opendtect seismic ascii file. Currently implemented, but untested.

SEGY 3D (Currently Not Implemented) ‐iy %filename Current not implemented

SEGY 2D ‐iy2d %filename SGY standard 2d format. Only tested for a coordinate transformation scalar of 1000. There are many different variations of SGY with different byte header settings. Your experience may vary.

Model Vision Poly File Format (Currently Untested) ‐imv %filename Model Vision Poly File format (currently untested)

ASCII XYZV (Scatter points) formatted File(2D) ‐ix2d %filename Interpolation modifiers must be set when using this input

Force grid mesh size must be set when using this input

Example:

The command "java ‐jar ‐Xmx16G JGeoTools.jar ‐iy2d example.sgy" will run JGeoTools and import the SGY into a rectilinear mesh to be used for processing

Data Output

Rectilinear Section/Volume ‐o %filename This is the default format for this program

ModEM file format ‐om %filename

WSINV 3D volume file format(3D) ‐ow %filename

VTK (Currently Not Implemented) ‐ov %filename Currently Not Implemented

Seismic ASCII ‐os %filename

SEGY 3D ‐oy %filename Currently Not Implemented

SEGY 2D ‐ot2d %filename Currently Not Implemented

ASCII XYZV (Scatter points) formatted File(2D) ‐ox %filename An x,y,z,value ASCII file. The implementation is not terribly advanced and is essentially for nearest neighbor gridding. Currently this is being used for Res2D gridding.

The command "java ‐jar ‐Xmx16G JGeoTools.jar ‐iy2d example.sgy ‐o example.ASCII" will run JGeoTools and import the SGY into a rectilinear mesh and export to the Rectilinear 2D ASCII file format

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Commands

Value Replacement

Replace Value ‐repv %search_value %replace_value Replace one value with another

Replace Value (Terrain & Halfspace) ‐rept %search_value %replace_value Replace one value with another within the terrain and halfspace. That is, it will start from the top of the trace and while the search value is found, values will be replaced until a non‐search value is found. This process is repeated from the bottom up.

Cluster Mapping ‐mapvc %inputvaluefile %mappingmode (mode: 1=mean, 2=median) Map values into clusters (or connected patches of same value)

Eliminate Duplicate Offsets ‐ed %offset_tolerance Remove offsets that are within a certain amount of another offset. Good for sanitizing input for SEGY conversion.

Project values onto a Mare2DEM Mesh ‐pjm2d %inputResistivityFile %inputPolyFile %outputResistivityFile Project the values of the input models onto a Mare2D mesh and export a new resistivity file.

Statistical

KMeans Statistical Clustering ‐kmeans %nClusters %maxIterations %kmeanssavefile This will run a if %maxIterations is set to ‐1, run until misfit is 0. Random numbers will be used to initialize the seed model if a kmeanssavefile is not specified. Values from the kmeans save

Transformation

Transform from local‐to‐global coordinates using an affine transform ‐tl %transformation_file The coordinates transform properties uses a Java properties file. The affine transform handles translation,

rotation and scaling. As long as you have three local coordinates and the three corresponding global

coordinates defined, transformations between both local and global coordinates can be made, even if it is

outside of the bounds of the provided coordinates.

Format: Example.properties

LOCAL_X1 = 0

LOCAL_X2 = 100

LOCAL_X3 = 100

LOCAL_Y1 = 0

LOCAL_Y2 = 0

LOCAL_Y3 = 100

GLOBAL_X1 = 3000

GLOBAL_X2 = 3100

GLOBAL_X3 = 3100

GLOBAL_Y1 = 6000

Transform from global‐to‐local coordinates using an affine transform ‐tg %transformation_file

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GLOBAL_Y2 = 6000

GLOBAL_Y3 = 6100

Transform from local‐to‐global coordinates using a non‐linear transform ‐tnl %non_linear_transformation_file If coordinate systems are not linearly mapped, for example when converting a jagged poly‐line in real

coordinates to a ModEM2d local coordaintes, then this is the transformation system that should be used. This

is used for coordinate transforms of 2D transect lines only, this is not to be used for 3D datasets. The format

is basically an offset‐ordered list of local X, Local Y and corresponding Global X, Global Y points.

LX LY GX GY

LocalX1 LocalY1 GlobalX1 GlobalY1

LocalX2 LocalY2 GlobalX2 GlobalY2

...

LocalXN LocalYN GlobalXN GlobalYN

Example Format (includes the header line LX LY GX GY):

LX LY GX GY

‐19227.073 0 ‐165332.0487 6927032.407

1772.927 0 ‐146163.7861 6932578.214

21772.927 0 ‐127908.2979 6937859.934

40772.927 0 ‐110565.5841 6942877.568

58772.927 0 ‐94135.64467 6947631.116

75772.927 0 ‐78618.47968 6952120.579

91772.927 0 ‐64014.0891 6956345.955

106772.927 0 ‐50322.47294 6960307.245

120772.927 0 ‐37543.63118 6964004.45

133772.927 0 ‐25677.56383 6967437.568

145772.927 0 ‐14724.2709 6970606.6

156772.927 0 ‐4683.752376 6973511.546

166772.927 0 4443.991736 6976152.406

175772.927 0 12658.96144 6978529.18

183772.927 0 19961.15673 6980641.869

190772.927 0 26350.5776 6982490.471

196772.927 0 31827.22407 6984074.987

201772.927 0 36391.09613 6985395.417

Transform from global‐to‐local coordinates using a non‐linear transform ‐tng %non_linear_transformation_file

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Filters

Maximum Frequency Filter ‐maxfq %filterx %filtery %filterz Selects the most frequently occuring Integer within filter. This filter converts all values to integers prior to running. The filter size (in terms of number of cells) is defined by filterx, filtery and filterz. In 2D cases, filtery is not used. Currently a dummy value must be included for 2D grids.

Modifiers

Interpolation ‐xm %mode These values are used when importing XYZV 2D scattered point datasets.

Modes:

1(Nearest Neighbour)

2(Inverse Distance)

3(Linear)

4(Cubic)

Interpolation Search x ‐xsx %distance These values are used when importing XYZV 2D scattered point datasets.

These values are the search distance

Interpolation Search y ‐xsy %distance

Interpolation Search z ‐xsz %distance

Spatial Resolution

This tag is used when importing XYZV 2D scattered point datasets and Model Vision poly files. They specify the output mesh resolution of scattered points.

Spatial Resolution x ‐dx %spacing Force Spatial Resolution in x direction (dx Spacing)

Spatial Resolution y ‐dy %spacing Force Spatial Resolution in y direction (dy Spacing)

Spatial Resolution z ‐dz %spacing Force Spatial Resolution in the vertical direction (dz Spacing)

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Mapping (to be used for mapping SEGY to Rectilinear Grid)

Global SEGY to Local Rectilinear Grid Transformation ‐fmt %tf_file (Input non‐linear transformation file)

This force mesh function is a specific function related to SEGY files.

Since SEGY files can become extremely big, computation on them

becomes memory and CPU intensive. In cases of k‐means clustering,

we want to subsample them down to a smaller mesh size upon

import. This is a less memory intensive way to subsampling SEGY

down into a smaller mesh size.

If the ‐fmt tag is used, the segy in global coordinates will be converted

via the transformation to the local rectilinear grid mesh.

SEGY map to lower/higher resolution mesh ‐fm %rectilinear_file (Input rectilinear file)

Examples

Example 1: Convert ModEM to Standard Rectilinear Format

This step converts ModEM models to the standardized ASCII rectilinear format

java ‐jar ../JGeoTools.jar ‐im Data/Input_Model.rho ‐o Data/Output_Model.txt

Breakdown:

java Java must be added to the system paths,this needs to be jdk/jre 1.8 or higher

‐jar a tag to tell java to open a Jar file

.../JGeotools.jar the path to the compiled JGeoTools API

‐im a tag saying the input file is a ModEM rho file

Data/Input_Model.rho path to input ASCII ModEM 2D file in local coordinates

Data/Output/MT/Model_01_Local.txt path to output ASCII rectilinear file in ModEM local coordinates

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Example 2: Downsampling SGY to ModEM grid

Running command to convert the original ModEM file from local coordinates to standardized rectilinear grid

This step is composed of smaller steps:

1. Load the SGY Seismic attribute

2. Map the seismic ascii data from global coordinates to local coordinates using the mesh from ModEM

3. Export the data to standard ascii format

java ‐jar ‐Xmx6000m ../JGeoTools.jar ‐iy2d Data/Original/Seismic_Attributes/YOM_Seismic_depth_Texture_Correlation_64.sgy ‐fm Data/Output/MT/Model_01_Local.txt ‐fmt

Transformation/YOMGlobalSeismicToLocalMTTransform ‐o Data/Output/Seismic/Downsampled/YOM_Seismic_depth_Texture_Correlation_64_Local.txt

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Breakdown:



‐Xmx6000m a tag to use more than 4gb memory. In this case 6000 mb is used— higher numbers can be used if an out of memory exception is encountered.


‐iy2d The following input file is in a 2D SGY file format

Data/Original/Seismic_Attributes/YOM_Seismic_

depth_Texture_Correlation_64.sgy

path to the 2D segy file format

‐fm The seismic data will be projected into a new rectilinear grid upon import

Data/Output/MT/Model_01_Local.txt The input model to be used to down‐sample the seismic section.

‐fmt This command is specific to mapping seismic SGY to local ModEM coordinates.

Transformation/YOMGlobalSeismicToLocalMTTra

nsform

Path for non‐linear local to global transformation file

‐o Output the result into the standard rectilinear file format

Data/Output/Seismic/Downsampled/YOM_Seism

ic_depth_Texture_Correlation_64_Local.txt

Output downsampled file

Example 3: KMeans Clustering

K‐Means clustering is described in the section K‐Means Clustering.

The usage:

The method ‐kmeans takes in any number of attributes (i.e., the k‐means dimensions), the number of target clusters, the maximum number of iterations and the K‐means clustering output file. All input files

selected will be included for the K‐means clustering method. Note that the input files must have the same rectilinear mesh, otherwise step 2 must be performed to normalize the grids. Another consideration is

that some clusters may not have any associated points, that is some centroids may represent outliers, a small number of points or no data at all.

The output file:

java ‐jar ‐Xmx6000m ../JGeoTools.jar ‐i %ATT0% ‐i %ATT1% ‐kmeans 3 200 Data/Output/KMeans/kmeans.txt ‐o Data/Output/KMeans/Clustering_Result_Local.txt

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Breakdown:





‐i Input File

%ATT0% %ATT1% Paths to the input files (i.e., dimension)

‐kmeans Tells JGeoTools to take the inputs for a K‐means clustering.

3 The number of target clusters (This number is just for illustration, use higher numbers when appropriate

200 Maximum number of iterations (This may not be reached if cluster assignment does not change in an iteration)

Data/Output/KMeans/kmeans.txt Path to the clustering result (See below for description). If you wish to continue k‐means clustering, point this path to the previous output clustering result.


Data/Output/KMeans/Clustering_Result_Local.txt Output Clustering file. This file will contain the grid with each cell assigned the corresponding cluster result (id of the centroid).

Example Output

Version: 1

NClusters: 10

NDimensions: 3

Iteration Cluster_0_dim_0 Cluster_0_dim_1 Cluster_1_dim_0 Cluster_1_dim_1 Cluster_2_dim_0 Cluster_2_dim_1

1 7.3087816 1.0047317 4.0743976 2.077148 3.32717 6.588672

1 7.3087816 1.0047317 4.0743976 2.077148 3.32717 6.588672

2 6.4140596 1.525588 3.1859615 3.493386 2.782297 6.28927

3 5.511826 1.7032617 2.580508 4.10425 2.310531 6.095014

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The result is stored for each iteration, with the final line of the file as the final iteration. The cluster number Cluster_x, where x is cluster number from 0 to N‐1. Dimension or dim_y, where y is the input file. In this

case dim_0 is the value of %ATT0%, dim_1 is the value of %ATT1%.

That is, the final iteration in this example produced the following centroid values:

Iteration 3

Cluster 0 : %ATT0%=5.511826 %ATT1%=1.7032617

Cluster 1 : %ATT0%=2.580508 %ATT1%=4.10425

Cluster 2 : %ATT0%=2.310531 %ATT1%=6.09014

Example 4: Most Frequent Filtering

Running this command will run a Most Frequent Number filter on the data. This filter is a rectangular filter and is currently implemented for 2D only, but takes in 3D input. The second y dimension is currently

ignored.

java ‐jar ‐Xmx6000m ../JGeoTools.jar ‐i Data/Output/KMeans/Clustering_Result_Local.txt ‐maxfq 2 2 1 ‐o Data/Output/KMeans/Clustering_Result_Most_Frequent_2_2_1_Local.txt

Breakdown:



‐Xmx6000m a tag to use more than 4gb memory. In this case 6000 mb is used— higher numbers can be used if an out of memory exception

is encountered.


‐i Input File

Data/Output/KMeans/Clustering_Result_Local.txt Paths to the input file to be used for the most frequent filter

‐maxfq Tells JGeoTools to run a max frequency filter on the input

2 2 1 The radius of the rectangular filter for the x, y and z dimensions. A value of 2 will include the indices [i‐2, i‐1,i,i+1, i+2].


Data/Output/KMeans/Clustering_Result_Most_Frequent_2_2_1_Local.txt Output Maximum frequency filtered file

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Example 5: Flood Fill Values by cluster group

Running command maps conductivities from the initial ModEM model onto the K‐means clustering result

A recursive flood fill algorithm is applied and may run out of memory. Increase the stack size using the ‐Xss tag. Currently set to 16 mb.

java ‐jar ‐Xmx6000m ‐Xss16m ../JGeoTools.jar ‐i Data/Output/KMeans/Clustering_Result_Most_Frequent_2_2_1_Local.txt ‐mapvc Data/Output/MT/Model_01_Local.txt 1 ‐o

Data/Output/MT/Model_01_Cluster_Mapped_Local.txt

Breakdown:



‐Xmx6000m a tag to use more than 4gb memory. In this case 6000 mb is used— higher numbers can be used if an out of memory exception is

encountered.

‐Xss16m Stack size in m. This function is a recursive function and may result in a stack overflow error. If this is encountered, increase the

stack memory value


‐i Input File

Data/Output/KMeans/Clustering_Result_Most_Frequent_2_2_1_Local.txt Path to the cluster groups (or the skeleton to be flood filled)

‐mapvc The values input file (values from this input will be used to fill the skeleton within regions of connected equal cluster/integer value)

Data/Output/MT/Model_01_Local.txt Tells JGeoTools to run a max frequency filter on the input

1 Grouping Mode. A value of 1 tells the program to flood fill using the mean value within that zone. If you wish to use the median

value instead use the value 2. (mean = 1, median = 2)


Data/Output/MT/Model_01_Cluster_Mapped_Local.txt Output flood filled values (averaged values for each connected cluster id mapped to the input skeleton)

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Example 6: Output ASCII to ModEM model file

This method converts the final flood filled conductivity values (or any input file) into the ModEM model file format.

The following function is called:

java ‐jar ‐Xmx6000m ../JGeoTools.jar ‐i Data/Output/MT/Model_01_Cluster_Mapped_Local.txt ‐om Data/Output/MT/Model_01_KMeans_Conductivity.rho

Breakdown





‐i Input File

Data/Output/MT/Model_01_Cluster_Mapped_Lo

cal.txt

Path to the cluster groups (or the skeleton to be flood filled)

‐om Output to ModEM model file format

Data/Output/MT/Model_01_KMeans_Conductivi

ty.rho

Path to output model file

Acknowledgments

Research Partners

MRIWA

Pawsey

Petrel

Geoscience Australia

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File Formats

This section overviews several of the major file formats that can be imported/exported in JGeoEarthTools.

SeismicASCII

The 2D Seismic ASCII File is based upon the simple file format in Opendtect. There are limitations applied in this software. These are:

Only ASCII is supported. Binary is currently not yet supported.

Trace numbers must be included for the 2D file (3D does not have trace numbers)

Otherwise follow the Opendtect specification below:

Simple File

The user can import simple ASCII or Binary file by using plain file Seismic I/O Plugin.

This can be reached via Survey > Import > Seismic > Simple File > 3D or 2D (Pre/Post‐stack) etc.

The input file must first be selected and its data format type specified, between ascii and binary (4‐bytes floats).

All data must be in the 'local' format, because a blunt binary read/write is performed.

(Part of) the input file can be visualized by pressing the examine button.

The data must consists of one trace per record (line). The samples are thus in columns,

from shallowest to deepest, with a regular step. The trace position and time/depth index can be

read from the input file, left of the trace, or can be provided.

If provided, start, step and number of samples are requested in the corresponding directions,

assuming the input file if regular and does not contain holes. Post‐stack volume must be sorted by

inlines, crossline, (offset), Z (time or depth).

Optionally, the user can scale the cube before loading as well by mentioning the amount of shift and

the corresponding factor. Either pass or discard the null traces before loading.

The easiest way to see what the format looks like is by producing a little export file from a bit of

seismics.

Excerpt from the Opendtect 6.0 Manual

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An example of the file is shown below:

8 5 9

1 50 0 * 10 90.5 * * * * * *

2 100 100 * * 82.7 84.2 85.3 84.7 * * *

3 150 200 * * 88.2 88.4 89.8 89.2 * * *

4 200 300 * * 92.6 90.5 91.5 90.4 * * *

5 250 400 * * 94.1 90.9 90.7 89.1 * * *

6 300 500 * * 94.9 90.5 88.5 86.1 * * *

7 350 600 * * * 86.9 85.1 83.2 * * *

8 400 700 * * * 87.5 86.2 84.0 * * *

9 450 800 * * * 87.9 87.3 85.6 * * *

10 500 900 * * * 86.8 87.0 86.6 * * *

11 550 1000 * * * 85.5 86.7 86.9 * * *

12 600 1100 * * * 84.5 84.7 84.8 80.3 * *

13 650 1200 * * * 81.2 81.0 81.3 76.5 * *

14 700 1300 * * * 83.2 80.8 79.6 73.9 * *

15 750 1400 * * * * * * * *

... Nth trace

Explanation

Line 1: Datum(8 m) dz_increment(5 m) trace_length(9 samples)

Line 2: Trace_Number(1) inline/easting(50) crossline/northing(0) sample_1(value=*=null) sample_2(value=10) ....

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How to Import a Seismic ASCII file and Export a SEGY file in Opendtect?

Step 1: Open Opendtect and select the Simple File import Option

Survey > Import > Seismic > Simple File > 2D/3D

Step 1: Importing Simple ASCII file

Step 2: Set up import options (From top to bottom)

ASCII must be selected

Select your ASCII file

Traces must start with a position

Trace number is included preceding the X/Y

No Ref/SP number is included after the trace number

File start does contain sampling info

Discard or Pass Null Traces

Set Output attribute

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Set Line name

Step 2: Setting up import options for Simple ASCII file

Step 3: Select SEG‐Y Export function

Survey > Export > Seismics > SEG‐Y > 2D/3D

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Step 3: Selecting SEGY Export

Step 4: Set up export options

Set up the options depending on your needs, but I select all data under the trace sub‐selection option button.

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Step 4: Setting up export options

XYZV Line

This format is an ascii format with scatter points in the form, x, y, z, value. There is a header line that is skipped.

X Y Z V

657815. 5733888. 41. 0.

657839. 5733891. 42. 1.

657840. 5733891. 45. 2.

657839. 5733891. 48. 3.

The following modifiers must be included:

‐xm ‐dx ‐dy ‐dz ‐xsx ‐xsy ‐xsz

These tags tell the specified interpolator how to construct a grid from the scattered points. A convex hull algorithm will eliminate data outside of the data extents.

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K-Means Clustering

K‐Means clustering is a simple statistical unsupervised learning algorithm. It works by grouping an N‐dimensional dataset into groups— categorizing each data point into a cluster as represented by a centroid. K‐Means predicts the centroid location through an iterative process. The coded solution has been based on the solution provided from DataOnFocus but has been completely rewritten for application in JGeoEarthTools:

Solution extremely modified from DataOnFocus and is almost all re‐written. The following is added:

Multi‐threading has been added. Parallelization is performed on each cluster group. For example 10 threads will be created if the number of clusters is chosen.

Maximum number of iterations can now be set in the inversion loop. This should be used for large datasets where computation of the solution is slow.

Enabled a N‐dimensional cluster group. Any number of clusters can be used, including one.

A constant Random number seed number is set so the initial clusters are consistent. A variable may be included in later releases if a different cluster output is required.

Output Cluster results. This ASCII file acts as a save‐state for the clustering result. This way the K‐Means clustering can be continued after being interrupted.

The method no longer accepts 1D arrays but rectilinear objects

It has been noticed that the cluster results are slightly different in the way the threading is performed. This may because random numbers are now accessed asynchronously rather than sequentially.

Setting up the K‐Means clustering is based on the following code:

1. It takes in the initial cluster points as input. This allows for any number of datasets.

2. The initial points are assigned to be random if a clustering groupings file is not chosen. The seed has been currently set to Random seed 1, which means, that each clustering result will be consistent each time you run the program.

3. A maximum number of iterations is allowed in case a perfect k‐means clustering result is not required. If a clustering groupings file has been selected then it will initialise the cluster point values with the last recorded index. This cluster file contains the grouping for all iterations.

4. It will then compute the error for each data‐point against all centroids. For each point, the centroid with the lowest error will be initialized with that initial cluster.

5. The centroids are then updated to minimize the global error. This is done through an "Expectant Minimization" algorithm (see Stanford Computer Science for more details).

6. The centroids are updated and errors recalculated

7. Steps 3‐6 are repeated until centroid assignment does not change from one iteration to the next or if the maximum number of iterations is reached

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public KMeans(String clusteringGroupingsFile, int [][] indices, float [][][] pts, int nClusters, int maxIterations, ArrayList<Double> minimumArray, ArrayList<Double> maximumArray) {

this.pts = pts;

this.indices = indices;

this.dim = pts.length;

this.nClusters = nClusters;

this.maxIterations = maxIterations;

this.clusteringGroupingsFile = clusteringGroupingsFile;

this.minimumArray = minimumArray;

this.maximumArray = maximumArray;

clusters = new ArrayList<>();

}

The code that performs the K‐Means clustering is:

public static RectilinearSection kmeans (String clusteringGroupingsFile, List<RectilinearSection> input, int nClusters, int maxIterations) {

logger.info("Running KMeans");

logger.info("...Number of Inputs=" + input.size());

logger.info("...Number of Clusters=" + nClusters);

logger.info("...Max Iterations=" + (maxIterations == ‐1 ? "Infinite" : maxIterations));

logger.info("...Validating Inputs");

maxIterations = maxIterations == ‐1 ? Integer.MAX_VALUE : maxIterations;

int nx = input.get(0).xs.size();

int nz = input.get(0).zs.size();

ArrayList<Double> offsets = input.get(0).offsets;

ArrayList<Double> xs = input.get(0).xs;

ArrayList<Double> ys = input.get(0).ys;

ArrayList<Double> zs = input.get(0).zs;

double originX = input.get(0).originX;

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double originY = input.get(0).originY;

double originZ = input.get(0).originZ;

boolean isValid = true;

int nth = 1;

LinkedHashMap<Integer, ArrayList<Integer>> validxs = new LinkedHashMap<>();

LinkedHashMap<Integer, ArrayList<Integer>> validys = new LinkedHashMap<>();

LinkedHashMap<Integer, ArrayList<Integer>> validzs = new LinkedHashMap<>();

ArrayList<Double> currentValidXs = new ArrayList<>(xs);

ArrayList<Double> currentValidYs = new ArrayList<>(ys);

ArrayList<Double> currentValidZs = new ArrayList<>(zs);

for(RectilinearSection s : input) {

ArrayList<Double> xts = new ArrayList<>(currentValidXs);

ArrayList<Double> yts = new ArrayList<>(currentValidYs);

ArrayList<Double> zts = new ArrayList<>(currentValidZs);

for(double d : s.xs) {

boolean found = false;

Double df = Double.NaN;

for(Double d2 : xts) {

if(approximatelyEqual(d,d2,TOLERANCE)) {

found = true;

df = d2;

}

}

xts.remove(df);

}

if(nx != s.xs.size()) {

int n = 1;

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for(RectilinearSection s2 : input) {

logger.info("Eastings do not match: Input " + n++ + "="+ + s2.xs.size());

}

isValid = false;

}

if(nz != s.zs.size()) {

isValid = false;

int n = 1;


logger.info("Northings do not match: Input " + n++ + "="+ s2.ys.size());

}

}

if(originX != s.originX) {

isValid = false;

logger.info("Origin X does not match: Input " + nth + "="+ s.originX);

int n = 1;


logger.info("‐‐‐Origin for " + n++ + "="+ s2.originX);

}

}

if(originY != s.originY) {

isValid = false;

logger.info("Origin Y does not match: Input " + nth + "="+ s.originY);

int n = 0;


logger.info("‐‐‐Origin for " + n++ + "="+ s2.originY);

}

}

if(originZ != s.originZ) {

isValid = false;

logger.info("Origin Z does not match: Input " + nth + "="+ + s.originZ);

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int n = 0;


logger.info("‐‐‐Origin for " + n++ + "="+ + s2.originZ);

}

}

nth++;

}

if(isValid) {

logger.info("...Valid!");

} else {

logger.info("...INVALID! Aborting.");

return null;

}

logger.info("...Creating Cluster Indices");

logger.info("..." + nx + " x " + nz + " x " + input.size());

int [][] indices = new int [nx][nz];

int nv = input.size();

logger.info("...Initializing blank array");

float [][][] pts = new float[nv][nx][nz];

logger.info("...Null Points Added");

logger.info("...Settings Points");

int countIncrement = (int) Math.min((double) nx/20.0,10000.0);

ArrayList<Double> minimumArray = new ArrayList<Double>();

ArrayList<Double> maximumArray = new ArrayList<Double>();

for(int k = 0 ; k < nv ; k++) {

RectilinearSection s = input.get(k);

double minVal = s.values.get(0).get(0);

double maxVal = s.values.get(0).get(0);

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for(int i = 0 ; i < nx ; i++) {

List<Double> vals = s.values.get(i);

if(((i+1)%countIncrement) == 0) System.out.println("...Trace " + i + " of " + nx + "(" + (100 *(double) i / (double) nx) + "%) complete");

for(int j = 0 ; j < nz ; j++) {

double val = vals.get(j);

pts[k][i][j] = (float) val;

minVal = Math.min(minVal, val);

maxVal = Math.max(maxVal, val);

}

}

minimumArray.add(new Double(minVal));

maximumArray.add(new Double(maxVal));

}

logger.info("...Computing K‐Means");

KMeans k = new KMeans(clusteringGroupingsFile, indices, pts, nClusters, maxIterations, minimumArray, maximumArray);

k.init();

k.calculate();

logger.info("...Formatting Output");

List<List<Double>> outputValues = new LinkedList<List<Double>>();

for(int i = 0 ; i < nx ; i++) {

int [] arr = indices[i];

List<Double> l = new LinkedList<Double>();

for(int j = 0 ; j < nz ; j++) {

l.add((double) arr[j]);

}

outputValues.add(l);

}

RectilinearSection section = new RectilinearSection(offsets, xs, ys, zs, outputValues, originX, originY, originZ);

return section; }

APPENDIX C

Cooperative Inversion Workflows Flowcharts

Prior Model Creation Using Unsupervised Clustering of Seismic Attributes (Automatised via batch files)

Prior Model Creation Using Structural Constraints (Manual via interpreted seismic sections)

K‐means clustering of seismic attributes files.

Convert ModEM model file to JGeo format

Map resistivities from converted ModEM file to the k‐means clusters

Downsample the k‐means cluster file to the

same mesh as the ModEM file

Apply a most frequency filter to provide a

smoother prior model

Convert the smoothed JGeo file back to

ModEM format as prior model for MT inversion

Structural/Stratigraphic Interpretation of Seismic

Section

Create Highres Bitmap of Interpreted Seismic with Geological Units

Load Bitmap into MTModelBuilder and assign Resistivities

Export suitable Prior Model File for Inversion

ProgramSet Meshing Parameters

Load suitable Datafile of Inversion Program

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Don’t Bury Western Australia’s Geophysical Data: Uncovering Prospective Mineral Terrains with Regional

Potential Field, Seismic and MT Transects through Cooperative Inversion

(MRIWA Project M493)

department of exploration geophysics · 2020-03-18 · while the mathematical details of...

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