data processing in mineral exploration

8/19/2019 Data processing in mineral exploration

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ASSIGNMENT COVER SHEET

Student Name: __________________________________________

Student ID: __________________________________________

Unit Name: __________________________________________

Lecturer’s Name: __________________________________________

Due Date: __________________________________________

Date Submitted: __________________________________________

DECLARATION

I have read and understood Curtin’s policy on plagiarism, and, except where indicated, thisassignment is my own work and has not been submitted for assessment in another unit orcourse. I have given appropriate references where ideas have been taken from the published orunpublished work of others, and clearly acknowledge where blocks of text have been taken fromother sources.

I have retained a copy of the assignment for my own records.

________________________________________

[Signature of student]

For Lecturer’s Use Only:

Overall Mark: ________ out of a total of _________ Percentage:

Lecturer’s Comments:

Lecturer’s Name: Date Returned:

Sutthisrisaarng Pholpark

17682974

Geophysical Data Processing 612 (Minerals)

Paul W.

14/11/2014

12/11/2014

Sutthisrisaarng Pholpark


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DEPARTMENT OF EXPLORATION GEOPHYSICS

GEOPHYSICS 312 – Radiometrics, digital terrain data, and magnetics

WORKSHOPS 1 and 2 - 5 and 12 August 2013

PROCESSING AND IMAGING DATA IN OASIS MONTAJ

TASKS

You are provided with digital terrain, airborne radiometric and aeromagnetic data from the Wongan Hills

area, WA. Geological map and notes for Moora are also provided.

Data set1vd, 1 vd.ers : First vertical derivative grid

dtm, dtm.ers : Digital terrain model grid

mag, mag.ers : Magnetics grid

pot, pot.ers : Potassium radiometric grid

tho, tho.ers : Thorium radiometric grids

ura, ura.ers : Uranium radiometric grids

sh5010.ecw sh5010.ers : Geological map

1. Maps display

This section is aimed to display the given data in Oasis montaj. All grids are displayed in color shadedoption with 45 degrees inclination/45 degrees declination. In addition, all grids from radiometric

survey is combined and displayed in ternary image.

First vertical derivative grid(nT/m)

Display the rate of change of magnetics respect to

z-axis. Can be used for detecting faults or rocks in

subsurface.

Digital terrain model grid(m)

Display bare ground elevation without effects of

buildings or environments.


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Magnetics grid(nT) Potassium radiometric grid(count/sec)

Thorium radiometric grids(count/sec) Uranium radiometric grids(count/sec)


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2. Faults, and rock units mapping

2.1 faults mapping

Step 1

Step 2

Step 3

Fault mapping method in

Oasis montaj

1. Use ‘Map Tools’ to draw a

direct line. The program will

save 2 coordinate points ofthe line and save in XYZ

format.

2. Load saved coordinate

points in to database.

3. Use coordinate points to

draw a line path in the map.


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First vertical derivative map can be used to locating faults as shown below. According to the given

geological maps the first estimated fault line is about the approximation fault and the second estimate

fault line is about the inferred fault.

2.2 Rocks mapping

Rock mapping method in Oasis montaj

Step 1

Draw a polygon line over magnetics map. The

program will automatically generate PLY file.

Step 2

Load save PLY file in to the map, in order to

place your drawing in to your map.


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GEOPHYSICS 312 – Radiometrics, dtm and magnetics

WORKSHOP 3 - August 20 and 27, 2014

PROCESSING AND IMAGING DATA IN ER MAPPER

TASKS

You are provided with digital terrain, airborne radiometric and aeromagnetic data from the Wongan Hills

area, WA. Geological map for the Moora 1:250 000 map sheet is also provided.

Given data sets

dtm, dtm.ers : Digital terrain model grid

mag, mag.ers : Magnetics grid

pot, pot.ers : Potassium radiometric grid

tho, tho.ers : Thorium radiometric grids

ura, ura.ers : Uranium radiometric grids

sh5010.ecw sh5010.ers : Geological map of Moora 1:250 000 map sheet

Tasks are including:

-

Display each data set effectively.

- Learn about filtering, colour look up tables, adding contours, legends, data stretches etc.

1. Start up ER mapper

1.1 Start ER mapper by loading geophysics tools into toolsbar.

1.2 Create new algorithm and open ‘Edit algorithm function’


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1.3 Open dataset that you want for image processing

1.4 Select appropriate colour mode and colour table for your data. Colour table will be used in stretching

process e.g. look up colour table to transform input image.

2. Processing the image

The goal of image processing for geophysics application is to enhance important geographic features in orde

to extract quantitative information and solve geological problems.

2.1 filtering

This function allow users to load filter algorithm for imaging processing. The example below is DEM

display after use aspect.ker filter. This filter help to enhance slope of DEM. Others filter e.g. upward

continuation, low pass filter can be selected depending on user’s objective.


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2.2 Edit transform limit or data stretching

Transform limit can be used to adjust image contrast. There are several parameters can be adjusted.

Users can adjust transform limit to control colour range so that only desired colours is enhanced. Users can

select transform method e.g. linear stretch, histogram equalize to enhance desired features. The transform

will map image values to colour table. For example, if an image has a range between 206 - 250, this number

will be mapping to value 0 - 255 for pseudo colour table so that new colours from colour table are assigned

an image. The pictures below show results from different transform setting.

If we change colour table in #2 to rainbow, lookup colour table in transform window will be changed to

rainbow as shown below.

2.3 Real time sunshade

Users can use ‘Realtime Sunshaded’ function to enhance features of an image. Users can adjust this

function in pseudo layer alone or add another layer for sun shaded. The later method significantly enhance

elevation features in the digital terrain model as shown below.


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2.4 Adding contour

Users can display contour in an image by go to algorithm and de the steps as shown below.

Then contour wizard will show up. There are several steps as shown below to complete creating you

contour.


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The pictures below display contour in different types of layer setting.

3. Annotation

Before adding any annotations, you need to set up your page.

By using annotation tools, Users can display your image professionally. Users can use page set up

function to adjust a size of your map. You can also add important map featues e.g. map detail box, scale bar

colour legend bar, direction annotation and grid on your map.


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3.1 Adding title box

You can use ‘Map rectangle’ to add different annotation features on your map. Firstly, you may add

title box to your map to start.

3.2 Adding grid

You can use ‘Map rectangle’ to add grid in to your map. The procedure is illustrated below. Note tha

you should not manually adjust your grid because the scale may not fit your actual map, you should select ‘F

gird’ instead. ER mapper will automatically fit your grid to your map with the correct scale.

3.3 Adding map scale


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3.4 Adding colour legend bar

Here you can set up font size, font colour, name, number of division etc. of colour legend bar.

3.5 Adding direction annotation

After you finish adding important annotations, you will have a map as shown below.


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4. Display image in 3D

5. Display ternary image

RGB funtion can be used to display radiometrics ternary image. Assigned colours are shown below.


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6. Display the given data sets

Digital terrain model

Contour, Sun-illumination, 3D


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Magnetics

Sun-illumination, 3D

Radiometrics ternary image


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GEOPHYSICS 312 / 612 – 4 different gridding methods

MINERALS WORKSHOP 4 – September 10 and 17, 2014

PROCESSING DATA IN OASIS MONTAJ

TASKS

Dataset: Shuttle Radar Topographic Mission data (SRTM) for part of the Perth area. (Scarborough to Gosnells)

File type: xyz file with 90 x 90 m spacing

File name: srtm scarb to gosnells .xyz ( 2.8 Mb)

1. Import the xyz file into a new database.

2. Create grids using four different methods – minimum curvature, kriging, spline gridding and tins.

Note that the grid cell size should be around 1/4 to 1/3 of the line space in order to increase the

resolution of the grid. So that the gird cell size 30 is used in this experiment. All gridding methods use Z

axis so that we can see the elevation trend from SRTM data.

Minimum curvature


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Kriging

Spline Gridding

TIN Gridding


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The results of 4 gridding methods are shown below.

3. Compare the grids and also compare against posted elevation values from the database.

In order to compare the grids from different gridding methods, the area of comparing need to

be specified to get a clear result. The area that appropriate for grid comparisons in this SRTM data is

the contact between Kings Park and Swan River because we can observe the data

overshoot/undershoot from elevation transitions of that area. The picture below shows satellite map

of the mentioned area which has been taken from ‘Google map’. We use ‘Window a Grid’ to capture

only the area that we want.


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Specified grid window

The pictures below show the results from different gridding methods.


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Grid display

Grid display with color-shaded and contour

Comment: From the displays we can clearly see that the colors of each gridding method assigned in sea-level

are different.


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Spline

Spline gridding method matchs the variations of

data between parallel lines very well (N-S),

however, it does not take variation of data in

orthogonal lines (E-W) into account. So that the

spline method is unable fully represent the data in

2 line directions.

TIN

TIN method is good for weighing data when there

are several values colleted at the same location. In

TIN gridding caluation, it use Tin mesh and Tinnodes in order to weight data. However, this data

is collected only single value at a particular

location with equally spaced lines. So when

weighting data, all of the points are equally

weighted by only taking adjacent values in

calculated. So that we can observe some strong

discontinuities of colors between data points e.g.

as shown shown in the circles.

4. Which methods work best with this data and why?

Do the grids overshoot / undershoot the data particularly in water areas like the Swan River?

In order to observe grid overshoot/undershoot, we can use ‘GX’ function to run ‘gridprof.gx’ then draw

a line across the grid to see profiles from different gridding methods along that line. So that we can see

the variations of values of each grid method.


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The picture on the left shows the

profiles from each gridding method.

The colors are assigned as below.

Minimum curvature: Pink

Krigging: Red

Spline: Blue

TIN: Green

From an observation, all profiles are

overshooting. However, krigging and

TIN gridding method yield the least

overshoot compared to others.


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Both spline and minimum curvature

gridding are overshooting and not

converge to 1. In addition minimum

curvature shows an oscillation in the

profile. On the other hand kriging

and TIN are less over shoot. The only

gridding method that rapidly

converges to 1 (water/sea level) iskrigging.

Judging from comparing posting values to the grid data and the profiles comparing, krigging shows the best

performance in color variation according to the real data, less overshooting and rapidly converge to 1. Hence

krigging is the best gridding method.

5. See gridding notes that are provided in the workshop folder.

6. Add notes on how each gridding method works. See Oasis Montaj help files and tutorials. Aim to writehalf to a page on each method.

6.1 Spline or Bi-directional Gridding

- Suitable for the data collected along lines that are roughly parallel, as in the following pictures.

(Oasis motaj manual)

- Suitable for data with high sample density down the lines relative to the line separation.

- Cannot be used with tie lines because the calculation method does not allow to.

- Cannot be used with random data, non-parallel line data and orthogonal line data as shown below.

(Oasis motaj manual)

There are 2 steps in Bi-Directional gridding (Oasis montaj manual).

1. Each line is interpolated along the original survey line to yield data values at the intersection of each

required grid line with the observed value.

2. The intersected points from each line are then interpolated in the across-line direction to produce a value

at each required grid point.


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6.2 Minimum Curvature or Random Gridding

- Use when data is relatively smooth between sample points or survey lines.

- Allows extrapolation of the surface beyond the outside boundary of the contact points.

- When data is randomly distributed and when data is sampled along arbitrary lines.

- Use when tie lines are included.

In minimum curvature process step, firstly it estimates grid values at the nodes of a coarse grid normally 8

times of the final grid cell size. The estimation performs based on the inverse distance average of the actual

data within a specified search radius. If there is no data point lying within the radius, the average of all datapoints in the grid is used instead. After that iteration process is performed in order to adjust the coarse grid

nodes to fit the nearest actual data points.

After the first step is completed (acceptable fit), the coarse cell size is divided by 2 and then the first step is

repeated (fit grid nodes to the nearest actual data points) until the minimum curvature surface is fit at the

final grid cell size.

The number of iterations is a very important factor in minimum curvature process, since the more number of

iterations, the closer final surface will be to a true minimum curvature surface. However, the number of

iterations is proportional to time.

Minimum Curvature stops iterating when:

It reaches a specified maximum number of iterations, or

A certain percentage of the observed points are within a limiting tolerance of the surface

By default these limits are 100 iterations and 99% of points within 1% of the data range (Oasis montaj

manual).

6.3 Kriging

- Use when data is variable between sample locations

- Use when the data known to be statistical in nature.

- Suitable for poorly sampled data.

Krigging method is based on a statistical local estimation technique that provides the best linear unbiased

estimate of an unknown characteristic being studied (Oasis montaj manual).

Firstly, it calculates a ‘variogram’ of the data showing the correlation of the data as a function of distance as shownbelow.

The equation shows that the greater distance between data points, the higher variation between the points.

There are 2 models in krigging calculation which are ordinary krigging and universal krigging used to estimate

the data values at the nodes of the grid. The main different between the introduced models is that a method

to define the variance of the data, however, both models based on a variogram.

The process of krigging is complicate, the size of the data set may limit the use of krigging since it is very time

consuming in a large dataset calculation.

In addition, KRIGRID in Oasis motaj is able to produce an error grid which is a by-product of kriging statistics.

This grid shows the degree of confidence at each grid node.


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6.4 TIN gridding (TIN = Triangular Irregular Network)

- Use when data is variable between sample locations.

- Use when data is highly irregular in distribution.

The advantage of TIN gridding over others method is that it has ability to sum and average multiple values

collected in the same location.

Several words are introduced in TIN gridding; TIN Nodes, the TIN Mesh (or Delaunay triangulation), the

Convex Hull, the Voronoi cells.TIN nodes: X,Y locations of the data.

Voronoi cells: Polygonal zones surrounding each node (Figure1). Any points inside the cell are closest to their

own node than other nodes.

TIN Mesh (or Delaunay triangulation): The Delaunay triangulation is the set of triangles created from

connections between nodes in the TIN, determined by the TIN algorithm (Figure 2) (Oasis montaj manual).

The criteria to create these triangles are that they need to be least ‘long and thin’ generated among possible

irregular distribution points by the principles of maximum-minimum internal angle and density-dependent

size.

The Convex Hull: The Convex Hull is the smallest convex set of nodes that enclose all nodes (Oasis montaj

manual) (Figure 2).

There are three methods in TIN gridding (Oasis montaj manual)

Nearest Neighbour: Use the values of the nodes closest to the given locations.

Linear: Triangles in the TIN are interpolated using a plane defined by the triangle vertices.

Natural Neighbour: Use the Natural Neighbour algorithm described by Sambride et al (1995)

(The pictures are taken from the given gridding note)


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Extras: How to download SRTM in any places from Oasis montaj.

This function allow you to down load SRTM data anywhere in the world.


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GEOPHYSICS 312 / 612 – Model vision

MINERALS WORKSHOP 5 - September 23, 2014

Forward magnetic modelling with Modelvision 12.0

Aim : Forward magnetic modelling with Modelvision 12.0 to create synthetic model and recovering modelparameters using Analytic Signal and Euler Deconvolution in Oasis Montaj

Outline of steps:

1. Start Encom program ModelVision

2. File >new project

3. Project properties – untick local grid, set datum to GDA94, Projection to Universal Transverse Mercator

proj/zone to SUTM50, Mag units SI, T=60000 nT, inc = -60.0, dec =0.0

4. Set up synthetic survey (Utility>synthetic lines) with 101 north south lines, ref pt x=500 000, y = 6 000

000, line length = 20000 m, pt spacing = 20 m, survey width 20000 m, line spacing = 200 m, azimuth = 0

degrees, select “create survey”


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5. Go to view to see plan of survey lines view>view map>stacked profiles

6. Create tabular model: model>body operations>create body>tabular, susceptibility = 0.1 SI units, strike

length = 10000 m

7. Go back to open map (step 5) and click on map to position centre of model on centre of grid of lines.

8. To see body parameters body properties > see or change thickness and extent – record these for later

use.


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9. To create model grid: model>grid control specify grid dimensions 50 x 50 metres.

10.

To see magnetic grid: view>map>grid image (Note that you need to activate your model before viewing

the grid)


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11. To see individual cross sections: view > X-section, select line eg 51 (central line)

12. To export model grid: file.export>ermapper ers format, use 8 byte real

13. In oasis montaj – create new project and view model grid using display grid etc


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15. Look at grid profiles in database (show profile etc) to see how well they recover model parameters –

location and depth to top etc.

Location accuracy

Direction Real edge position Inflection point of AS %error

N-S6005000 6005840.2 8.402

5995000 5995563.5 5.635

E-W 499850 497883.4 19.666

500150 502343.1 21.931

Comment on location accuracy: The N-S analytic signal profile has higher accuracy in locating the target

edge that the E-W profile. However, overall of analytic signal performances in locating the target’s edg

are within 20% error.

Depth and width accuracy

Note: The half-maximum points (on the left and the right) of N-S profile come from extrapolation between dat

points in order to obtain the exact y values according to the maximum of analytic signal (2.605341).

Comment on depth (to the top) and target width accuracy: Analytic signal can recover the width of the

target very well with less than 15% errors. However, when using Half-height Half-width (analytic signal) metho

EW profile nT/m x

AS maximum point 4.020358 500016.3

1/2 AS maximum point 2.010179

1/2 AS maximum point LEF 1.919922 496913.9

1/2 AS maximum point RIG 1.892624 503118.7

Width 6204.79 m

Half-Width 3102.395 m

Depth 3102.395 m

NS profile nT/m y

AS maximum point 5.211882 500016.3366

1/2 AS maximum point 2.605941

1/2 AS maximum point LEFT 2.605941 6006809.674

1/2 AS maximum point RIGHT 2.605941 5995563.474

Width 11246.19947 m

Half-Width 5623.099734 m

Depth 5623.099734 m

Profile Calculated Model %error

N-S Depth 5623.099734 2000 36.231

N-S Width 11246.19947 10000 12.46199

E-W Depth 6204.79 2000 42.0479

E-W Width 3102.395 3000 1.02395


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16. Run located Euler deconvolution to also recover model parameters.

Located Euler Steps

After complete all the steps, Euler solutions are saved in the database. You can use solutions plot to see

where are the solutions on your magnetic map.

Comment: From the solution plot, we can see that the location of the solution is lying in the target’s

body. The error of the computed located Euler solution to locate the target depth is 40%. The error is

close to computed depth from analytic signal E-W profile. Located Euler is unable to map the target’s

edge. If the extension of the target is required, then standard Euler should be used instead.


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17. Record all steps and results in your electronic lab books. How well have Analytic Signal and Euler

Deconvolution worked in recovering model parameters?

In target’s depth recovery, depth calculations from analytic signal show better accuracy than the depth

computed from located Euler. Even though these technic yield error more than 30% in the depth from

surface to the target, the range of the depths is still lying within the target’s body.The accuracies of bot

technics are shown in the topic 16 for analytic signal and the topic 17 for located Euler.


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GEOPHYSICS 312 – OASIS Montaj and Modelvision

WORKSHOP 6 - October 8 and 15 and also Assignment 2, 2014

terpreting gravity data from Moora

ou are provided with gravity data from Moora, WA

bjective

• Learn gravity data processing: import data, gridding, horizontal gradient filter, regional-residual separation,

and map display.

• Locating the fault and calculating fault-throw from a given gravity data.

• Learn to use model vision to model fault and bedrock topography from gravity data.

• Learn how to interpret gravity data with related geology information.

ASKS

Import file Moora.csv into Oasis montaj.


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fter imported Moora.csv into the created database, the coordinate of the file need to be set before processing in

rther steps.

Produce maps showing station locations and grid Bouguer Anomaly and height above Australian Height

datum ( AHD )

2.1

Station locations

Station locations plot shows location coordinates where the data has been collected.


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After step 3, the station locations are automatically in the created map. Station spacing can be obtained

from the distance between the centers of adjacent stations. Station spacing = 50 m and the appropriate

grid cell size (for gridding) is ¼ of the station spacing = 50 m.

Base map is created with scale 1:40000 m. The station locations plot is showed below.


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he picture below shows colour-shadded grid with 45 incl/45 decl in the base map scale 1:40000 m with contour an

olour legend bar.

Figure 2 Bouguer anomaly colour-shadded grid with 45 incl/45 decl in the base map scale 1:40000 m with contour and colour legend b

ote: The contours are significantly dense in the middle of the grid which indicates the high bouguer anomaly

adient in the middle area. This attributes to the underlying fault (Darling fault).

2.3

Australian Height datum ( AHD )

AHD displays the topography of Moora area. In addition, it can be used in a terrain correction process in

order to remove terrain effects in gravity data which mask the target (not included in this lab).

The picture below shows colour-shadded grid with 45 incl/45 decl in the base map scale 1:40000 m with

contour and colour legend bar. The process steps are similar to bouguer anomaly grid display in topic 2.3. Tunit displays here is in metre.


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gure 3 Australian Height datum (AHD ) colour-shadded grid with 45 incl/45 decl in the base map scale 1:40000 m with contour and co

legend bar

You will see that the data is very dominated by an east west gradient due to the presence of the fault.

Can you locate the position of the Darling Fault ?

There are 2 methods to locate the fault which are (1) by using gridprof to create bouguer anomaly profil

across the fault and then use horizontal gradient (database filter) to locate the inflection point of the bougu

anomaly profile, the center of the fault is at the inflection point, (2) by using horizontal gradient filter for

bouguer anomaly grid, the center of the fault is at maximum point of horizontal gradient.

We use an inflection point of the bouguer anomaly

and the maximum point of HGRAD to locate the faul

because an abruptly change of density contrast and

mass caused an abruptly in gravity data. Hence the

maximum rate of variation is the fault location.


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3.1

Gridprof method

3.1.1 Create new database for gridprof. The new line paths and profiles will be stored in this

database.

hen draw a line across bouguer anomaly grid and save to the new database.

3.1.2 Go to the created database. Use database filter to calculate horizontal gradient of the selecte

profile. The peak of HGRAD represents where the bouguer anomaly has the maximum slope

which indicates center of the fault.


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3.1.3 Use gridprof.gx to draw 2 more line paths (3 lines altogether). Then you can use ‘map tools” t

draw (1) line path, (2) bouguer anomaly profile, (3) HGRAD profile. We can locate the central

the fault location by use maxima point of HGRAD as show in the picture below (figure 4). As

seen in figure 4, the estimate location of the faults is mismatch with the fault location in the

geology map.

Figure 4 Fault location estimation from “gridprof” method

3.2

Horizontal gradient filter method

3.2.1

Compute HGRAD grid by HGRAD filter.


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The map below shows the result from HGRAD filter. The unit of the map is mGal/m. The high values

located around the central of the map which attributes to the effect of the fault.

Figure 5 HGRAD of bouguer anomaly.

3.2.2 Create a new database and use gridprof to draw line paths across HGRAD grid. Plot the

maximum points of (1) line path, (2) bouguer anomaly profile, (3) HGRAD profile on the map

The maximum point of each HGRAD profile is the central of the fault.


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The result is similar to gridprof method.

Figure 6 Fault location estimation from “HGRAD filter” method

Can you estimate throw on the Fault?

∆ () = 2 × ∆ × ℎ

49.3 = (6×6.61×1011) × ∆ × ℎ()×1011

49.3 = 39.66 × ∆ × ℎ 49.3 = 39.66 × 0.4 × ℎ

ℎ ℎ = 3.10


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3. Fill in model settings.


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4. Edit regional and active all lines.

5. Use X-section to view the selected line.


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om the modeling, the model profile does not well match with the anomaly profile. However, we can observe that

e effect of the fault (the purple body) yields the profile similar to the fault anomaly e.g. shape and gradient. In

ddition, the created fault model is similar to the geological fault model in figure 8. In summarize, we can confirm

at the effect of east-west gradient is due to the present of the fault in that area and the fault dip down direction i

s in figure 7.

Figure 8 Fault model

(after http://geophysics.geoscienceworld.org)


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What we are interested in is the bedrock topography under the town of Moora.

How do we minimize the effect of the Darling Fault and look in detail at bedrock topography? Use regional /

residual separation in Oasis Montaj.

The effect of the fault in bouguer anomaly gravity masks the effect of underlying bedrock, hence, in order to

observe bedrock topography in the data, the regional effect caused by the bedrock need to be removed. In

Oasis montaj, we can use trend filter to remove the effect of regional gravity. The output of the trend filter i

residual.

Note: Bouguer anomaly = regional + residual, what we want to observe is residual.

he result of ‘trend filter’ is showed in figure 8.

Figure 9 (left) Bouguer anomaly and (right) Residual anomaly after ‘trend filter’


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Figure 10 Residual grid with 45 incl/45 decl in the base map scale 1:40000 m with contour and colour legend bar

ote: We can use grid math to compute regional grid, to see how it affects the bouguer anomaly. From the regionagrid, we can clearly see that the effect of the fault caused a gravity gradient from the east towards the west

direction.


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Use ModelVision to model bedrock topography – using grid profiles from Oasis Montaj (use gridprof.gx ).

Note: The instruction of using gridprof is shown in “the topic 3.1”

The background density of the area is 2.67, for the model is 2.27.


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** Note: The model in line 3 is very distinctive from others. This may attributes to the location of the profile is f

om the survey station, hence its value came from the extrapolation in minimum curvature process instead of

eological influence. So that the model from line 3 should not take in to account of the interpretation. ***


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nce line3 is removed, it is reselected as show in the grid below. The new model is more geological possible the

revious model.

he new 3D perspective is created.


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Write up interpretation and include models in your electronic workbooks.

6.1 The fault and its throw

The location of fault is approximately in the middle of the map as show below.

The fault dipped down in the west (as in the picture below) and its vertical throw is approximately 3.1 km

6.2

Bedrock topographyFrom the residual anomaly profiles and models as shown below, there is a plausible presence of channe

filled with low density sediments below the survey area surrounded by granite bed rock.


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GEOPHYSICS 312 – OASIS Montaj

WORKSHOPS 7 and 8 - October 22 and 29, 2014

Falcon gravity gradiometry data from Vredefort Dome, South Africa

You are provided with Falcon airborne gravity gradiometry data from Vredefort Dome, South Africa. This is thsite of the world’s largest known meteorite impact crater.

Google “Vredefort dome” for more information.

This is a brand new pair of workshops and will evolve during the workshops!

TASKS

1. Copy into your working directories the FALCON vredefort database.

2. Open the database in OASIS Montaj.

3.

Produce map showing flight lines. What are line direction and spacing? Projection details are WGS84and UTM zone 35S.


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Figure 1 Flight lines

Line spacing is 1000 m. Hence the appropriate grid cell size is ¼ of line spacing = 250 m

Figure 2 Line spacing


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4. Grid the Bouguer gravity (Gd), Gravity vertical gradient (Gdd) and DEM data and produce maps with

base maps for all 3 grids. The channels you will need are: easting, northing, DEM,

gD_FOURIER_2p67_1000 , GDD_FOURIER_2p67_1000

Figure 3 (left) Bouguer gravity (Gd) and (right) Gravity vertical derivative (Gdd) both display with color-shaded grid display 45incl/45decli

Figure 4 Digital Elevation model display with color-shaded grid display

45incl/45decli

Figure 3 (left) shows the grid of measured gravity

data while the one on the left shows gravity verticalgradient grid taken from gravity gradiometry

measurement. We can clearly see the edge of

anomalies extend in gravity vertical gradient grid.

Note that the unit of gravity gradiometer

measurement is in Eotvos.

Figure 4, Digital Elevation Model (DEM) shows the

ground elevation of the area which indicates

vredefort topography.


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5. How do the gravity grids relate to known geology and also to aeromagnetic data?

The steps of georeferencing method are shown in the extra part of this lab. After the geological map is

warped by correct coordinates, it can be used to overly by any survey data which has the same

georeferencing. The results are shown below.


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6. Run Euler deconvolution on the gravity grids to determine source positions and depths.

Use Structural index of 0.5 and 5 % tolerance. This survey was flown 80 metres above ground level.

In order to run Euler deconvolution, the

derivative grids of gravity need to be computed.

As shown on the left, we can see that dz of

gravity is very similar to measured data from

gravity gradiometry except the noisy lines. So

we may use gravity gradiometry data instead of

dz.

SI is directly related to field fall-off rate to the target. Different target shapes need to be

assigned appropriate SI to obtain accurate Euler solutions. For gravity data, SI = 0 is appropriate for Sil

/ Dyke / Ribbon / Step and SI = 1 is appropriate for Cylinder / Pipe.

After performed standard Euler deconvolution with the data set, it gives too many solutions,

hence located Euler is performed instead.

Located Euler deconvolution solutions with SI = 0.5 and SI = 1 are plotted with size and color

proportional to depth as shown below.

With SI = 0.5, it yields 89 solutions whereas SI = 1 yields 92 solutions. The solutions are screened by mask

channel which allow showing only solutions with STD less than 5%. Solution locations computed from SI = 0.5


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Extra: How to mapping coordinate in to your geological map e.g. JPEG file. (Georeferencing)1. Import your geological map into oasis.

2. Pick 3 points and put them in to excel file.

3. Save your excel file and import it to oasis.

4. Convert your geographic coordinates to projected x,y coordinates.


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5. Define your warp by picking 3 points in the map as in steps 2.

6. After step 5, you should have .wrp file. Then you have to attach your .wrp file to your map.

Finally, coordinate of your geological map is created. Now it is ready to be used in your interpretation

e.g. overlay it in magnetics map!!!


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References:

in the workshop 7 and 8 folder on Blackboard

Cowan and Cooper, 2009

Muundjua et al, 2007

Schedule 2 – Data processing and deliverables

Technical notes on Falcon preliminary processing

data processing in mineral exploration

Documents