The Predictive Power of Hyperspectral Core Imaging, Applications to Grade and Geometallurgical Parameters

Tom Carmichael1, Brenton Crawford1, Sam Scher2+, Brigette A. Martini3

1Solve Geosolutions, Melbourne, Victoria, Australia (information@solvegeosolutions.com) 2Corescan SpA, San Pablo 9900, Pudaheul, Santago de Chile (sam.scher@corescan.cl) 3Corescan, 1055 W. Hastings, Suite 1900, Vancouver, BC, V6E 2E9, Canada (brigette.martini@corescan.com.au) +Corresponding Author

Abstract

Hyperspectral core imaging data is a unique dataset in geology as it is consistent, precise and continuous through drillholes. High resolution, information rich datasets such as this are ideal for machine learning classification and regression models to predict geometallurgical parameters using coincident regions where both datasets are collected, then using these regions to project the model across a larger region where the hyperspectral core imaging is collected. Investigated within are a Peruvian skarn and Chilean porphyry deposit where machine learning has been successfully applied to predict Au grade in the skarn and the presence or absence of skarn, as well as to extrapolate rock hardness parameters to areas of poor rock quality.

Resumen

El Poder predictivo de las imágenes hiperespectrales de sondajes, aplicaciones a la ley y a los parámetros geometalúrgicos Los datos de imágenes hiperespectrales de sondajes son un conjunto de datos único en geología, ya que son consistentes, precisos y continuos a lo largo de los sondajes. Los conjuntos de datos ricos en información de alta resolución de este tipo, son ideales para la elaboración de modelos de regresión y de clasificación automatizada, los cuales se utilizan para predecir parámetros geometalúrgicos utilizando ámbitos coincidentes en donde se recopila ambos conjuntos de datos. Consecuentemente se puede usar estas interpretaciones para proyectar el modelo a lo largo de una región mayor, en la cual se recopilan las imágenes hiperespectrales. Esta metodología ha sido utilizada en un depósito de tipo skarn en el Perú y en un pórfido de cobre en Chile, en donde la evaluación automática ha sido aplicada con éxito para predecir la ley de Au y la presencia o ausencia de skarn, así como para extrapolar parámetros de dureza de roca en áreas de roca de baja calidad. Keywords: hyperspectral core imaging, predictive learning.

1. Introduction Most machine learning workflows require large amounts of high resolution and consistent data. Corescan hyperspectral core imaging data collects approximately 200,000 contiguous pixels of data per meter that additionally provides spatial relationship data between the pixels in the form of an image. As a result, the mineral identification is known at any given pixel as well as the spatial arrangement of other surrounding pixels and their mineral identification in relation to it. Therefore, once the relationship between hyperspectral and another dataset are established, areas with no measurement or geological log can be predicted using the relationships first identified using the hyperspectral data. The efficient use of predictive modeling may allow for significant cost reduction, as well as to extend parameters to areas where measurements are unable to be taken or the accuracy of those measurements is unknown. This study investigates the application of supervised machine learning techniques to a Chilean porphyry and a Peruvian skarn. High resolution hardness data was collected at a Chilean porphyry and the data is applied to predict hardness particularly across intervals where it was impossible to collect data due to poor rock quality (although there are additional implications as this data is time-consuming and difficult to collect). In the Peruvian case study Au is predicted within the general sedimentary rock package and skarns are predicted against background and speciated within the same rock package. The goal of the skarn study was to differentiate the multiple types of skarn and to confirm that hyperspectral data could be trained to detect skarn automatedly (using human input and machine learning) to use as a first pass log.

2. Methodology

2.1. Hyperspectral Core Imaging

Digital infrared imaging spectroscopy (or hyperspectral imaging) for identification of earth materials has been in use for over thirty

years (Goetz et al., 1985). Hyperspectral imagers identify and spatially map terrestrial materials via the collection of both reflected and emitted energy across the electromagnetic spectrum using various detector array configurations and materials. The interaction of this energy (or photons) with earth materials is measured and quantified into spectral signatures whose specific geometries relate to molecular level composition (Martini et al., 2017).

2.1.1. Corescan HCI-III System Spectral imaging data measured with the Corescan© Hyperspectral Core Imager, Mark III system (HCI-III) operates across the Visible Near InfraRed (VNIR) and Shortwave InfraRed (SWIR) bands from 450nm-2500nm at an average spectral resolution (or bandwidth) of ~4nm (where the VNIR bands measure at 2.8nm and the SWIR bands measure at 4.5nm). The HCI-III continuously measures the surface of rock material samples at 500 μm spatial resolution.

High quality optics with directed illumination from dual quartz halogen lamps, focuses the spectral measurement to this 500 μm point on the core, maximizing signal (average 2000:1 across the measured spectrum). This results in ~200,000 spectra per meter of scanned rock material (Martini et al., 2017).

2.2. Data Analytics Machine learning is a branch or subset of Artificial Intelligence (AI) that is focused on systems that learn from their environment, e.g. data (Bishop, 2006). It is a method of data analysis that automates analytical model building by using algorithms that iteratively learn from data to find hidden insights and structure without being explicitly programmed where and how to look. The key aim in machine learning is to build a model that is general, which means it will behave intelligently in new environments, e.g. with new data that the model has never seen before. In large datasets with many variables there is a large number of variables that influence the prediction or classification of

variables and in situations such as these, machine learning algorithms are used to determine the rules from the data.

2.2.1. Classification and Regression Models Applying machine learning algorithms and classification models, i.e. supervised learning, can predict the probability and class of different categories based on existing downhole data, e.g. using hyperspectral core imaging mineralogical data to predict the presence or absence of detection limit Au, lithological classes and rock hardness measures. Random forest classification models (Brieman, 2001) were built to (1) predict hardness in the Chilean porphyry, (2) predict Au grade within a specific rock package, and (3) predict the presence of skarns within the aforementioned rock package. Variable importance analyses (Granitto et al., 2006) were conducted to determine the most effective minerals for predicting the above parameters, e.g. hardness, Au grade, and location of skarn. Note that while some features, e.g. minerals, may be unimportant alone, they may become useful when considered in combination with other features; likewise, some features may be redundant and carry the same information as other features, hence the importance of the variable importance analysis. Portable hardness measurements (using an Equotip® system) collected at 2cm intervals formed the hardness parameter dataset. Due to grain and other localization effects, Equotip data is noisy; processing and upscaling of the data was required to compare it at the 25cm sample scale of the hyperspectral data. A 20-point symmetrical mean smoothing filter was applied and used as the basis for the regression to reduce noise and increase data fidelity. Sections of very discontinuous Equotip measurements with intervals that approached the size of the smoothing window were removed from the analysis. Before performing the regression, one drillhole was removed in order to test the model, thus forming a training set (i.e. the data to train the regression model that will learn the relationship between the hyperspectral variables and the Equotip values) and a test set (i.e. the data that the model is tested on and not included in the training data). Initially some simple linear

regressions using individual minerals obtained from hyperspectral measurements were performed to observe how each mineral phase could perform alone, but variable importance analysis was utilized to determine the most important mineral phases to use in an array of regression models, including robust linear regressions and Random Forest regressions. In order to predict the Au grade, the geology provided by the company was divided first into two classes, high Au and low Au based on a threshold value of 0.1g/t. Additionally the only rock packages selected were those of the skarns and sediments, precluding prediction across different styles of mineralization (the project contains epithermal, porphyry and skarn mineralization). Variable importance analysis was performed to determine the most important Corescan variables for distinguishing high-Au grade from barren intervals within the rock package. The top variables were subsequently chosen to construct a machine learning classification model that aimed to predict high Au from low Au. Lastly, the classification model was trained to learn the signature of Au in all drillholes with the exception of one drillhole, which was used to test the model. This process was conducted iteratively through all the drillholes so that each drillhole was used as a “test” drillhole throughout the process. Similar to the Au grade prediction, the presence of skarn was predicted by first dividing the geology into two classes: skarn and not skarn (i.e. background) based on the logged lithology; a so-called ‘one vs. all’ classification. Variable importance analysis was performed to determine the most important hyperspectral variables for distinguishing a skarn from background lithological classes. The top variables were chosen to construct a machine learning classification model that aimed to predict the location of skarns across the deposit. The model was trained to learn the signature of skarn on all drillholes except one; the model was used to predict skarn presence on the hole not used in the training dataset. The final step was repeated for every hole in the deposit.

3. Results

3.1. Hardness Prediction

Visual assessment of the mineralogical and Equotip data downhole suggest that the alternating domains of chlorite and white mica showed a strong correlation with the Equotip data. Indeed, Equotip has an inverse Pearson correlation with white mica (mostly muscovite-phengite) and white mica crystallinity, while it has a strong positive Pearson correlation with chlorite, epidote and a general Fe-silicate species. There appears to be a relationship between the Equotip hardness and the variability in adjacent Equotip measurements. The softer rocks have higher variability than harder ones. Chlorite abundance shows the strongest control on the Equotip data, with high chlorite samples producing predominantly high hardness values.

The above observations were confirmed with robust linear and random forest regressions. The regression results have a strong correlation with the measured Equotip (Figure 1). In this particular case, chlorite and white mica distribution showed a strong relationship with the Equotip data, along with some input from epidote and the general Fe-silicate species. Other minerals including gypsum were locally important.

Figure 1. Gray line is the measured Equotip values and

colored lines are the different regression models to

predict hardness.

3.2. Gold Grade Prediction in the Skarn

The classification model was trained at 25cm, 40cm and 2m (assay interval) to learn the signature of Corescan where there is Au present. The variable importance analysis demonstrated that the minerals most important in predicting Au grade were, in order of importance, magnetite, white mica, carbonate, aspectral (combined group that includes unaltered feldspars and microcrystalline quartz), iron oxides, montmorillonite, chlorite, gypsum, epidote, and Mg-chlorite.

There is excellent visual agreement with the Au random forest model generated from the hyperspectral data (Figure 2). Interestingly, the hyperspectral mineralogy sees changes in the gold on a smaller scale than the assay data (the prediction can be confirmed with higher resolution assay data).

Figure 2. From left to right, logged lithology 1,

predicted lithology 1, logged lithology 2, predicted

lithology 2, Au grade from assay, and Au grade

predicted from hyperspectral mineralogical data.

3.3. Skarn Prediction The model was trained to learn where there is skarn and where there is no skarn and subsequently to speciate the ten skarns recognized at the project using the core logging data as its training dataset. The variable importance analysis determined that the most important variables for prediction of skarn versus background are, in order of importance, aspectral, white mica, iron oxides, montmorillonite, epidote, magnetite, chlorite, carbonate, gypsum, and garnet. In comparison with the core logging results, the skarn prediction agreed remarkably well, with the exception of two <2m intervals that contain previously unidentified skarn (Figure 2).

4. Discussion

Training machine learning algorithms to learn the signatures of Corescan where there is grade and geometallurgical parameters available is an important tool for mineral exploration and exploitation allowing for timelier, well informed decisions to be made by a geologist or geometallurgist. If the signature is robust, as shown in the examples above, it is possible to predict Au assay, lithological/alteration domains and

geometallurgical variables everywhere there is hyperspectral data.

The power of building predictive models is that the data can be used to predict in areas where a certain parameter has not been collected in the past and where at present day there is no sample available due to prior destructive sampling techniques. In the case of the hardness testing, data can be predicted where the rock is broken, and no sample can ever be collected (Figure 3).

Figure 3. Applying a regression model (blue line) to

predict hardness in an area where no data was able to

be collected (data collected is in gray).

5. Conclusions New technologies, particularly those with heavy, ‘big-data’ profiles, require exploration and mining professionals to become increasingly sophisticated and efficient in data handling and analysis. The inherent multi-variate nature of geological systems and problems, lend themselves well to advanced data analytical tools such as the machine learning tools discussed in this paper. Rather than only use some small portion of the pipeline of data coming out of our prospects and mines, we can leverage these trained algorithms to help sift through and analyze these huge volumes of data, and ultimately enhance geological and metallurgical models.

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NOTASTHE PREDICTIVE POWER OF

HYPERSPECTRAL CORE IMAGING,

APPLICATIONS TO GRADE AND

GEOMETALLURGICAL PARAMETERS

Tom Carmichael, Brenton Crawford, Sam Scher, Brigette

A. Martini