Improving Automated Geological Logging of Drill Holes by Incorporating Multiscale Spatial Methods

Abstract

Manually interpreting multivariate drill hole data is very time-consuming, and different geologists will produce different results due to the subjective nature of geological interpretation. Automated or semi-automated interpretation of numerical drill hole data is required to reduce time and subjectivity of this process. However, results from machine learning algorithms applied to drill holes, without reference to spatial information, typically result in numerous small-scale units. These small-scale units result not only from the presence of very small rock units, which may be below the scale of interest, but also from misclassification. A novel method is proposed that uses the continuous wavelet transform to identify geological boundaries and uses wavelet coefficients to indicate boundary strength. The wavelet coefficient is a useful measure of boundary strength because it reflects both wavelength and amplitude of features in the signal. This means that boundary strength is an indicator of the apparent thickness of geological units and the amount of change occurring at each geological boundary. For multivariate data, boundaries from multiple variables are combined and multiscale domains are calculated using the combined boundary strengths. The method is demonstrated using multi-element geochemical data from mineral exploration drill holes. The method is fast, reduces misclassification, provides a choice of scales of interpretation and results in hierarchical classification for large scales where domains may contain more than one rock type.

Integrated Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Wavelet Tessellation: A New, Rapid Approach for Lithogeochemical Analysis and Interpretation

“…In the mineral exploration context, Hill and Uvarova (2018) [5] extracted geological information from geochemical data derived from exploration drill holes and generated pseudologs with less misclassifications when compared with geologists logging of the same drill cores. Both of those applications were using univariate datasets, and more recently, a multivariate version of the wavelet tessellation method, Data Mosaic, is described in Hill et al (2020) [4] which is accessible through the Data Mosaic web app (https://datamosaic.geoanalytics.group/) (access date: 28 January 2021).…”

“…Hill et al, (2020) [4] highlight that success in generating pseudologs utilising the Data Mosaic method is dependent on factors including: regular sampling intervals at the highest possible resolution; at least one pure (nonmixed) sample for correct classification of rock units (i.e., the smallest lithology intersection must be at least two sampling intervals in length); the careful selection of variables that represent geological processes of interest for the application. For example, if the goal is primary rock type classification, then selection of immobile elements is more appropriate than trace metals or highly mobile elements.…”

“…Prior to LIBS analyses, the rock-block surface was slightly polished to remove any saw marks. The primary advantage of the Data Mosaic wavelet tessellation method [4] is its multivariate capability in which multiple variables can be integrated by combining their boundary locations and strengths, generating multivariate mosaic plots. Boundaries are extracted for each variable, boundaries strengths, which are based on first wavelet coefficients, are rescaled for each variable, so the boundaries are comparable and boundaries for all variables are added into a single set.…”

“…Each method has inherent limitations in its application and algorithm selection is often dependant on the question being asked, the available data, and user preference. For example, Hill et al (2020) [4] applied the k-means method for the generation of 20 clusters correspondent to the 20 logged lithological units for lithogeochemical analysis through wavelet tessellation and analysis of classified mosaic plots. The choice of the k-means method was based on its common application as a benchmark machine learning algorithm for comparison to other algorithms and that that k-means generally works reasonably well for large datasets.…”

“…Such techniques include the recently developed Data Mosaic web application [3,4], which utilises continuous wavelet transform to identify geological boundaries from numerical data, such as geochemistry, and can generate pseudologs. The conversion of streams of geochemical data into pseudologs provides a useful visual summary of the information that can be assessed at various scales to extract spatial information on broad lithological boundaries and lithogeochemical characterisation [4][5][6]. Applications such as Data Mosaic have the additional advantage of providing an objective assessment of geochemical data.…”