T5 Innovations in Geological Mapping

Sunday, 22 September 2024
8:00 AM - 12:00 PM
206B

MODELLING THE THICKNESS AND 3D LITHOLOGICAL PROPERTIES OF SEDIMENTS ABOVE BEDROCK ACROSS ALBERTA USING SPATIALLY AUGMENTED MACHINE LEARNING ALGORITHMS

Paper number: T5-4
Program: Technical Sessions
Time: 8:45 AM – 9:00 AM

Authors

Abstract

Information on the thickness and composition of sediments above bedrock is fundamental to many geoscience studies and is a prerequisite for regional hydrogeological and geotechnical investigations. However, it can be difficult to estimate these properties across large regions containing varied physiography; traditional methods of interpolating bedrock elevation often underestimate or overestimate bedrock elevation in areas of rugged terrain, and lithological composition is usually inferred from stratigraphic modelling, which is difficult/costly to apply to regions with poorly defined stratigraphy, or where the geology exhibits a large degree of inherent lithological variability.

Here, we describe a machine learning spatial prediction workflow that uses information from traditional digital elevation model derived estimates of terrain morphometry and satellite imagery, augmented with spatial feature engineering techniques to predict sediment thickness and 3D lithological information across Alberta, Canada.

First, compiled measurements of sediment thickness were used with a natural language model lift to predict bedrock depth across all available lithologs (> 300,000). The combined data were used for sediment thickness modelling employing several machine learning algorithms (XGBoost, Random forests, and Cubist) and spatial feature engineering techniques, with the use of spatial lag variables being shown to significantly improve predictive performances. Finally, the properties of the sediments above bedrock were estimated based on the predicted probabilities from a binary coarse/fine-grained 3D machine learning classification model. The results, when evaluated in 3D as iso-surfaces, or sliced at specific depth intervals, for example to show the probability of coarse-grained deposits occurred immediately above the bedrock contact, were successful at delineating the distribution of many major sand and gravel dominant geobodies at provincial and sub-regional scales across Alberta.

Geological Society of America Abstracts with Programs. Vol. 56, No. 5

doi: 10.1130/abs/2024AM-404152

© Copyright 2024 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.