Machine learning and tectonic setting determination: Bridging the gap between Earth scientists and data scientists

Abstract

Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies, because current methods are designed using limited number of data and cannot handle large datasets. In geoscience, tectonic discrimination illustrates this issue, using geochemical analyses to define tectonic settings when most of the geological record is missing, which is the case for most of the older portion of the Earth’s crust. Basalts are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings, and their chemical compositions can be an effective way to understand tectonics-related mantle processes. However, the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique solutions. Also, weathering, erosion, and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic records. To address those limitations, supervised machine learning techniques (a part of artificial intelligence) are being utilized more often as a tool to analyze multidimensional datasets and statistically process data to tackle big data challenges. This contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine learning. Deep learning, especially Convolutional Neural Network (CNN) is the most accurate approach. However, it requires a large dataset and considerable processing time, and the gain of accuracy can be at the expense of interpretability. Therefore, this study designed guidelines for data pre-processing, tectonic setting classification and objectively evaluating the model performance. We also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research, aimed at closing the divide between earth scientists and data scientists.

Keywords

• Machine learning
• Artificial intelligence
• Geochemistry
• Igneous petrology
• Basalts
• Tectonic settings