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

作     者：Pratchaya Takaew Jianhong Cecilia Xia Luc S.Doucet Pratchaya Takaew;Jianhong Cecilia Xia;Luc S.Doucet

作者机构：Department of Spatial SciencesSchool of Earth and Planetary SciencesCurtin University6845 PerthWAAustralia Earth Dynamics Research GroupSchool of Earth and Planetary SciencesCurtin University6845 PerthWAAustralia State Key Laboratory of Geological Processes and Mineral ResourcesChina University of GeosciencesWuhanHubei 430074China 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2024年第15卷第1期

页      面：339-353页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0708[理学-地球物理学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported the open fund of State Key Laboratory of Geological Processes and Mineral Resources China University of Geosciences Wuhan (Grant No.GPMR202201) 

主　　题：Machine learning Artificial intelligence Geochemistry Igneous petrology Basalts Tectonic settings 

摘      要：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 *** 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 *** 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 ***,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 ***,weathering,erosion,and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic *** 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 *** contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine *** learning,especially Convolutional Neural Network(CNN)is the most accurate ***,it requires a large dataset and considerable processing time,and the gain of accuracy can be at the expense of ***,this study designed guidelines for data pre-processing,tectonic setting classification and objectively evaluating the model *** 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 scie