A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications

Yuyang He, You Zhou, Tao Wen, Shuang Zhang, Fang Huang, Xinyu Zou, Xiaogang Ma, Yueqin Zhu

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

The development of analytical and computational techniques and growing scientific funds collectively contribute to the rapid accumulation of geoscience data. The massive amount of existing data, the increasing complexity, and the rapid acquisition rates require novel approaches to efficiently discover scientific stories embedded in the data related to geochemistry and cosmochemistry. Machine learning methods can discover and describe the hidden patterns in intricate geochemical and cosmochemical big data. In recent years, considerable efforts have been devoted to the applications of machine learning methods in geochemistry and cosmochemistry. Here, we review the main applications including rock and sediment identification, digital mapping, water and soil quality prediction, and deep space exploration. Research method improvements, such as spectroscopy interpretation, numerical modeling, and molecular machine learning, are also discussed. Based on the up-to-date machine learning/deep learning techniques, we foresee the vast opportunities of implementing artificial intelligence and developing databases in geochemistry and cosmochemistry studies, as well as communicating geochemists/cosmochemists and data scientists.

Original languageEnglish (US)
Article number105273
JournalApplied Geochemistry
Volume140
DOIs
StatePublished - May 2022

Keywords

  • LIBS
  • Mapping
  • Molecular machine learning
  • Reactive-transport modeling
  • Water/soil prediction
  • XAFS

ASJC Scopus subject areas

  • Environmental Chemistry
  • Pollution
  • Geochemistry and Petrology

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