Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: Comprehensive review and future challenge

The so-called Fourth Paradigm has witnessed a boom during the past two decades, with large volumes of observational data becoming available to scientists and engineers. Big data is characterized by the rule of the five Vs: Volume, Variety, Value, Velocity and Veracity. The concept of big data naturally matches well with the features of geoengineering and geoscience. Large-scale, comprehensive, multidirectional and multifield geotechnical data analysis is becoming a trend. On the other hand, Machine learning (ML), Deep Learning (DL) and Optimization Algorithm (OA) provide the ability to learn from data and deliver in-depth insight into geotechnical problems. Researchers use different ML, DL and OA models to solve various problems associated with geoengineering and geoscience. Consequently, there is a need to extend its research with big data research through integrating the use of ML, DL and OA techniques. This work focuses on a systematic review on the state-of-the-art application of ML, DL and OA algo-rithms in geoengineering and geoscience. Various ML, DL, and OA approaches are firstly concisely intro-duced, concerning mainly the supervised learning, unsupervised learning, deep learning and optimization algorithms. Then their representative applications in the geoengineering and geoscience are summarized via VOSviewer demonstration. The authors also provided their own thoughts learnt from these applica-tions as well as work ongoing and future recommendations. This review paper aims to make a compre-hensive summary and provide fundamental guidelines for researchers and engineers in the discipline of geoengineering and geoscience or similar research areas on how to integrate and apply ML, DL and OA methods.(c) 2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

Automated summary

The so-called Fourth Paradigm has led to the availability of large volumes of observational data for scientists and engineers. The concept of big data aligns well with geoengineering and geoscience, and machine learning, deep learning, and optimization algorithms offer insights into geotechnical problems. This review paper comprehensively summarizes the state-of-the-art applications of these algorithms in geoengineering and geoscience and provides guidelines for researchers and engineers in integrating and applying these methods.