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

• Published in: Gondwana research Vol. 109; pp. 1 - 17
• Main Authors: Zhang, Wengang, Gu, Xin, Tang, Libin, Yin, Yueping, Liu, Dongsheng, Zhang, Yanmei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2022
• Subjects: Deep learning; Geoengineering and geoscience; Machine learning; Optimization algorithms; VOSviewer; Deep learning; Geoengineering and geoscience; Optimization algorithms; VOSviewer; Machine learning
• ISSN: 1342-937X; 1878-0571
• DOI: 10.1016/j.gr.2022.03.015

[Display omitted] •Common artificial intelligence (AI) algorithms in geoscience and geoengineering introduced.•What can be learnt from application of AI algorithms summarized.•Ongoing work and future recommendations provided. 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 algorithms in geoengineering and geoscience. Various ML, DL, and OA approaches are firstly concisely introduced, 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 applications as well as work ongoing and future recommendations. This review paper aims to make a comprehensive 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.