Machine learning has evolved over several decades. Since the mid-2000s, neural networks re-emerged along with various deep learning architectures. These advances have enabled successful applications of deep learning methods in many industries. However, these methods are not being fully exploited in ...
This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the co-developments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging...
Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the ...
sion or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of so...
F. Leung. Big data and machine learning in geoscience and geoengineering: Introduction[J]. Geoscience Frontiers, 2021, 12(1): 327-329. DOI: 10.1016/j.gsf.2020.05.006 Citation: Wengang Zhang, Wengang Zhang, Jianye Ching, Andy Y. F. Leung. Big data and machine learning in geoscience ...
Annette L. Walkerd a.Hanson Center for Space Science, University of Texas at Dallas, Richardson, TX 75080, USA; b.Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA; c.BEACON Center for the Study of Evolution in Action, Michigan State Un...
Explainability can foster trust in artificial intelligence in geoscience Jesper Sören Dramsch Monique M. Kuglitsch Arthur Hrast Essenfelder Nature Geoscience(2025) Techniques and methods for seafloor topography mapping: past, present, and future ...
This paper reviews the progress of four advanced machine learning methods for spatial data handling, namely, support vector machine (SVM)-based kernel learning, semi-supervised and active learning, ensemble learning, and deep learning. These four machine learning modes are representative because they ...
Maniar H, Ryali S, Kulkarni MS, Abubakar A (2018) Machine-learning methods in geoscience. In: SEG technical program expanded abstracts 2018. Society of exploration geophysicists, pp. 4638–4642. https://doi.org/10.1190/segam2018-2997218.1 Manshad A, Rostami H, Niknafs H, Mohammadi A (2017)...
Recently, using machine learning methods in geoscience studies has been increasing in various applications, including mapping land-cover types and monitoring land usage. Some work has been carried out in the field of identifying and classifying lithologies of valuable minerals [56]. Digital image cl...