Machine learning in geo- and environmental sciences: From small to large scale. (August 2020) - Tahmasebi, Pejman; Kamrava, Serveh; Bai, Tao; Sahimi, Muhammad
Highlights: The important methods of data mining and machine learning are reviewed and discussed. The machine learning methods are reviewed for various applications in environmental science. Deep learning methods for various problems and scales are discussed. Several problems, the existing solutions, and potential directions are discussed. Abstract: In recent years significant breakthroughs in exploring big data, recognition of complex patterns, and predicting intricate variables have been made. One efficient way of analyzing big data, recognizing complex patterns, and extracting trends is through machine-learning (ML) algorithms. The field of porous media, and more generally geoscience, have also witnessed much progress, and recent progress in developing various ML techniques have benefitted various problems in porous media and geoscience across disparate scales. Thus, it is becoming increasingly clear that it is imperative to adopt advanced ML methods for the problems in porous media and geoscience because they enable researchers to solve many difficult problems. At the same time, one can use the already existing extensive knowledge of porous media to endow ML algorithms and develop novel physics-guided methods. The goal of this review paper is to provide the first comprehensive review of the recently developed methods in the ML algorithms and describe their application to porous media and geoscience. Thus, we review the basic concept of the ML and describe more advanced methods, known as deep-learning algorithms. Then, the application of such methods to various problems in porous media and geoscience, such as hydrological modeling, fluid flow in porous media, and (sub)surface characterization, are reviewed. We also provide a discussion of future directions in this rapidly developing field.