Machine Learning for the Geosciences

Neta Rabin () and Yuri Bregman ()
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Neta Rabin: Tel-Aviv University
Yuri Bregman: Soreq Nuclear Research Center

A chapter in Machine Learning for Data Science Handbook, 2023, pp 779-800 from Springer

Abstract: Abstract This chapter provides a review on data-driven problems in geoscience, with a special focus on the sub-field of seismology. Geoscience phenomena are often studied by using data-driven models, which are based on various types of data that are monitored and sensed using sophisticated equipment. The large amounts of gathered data make it very attractive for incorporating machine learning and deep learning techniques for advancing research challenges and for promoting the social benefit such as hazard predictions and preservation of natural resources. In the field of seismology, the tasks include seismic event detection, localization, and classification. These have been approached by machine learning techniques, mainly by supervised machine learning methods, since the 90s. Nowadays, deep learning architectures are applied for modeling larger amounts of seismic data in order to provide fast and accurate event detection and classification solutions that are incorporated into real-time analysis systems. We review the noticeable research trends and developments in the field and conclude by discussing advantages, drawbacks, and future possible research directions.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_34

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DOI: 10.1007/978-3-031-24628-9_34

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