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A Primer on Machine Learning in Subsurface GeosciencesA Brief Review of Popular Machine Learning Algorithms in Geosciences

A Primer on Machine Learning in Subsurface Geosciences: A Brief Review of Popular Machine... [In the last several decades, computer scientists and statisticians have developed and implemented a plethora of machine learning (ML) algorithms. Although the application of data-driven modeling is relatively new to geoscience, we can trace back some of its early applications to the 1980’s and 1990’s. This chapter will discuss the fundamental theory and analytic framework of many popular ML algorithms. Understanding the fundamentals of these algorithms, network-specific hyperparameters, and their meaning is essential to better implement these algorithms in our datasets and enhance the success rate of data-driven modeling. These algorithms are based on solid mathematical and statistical theories. Indeed, some algorithms are better than others for certain types of applications; however, sometimes, our lack of understanding of algorithms and the nuances of their applications to specific datasets cause them to underperform compared to others. Once we understand the fundamentals of algorithms and our datasets, ML will be more fun and provoking, which will facilitate further progress of geo-data science.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Primer on Machine Learning in Subsurface GeosciencesA Brief Review of Popular Machine Learning Algorithms in Geosciences

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References (76)

Publisher
Springer International Publishing
Copyright
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-71767-4
Pages
81 –121
DOI
10.1007/978-3-030-71768-1_4
Publisher site
See Chapter on Publisher Site

Abstract

[In the last several decades, computer scientists and statisticians have developed and implemented a plethora of machine learning (ML) algorithms. Although the application of data-driven modeling is relatively new to geoscience, we can trace back some of its early applications to the 1980’s and 1990’s. This chapter will discuss the fundamental theory and analytic framework of many popular ML algorithms. Understanding the fundamentals of these algorithms, network-specific hyperparameters, and their meaning is essential to better implement these algorithms in our datasets and enhance the success rate of data-driven modeling. These algorithms are based on solid mathematical and statistical theories. Indeed, some algorithms are better than others for certain types of applications; however, sometimes, our lack of understanding of algorithms and the nuances of their applications to specific datasets cause them to underperform compared to others. Once we understand the fundamentals of algorithms and our datasets, ML will be more fun and provoking, which will facilitate further progress of geo-data science.]

Published: May 4, 2021

Keywords: Machine learning algorithms; Model hyperparameters; Clustering; Neural networks; Decision trees; Deep learning; Ensemble learning; Physics-informed machine learning

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