70 years of machine learning in geoscience in review
- URL: http://arxiv.org/abs/2006.13311v3
- Date: Wed, 26 Aug 2020 12:34:24 GMT
- Title: 70 years of machine learning in geoscience in review
- Authors: Jesper S\"oren Dramsch
- Abstract summary: This review gives an overview of the development of machine learning in geoscience.
I explore the shift of kriging towards a mainstream machine learning method and the historic application of neural networks in geoscience.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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
towards a mainstream machine learning method and the historic application of
neural networks in geoscience, following the general trend of machine learning
enthusiasm through the decades. Furthermore, this chapter explores the shift
from mathematical fundamentals and knowledge in software development towards
skills in model validation, applied statistics, and integrated subject matter
expertise. The review is interspersed with code examples to complement the
theoretical foundations and illustrate model validation and machine learning
explainability for science. The scope of this review includes various shallow
machine learning methods, e.g. Decision Trees, Random Forests, Support-Vector
Machines, and Gaussian Processes, as well as, deep neural networks, including
feed-forward neural networks, convolutional neural networks, recurrent neural
networks and generative adversarial networks. Regarding geoscience, the review
has a bias towards geophysics but aims to strike a balance with geochemistry,
geostatistics, and geology, however excludes remote sensing, as this would
exceed the scope. In general, I aim to provide context for the recent
enthusiasm surrounding deep learning with respect to research, hardware, and
software developments that enable successful application of shallow and deep
machine learning in all disciplines of Earth science.
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