70 years of machine learning in geoscience in review

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.

arXiv comment36 pages, 17 figures, book chapter

References in corpus (13)

• Recursive Partitioning for Heterogeneous Causal Effects
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• A trans-disciplinary review of deep learning research for water resources scientists
• Using Transfer Learning for Image-Based Cassava Disease Detection
• Reconstruction of three-dimensional porous media using generative adversarial neural networks
• Generalized Seismic Phase Detection with Deep Learning
• P-wave arrival picking and first-motion polarity determination with deep learning
• Machine Learning Predicts Laboratory Earthquakes
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Combining Physically-Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn from Mismatch?
• Estimating Fault Friction from Seismic Signals in the Laboratory
• Modeling flow and transport in fracture networks using graphs
• Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks

Cited by in corpus (3)

• Complex-valued neural networks for machine learning on non-stationary physical data
• Automation and AI Technology in Surface Mining With a Brief Introduction to Open-Pit Operations in the Pilbara
• Using deep generative neural networks to account for model errors in Markov chain Monte Carlo inversion