# 70 years of machine learning in geoscience in review

arXiv: https://arxiv.org/abs/2006.13311 · HTML: https://mlsanity.com/paper/2006.13311/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review](https://mlsanity.com/paper/2303.10462/) (2023)
- [A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales](https://mlsanity.com/paper/2108.11875/) (2021)
- [Using explainability to design physics-aware CNNs for solving subsurface inverse problems](https://mlsanity.com/paper/2211.08651/) (2022)
- [Machine learning applications in archaeological practices: a review](https://mlsanity.com/paper/2501.03840/) (2025)
- [PetroGAN: A novel GAN-based approach to generate realistic, label-free petrographic datasets](https://mlsanity.com/paper/2204.05114/) (2022)
- [An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone](https://mlsanity.com/paper/2309.07992/) (2023)
- [A Survey on Mathematical Aspects of Machine Learning in GeoPhysics: The Cases of Weather Forecast, Wind Energy, Wave Energy, Oil and Gas Exploration](https://mlsanity.com/paper/2102.03206/) (2021)
- [End-to-End Intelligent Framework for Rockfall Detection](https://mlsanity.com/paper/2102.06491/) (2021)
- [Geospatial Soil Quality Analysis: A Roadmap for Integrated Systems](https://mlsanity.com/paper/2512.09817/) (2025)
- [Machine Learning in Fish Farming](https://mlsanity.com/paper/2609.13919/) (2026)

## Abstract (arXiv)

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.