70 years of machine learning in geoscience in review

Jesper Sören Dramsch

doi:10.1016/bs.agph.2020.08.002

Home/Archive/arXiv

arXiv · 2006.13311

70 years of machine learning in geoscience in review

Jesper Sören Dramsch

View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

physics.geo-ph cs.CV cs.LG eess.IV

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Jesper Sören Dramsch. 2020-08-26. 70 years of machine learning in geoscience in review. https://doi.org/10.1016/bs.agph.2020.08.002

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Direction-Aware Masked Pretraining for 3D Seismic Representation Learning and Transfer to Cross-Area Acoustic Impedance Inversion

Large archives of unlabeled three-dimensional seismic data offer opportunities for self-supervised representation learning and subsequent transfer to acoustic impedance inversion. However, conventional masked pretraining often treats three axes equivalently, overlooking differences between lateral reflector structure and vertical waveform characteristics. We propose a direction-aware masked autoencoder for three-dimensional post-stack seismic data, combining anisotropic tokenization, direction-aware representation, trace-aligned tube masking, and reconstruction constraints designed for reflector continuity and waveform characteristics. We evaluate reconstruction quality and downstream transferability using field data through masked reconstruction and cross-area acoustic impedance inversion. Reconstruction is more sensitive to lateral token resolution than to moderate changes in vertical patch length. Within the evaluated configurations, increasing encoder capacity does not fully compensate for reconstruction fidelity loss associated with coarser tokenization. Preferred token scales and masking strategies differ between reconstruction and inversion, indicating that reconstruction fidelity alone is not a reliable indicator of transferability. For cross-area inversion, the pretrained model is fine-tuned in the source area and applied to the target area without further parameter updates. With limited target-area well control, the proposed framework reduces normalized root-mean-square error by 20.8% relative to a pretrained conventional masked autoencoder across eight target-area test wells under matched tokenization and downstream settings. These results demonstrate the value of direction-aware masked pretraining for field seismic inversion and show that token scale and masking strategy should be selected according to downstream-task requirements rather than reconstruction accuracy alone.

physics.geo-ph↗

arXiv2026

Predicting the Elastic Properties of a Cemented Granular Material during Chemical Damage (Debonding)

While underground reservoirs emerge as essential elements to face global warming, these systems represent complex multi-physical and multiscale problems. The considered injection of fluids during hydrogen storage, carbon dioxide sequestration, or geothermal energy recovery involves a modification of the chemical equilibrium of the fluid in the porous reservoir. Chemical reactions can induce microstructural changes of the rock matrix, leading to a reduction of elastic properties of the material, and to potential settlement or stress redistribution. Consequently, it becomes pivotal to establish predictive behavior laws to describe the effect of chemical damage on elastic properties. Facing the difficulties to estimate experimentally the impact of chemical damage on mechanical properties, a Digital Rock Physics approach is proposed in this contribution. This numerical homogenization scheme is used to compare two distinct types of microstructure models: the first one consists in a Discrete Element Model, while the second one employs a continuous description. This continuous formulation is based on a Phase-Field description to predict the evolution of the microstructure subjected to chemical alterations and on the Fast Fourier Transform to estimate the macroscopic properties of the material. Finally, these frameworks establish different softening laws that can be used as constitutive ingredients for a cemented material during its weathering.

physics.geo-ph↗

arXiv2026

Determination of Physical Height Differences from Time Transfer via the ACES Mission -- A Simulation Study

The determination of physical height differences using highly stable atomic clocks has emerged as a novel approach in relativistic geodesy, exploiting the gravitational redshift as a direct observable of geopotential differences. In this study, we investigate the feasibility of satellite-based clock comparisons using the Atomic Clock Ensemble in Space (ACES) onboard the International Space Station, which enables time transfer via microwave (MWL) and optical (ELT) links. Since operational optical data are not yet available, a comprehensive full-scale simulation of realistic ACES observation scenarios is performed, including detailed noise models of clocks and links. A slope-based estimation method is applied to time series of clock comparisons in order to extract the relativistic redshift signal and derive height differences between the ground stations. The performance of the approach is evaluated for quasi-common view, non-common view, and split non-common view configurations, where the latter divides the observation period into shorter intervals. The results show that optical links enable faster convergence and can achieve height accuracies at the decimeter level within a few days and at the centimeter level over longer periods, while microwave links are more strongly affected by noise and bias contributions. Non-common view processing significantly increases observation availability with only minor loss in accuracy, and the split approach provides robust solutions for larger networks. These findings demonstrate the strong potential of satellite-based clock comparisons as a remote-sensing technique for determining physical height differences on a continental scale.

physics.geo-ph↗