3D inversion of joint gravity and magnetic data using a random forest classifier

Clicks: 1
ID: 315849
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #185 of 220 articles by views in geophysical journal international

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 220 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Summary Machine learning offers new opportunities for geophysical inverse problems, yet conventional regularized inversions of potential field data remain limited by global smoothing constraints and low structural resolution. We propose a locally adaptive, data-driven framework that combines synthetic Earth model generation and ensemble learning for joint gravity and magnetic interpretation. Training models are generated using geologically informed Voronoi-based geometries and planar structures, and a random forest classifier is trained on local statistical features of gravity and magnetic anomalies. The method yields geologically consistent subsurface models that reproduce observed anomaly characteristics without explicit regularization or iterative inversion. Compared with nonlinear Bayesian and traditional regularized inversions applied to the same dataset, the approach provides a substantial reduction in computational cost while preserving key structural features. The performance of the method is inherently linked to how representative the training ensemble is with respect to the target structure, and the results should be interpreted within this context. This framework demonstrates a practical and efficient alternative for potential field inversion using machine learning.
Reference Key
openalex_W7163568917 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Emad Ghalenoei*, Jan Dettmer, Mohammed Y Ali, Jeong Woo Kim
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag160
URL
https://doi.org/10.1093/gji/ggag160
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.