A Bayesian Gaussian Process Framework for the Discrete Inversion of 2D Gravity Data: Applications to the West Korea and Godavari Basins

Clicks: 19
ID: 328362
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
Steady

Ranked #193 of 338 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 338 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 While Bayesian inference is widely applied to magnetotelluric, seismic, and magnetic datasets, its application to gravity anomalies remains relatively unexplored. To address this, we introduce InDIA (Inversion of Density Interface and its Application), a Python-based Bayesian inference framework utilizing a structural Gaussian process. InDIA is developed to estimate discretized subsurface layer depths and densities while explicitly quantifying uncertainty. Unlike previous techniques restricted to continuous depth profiles and localized issues, InDIA integrates diverse prior information to effectively resolve both local and regional gravity anomalies. This approach successfully mitigates the multi-parameter challenges and convergence issues common in local gradient-based optimization techniques, such as Adam. The superiority of this algorithm has been validated through various synthetic models (involving multi-prism configurations, two-layer models with lateral density variations, heterogenous subsurface model with lateral and vertical density variation along with faulted dipping models) featuring both constant and variable density distributions—whether lateral, vertical (prior-constrained), or both—incorporating Gaussian noise, Random-walk noise, Systematic noise and Salt and Pepper noise to replicate real conditions. Furthermore, the framework’s field applicability is demonstrated using two real gravity datasets. First, it tackles multi-layered subsurface profiling in the West Korea Basin. Second, it simultaneously resolves depth and prior-constrained vertical density variations in the Godavari Basin, India. In both field applications, the inverted parameters are geologically viable and closely align with previously established models, confirming InDIA as a reliable tool for gravity data inversion.
Reference Key
openalex_W7212373457 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Isani Saha, Chandra Prakash Dubey
Are you one of these authors? Claim your profile
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag375
URL
https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggag375/71046749/ggag375.pdf
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.