Pseudo trans-dimensional 3d geometrical inversion: a proof of concept using gravity data

Abstract

We present and apply a pseudo trans-dimensional inversion method for 3D geometrical gravity inversion, in which the number of rock units, their geometry, and their density can vary during sampling. The method is designed for efficient exploration of the model space and to infer the presence and properties of units not directly observable but detectable with geophysical data. Sampling relies on a non-reversible Metropolis-Hastings algorithm, during which rock units can be added or removed from the model, interface geometries are perturbed using random fields, and densities are sampled from distributions informed by prior information. To visualise the space of sampled models and to aid interpretation, a workflow is proposed that combines dimensionality reduction with the clustering of models in families. The capabilities of the inversion method are evaluated using two synthetic cases. The first is a motivating example aimed at recovering an intrusion missing from the prior model. It features a horizontal layer-cake where fixed-dimensional inversion fails to adequately fit the data and sample models close to the true model, while the proposed pseudo trans-dimensional approach is much more successful. The second case investigates the recovery of two missing units and the capability to overcome prior model biases. Results show the potential of our method to infer the presence of unseen geological features such as intrusions. However, they suggest that with biased prior geological modelling, it may be challenging to infer with certainty the presence of more than two previously unknown rock units at depth.

A Spectral-Domain Pseudo-Inverse Method for True 3D Gravity Inversion
Shengchang Chen
arXiv13 Oct 2026PreprintCC BY-NC-SA 4.0Full textGeophysics & seismology Numerical analysis & scientific computingarXiv
EnviFlux (v1.0): a simplified surface flux inversion tool based on four-dimensional variational data assimilation (4D-Var)
Ross N. Bannister
Geoscientific Model Development12 Oct 2026Peer-reviewedCC BY 4.0Full textClimate scienceCrossref
Asymptotic behavior of lidar scattering properties of absorbing dust aerosols across Rayleigh and geometrical-optics regimes: theory and implications
Anthony La Luna, Zhibo Zhang, Qianqian Song +3
Atmospheric Chemistry and Physics13 Oct 2026Peer-reviewedCC BY 4.0Full text1 citedClimate scienceCrossref
Flow dependence of boundary-layer scheme parameters revealed by stochastic inversion
Magdalena Fritz, Stefano Serafin, Tomislava Vukicevic +1
EGUsphere5 Nov 2026PreprintCC BY 4.0General earth scienceEGUsphere
Selective Impacts of Prior, Background-Concentration, and Observation-Error Assumptions on Urban CO2 Inversion Estimates
Munsu Park, Sujong Jeong, Sojung Sim
EGUsphere3 Nov 2026PreprintCC BY 4.0General earth scienceEGUsphere

Cite

BibTeX RIS

BibTeX

@article{giraud2026pseudo,
  title = {{Pseudo trans-dimensional 3d geometrical inversion: a proof of concept using gravity data}},
  author = {Jérémie Giraud and Vitaliy Ogarko and Guillaume Caumon and Guillaume Pirot and Leonardo Portes and Paul Cupillard and Julien Herrero},
  journal = {Geophysical Journal International},
  year = {2026},
  month = sep,
  doi = {10.1093/gji/ggaf501},
  url = {https://hal.science/hal-05432366v1},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - Pseudo trans-dimensional 3d geometrical inversion: a proof of concept using gravity data
AU  - Jérémie Giraud
AU  - Vitaliy Ogarko
AU  - Guillaume Caumon
AU  - Guillaume Pirot
AU  - Leonardo Portes
AU  - Paul Cupillard
AU  - Julien Herrero
PY  - 2026
DA  - 2026/09/04
JO  - Geophysical Journal International
DO  - 10.1093/gji/ggaf501
UR  - https://hal.science/hal-05432366v1
AB  - We present and apply a pseudo trans-dimensional inversion method for 3D geometrical gravity inversion, in which the number of rock units, their geometry, and their density can vary during sampling. The method is designed for efficient exploration of the model space and to infer the presence and properties of units not directly observable but detectable with geophysical data. Sampling relies on a non-reversible Metropolis-Hastings algorithm, during which rock units can be added or removed from the model, interface geometries are perturbed using random fields, and densities are sampled from distributions informed by prior information. To visualise the space of sampled models and to aid interpretation, a workflow is proposed that combines dimensionality reduction with the clustering of models in families. The capabilities of the inversion method are evaluated using two synthetic cases. The first is a motivating example aimed at recovering an intrusion missing from the prior model. It features a horizontal layer-cake where fixed-dimensional inversion fails to adequately fit the data and sample models close to the true model, while the proposed pseudo trans-dimensional approach is much more successful. The second case investigates the recovery of two missing units and the capability to overcome prior model biases. Results show the potential of our method to infer the presence of unseen geological features such as intrusions. However, they suggest that with biased prior geological modelling, it may be challenging to infer with certainty the presence of more than two previously unknown rock units at depth.
SN  - 0956-540X
SN  - 1365-246X
ER  -

Abstract

Related

Cite