Bayesian Inference and Its Application to Geophysical Inversion

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geophysics".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 4508

Special Issue Editor

School of Geosciences, University of Edinburgh, Edinburgh, UK
Interests: bayesian inference; seismic inversion; tomography

Special Issue Information

Dear Colleagues,

Geophysical observations are generally collected in remote sensing-type experiments, which do not represent the Earth’s interior directly. Geophysical inversion is, therefore, required to characterize properties of the Earth’s interior from these measurements.

Geophysical inverse problems are usually ill-conditioned and have nonunique solutions due to the nonlinearity of the physical relationships between the model parameters and data, to insufficient data sampling and to noise in the data. It is, therefore, necessary to quantify solution uncertainties in order to interpret the inversion results correctly.

Bayesian inference provides a powerful theoretical framework for solving inverse problems and to quantify uncertainties, having become popular in geophysics in the past decades, and having shown great potential in solving various geophysical inverse problems.

This Special Issue aims to collect all research developments related to Bayesian inference in geophysics, from method developments to various applications, including seismic, gravitational, electromagnetic inversion, etc., in order to provide a comprehensive update of the state of the art in this field.

Dr. Xin Zhang
Guest Editor

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Keywords

  • Bayesian inference
  • Uncertainty quantification
  • Geophysical inversion
  • Inverse problem
  • Probability

Published Papers (2 papers)

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22 pages, 6802 KiB  
Article
Principles of a Fast Probability-Based, Data-Adaptive Gravity Inversion Method for 3D Mass Density Modelling
by Marilena Cozzolino, Paolo Mauriello and Domenico Patella
Geosciences 2022, 12(8), 306; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences12080306 - 12 Aug 2022
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Abstract
The aim of this paper is to present a 3D Probability-based Earth Density Tomography Inversion (PEDTI) method derived from the principles of the Gravity Probability Tomography (GPT). The new method follows the rationale of a previous Probability-based Electrical Resistivity Inversion (PERTI) method, which [...] Read more.
The aim of this paper is to present a 3D Probability-based Earth Density Tomography Inversion (PEDTI) method derived from the principles of the Gravity Probability Tomography (GPT). The new method follows the rationale of a previous Probability-based Electrical Resistivity Inversion (PERTI) method, which has proved to be a fast and versatile user-friendly approach. Along with PERTI, PEDTI requires no external a priori information. In this paper, after recalling the GPT imaging method, the PEDTI theory is developed and concluded with a key inversion formula that allows a wide class of equivalent solutions to be computed. Two synthetic cases are discussed to show the resolution that can be achieved in the determination of density contrasts and to examine the nature of the gravity non-uniqueness problem. Regarding the first issue, it is shown that the estimate of the density by PEDTI can change by about two orders of magnitude and get closer to reality with a more focused solution on a specific source body. Regarding the second problem, it is shown that two levels of equivalence can be classified, i.e., weak and strong equivalence, for a finer selection among the solutions. This is obtained by defining two appropriate statistical indices based on the information power of both the input and output gravity datasets. Full article
(This article belongs to the Special Issue Bayesian Inference and Its Application to Geophysical Inversion)
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17 pages, 26225 KiB  
Article
Characterizing a Wedged Chalk Prospect in the Danish Central Graben Using Direct Probabilistic Inversion
by Kenneth Bredesen, Ian Herbert, Florian Smit, Ask Frode Jakobsen, Peter Frykman and Anders Bruun
Geosciences 2022, 12(5), 194; https://0-doi-org.brum.beds.ac.uk/10.3390/geosciences12050194 - 29 Apr 2022
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Abstract
A novel direct probabilistic inversion using seismic pre-stack data as input to characterize a wedged chalk reservoir prospect was demonstrated from the Upper Cretaceous unit, Danish North Sea. The objective was to better resolve the lateral extent and pinch-out of the chalk prospect [...] Read more.
A novel direct probabilistic inversion using seismic pre-stack data as input to characterize a wedged chalk reservoir prospect was demonstrated from the Upper Cretaceous unit, Danish North Sea. The objective was to better resolve the lateral extent and pinch-out of the chalk prospect in a frontier exploration setting and compare the results with a more traditional deterministic inversion and geostatistical reservoir modeling. The direct probabilistic inversion results provided additional reservoir insights that were challenging to obtain from the more traditional workflows and are also more flexible for associated uncertainty assessments. Hence, this study demonstrates the usefulness of such direct probabilistic inversions even with suboptimal data availability. Full article
(This article belongs to the Special Issue Bayesian Inference and Its Application to Geophysical Inversion)
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