Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data
Geophysical methods provide remotely sensed data that are sensitive to subsurface properties and interfaces. Knowledge about discontinuities is important throughout the Earth sciences: for example, the saltwater/freshwater interface in coastal areas drive mixing processes; the temporal development o...
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fttriple:oai:gotriple.eu:10670/1.3ck4ns 2023-05-15T17:58:03+02:00 Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data de Pasquale, Giulia Linde, Niklas Doetsch, Joseph Holbrook, W. Steven 2019-01-01 https://doi.org/10.1093/gji/ggz055 http://www.scopus.com/inward/record.url?eid=2-s2.0-85063732032&partnerID=MN8TOARS en eng doi:10.1093/gji/ggz055 10670/1.3ck4ns http://www.scopus.com/inward/record.url?eid=2-s2.0-85063732032&partnerID=MN8TOARS undefined Serveur académique Lausannois Geophysical Journal International geo stat Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2019 fttriple https://doi.org/10.1093/gji/ggz055 2023-01-22T18:21:06Z Geophysical methods provide remotely sensed data that are sensitive to subsurface properties and interfaces. Knowledge about discontinuities is important throughout the Earth sciences: for example, the saltwater/freshwater interface in coastal areas drive mixing processes; the temporal development of the discontinuity between frozen and unfrozen ground is indicative of permafrost development; and the regolith-bedrock interface often plays a predominant role in both landslide and critical-zone investigations. Accurate detection of subsurface boundaries and their geometry is challenging when using common inversion routines that rely on smoothness constraints that smear out any naturally occurring interfaces. Moreover, uncertainty quantification of interface geometry based on such inversions is very difficult. In this paper, we present a probabilistic formulation and solution to the geophysical inverse problem of inferring interfaces in the presence of significant subsurface heterogeneity. We implement an empirical-Bayes-within-Gibbs formulation that separates the interface and physical property updates within a Markov chain Monte Carlo scheme. Both the interface and the physical properties of the two sub-domains are constrained to favour smooth spatial transitions and pre-defined property bounds. Our methodology is demonstrated on synthetic and actual surface-based electrical resistivity tomography data sets, with the aim of inferring regolith-bedrock interfaces. Even if we are unable to achieve formal convergence of the Markov chains for all model parameters, we demonstrate that the proposed algorithm offers distinct advantages compared to manual- or algorithm-based interface detection using deterministic geophysical tomograms. Moreover, we obtain more reliable estimates of bedrock resistivity and its spatial variations. Article in Journal/Newspaper permafrost Unknown Geophysical Journal International |
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geo stat de Pasquale, Giulia Linde, Niklas Doetsch, Joseph Holbrook, W. Steven Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data |
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geo stat |
description |
Geophysical methods provide remotely sensed data that are sensitive to subsurface properties and interfaces. Knowledge about discontinuities is important throughout the Earth sciences: for example, the saltwater/freshwater interface in coastal areas drive mixing processes; the temporal development of the discontinuity between frozen and unfrozen ground is indicative of permafrost development; and the regolith-bedrock interface often plays a predominant role in both landslide and critical-zone investigations. Accurate detection of subsurface boundaries and their geometry is challenging when using common inversion routines that rely on smoothness constraints that smear out any naturally occurring interfaces. Moreover, uncertainty quantification of interface geometry based on such inversions is very difficult. In this paper, we present a probabilistic formulation and solution to the geophysical inverse problem of inferring interfaces in the presence of significant subsurface heterogeneity. We implement an empirical-Bayes-within-Gibbs formulation that separates the interface and physical property updates within a Markov chain Monte Carlo scheme. Both the interface and the physical properties of the two sub-domains are constrained to favour smooth spatial transitions and pre-defined property bounds. Our methodology is demonstrated on synthetic and actual surface-based electrical resistivity tomography data sets, with the aim of inferring regolith-bedrock interfaces. Even if we are unable to achieve formal convergence of the Markov chains for all model parameters, we demonstrate that the proposed algorithm offers distinct advantages compared to manual- or algorithm-based interface detection using deterministic geophysical tomograms. Moreover, we obtain more reliable estimates of bedrock resistivity and its spatial variations. |
format |
Article in Journal/Newspaper |
author |
de Pasquale, Giulia Linde, Niklas Doetsch, Joseph Holbrook, W. Steven |
author_facet |
de Pasquale, Giulia Linde, Niklas Doetsch, Joseph Holbrook, W. Steven |
author_sort |
de Pasquale, Giulia |
title |
Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data |
title_short |
Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data |
title_full |
Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data |
title_fullStr |
Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data |
title_full_unstemmed |
Probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data |
title_sort |
probabilistic inference of subsurface heterogeneity and interface geometry using geophysical data |
publishDate |
2019 |
url |
https://doi.org/10.1093/gji/ggz055 http://www.scopus.com/inward/record.url?eid=2-s2.0-85063732032&partnerID=MN8TOARS |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Serveur académique Lausannois Geophysical Journal International |
op_relation |
doi:10.1093/gji/ggz055 10670/1.3ck4ns http://www.scopus.com/inward/record.url?eid=2-s2.0-85063732032&partnerID=MN8TOARS |
op_rights |
undefined |
op_doi |
https://doi.org/10.1093/gji/ggz055 |
container_title |
Geophysical Journal International |
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1766166589473816576 |