Bayesian inversion of CSEM and magnetotelluric data

Buland, A.; Kolbjørnsen, Odd

doi:10.1190/geo2010-0298.1

Cited by 53 publications

(20 citation statements)

References 38 publications

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“…Although the Occam inversion outputs the smoothest model, it is also possible to fit more complicated models to the data for the given error. Unlike a Bayesian inversion (Chen et al 2007;Buland & Kolbjørnsen 2012;Ray & Key 2012), it does not provide any information about model uncertainty. Finding the suite of these models would require the application of many perturbations to the final model, that is impractical, given model run times (On 64 computer nodes 4x Xeon E5/Core i7 processors, the OBE inversions take approximately 20 hr and the Vulcan inversions take approximately 14 hr for Line 1 and 8 hr for Line 2).…”

Section: Linementioning

confidence: 99%

Resistivity image beneath an area of active methane seeps in the west Svalbard continental slope

Goswami¹,

Weitemeyer

Minshull³

et al. 2016

Geophys. J. Int.

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S U M M A R YThe Arctic continental margin contains large amounts of methane in the form of methane hydrates. The west Svalbard continental slope is an area where active methane seeps have been reported near the landward limit of the hydrate stability zone. The presence of bottom simulating reflectors (BSRs) on seismic reflection data in water depths greater than 600 m suggests the presence of free gas beneath gas hydrates in the area. Resistivity obtained from marine controlled source electromagnetic (CSEM) data provides a useful complement to seismic methods for detecting shallow hydrate and gas as they are more resistive than surrounding water saturated sediments. We acquired two CSEM lines in the west Svalbard continental slope, extending from the edge of the continental shelf (250 m water depth) to water depths of around 800 m. High resistivities (5-12 m) observed above the BSR support the presence of gas hydrate in water depths greater than 600 m. High resistivities (3-4 m) at 390-600 m water depth also suggest possible hydrate occurrence within the gas hydrate stability zone (GHSZ) of the continental slope. In addition, high resistivities (4-8 m) landward of the GHSZ are coincident with high-amplitude reflectors and low velocities reported in seismic data that indicate the likely presence of free gas. Pore space saturation estimates using a connectivity equation suggest 20-50 per cent hydrate within the lower slope sediments and less than 12 per cent within the upper slope sediments. A free gas zone beneath the GHSZ (10-20 per cent gas saturation) is connected to the high free gas saturated (10-45 per cent) area at the edge of the continental shelf, where most of the seeps are observed. This evidence supports the presence of lateral free gas migration beneath the GHSZ towards the continental shelf.

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Section: Linementioning

confidence: 99%

Resistivity image beneath an area of active methane seeps in the west Svalbard continental slope

Goswami¹,

Weitemeyer

Minshull³

et al. 2016

Geophys. J. Int.

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show abstract

“…Trainor-Guitton & Hoversten (2011) use a sampling scheme which involves both the Metropolis-Hastings algorithm (Hastings 1970) and slice sampling (Neal 2003) in order to improve convergence upon the PDF of solution models. Buland & Kolbjornsen (2012) apply the MetropolisHastings algorithm to invert marine CSEM data together with magnetotelluric (MT) data in order to constrain the range of likely resistivities as a function of depth. In all these studies, with the exception of Gunning et al (2010) who use a maximum a posteriori estimate-based layer splitting approach, the parametrization is fixed at the outset by the user.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization

Ray¹,

Key²,

Bodin

et al. 2014

Geophysical Journal International

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S U M M A R YWe apply a reversible-jump Markov chain Monte Carlo method to sample the Bayesian posterior model probability density function of 2-D seafloor resistivity as constrained by marine controlled source electromagnetic data. This density function of earth models conveys information on which parts of the model space are illuminated by the data. Whereas conventional gradient-based inversion approaches require subjective regularization choices to stabilize this highly non-linear and non-unique inverse problem and provide only a single solution with no model uncertainty information, the method we use entirely avoids model regularization. The result of our approach is an ensemble of models that can be visualized and queried to provide meaningful information about the sensitivity of the data to the subsurface, and the level of resolution of model parameters. We represent models in 2-D using a Voronoi cell parametrization. To make the 2-D problem practical, we use a source-receiver common midpoint approximation with 1-D forward modelling. Our algorithm is transdimensional and self-parametrizing where the number of resistivity cells within a 2-D depth section is variable, as are their positions and geometries. Two synthetic studies demonstrate the algorithm's use in the appraisal of a thin, segmented, resistive reservoir which makes for a challenging exploration target. As a demonstration example, we apply our method to survey data collected over the Scarborough gas field on the Northwest Australian shelf.

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“…Applications include the inversion of controlled-source electromagnetic marine (CSEM) data (e.g., Gunning et al, 2010;Buland and Kolbjørnsen, 2012), AEM data (e.g., Minsley, 2011;Brodie and Sambridge, 2012), and direct current resistivity sounding (e.g., Malinverno, 2002). All aforementioned MCMC algorithms use a 1D forward kernel, and they are applied on a station-by-station basis, in which the number of layers is often treated as an additional unknown.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic inversion of airborne electromagnetic data under spatial constraints

2015

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Probabilistic 1D inversions of airborne electromagnetic (AEM) surveys allow an exhaustive search of model space for each station, but they often assume that there is no spatial correlation between neighboring stations. This can result in abrupt transverse model discontinuities when attempting to construct a 3D model. In contrast to this, fully spatially regularized deterministic inversions can take spatial correlation between 1D models into account, but they do not explore the model space sufficiently to be able to evaluate model robustness. The Bayesian parametric bootstrap (BPB) approach that we developed is a practical compromise between computationally expensive exhaustive search techniques and computationally efficient deterministic inversions. Using a 1D kernel, we inverted for the interfaces, layer properties, and related uncertainties, taking lateral spatial correlations and additional prior information into account. Numerical examples revealed that a BPB technique was likely to explore the model space sufficiently for nonpathological situations. Using a subset of a large AEM survey collected in northwest Australia for aquifer mapping, we show how the BPB approach can be used to produce a spatially coherent map of the base of the Broome sandstone aquifer. The recovered uncertainties, which are likely to be one of the main sources of uncertainty in any groundwater model, exhibited the wellknown increase in uncertainty of a depth to interface with increasing depth to the interface.

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Bayesian inversion of CSEM and magnetotelluric data

Cited by 53 publications

References 38 publications

Resistivity image beneath an area of active methane seeps in the west Svalbard continental slope

Resistivity image beneath an area of active methane seeps in the west Svalbard continental slope

Bayesian inversion of marine CSEM data from the Scarborough gas field using a transdimensional 2-D parametrization

Probabilistic inversion of airborne electromagnetic data under spatial constraints

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