A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

JafarGandomi, Arash; Binley, Andrew

doi:10.1016/j.jappgeo.2013.06.004

Cited by 22 publications

(7 citation statements)

References 64 publications

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“…We have demonstrated in our 2-D examples that horizontal resistivity structure can be accurately resolved by inverting multiple independent 1-D soundings along a survey line. Although, to improve the spatial resolution and structure of the final 2-D model, in particular between soundings, additional spatial fusing methods, such as Bayesian maximum entropy (JafarGandomi and Binley, 2013), could be used for horizontally gridding the multiple 1-D soundings. The Bayesian maximum entropy method could incorporate the different uncertainties associated to each 1-D resistivity profile, calculated from their PDFs, instead of applying a linear interpolation, when horizontal gridding along the 2-D line.…”

Section: Discussionmentioning

confidence: 99%

A quantitative method for deriving salinity of subglacial water using ground-based transient electromagnetics

2021

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Liquid water can exist at temperatures well below freezing beneath glaciers and ice sheets, where subglacial water systems, fresh and saline, have been shown to host unique microbial ecosystems. Geophysical techniques sensitive to fluid-content contrasts, e.g. electromagnetics, can characterize subglacial water and its salinity. Here, we assess the ground-based transient electromagnetic (TEM) method for deriving the resistivity and salinity of subglacial water. We adapt an existing open-source Bayesian inversion algorithm, which uses independent depth constraints, to output posterior distributions of resistivity and pore fluid salinity with depth. A variety of synthetic models, including a thin (5 m), conductive (0.16 Ωm), hypersaline (147 psu) subglacial lake, are used to evaluate the TEM method for imaging under 800 m-thick ice. The study demonstrates that TEM methods can resolve conductive, saline bodies accurately using external depth constraints, for example, from radar or seismic data. The depth resolution of TEM can be limited beneath deep (>800 m), thick (>50 m) conductive, water bodies and additional constraints from passive electromagnetic (EM) methods could be used to reduce ambiguities in the TEM results. Subsequently, non-invasive active and passive EM methods could provide profound insights into remote aqueous systems under glaciers and ice sheets.

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Section: Discussionmentioning

confidence: 99%

A quantitative method for deriving salinity of subglacial water using ground-based transient electromagnetics

2021

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“…To proceed with Bayesian inference we define distributions for each stochastic variable of θ. Following work described in, 8,11 one simple choice is to assign uniform priors for each parameter. However, despite their apparent attractiveness as non-informative (of course they are not really non-informative since a range must be defined), uniform priors can become a problem if the true posterior distribution is non-vanishing outside the prior's support.…”

Section: The Prior Distributionmentioning

confidence: 99%

Bayesian signal processing techniques for the detection of highly localised gravity anomalies using quantum interferometry technology

et al. 2016

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“…The rjMCMC was first applied in geophysics in 2000 in an inversion study of zero-offset vertical seismic profiles (Malinverno, 2000;Malinverno and Leaney, 2000). Since then, it has been utilized in a variety of geophysical inverse problems, including earthquake seismology and tomography Agostinetti and Malinverno, 2010;Bodin et al, 2012a,b;Young et al, 2013;Zulfakriza et al, 2014;Kolb and Lekić, 2014;Galetti et al, 2015), geoacoustic inversion (Dettmer et al, 2010;Dettmer and Dosso, 2012;Steininger et al, 2013;Dettmer et al, 2013;Dosso et al, 2014), and electrical and magnetotelluric geophysics (Malinverno, 2002;Minsley, 2011;Brodie and Sambridge, 2012;Ray and Key, 2012;JafarGandomi and Binley, 2013;Ray et al, 2014;Gehrmann et al, 2015). Dadi (2014) and Dadi et al (2015) used rjMCMC for seismic impedance inversion, uncertainty estimation and well log upscaling.…”

Section: Overview Of the Transdimensional Approach Rjmcmcmentioning

confidence: 99%

“…The difference of the modeled and observed data can be calculated in many ways, such as the L2-norm error function, a cross correlation , and Shannon's entropy (JafarGandomi and Binley, 2013). L2-norm is the square root of the sum of the squares of all the samples (equation (2.11)), whereas the L1-norm is defined as the sum of the absolute values of all the samples (equation (2.12)).…”

Section: The Likelihood Functionmentioning

confidence: 99%

Seismic inversion and uncertainty analysis using a transdimensional Markov chain Monte Carlo method

Zhu

Gibson

2016

SEG Technical Program Expanded Abstracts 2016

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We use a transdimensional inversion algorithm, reversible jump MCMC (rjMCMC), in the seismic waveform inversion of post-stack and prestack data to characterize reservoir properties such as seismic wave velocity, density as well as impedance and then estimate uncertainty. Each seismic trace is inverted independently based on a layered earth model. The model dimensionality is defined as the number of the layers multiplied with the number of model parameters per layer. The rjMCMC is able to infer the number of model parameters from data itself by allowing it to vary in the iterative inversion process, converge to proper parameterization and prevent underparameterization and overparameterization. We also use rjMCMC to enhance uncertainty estimation since it can transdimensionally sample different model spaces of different dimensionalities and can prevent a biased sampling in only one space which may have a different dimensionality than that of the true model space. An ensemble of solutions from difference spaces can statistically reduce the bias for parameter estimation and uncertainty quantification. Inversion uncertainty is comprised of property uncertainty and location uncertainty. Our study revealed that the inversion uncertainty is correlated with the discontinuity of property in such a way that 1) a smaller discontinuity will induce a lower uncertainty in property at the discontinuity but also a higher uncertainty of the location of that discontinuity and 2) a larger discontinuity will induce a higher uncertainty in property at the discontinuity but also a higher ''certainty'' of the location of that discontinuity. Therefore, there is a trade-off between the property uncertainty and the location uncertainty. To our surprise, there is a lot of hidden information in the uncertainty result that we can actually take advantage of due to this trade-off effect. On the basis of our study ii using rjMCMC, we propose to use the inversion uncertainty as a novel attribute in an optimistic way to characterize the magnitude and the location of subsurface discontinuities and reflectors. important it is to estimate the uncertainty, and I used another method for two years but it turned out that method was unsuccessful for solving my research problems because it couldn't answer the question for uncertainty assessment. This is one of the biggest mistakes that I have ever made because I wasn't aware of the importance of my advisor's words and questions. Indeed, research is not always successful and smooth and I did encounter failures, but I didn't give up.

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A Bayesian trans-dimensional approach for the fusion of multiple geophysical datasets

Cited by 22 publications

References 64 publications

A quantitative method for deriving salinity of subglacial water using ground-based transient electromagnetics

A quantitative method for deriving salinity of subglacial water using ground-based transient electromagnetics

Bayesian signal processing techniques for the detection of highly localised gravity anomalies using quantum interferometry technology

Seismic inversion and uncertainty analysis using a transdimensional Markov chain Monte Carlo method

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