Generalized joint inversion of multimodal geophysical data using Gramian constraints

Zhdanov, Michael S.; Gribenko, Alexander; Wilson, Glenn A.

doi:10.1029/2012gl051233

Cited by 135 publications

(67 citation statements)

References 17 publications

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“…It is shown in Zhdanov et al, (2012) that the Gramian provides a measure of correlation between the different model parameters or their attributes. By imposing the additional requirement of the minimum of the Gramian in regularized inversion, we obtain multimodal inverse solutions with enhanced correlations between the different model parameters or their attributes.…”

Section: Principles Of Joint Inversion Using Gramian Constraintsmentioning

confidence: 99%

Gramian constraints in the joint inversion of airborne gravity gradiometry and magnetic data

Zhu

Zhdanov

C̆uma

2013

SEG Technical Program Expanded Abstracts 2013

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We apply Gramian constraints for the joint inversion of airborne gravity gradiometry and magnetic data. The method does not require any a priori knowledge about the types of relationships between the different model parameters, but instead determines the form of these relationships in the process of the inversion. The Gramian constraints make it possible to consider both linear and nonlinear relationships between the different physical parameters of a geological model. As an illustration, we consider in this paper polynomial relationships between different model parameters. The case study includes joint inversion of airborne gravity gradiometer (AGG) and magnetic data collected by Fugro Airborne Surveys in the area of McFaulds Lake located in northwestern Ontario. This case study demonstrates how joint inversion may enhance the produced subsurface images of a deposit.

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Section: Principles Of Joint Inversion Using Gramian Constraintsmentioning

confidence: 99%

Gramian constraints in the joint inversion of airborne gravity gradiometry and magnetic data

Zhu

Zhdanov

C̆uma

2013

SEG Technical Program Expanded Abstracts 2013

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“…This multimodality shows how it can often be inappropriate to represent the resulting geophysical parameter constraint as some uncertainty around a central average value. In recent years, however, a few authors, such as Zhdanov et al (2012), have made progress in developing methods for joint inversion schemes for multimodal parameter spaces. This is further highlighted by Figures 16-19.…”

Section: Model Representationmentioning

confidence: 99%

Joint stochastic constraint of a large data set from a salt dome

et al. 2016

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'Joint stochastic constraint of a large data set from a salt dome.', Geophysics., 81 (2). ID1-ID24.Further information on publisher's website:http://dx.doi.org/10.1190/geo2015-0127.1Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTUnderstanding the uncertainty associated with large joint geophysical surveys, such as 3D seismic, gravity, and magnetotelluric (MT) studies, is a challenge, conceptually and practically. By demonstrating the use of emulators, we have adopted a Monte Carlo forward screening scheme to globally test a prior model space for plausibility. This methodology means that the incorporation of all types of uncertainty is made conceptually straightforward, by designing an appropriate prior model space, upon which the results are dependent, from which to draw candidate models. We have tested the approach on a salt dome target, over which three data sets had been obtained; wide-angle seismic refraction, MT and gravity data. We have considered the data sets together using an empirically measured uncertain physical relationship connecting the three different model parameters: seismic velocity, density, and resistivity, and we have indicated the value of a joint approach, rather than considering individual parameter models. The results were probability density functions over the model parameters, together with a halite probability map. The emulators give a considerable speed advantage over running the full simulator codes, and we consider their use to have great potential in the development of geophysical statistical constraint methods.

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“…Thus, Gallardo et al (2012) use the cross-gradient (Gallardo and Meju 2004, 2007Meju and Gallardo 2016), a versatile structural constraint, that has established itself as one of the most popular coupling approaches in joint inversion (e.g., Linde et al 2008;Doetsch et al 2010;Lochbühler et al 2013;Sánchez and Delgado 2015;Tarits et al 2015;Zhou et al 2015). Other structural constraints include curvature based measures (Haber and Oldenburg 1997), directed constraints (Molodtsov et al 2013), using the roughness of another model to modify the regularization (Günther and Rücker 2006), Gramian constraints (Zhdanov et al 2012) and joint total variation (Haber and Holtzman Gazit 2013). Depending on the scenario, these coupling methods can have superior properties under certain circumstances.…”

Section: Joint Inversion With Structural Constraintsmentioning

confidence: 99%

Integrating Electromagnetic Data with Other Geophysical Observations for Enhanced Imaging of the Earth: A Tutorial and Review

Moorkamp

2017

Surv Geophys

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In this review, I discuss the basic principles of joint inversion and constrained inversion approaches and show a few instructive examples of applications of these approaches in the literature. Starting with some basic definitions of the terms joint inversion and constrained inversion, I use a simple three-layered model as a tutorial example that demonstrates the general properties of joint inversion with different coupling methods. In particular, I investigate to which extent combining different geophysical methods can restrict the set of acceptable models and under which circumstances the results can be biased. Some ideas on how to identify such biased results and how negative results can be interpreted conclude the tutorial part. The case studies in the second part have been selected to highlight specific issues such as choosing an appropriate parameter relationship to couple seismic and electromagnetic data and demonstrate the most commonly used approaches, e.g., the cross-gradient constraint and direct parameter coupling. Throughout the discussion, I try to identify topics for future work. Overall, it appears that integrating electromagnetic data with other observations has reached a level of maturity and is starting to move away from fundamental proof-of-concept studies to answering questions about the structure of the subsurface. With a wide selection of coupling methods suited to different geological scenarios, integrated approaches can be applied on all scales and have the potential to deliver new answers to important geological questions.

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Generalized joint inversion of multimodal geophysical data using Gramian constraints

Cited by 135 publications

References 17 publications

Gramian constraints in the joint inversion of airborne gravity gradiometry and magnetic data

Gramian constraints in the joint inversion of airborne gravity gradiometry and magnetic data

Joint stochastic constraint of a large data set from a salt dome

Integrating Electromagnetic Data with Other Geophysical Observations for Enhanced Imaging of the Earth: A Tutorial and Review

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