Geologically constrained 2D and 3D airborne EM inversion through cross-gradient regularization and multi-grid efficiency

Scholl, Carsten; Hallinan, S.; Neumann, Julia; Mulè, Shane; Watts, M. D.

doi:10.1071/aseg2016ab229

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Successful case studies have shown the relevance of integrating different geophysical datasets in complex scenarios using some of the methodologies listed above (see for example Colombo and De Stefano, 2007;Gallardo et al, 2012;De Stefano et al, 2011;Reid et al 2013 and Medina et al, 2015). However, while geological measurements and orientation data can be used as constraints during inversion (Fullagar et al, 2008, Lelièvre and Oldenburg, 2009, and Scholl et al, 2016, less effort has been put on the quantitative integration of geostatistical modeling into geophysical joint inversion. Several studies show examples where different disciplines of geology and geophysics are integrated in a cooperative manner using expert knowledge (Jessell and Valenta, 1996, Betts et al, 2003, Lane et al, 2009, and more recently Mantovani et al, 2016 andTschirhart et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

et al. 2017

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We introduce a joint geophysical inversion workflow that aims to improve subsurface imaging and decrease uncertainty by integrating petrophysical constraints and geological data. In this framework, probabilistic geological modeling is used as a source of information to condition the petrophysical constraints spatially and to derive starting models. The workflow then utilizes petrophysical measurements to constrain the values retrieved by geophysical joint inversion. The different sources of constraints are integrated into a least-square framework to capture and integrate information related to geophysical, petrophysical and geological data. This allows us to quantify the posterior state of knowledge and to calculate posterior statistical indicators. To test this workflow, using geological field data we have generated a set of geological models, which we used to derive a probabilistic geological model. In this synthetic case study, we show that the integration of geological information and petrophysical constraints in geophysical joint inversion can reduce uncertainty and improve imaging. In particular, the use of petrophysical constraints retrieves sharper boundaries and better reproduces the statistics of the observed petrophysical measurements. The integration of probabilistic geological modeling permits more accurate retrieval of model geometry, and better constrains the solution 2 while still satisfying the statistics derived from geological data. The analysis of statistical indicators at each step of the workflow shows that 1) the inversion methodology is effective when applied to complex geology, and 2) the integration of prior information and constraints from geology and petrophysics significantly improves the inversion results while decreasing uncertainty. Lastly, the analysis of uncertainty to the integration of the conditioned petrophysical constraints also shows that, for this example, the best results are obtained for joint inversion using petrophysical constraints spatially conditioned by geological modeling. * i-th geological model cell Conditioning of petrophysical constraints

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Section: Introductionmentioning

confidence: 99%

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

et al. 2017

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“…Peng et al developed a joint inversion algorithm of receiver functions and magnetotelluric data to determine the crustal and mantle structure beneath central Namche Barwa in the eastern Himalayan syntaxis [14]. Scholl et al applied the cross-gradient constraint to the joint inversion of three-dimensional airborne electromagnetic data and gravity data [15]. Peng et al combined magnetotelluric and wide-angle seismic reflection/refraction inversion to detect crustal structures and Moho discontinuities [16].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Joint Inversion of the Resistivity Method and Time-Domain-Induced Polarization Based on the Cross-Gradient Constraints

et al. 2023

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The resistivity method and time-domain-induced polarization (TDIP) are two branches of electric exploration that are used to solve problems in mineral exploration, hydrogeology and engineering geology. In recent years, integrating different physical parameters for joint inversion to improve the accuracy of inversion results has been extensively examined; however, three-dimensional joint inversion of the two methods above has not been realized. To further address this issue, in this research, we used the limited-memory BFGS (L-BFGS) method to develop a three-dimensional joint inversion algorithm of the resistivity method and TDIP based on the cross-gradient constraints. In the new algorithm, the resistivity method and TDIP inversion were iteratively updated alternately to ensure that the inversion results can simultaneously meet the two conditions of obtaining minimum data misfits and finding structural similarity. The three-dimensional synthetic dataset inversion results showed that the models obtained by joint inversion are more accurate in the recovery of both the boundaries and the values of the anomalies. Especially in the background of high noise, joint inversion has higher resolution for the target body. The joint inversion algorithm was also successfully applied to a groundwater detection practice in Beijing, China, in which the practicability of the algorithm was confirmed.

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“…P4 l14: "single unique" → "unique" is enough.P4 l16: There are also other works you may want to cite when it comes to using infomation derived from geological measurements or modelling directly into geophysical inversion. For instance,Fullagar et al (2008),Guillen et al (2008),Scholl et al (2016) integrate geological information or modelling in their inversion algorithm. Publications relating to works using level-set inversion also rely on geological models (see for exampleBijani et al, 2017, and Zheglova et al, 2018, for joint inversion).…”

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confidence: 99%

Comments to submission by Olierook et al.: "Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models"

Anonymous

2019

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Geologically constrained 2D and 3D airborne EM inversion through cross-gradient regularization and multi-grid efficiency

Abstract: View related articlesGeologically constrained 2D and 3D airborne EM inversion through cross-gradient regularization and multi-grid efficiency C . S c h o l l J . N e u m a n n M . D . W a t t s

Cited by 6 publications

References 8 publications

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Three-Dimensional Joint Inversion of the Resistivity Method and Time-Domain-Induced Polarization Based on the Cross-Gradient Constraints

Comments to submission by Olierook et al.: "Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models"

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Geologically constrained 2D and 3D airborne EM inversion through cross-gradient regularization and multi-grid efficiency

Abstract: View related articlesGeologically constrained 2D and 3D airborne EM inversion through cross-gradient regularization and multi-grid efficiency C . S c h o l l J . N e u m a n n M . D . W a t t s

Cited by 6 publications

References 8 publications

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Uncertainty reduction through geologically conditioned petrophysical constraints in joint inversion

Three-Dimensional Joint Inversion of the Resistivity Method and Time-Domain-Induced Polarization Based on the Cross-Gradient Constraints

Comments to submission by Olierook et al.: &quot;Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models&quot;

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Comments to submission by Olierook et al.: "Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models"