Regularized joint inversion based on Gramian constraints is a hot research topic in the field of geophysical joint inversion. Given the difficulty in selecting weighted factors of the regularization and constraint items, it is necessary to introduce inequality constraints into the regularized joint inversion. To investigate the regularized joint inversion of magnetotelluric (MT) and gravity data based on Gramian constraints, this study compared the application effects of the penalty function method and the transform function method in the joint inversion and processed the measured data of a survey line in Xiangshan, Jiangxi Province. According to the results from model experiments, both methods can effectively constrain petrophysical parameters, and the penalty function method has higher flexibility but requires the artificial setting of the weighted factors. Moreover, the processing of the measured data shows that the joint inversion based on inequality and Gramian constraints is highly practical and can improve the precision of geophysical interpretation.
CHEN Xiao, ZENG Zhi-Wen, DENG Ju-Zhi, ZHANG Zhi-Yong, CHEN Hui, YU Hui, WANG Yan-Guo. Regularized joint inversion of magnetotelluric and gravity data based on inequality and Gramian constraints[J]. Geophysical and Geochemical Exploration, 2023, 47(3): 575-583 doi:10.11720/wtyht.2023.1474
Fig.1
Joint inversed results of different projects for the model test 1
a—designed residual density model;b—designed resistivity model;c—residual density result of project 1;d—resistivity result of project 1; e—residual density result of project 2;f—resistivity result of project 2;g—residual density result of project 3 ;h—resistivity result of project 3;i—fitting curves of gravity anomalies
Fig.2
Joint inversed results of different projects for the model test 2
a—residual density result of project 1;b—resistivity result of project 1; c—residual density result of project 2;d—resistivity result of project 2;e—residual density result of project 3;f—resistivity result of project 3;g—fitting curves of gravity anomalies
Fig.5
Joint inversed results of different projects
a—joint inversed residual density result of project 1;b—joint inversed residual density result of project 2; c—gravity anomaly fitting curves of project 1;d—gravity anomaly fitting curves of project 2;e—coupling relationship for joint inversed results of project 1;f—coupling relationship for joint inversed results of project 2
Joint inversion of multiple geophysical data has become an active area of research due to its potential to greatly enhance the fidelity of inverted models. Many open questions and challenges still remain. One of them is how to effectively incorporate into joint inversion multimodal petrophysical information that describes the statistical behavior of physical property values in the parameter domain (i.e., in a crossplot). We have regarded the multimodal petrophysical data as different clusters in the parameter domain and developed an approach that handles multimodal petrophysical information through guided fuzzy c-means (FCM) clustering in the parameter domain. We inverted the petrophysical data in the parameter domain in a similar manner to and simultaneously with the geophysical data in the spatial domain through minimizing one common objective function. Numerical examples have determined that the resulting models from this multidomain joint-inversion strategy are able to reproduce both the geophysical and the petrophysical data. In addition to incorporating a priori multimodal petrophysical information into inversion, guided FCM clustering also allows us to integrate geology differentiation and geophysical inversion into one unified framework that makes these two components positively affect each other. Geology differentiation results were obtained as a direct output from joint inversion. We have also developed a strategy for imposing different clustering constraints in different model regions, allowing region-specific a priori petrophysical information to be incorporated into inversion. We have applied our joint-inversion algorithm to the SEG/EAGE salt model in four different scenarios, and we found that the proposed algorithm produced much better geophysical models and geology differentiation results than separate inversions.
Joint inversion, the inversion of multiple geophysical data sets containing complementary information about the subsurface, has the potential to significantly improve inversion results by reducing the nonuniqueness of the inverse problem. One of the challenges of joint inversion is deciding how to couple the multiple physical property models. If a coupling approach is used that is inconsistent with the physical truth, then inversion artifacts can occur and may lead to incorrect interpretations. In this paper, we investigated the fuzzy c-means (FCM) clustering approach to provide a lithological coupling of the seismic velocity and density models in joint 2D inversions of first-arrival traveltimes and gravity data. Even though this coupling approach has been used in previous works, recommendations for its effective use have not yet been developed. We conducted a suite of joint inversion tests on synthetic data generated from a geologically realistic model based on magmatic massive sulfide deposits. There is a known relationship between seismic velocity and density for the silicate rocks and sulfide minerals involved; this lithological relationship was used to design a clustered coupling strategy in the joint inversions. The tests we conducted clearly exhibited the benefits of joint inversion using FCM coupling. Our work revealed the effects of including inaccurate a priori physical property information. We also evaluated approaches to assess whether such inaccurate information may have been used.
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Verification of velocity-resistivity relationships derived from structural joint inversion with borehole data
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We have developed a frequency-domain joint electromagnetic (EM) and seismic inversion algorithm for reservoir evaluation and exploration applications. EM and seismic data are jointly inverted using a cross-gradient constraint that enforces structural similarity between the conductivity image and the compressional wave (P-wave) velocity image. The inversion algorithm is based on a Gauss-Newton optimization approach. Because of the ill-posed nature of the inverse problem, regularization is used to constrain the solution. The multiplicative regularization technique selects the regularization parameters automatically, improving the robustness of the algorithm. A multifrequency data-weighting scheme prevents the high-frequency data from dominating the inversion process. When the joint-inversion algorithm is applied in integrating marine controlled-source electromagnetic data with surface seismic data for subsea reservoir exploration applications and in integrating crosswell EM and sonic data for reservoir monitoring and evaluation applications, results improve significantly over those obtained from separate EM or seismic inversions.
YuH.Three-dimensional fine structure and geodynamics setting of transcrustal magmatic system beneath the Xiangshan volcanic basin[D]. Nanchang: East China University of Technology, 2021.
GuoY H.Study on regularized joint inversion of magnetotelluric and gravity based on Gramian constraints[D]. Nanchang: East China University of Technology, 2020.
