none 10.1190/geo2021-0795.1 Society of Exploration Geophysicists Society of Exploration Geophysicists 186 142963317 5981 2025121212074582900 10.1190 2025-12-12T17:07:53Z 2022-12-05T11:21:55Z 7 Geophysics 0016-8033 1942-2156 03 01 2023 88 2 Rongzhe Zhang Jilin University 1 , College of Geo-Exploration Sciences and Technology, Changchun, China. zhangrz@jlu.edu.cn http://orcid.org/0000-0001-7870-2238 Tonglin Li Jilin University 1 , College of Geo-Exploration Sciences and Technology, Changchun, China. litl@jlu.edu.cn (corresponding author) http://orcid.org/0000-0002-2874-8112 Cai Liu Jilin University 1 , College of Geo-Exploration Sciences and Technology, Changchun, China. liucai@jlu.edu.cn http://orcid.org/0000-0002-7852-3894 Haoyuan He Jilin University 1 , College of Geo-Exploration Sciences and Technology, Changchun, China. hyhe20@mails.jlu.edu.cn http://orcid.org/0000-0003-2204-7179 Xingguo Huang Jilin University 2 , College of Instrumentation and Electrical Engineering, Changchun, China. xingguohuang@jlu.edu.cn http://orcid.org/0000-0001-9719-6297 Saeed Vatankah Jilin University 2 , College of Instrumentation and Electrical Engineering, Changchun, China. svatan@ut.ac.ir ABSTRACT Joint inversion algorithms continue to receive a lot of interest in the application of geophysical interpretation due to the reduction of uncertainty of the inverse solutions. Over the past decade, several coupling constraints, structural or rock physics, have been introduced to the joint inversion of multiple geophysical data sets. However, a single coupling constraint may not provide the optimum solution for all geologic scenarios. To improve the application of the multiple geophysical data sets, in which some rock-physics information is available, we have developed a new joint inversion algorithm based on a mixed structural and rock-physics coupling constraints. The presented algorithm incorporates the cosine dot product gradient function, a structural coupling constraint, with a guided fuzzy c-means clustering strategy, which is a rock-physics coupling constraint. The former imposes the structural similarity among different model parameters, whereas the latter improves the linear or nonlinear correlation among model parameters. We use the exact structural resemblance algorithm to numerically solve the mixed coupling constraint joint inversion objective function. The developed algorithm is applied on 2D synthetic and field data examples. Our results find a significant improvement in the resolution of the reconstructed models, in comparison with traditional single coupling algorithms, even when the rock-physics information is not complete. This algorithm is more conducive to obtaining clear geologic unit boundaries and provides a favorable basis for delineating metallogenic target areas. 02 21 2023 03 01 2023 12 05 2022 K27 K37 National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809 42104134 Geophysical Computational Imaging and Instrumentation Group at Jilin University Scientific Research Project of Education Department of Jilin Province JJKH20221015KJ 1 10.1190/crossmark-policy library.seg.org true 2021-12-09 2022-11-15 2022-11-15 2023-02-21 National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809 42104134 Geophysical Computational Imaging and Instrumentation Group at Jilin University Scientific Research Project of Education Department of Jilin Province JJKH20221015KJ 10.1190/geo2021-0795.1 https://pubs.geoscienceworld.org/geophysics/article/88/2/K27/620537/Geophysical-joint-inversion-based-on-mixed 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