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Didar, Fatemeh, Hosseini, Mojtaba, Abedi, Maysam, Ebrahimzadeh Ardestani, Vahid. (1405). Mixed-Norm Inversion of Full-Tensor Gravity and Magnetic Data for High-Resolution Subsurface Imaging. , 58(1), 13-27. doi: 10.22059/jac.2026.414248.1261
Fatemeh Didar; Mojtaba Hosseini; Maysam Abedi; Vahid Ebrahimzadeh Ardestani. "Mixed-Norm Inversion of Full-Tensor Gravity and Magnetic Data for High-Resolution Subsurface Imaging". , 58, 1, 1405, 13-27. doi: 10.22059/jac.2026.414248.1261
Didar, Fatemeh, Hosseini, Mojtaba, Abedi, Maysam, Ebrahimzadeh Ardestani, Vahid. (1405). 'Mixed-Norm Inversion of Full-Tensor Gravity and Magnetic Data for High-Resolution Subsurface Imaging', , 58(1), pp. 13-27. doi: 10.22059/jac.2026.414248.1261
Didar, Fatemeh, Hosseini, Mojtaba, Abedi, Maysam, Ebrahimzadeh Ardestani, Vahid. Mixed-Norm Inversion of Full-Tensor Gravity and Magnetic Data for High-Resolution Subsurface Imaging. , 1405; 58(1): 13-27. doi: 10.22059/jac.2026.414248.1261

Mixed-Norm Inversion of Full-Tensor Gravity and Magnetic Data for High-Resolution Subsurface Imaging

Journal of Algorithms and Computation
دوره 58، شماره 1، مهر 2026، صفحه 13-27    اصل مقاله (1.44 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2026.414248.1261
نویسندگان
Fatemeh Didar1؛ Mojtaba Hosseini1؛ Maysam Abedi* 2؛ Vahid Ebrahimzadeh Ardestani3
1Petroleum Engineering and Geophysics Laboratory, School of Mining Engineering, College of Engineering, University of Tehran, Iran.
2Petroleum Engineering and Geophysics Laboratory, School of Mining Engineering, College of Engineering University of Tehran, Iran
3Institute of Geophysics, University of Tehran, Iran.
چکیده
This study develops a unified framework for full-tensor gravity-gradient and full-tensor magnetic forward modelling and inversion using a mixed-norm regularization approach for subsurface physical property reconstruction. By utilizing all components of both gravity and magnetic gradient tensors, the forward modelling stage captures the full directional sensitivity of the potential fields, providing a more informative representation of subsurface structures than conventional approaches. The inversion is formulated as a constrained optimization problem and solved using a projected Gauss–Newton conjugate gradient method combined with an iterative reweighted least squares scheme. Mixed-norm ensures a balance between compactness and smoothness, enabling recovery of sharp interfaces and smooth background variations. Sensitivity-based weighting and physical property bounds enhance stability and robustness. The results demonstrate reliable reconstruction of density and susceptibility models with good agreement between observed and predicted full-tensor responses. Overall, the study highlights the effectiveness of full-tensor gravity and magnetic inversion for improved subsurface imaging.
کلیدواژه‌ها
Full-tensor gravity gradient (FTG)؛ Full-tensor magnetic gradient (FTM)؛ Mixed-norm regularization؛ Forward modeling؛ Geophysical inversion
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تعداد مشاهده مقاله: 61
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