Sains Malaysiana 55(6)(2026): 959-973

http://doi.org/10.17576/jsm-2026-5506-02

 

Stability of Cross-Gradient Joint Inversion under Noisy Conditions: A Systematic Review

(Kestabilan Penyongsangan Bersama Kecerunan Silang di bawah Keadaan Hingar: Satu Kajian Sistematik)

 

MUHAMMAD FAWZY ISMULLAH MASSINAI1,2, MUHAMMAD TAQIUDDIN ZAKARIA1,*, MOHD HARIRI ARIFIN1, NURUL ASIKIN MOHD ARAHA1 & NUR IRDINA INSYIRAAH MOHD SALWIRA1

 

1Geology Programme, Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

2Department of Geophysics, Faculty of Mathematics and Natural Science, Hasanuddin University, Makassar 90245, Indonesia

 

Diserahkan: 15 Disember 2025/Diterima: 20 Mei 2026

 

Abstract

Cross-gradient joint inversion (CGJI) is a widely applied geophysical method that integrates multiple parameters by enforcing structural similarity through cross-gradient constraints. While its effectiveness has been demonstrated across diverse geological settings, its stability under noisy conditions remains insufficiently characterised. This study presents a systematic literature review (SLR) of 68 publications between 2015 and 2024, following a structured identification, screening, and synthesis protocol based on PRISMA guidelines. The reviewed studies were analysed in terms of applications, methodological developments, and noise stability using a semi-quantitative synthesis approach. The results indicate that CGJI is most commonly applied in geodynamics–tectonics and petroleum exploration, with increasing methodological developments driven by advances in machine learning. Its performance is strongly dependent on the integration of datasets with complementary sensitivities, rather than on any specific geophysical method. Most studies report improved structural delineation and reduced inversion ambiguity compared to single-method approaches, particularly under low-to-moderate noise conditions (≤ 20%). However, a critical gap remains: existing evaluations are predominantly based on synthetic datasets, and noise levels rarely exceed 20%, which may not reflect realistic field conditions. Although metrics such as Root Mean Square (RMS) and Structural Similarity Index Measure (SSIM) are used to assess noise sensitivity, their application remains limited and inconsistent. These findings highlight the need for systematic evaluation of CGJI under higher noise levels and real field conditions to ensure its reliability and practical applicability.

Keywords: Cross gradient joint inversion; geophysical applications; methodological development; noise stability; systematic literature review

 

Abstrak

Penyongsangan bersama kecerunan silang (CGJI) ialah kaedah geofizik yang digunakan secara meluas yang mengintegrasikan pelbagai parameter dengan menguatkuasakan keserupaan struktur melalui kekangan kecerunan silang. Walaupun keberkesanannya telah ditunjukkan merentasi pelbagai persekitaran geologi, kestabilannya di bawah keadaan hingar masih belum dicirikan dengan mencukupi. Penyelidikan ini membentangkan satu tinjauan kepustakaan sistematik (SLR) terhadap 68 penerbitan antara tahun 2015 hingga 2024, mengikut protokol pengenalpastian, penapisan dan sintesis yang berstruktur berdasarkan garis panduan PRISMA. Kajian yang dikaji dianalisis dari segi aplikasi, perkembangan metodologi dan kestabilan hingar menggunakan pendekatan sintesis separa kuantitatif. Keputusan menunjukkan bahawa CGJI paling kerap digunakan dalam geodinamik-tektonik dan penerokaan petroleum dengan peningkatan perkembangan metodologi didorong oleh kemajuan dalam pembelajaran mesin. Prestasinya sangat bergantung pada penyepaduan set data dengan kepekaan pelengkap dan bukannya pada mana-mana kaedah geofizik tertentu. Kebanyakan kajian melaporkan peningkatan dalam penentuan struktur dan pengurangan ketaksaan penyongsangan berbanding pendekatan kaedah tunggal, terutamanya di bawah keadaan hingar rendah hingga sederhana (≤ 20%). Walau bagaimanapun, terdapat jurang kritikal masih wujud, penilaian sedia ada kebanyakannya berdasarkan data sintetik dan tahap hingar jarang melebihi 20% yang mungkin tidak mencerminkan keadaan lapangan sebenar. Walaupun metriks seperti ralat Punca Purata Kuasa Dua (RMS) dan Ukuran Indeks Kesamaan (SSIM) digunakan untuk menilai kepekaan hingar, penggunaannya masih terhad dan tidak tekal. Keputusan ini menekankan keperluan untuk penilaian sistematik terhada CGJI di bawah tahap hingar yang lebih tinggi dan keadaan lapangan sebenar bagi memastikan kebolehpercayaan dan kebolehgunaan praktikalnya.

Kata kunci: Aplikasi geofizik; kestabilan hingar; penyongsangan bersama kecerunan silang; perkembangan metodologi; tinjauan kepustakaan sistematik

 

RUJUKAN

Abdullah Sanusi, N.H., Ismail, N.A., Roslan, I.Z., Saleh, A.S. & Rosli, N. 2021. Cross-gradient joint inversion of 2D resistivity and seismic refraction methods for subsurface mapping. Journal of Mines, Metals and Fuels 69(8): 402-412.

Al Nasser, S. & Morgan, F.D. 2021. Joint inversion for an improved reservoir modeling and an accurate history matching. First International Meeting for Applied Geoscience & Energy. Paper No: segam2021-3582926.1. pp. 2159-2162.

Amin, M.A., Shukor, H., Makhtar, M.M.Z., Ismail, M.I., Yaakop, N.A.S., Shafiq, M.D. & Shoparwe, N.F. 2024. Bibliometric analysis on biobutanol production research trends from 2010-2022 using Scopus database. Sains Malaysiana 53(3): 635-651.

Carrillo, J. & Gallardo, L.A. 2018. Joint two-dimensional inversion of magnetotelluric and gravity data using correspondence maps. Geophysical Journal International 214(2): 1061-1071.

Colombo, D., Rovetta, D. & Turkoglu, E. 2018. CSEM-regularized seismic velocity inversion: A multiscale, hierarchical workflow for subsalt imaging. Geophysics 83(5): B241-B252.

de Peppo, G.P., Cercato, M. & De Donno, G. 2024. Cross-gradient joint inversion and clustering of ERT and SRT data on structured meshes incorporating topography. Geophysical Journal International 239: 1155-1169.

Demirci, İ., Dikmen, Ü. & Candansayar, M.E. 2018. Two-dimensional joint inversion of magnetotelluric and local earthquake data: Discussion on the contribution to the solution of deep subsurface structures. Physics of the Earth and Planetary Interiors 275(January 2017): 56-68.

Demirci, İ., Candansayar, M.E., Vafidis, A. & Soupios, P. 2017. Two dimensional joint inversion of direct current resistivity, radio-magnetotelluric and seismic refraction data: An application from Bafra Plain, Turkey. Journal of Applied Geophysics 139: 316-330.

Fang, Y., Wang, J., Meng, X., Zheng, S. & Tang, H. 2022. An efficient cross-gradient joint inversion algorithm for gravity and magnetic data using a sequential strategy. IEEE Transactions on Geoscience and Remote Sensing 60: 4510116.

Feng, X., Nilot, E., Liu, C., Zhang, M., Yu, H., Zhao, J. & Sun, C. 2018. Joint inversion of seismic and audio magnetotelluric data with structural constraint for metallic deposit. Journal of Environmental and Engineering Geophysics 23(2): 159-169.

Fregoso, E., Palafox, A. & Moreles, M.A. 2020. Initializing cross-gradients joint inversion of gravity and magnetic data with a Bayesian surrogate gravity model. Pure and Applied Geophysics 177: 1029-1041.

Fregoso, E., Gallardo, L.A. & García-Abdeslem, J. 2015. Structural joint inversion coupled with Euler deconvolution of isolated gravity and magnetic anomalies. Geophysics 80(2): G67-G79.

Gallardo, L.A. & Meju, M.A. 2003. Characterization of heterogeneous near-surface materials by joint 2D inversion of dc resistivity and seismic data. Geophysical Research Letters 30(13): 1658.

Gao, J. & Zhang, H. 2018. An efficient sequential strategy for realizing cross-gradient joint inversion: Method and its application to 2-D cross borehole seismic traveltime and DC resistivity tomography. Geophysical Journal International 213(2): 1044-1055.

Geng, M., Yang, Q. & Huang, D. 2017. 3D joint inversion of gravity-gradient and borehole gravity data. Exploration Geophysics 48(2): 151-165.

Gessner, K., Gallardo, L.A., Wedin, F. & Sener, K. 2016. Crustal structure of the northern Menderes Massif, western Turkey, imaged by joint gravity and magnetic inversion. International Journal of Earth Sciences 105(7): 2133-2148.

Ghari, H., Parnow, S., Varfinezhad, R., Milano, M., Fourie, F.D. & Tosti, F. 2024. Cross-gradient joint inversion of DC resistivity and gravity gradient data: A multi-disciplinary approach for geoscience, heritage, and the built environment. Remote Sensing 16(23): 4468.

Giraud, J., Ogarko, V., Martin, R., Jessell, M. & Lindsay, M. 2021. Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code. Geoscientific Model Development 14(11): 6681-6709.

Gosselin, J.M., Dosso, S.E., Askan, A., Wathelet, M., Savvaidis, A. & Cassidy, J.F. 2022. A review of inverse methods in seismic site characterization. Journal of Seismology 26(4): 781-821.

Gross, L. 2019. Weighted cross-gradient function for joint inversion with the application to regional 3-D gravity and magnetic anomalies. Geophysical Journal International 217(3): 2035–2046.

Harzing, A.W. 2007. Publish or Perish.

He, H., Li, T. & Zhang, R. 2022. Joint inversion of 3D gravity and magnetic data under undulating terrain based on combined hexahedral grid. Remote Sensing 14: 4651.

Huang, Y., Moorkamp, M., Gao, J. & Zhang, H. 2023. Seismogenic structure of the 2014 M6.5 ludian earthquake from three-dimensional joint inversion of magnetotelluric data and seismic arrival times. Journal of Geophysical Research: Solid Earth 128(7): e2022JB026151.

Hu, Y., Wei, X., Wu, X., Sun, J., Huang, Y. & Chen, J. 2024. Three dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques. Geophysics 89(1): WB67-WB79.

Hu, Y., Wei, X., Wu, X., Sun, J., Chen, J., Huang, Y. & Chen, J. 2023. A deep learning enhanced framework for multi-physics joint inversion. Geophysics 88(1): K13-K26.

Jordi, C., Doetsch, J., Günther, T., Schmelzbach, C., Maurer, H. & Robertsson, J.O.A. 2020. Structural joint inversion on irregular meshes. Geophysical Journal International 220(3): 1995-2008.

Joulidehsar, F., Moradzadeh, A. & Ardejani, F.D. 2018. An improved 3D joint inversion method of potential field data using cross-gradient constraint and LSQR method. Pure and Applied Geophysics 175(12): 4389-4409.

Lan, T., Liu, H., Liu, N., Li, J., Han, F. & Liu, Q.H. 2018. Joint inversion of electromagnetic and seismic data based on structural constraints using variational born iteration method. IEEE Transaction on Geoscience and Remote Sensing 56(1): 436-445.

León-Sánchez, A.M., Gallardo, L.A. & Ley-Cooper, A.Y. 2018. Two dimensional cross-gradient joint inversion of gravity and magnetic data sets constrained by airborne electromagnetic resistivity in the Capricorn Orogen, Western Australia. Exploration Geophysics 49(6): 940-951.

Li, G., Li, T. & Zhang, R. 2022. 2-D magnetotelluric multiparameter joint inversion considering the induced polarization effect. IEEE Transactions on Geoscience and Remote Sensing 60: 5923809.

Li, Z., Ma, G., Meng, Q., Wang, T. & Li, L. 2023. Gravity and magnetic fast inversion method with cross-gradient based on function fitting. Geophysical Journal International 232(2): 1209-1218.

Liao, C., Hu, X., Zhang, S., Li, X., Yin, Q., Zhang, Z. & Zhang, L. 2022. Joint inversion of gravity, magnetotelluric and seismic data using the alternating direction method of multipliers. Geophysical Journal International 229(1): 203-218.

Liu, S., Tang, Y., Lu, F., Sun, D., Jia, B. & Ma, Y. 2024. Joint inversion of gravity and magnetic data based on the modified structural similarity index for the structural consistency constraint. Earth and Planetary Sciences 3(1): 44-54.

Liu, S., Wan, X., Jin, S., Jia, B., Lou, Q. & Xuan, S. 2023a. Joint inversion of gravity and vertical gradient data based on modified structural similarity index for the structural and petrophysical consistency constraint. Geodesy and Geodynamics 14(5): 485-499.

Liu, S., Wan, X., Jin, S., Jia, B., Xuan, S., Lou, Q., Qin, B., Peng, R. & Sun, D. 2023b. Fast 3D joint inversion of gravity and magnetic data based on cross gradient constraint. Geodesy and Geodynamics 14(4): 331-346.

Ma, G., Gao, T., Niu, R., Li, L., Wang, T. & Li, D. 2022. Cross-gradient joint inversion of gravity and seismic data with triangular grid division by the second-order finite-difference method. IEEE Transactions on Geoscience and Remote Sensing 60: 5919410.

Ma, G., Gao, T., Li, L., Wang, T., Niu, R. & Li, X. 2021. High-resolution cooperate density-integrated inversion method of airborne gravity and its gradient data. Remote Sensing 13(20): 4157.

Ma, J., Deng, Y., Li, X., Guo, R., Zhou, H. & Li, M. 2024. Recent advances in machine learning ‑ Enhanced joint inversion of seismic and electromagnetic data. Surveys in Geophysics 46: 197-225.

Martin, R., Giraud, J., Ogarko, V., Chevrot, S., Beller, S., Gégout, P. & Jessell, M. 2021. Three-dimensional gravity anomaly data inversion in the Pyrenees using compressional seismic velocity model as structural similarity constraints. Geophysical Journal International 225(2): 1063-1085.

Meju, M.A., Saleh, A.S., Karpiah, A.B., Masnan, M.S., Miller, R.V., Legrand, X. & Kho, J.H.W. 2023. Three-dimensional anisotropic inversion and electrostratigraphic imaging of marine magnetotelluric data to understand the control of crustal deformations by pre-existing lithospheric structures in the Mexican Ridges Fold belt, Southwestern Gulf of Mexico. Geophysical Journal International 234: 1032-1050.

Meju, M.A., Mackie, R.L., Miorelli, F., Saleh, A.S. & Miller, R.V. 2019. Structurally-tailored 3D anisotropic CSEM resistivity inversion with cross-gradients criterion and simultaneous model calibration. Geophysics 84(6): E387-E402.

Meng, Q., Ma, G., Li, L., Wang, T. & Han, J. 2022. 3-D cross-gradient joint inversion method for gravity and magnetic data with unstructured grids based on second-order Taylor formula: Its application to the southern Greater Khingan Range. IEEE Transactions on Geoscience and Remote Sensing 60: 5914816.

Molodtsov, D., Kiyan, D. & Bean, C.J. 2024. Decoupled joint inversion with variable splitting: Example scheme for magnetotelluric, seismic and gravity data. Geophysical Journal International 239: 706-724.

Moorkamp, M. 2017. Integrating electromagnetic data with other geophysical observations for enhanced imaging of the earth: A tutorial and review. Surveys in Geophysics 38(5): 935-962.

Niu, R., Ma, G., Wang, T., Li, L. & Gao, T. 2023. Joint inversion method of gravity and magnetic analytic signal data with adaptive unstructured tetrahedral subdivision. IEEE Transactions on Geoscience and Remote Sensing 61: 5915309.

Oliver-Ocaño, F.M., Gallardo, L.A., Romo-Jones, J.M. & Pérez-Flores, M.A. 2019. Structure of the Cerro Prieto Pull-apart basin from joint inversion of gravity, magnetic and magnetotelluric data. Journal of Applied Geophysics 170: 103835.

Pak, Y.C., Li, T. & Kim, G.S. 2017. 2D data-space cross-gradient joint inversion of MT, gravity and magnetic data. Journal of Applied Geophysics 143: 212-222.

Peng, M., Tan, H. & Moorkamp, M. 2019. Structure-coupled 3-D imaging of magnetotelluric and wide-angle seismic reflection/refraction data with interfaces. Journal of Geophysical Research: Solid Earth 124(10): 10309-10330.

Qiao, Z., Zhang, Z., Hu, R., Shen, Z., Yuan, P., Zhou, H. & Huang, X. 2024. Joint inversion of gravity and gravity gradient data based on cross-gradient function. IEEE Sensors Journal 24(13): 20940-20948.

Qin, T., Bohlen, T. & Pan, Y. 2024. Indirect joint petrophysical inversion of shallow-seismic and multi-offset ground penetrating radar field data. Geophysical Journal International 237: 974-988.

Ragueh, R.R., Tarits, P., Hautot, S. & Jalludin, M. 2024. Inversion of gravity data constrained by a magnetotelluric resistivity model: Application to the Asal Rift, Djibouti. Journal of Geophysical Research: Solid Earth 129(8): e2023JB028484.

Rittgers, J.B., Revil, A., Mooney, M.A., Karaoulis, M., Wodajo, L. & Hickey, C.J. 2016. Time-lapse joint inversion of geophysical data with automatic joint constraints and dynamic attributes. Geophysical Journal International 207: 1401-1419.

Saleh, A.S., Meju, M.A., Mackie, R.L., Andersen, E., Ismail, N.A. & Nawawi, M. 2023. Seismic-electromagnetic projection attribute: Application in integrating seismic quantitative interpretation and 3D controlled-source electromagnetic-magnetotelluric broadband data inversion for robust ranking and sweet spotting of hydrocarbon prospects. Geophysics 88(6): B329-B341.

Saleh, A.S., Meju, M.A., Ismail, N.A. & Nordin, M.N.M. 2022. Optimization of seismic-guided 3-D marine magnetotelluric imaging in a complex fold-thrust belt setting in NW Borneo, Malaysia. Geophysical Journal International 230: 464-479.

Sharom, N.A., Talib, N.K., Muztaza, N.M., Masnan, S.S.K. & Bujeng, V. 2025. Penyelidikan arkeologi menggunakan kaedah keberintangan elektrik di Bukit Suring, Lenggong, Perak, Malaysia. Sains Malaysiana 54(10): 2337-2351.

Shi, Z., Hobbs, R.W., Moorkamp, M., Tian, G. & Jiang, L. 2017. 3-D cross-gradient joint inversion of seismic refraction and DC resistivity data. Journal of Applied Geophysics 141: 54-67.

Song, X., Li, M., Yang, F., Xu, S. & Abubakar, A. 2019. Three-dimensional joint inversion of acoustic and electromagnetic data based on contrast source inversion. 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), Granada, Spain. pp. 875-878.

Sun, C. & Wang, Y. 2020. Gravity-magnetic cross-gradient joint inversion by the cyclic gradient method. Optimization Methods and Software 35(5): 982-1001.

Tarits, P., Hautot, S., Roach, P. & Magareh, H.M. 2015. Mapping density models onto resistivity structure through joint inversion of gravity and MT. SEG Technical Program. pp. 854-858.

Tavakoli, M., Kalateh, A.N., Rezaie, M., Gross, L. & Fedi, M. 2021. Sequential joint inversion of gravity and magnetic data via the cross-gradient constraint. Geophysical Prospecting 69(7): 1542-1559.

Turkoglu, E., Colombo, D., McNeice, G. & Sandoval-Curiel, E. 2018. Quantitative integration of seismic and electromagnetics for enhancing subsalt imaging. Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2018, ADIPEC 2018, SPE-192615-MS.

Varfinezhad, R., Oskooi, B. & Fedi, M. 2020. Joint inversion of DC resistivity and magnetic data, constrained by cross gradients, compactness and depth weighting. Pure and Applied Geophysics 177(9): 4325-4343.

Vatankhah, S., Huang, X., Renaut, R.A., Mickus, K., Kabirzadeh, H. & Lin, J. 2023. Efficiently implementing and balancing the mixed Lp-Norm joint inversion of gravity and magnetic data. IEEE Transactions on Geoscience and Remote Sensing 61: 5614917.

Vatankhah, S., Renaut, R.A., Mickus, K., Liu, S. & Matende, K. 2022. A comparison of the joint and independent inversions for magnetic and gravity data over kimberlites in Botswana. Geophysical Prospecting 70(9): 1602-1616.

von Ketelhodt, J.K., Manzi, M.S.D., Durrheim, R.J. & Fechner, T. 2019. Seismic vertical transversely isotropic parameter inversion from P- and S-wave cross-borehole measurements in an aquifer environment. Geophysics 84(3): D101-D116.

Wang, K-P., Tan, H-D. & Wang, T. 2017. 2D joint inversion of CSAMT and magnetic data based on cross-gradient theory. Applied Geophysics 14(2): 279-290.

Wang, N., Ma, G., Li, L., Wang, T. & Li, D. 2022. A density-weighted and cross-gradient constrained joint inversion method of gravity and vertical gravity gradient data in spherical coordinates and its application to lunar data. IEEE Transactions on Geoscience and Remote Sensing 60: 4511211.

Wang, Z., Cai, Y., Liu, D., Lu, J., Qiu, F., Hu, J., Li, Z. & Gamage, R.P. 2024. A review of machine learning applications to geophysical logging inversion of unconventional gas reservoir parameters. Earth-Science Reviews 258: 104969.

Wei, X., Li, K. & Sun, J. 2023. Mapping critical mineral resources using airborne geophysics, 3D joint inversion and geology differentiation: A case study of a buried niobium deposit in the Elk Creek carbonatite, Nebraska, USA. Geophysical Prospecting 71(7): 1247-1266.

Wu, P., Tan, H., Ding, Z., Kong, W., Peng, M., Wang, X. & Xu, L. 2022. Joint inversion of 3-D magnetotelluric and ambient noise dispersion data sets with cross-gradient constraints: Methodology and application. Geophysical Journal International 230: 714-732.

Wu, P., Tan, H., Lin, C., Peng, M., Ma, H. & Yan, Z. 2020. Joint inversion of two-dimensional magnetotelluric and surface wave dispersion data with cross-gradient constraints. Geophysical Journal International 221(2): 938-950.

Xu, Z., Sun, F., Xin, W., Sun, N., Li, F., Niu, J., Li, L. & Li, G. 2019. Formation and evolution of Paleoproterozoic orogenic belt in southern Jilin, Jiao–Liao–Ji Belt, North China Craton: Constraints from geophysics. Precambrian Research 333(2199): 105433.

Yadav, K. & Sircar, A. 2019. Integrated 2D joint inversion models of gravity, magnetic, and MT for geothermal potentials: A case study from Gujarat, India. Modeling Earth Systems and Environment 5(3): 963-983.

Yang, H., Li, T., Zhang, R., Dong, X. & Zhuang, Y. 2023. 3-D joint inversion of DC resistivity and time-domain induced polarization with structural constraints in undulating topography. IEEE Transactions on Geoscience and Remote Sensing 61: 5920412.

Yari, M., Nabi-Bidhendi, M., Ghanati, R. & Shomali, Z.H. 2021. Hidden layer imaging using joint inversion of P-wave travel-time and electrical resistivity data. Near Surface Geophysics 19(3): 297-313.

Zaid, H.A.H., Arifin, M.H., Kayode, J.S. & Iswadi, M.B. 2023. Application of 2-D electrical resistivity imaging, and induced polarization methods for delineating gold mineralization at Felda Chiku 3, Kelantan, Malaysia. Sains Malaysiana 52(1): 305-320.

Zakaria, M.T., Mohd Muztaza, N., Abir, I.A., Ismail, N.A. & Masnan, S.S.K. 2025. Integration of geophysical applications in heterogeneous near-subsurface environments for archaeological investigations. Sains Malaysiana 54(2): 343-360.

Zakaria, M.T., Ismail, N.A., Mohd Muztaza, N. & Mohamad Zaki, M.F. 2024. Integrated geophysical models for interpretations of heterogeneous subsurface environments. Sains Malaysiana 53(5): 1021-1031.

Zakaria, M.T., Muztaza, N.M., Zabidi, H., Salleh, A.N., Mahmud, N. & Rosli, F.N. 2022. Integrated analysis of geophysical approaches for slope failure characterisation. Environmental Earth Sciences 81: 299.

Zhang, R. & Li, T. 2019. Joint inversion of 2D gravity gradiometry and magnetotelluric data in mineral exploration. Minerals 9: 541.

Zhang, R., Li, T., Deng, X., Huang, X. & Pak, Y. 2019a. Two-dimensional data-space joint inversion of magnetotelluric, gravity, magnetic and seismic data with cross-gradient constraints. Geophysical Prospecting 68(2): 721-731.

Zhang, R., Li, T., Zhou, S. & Deng, X. 2019b. Joint MT and gravity inversion using structural constraints: A case study from the linjiang copper mining area, Jilin, China. Minerals 9: 407.

Zhang, Y. & Wang, Y. 2019. Three-dimensional gravity-magnetic cross-gradient joint inversion based on structural coupling and a fast gradient method. Journal of Computational Mathematics 37(6): 758-777.

Zhao, R., Xiong, Q., Liu, Z., Liu, S., Ma, X. & Mao, D. 2023. Reconstruction of hydrogeological parameter distributions by exploiting structural similarities. Advances in Water Resources 173: 104404.

Zhao, X., Zeng, Z., Wu, Y., He, R., Wu, Q. & Zhang, S. 2020. Interpretation of gravity and magnetic data on the hot dry rocks (HDR) delineation for the enhanced geothermal system (EGS) in Gonghe town, China. Environmental Earth Sciences 79: 390.

Zhong, Y., Ren, Z., Tang, J., Lin, Y., Chen, B., Deng, Y. & Jiang, Y. 2022. Constrained gravity inversion with adaptive inversion grid refinement in spherical coordinates and its application to mantle structure beneath Tibetan Plateau. Journal of Geophysical Research: Solid Earth 127(5): e2021JB022916.

Zhou, J., Meng, X., Guo, L. & Zhang, S. 2015. Three-dimensional cross-gradient joint inversion of gravity and normalized magnetic source strength data in the presence of remanent magnetization. Journal of Applied Geophysics 119: 51-60.

Zhu, D., Tan, H., Peng, M. & Wang, T. 2023. Three-dimensional joint inversion of the resistivity method and time-domain-induced polarization based on the cross-gradient constraints. Applied Sciences (Switzerland) 13(14): 8145.

Zhu, T. & Harris, J.M. 2015. Improved estimation of P-wave velocity, S-wave velocity, and attenuation factor by iterative structural joint inversion of crosswell seismic data. Journal of Applied Geophysics 123: 71-80.

 

*Pengarang untuk surat-menyurat; email: taqiuddin@ukm.edu.my

 

 

 

 

 

 

 

           

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