تعداد نشریات | 161 |
تعداد شمارهها | 6,500 |
تعداد مقالات | 70,262 |
تعداد مشاهده مقاله | 123,480,781 |
تعداد دریافت فایل اصل مقاله | 96,708,829 |
A comparative study of Sparse and Tikhonov regularization methods in gravity inversion: a case study of manganese deposit In Iran | ||
International Journal of Mining and Geo-Engineering | ||
مقاله 5، دوره 58، شماره 2، شهریور 2024، صفحه 161-169 اصل مقاله (1.15 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijmge.2024.368515.595125 | ||
نویسندگان | ||
Bardiya Sadraeifar1؛ Maysam Abedi* 2 | ||
1Institute of Geophysics, University of Tehran, Iran. | ||
2School of Mining Engineering, Faculty of Engineering, University of Tehran, Iran. | ||
چکیده | ||
Gravity inversion methods play a fundamental role in subsurface exploration, facilitating the characterization of geological structures and economic deposits. In this study, we conduct a comparative analysis of two widely used regularization methods, Tikhonov (L2) and Sparse (L1) regularization, within the framework of gravity inversion. To assess their performance, we constructed two distinct synthetic models by implementing tensor meshes, considering station spacing to discretize the subsurface environment precisely. Both methods have proven ability to recover density distributions while minimizing the inherent non-uniqueness and ill-posed nature of gravity inversion problems. Tikhonov regularization yields stable results, presenting smooth model parameters even with limited prior information and noisy data. Conversely, sparse regularization, utilizing sparsity-promoting penalties, excels in capturing sharp geological features and identifying anomalous regions, such as mineralized zones. Applying these methodologies to real gravity data from the Safu manganese deposit in northwest Iran, we assess their efficacy in recovering the geometry of dense ore deposits. Sparse regularization demonstrates superior performance, yielding lower misfit values and sharper boundaries during individual inversions. This underscores its capacity to provide a more accurate representation of the depth and edges of anomalous targets in this specific case. However, both methods represent the same top depth of the target in the real case study, but the lower depth and density distribution were not the same in the XZ cross-sections. Inversion results imply the presence of a near-surface deposit characterized by a high-density contrast and linear distribution, attributed to the high grade of manganese mineralization. | ||
کلیدواژهها | ||
Tikhonov regularization؛ Sparse regularization؛ Synthetic models؛ Tensor meshes؛ Manganese deposit | ||
مراجع | ||
[1] Wang, Y., Leonov, A. S., Lukyanenko, D. V., & Yagola, A. G. (2020). General Tikhonov Regularization with Applications in Geoscience. CSIAM Transactions on Applied Mathematics, 1(1), 53–85.
[2] Tarantola, A. (2005). Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics (SIAM). (p. 168).
[3] Jordi, C., Doetsch, J., Günther, T., Schmelzbach, C., & Robertsson, J. O. A. (2018). Geostatistical regularization operators for geophysical inverse problems on irregular meshes. Geophysical Journal International, 213(2), 1374–1386.Top of FormTop of Form
[4] Menke, W., & Eilon, Z. (2015). Relationship between Data Smoothing and the Regularization of Inverse Problems. Pure and Applied Geophysics, 172, 2711-2726.
[5] Aster, R. C., Borchers, B., & Thurber, C. H. (2019). Parameter Estimation and Inverse Problems (3rd ed.). Elsevier.
[6] Silva, J. B. C., Medeiros, W. E., & Barbosa, V. C. F. (2001). Potential-field inversion: Choosing the appropriate technique to solve a geologic problem. Geophysics, 66(2), 511–520.
[7] Hong, B. W., Koo, J. K., Dirks, H., & Burger, M. (2017). Adaptive Regularization in Convex Composite Optimization for Variational Imaging Problems. Lecture Notes in Computer Science, 10496, 268–280.
[8] Tikhonov, A. N., & Arsenin, V. Y. (1977). Solutions of Ill-posed Problems. VH Winston and Sons, Washington, DC.
[9] Toushmalani, R., & Saibi, H. (2015). 3D Gravity Inversion using Tikhonov Regularization. Acta Geophysica, 63, 1044–1065.
[10] Fernández-Martínez, J. L., Pallero, J. L. G., Fernández-Muñiz, Z., & Pedruelo-González, L. M. (2014). The effect of noise and Tikhonov's regularization in inverse problems. Part I: The linear case. Journal of Applied Geophysics, 108, 176–185.
[11] Gholami, A., & Siahkoohi, H. R. (2010). Regularization of linear and non-linear geophysical ill-posed problems with joint sparsity constraints. Geophysical Journal International, 180(2), 871–882.
[12] Grasmair, M., Haltmeier, M., & Scherzer, O. (2015). Sparsity in Inverse Geophysical Problems. In W. Freeden, M. Nashed, & T. Sonar (Eds.), Handbook of Geomathematics (p. 1680). Springer
[13] Lin, Y., & Huang, L. (2015). Quantifying subsurface geophysical properties changes using double-difference seismic-waveform inversion with a modified total-variation regularization scheme. Geophysical Supplements to the Monthly Notices of the Royal Astronomical Society, 203(3), 2125-2149.
[14] Strong, D., & Chan, T. (2003). Edge-preserving and scale-dependent properties of total variation regularization. Inverse problems, 19(6), S165.
[15] Du, Z., Liu, D., Wu, G., Cai, J., Yu, X., & Hu, G. (2021). A high-order total-variation regularization method for full-waveform inversion. Journal of Geophysics and Engineering, 18(2), 241–252.
[16] Lima, W. A., Martins, C. M., Silva, J. B., & Barbosa, V. C. (2011). Total variation regularization for depth-to-basement estimate: Part 2 — Physicogeologic meaning and comparisons with previous inversion methods. GEOPHYSICS, 76, I13-I20.
[17] Sen, M. K., & Stoffa, P. L. (2013). Global Optimization Methods in Geophysical Inversion. Cambridge University Press. (p. 37)
[18] Villalon-Turrubiates, I. E., & Herrera-Nuñez, A. (2009). Performance study of the robust bayesian regularization technique for remote sensing imaging in geophysical applications. Mexican International Conference on Computer Science (pp. 3-12).
[19] Vatankhah, S., Ardestani, V. E., & Renaut, R. A. (2014). Automatic estimation of the regularization parameter in 2D focusing gravity inversion: application of the method to the Safo manganese mine in the northwest of Iran. Journal of Geophysics and Engineering, 11(4), 045001.
[20] Varfinezhad, R., & Ardestani, V. (2021). 2D inversion of gravity data including depth weighting and compactness constraints: a case study on the data set of the manganese mine of Safo. 19th Iranian Geophysical Conference–November, 2020 (pp. 152-156).
[21] Cockett, R., Kang, S., Heagy, L. J., Pidlisecky, A., & Oldenburg, D. W. (2015). SimPEG: An open source framework for simulation and gradient-based parameter estimation in geophysical applications. Computers & Geosciences, 85, 142-154.
[22] Ardestani, V. E. (2023). Sparse norm and cross-gradient inversions of gravity and magnetic data sets utilizing open-source resources in Python: Case study of hematite ore body in Jalal Abad area (Iran). Journal of the Earth and Space Physics.
[23] Pluff, D., (1976). Gravity and Magnetic fields of polygonal prisms and application to magnetic terrain corrections, Geophysics, 41, 727-41.
[24] De Bremaecker, J. Cl. (1966). On: “The gravitational attraction of a right rectangular prism,” by Dezsö Nagy (Geophysics, April, 1966, pp. 362–371). Geophysics, 31, 987-987.
[25] Li, Y., & Oldenburg, D. W. (1996). 3-D inversion of magnetic data. Geophysics, 61, 394-408.
[26] Oldenburg, D. W., & Li, Y. (2005). Inversion for applied geophysics: A tutorial. Investigations in Geophysics, 89-150.
[27] Abedi, M., Gholami, A., & Norouzi, G.H. (2014). A new stable downward continuation of airborne magnetic data based on Wavelet deconvolution. Near Surface Geophysics, 12, 751-762.
[28] Abedi, M., Gholami, A., & Norouzi, G.H. (2014). 3D inversion of magnetic data seeking sharp boundaries: a case study for a porphyry copper deposit from Now Chun in central Iran. Near Surface Geophysics, 12, 657-666.
[29] Abedi, M. (2019). AIRRLS: An Augmented Iteratively Re-weighted and Refined Least Squares Algorithm for Inverse Modeling of Magnetometry Data. Journal of Geological Research,1, 16-27.
[30] Maghfouri, S., Rastad, E., Movahednia, M., Lentz, D. R., Hosseinzadeh, M. R., Ye, L., & Mousivand, F. (2019). Metallogeny and temporal–spatial distribution of manganese mineralizations in Iran: Implications for future exploration. Ore Geology Reviews, 115, 103026.
[31] Soltanabadi, R., & Ardestani, V. E. (2018). 3D inversion of gravity data of Safo mining site using linear programming and simplex algorithm. Journal of Research on Applied Geophysics, 4(1), 137-155.
[32] Imamalipour, A. (2005). Geochemistry, mineralogy, and origin of Safu manganese deposit. 9th Meeting of Geological Society of Iran (pp. 256–269).
[33] Beltrão, J. F., Silva, J. B. C., & Costa, J. C. (1991). Robust polynomial fitting method for regional gravity estimation. GEOPHYSICS, 56, 80-89. | ||
آمار تعداد مشاهده مقاله: 206 تعداد دریافت فایل اصل مقاله: 170 |