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A simple but efficient non-linear method for 2D inversion of magnetic field data based on Ridge-Regression algorithm | ||
| International Journal of Mining and Geo-Engineering | ||
| دوره 55، شماره 1، شهریور 2021، صفحه 73-79 اصل مقاله (1.12 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2021.254258.594724 | ||
| نویسندگان | ||
| Ali Moradzadeh1؛ Ali Nejati* 2؛ Fuad Meysami2؛ Saeed Mojarad2 | ||
| 1School of Mining, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran | ||
| چکیده | ||
| In geophysical exploration, inversion is carried out on the observed data to generate a geophysical model, approximating the subsurface geological structure. In the interpretation of magnetic data, the subsurface model parameters are found by a proper inversion scheme. Hence, it will be possible to obtain the entire parameters of any features (e.g. Dike) including depth, width, and location. In this paper, theoretical and field studies were carried out to interpret the total components of magnetic anomalies of dikes at the finite depth. Moreover, a least-squares approach was used for depth determination using anomalous magnetic data. Potential field data inversion can be achieved through many optimization techniques. This study, however, it is attempted to develop an efficient two-dimensional (2D) inversion algorithm based on the Ridge Regression routine. The developed method was programmed using Matlab software and applied to three sets of synthetic magnetic data containing different percent of random noise to find out how good the results are. It was found that the proposed 2D inversion method can produce an accurate subsurface model that precisely explains the synthetic data in each case of data inversion. Finally, the method was applied to the real total magnetic field (TMF) data of Moghan Sedimentary basin. In that case, the estimated sedimentary basement depths were found to be in good agreement with that of the seismic data acquired before. | ||
| کلیدواژهها | ||
| Total magnetic field data؛ 2D inversion؛ basement depth؛ least-square method؛ sedimentary basins | ||
| مراجع | ||
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[1] Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019). Performance evaluation of RBF-and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of SA multifractal model and mineralization controls. Earth Science Informatics, 1-17.
[2] Parsa, M., Maghsoudi, A., Yousefi, M., & Carranza, E. J. M. (2017). Multifractal interpolation and spectrum–area fractal modeling of stream sediment geochemical data: implications for mapping exploration targets. Journal of African Earth Sciences, 128, 5-15.
[3] Cao, L., Cheng, Q. (2012). Quantification of anisotropic scale invariance of geochemical anomalies associated with Sn-Cu mineralization in Gejiu, Yunan Province, China, Geochemical Exploration 122, 47–54.
[4] Fyzollahhi, N., Torshizian, H., Afzal, P., & Jafari, M. R. (2018). Determination of lithium prospects using fractal modelling and staged factor analysis in Torud region, NE Iran. Journal of Geochemical Exploration, 189, 2-10.
[5] Zuo, R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal the Gangdese Belt, Tibet (China). J. Geochemical Exploration. 111, 13-22.
[6] Zuo, R. (2011). Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Applied Geochemistry 26, S271-S273.
[7] Zuo, R., Carranza, E.J.M. & Cheng, Q. (2012). Fractal/multifractal modelling of geochemical exploration data. Journal of Geochemical Exploration 122, 1-3.
[8] Zuo, R., Xia, Q. & Zhang, D. (2013). A comparison study of the C-A H. Mahdiyanfar & M. Farzamian / Int. J. Min. & Geo-Eng. (IJMGE), 55-1 (2021) 81-89 89 and S-A models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry 33, 165-172.
[9] Ghezelbash, R., Maghsoudi, A., & Daviran, M. (2019). Combination of multifractal geostatistical interpolation and spectrum–area (S– A) fractal model for Cu–Au geochemical prospects in Feizabad district, NE Iran. Arabian Journal of Geosciences, 12(5), 152.
[10] Shokouh Saljoughi, B., Hezarkhani, A., & Farahbakhsh, E. (2018). A comparative study of fractal models and U-statistic method to identify geochemical anomalies; case study of Avanj porphyry system, Central Iran. Journal of Mining and Environment, 9(1), 209-227.
[11] Zuo, R., Wang, J. (2015). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration.
[12] Wang, H., Zuo, R. (2015). A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies. Journal of Geochemical Exploration, 155, 84-90.
[13] Wang, J., Zuo, R. (2015). A MATLAB-based program for processing geochemical data using fractal/multifractal modeling. Earth Science Informatics, 1-11.
[14] Shahi, H., Ghavami, R., Kamkar Rouhani, A. & Asadi-Haroni, H. (2014). Identification of mineralization features and deep geochemical anomalies using a new FT-PCA approach, journal of Geopersia, 4 (2), 101-110.
[15] Shahi, H., Ghavami Riabi, R., Kamkar Ruhani, A. & Asadi Haroni, H. (2015). Prediction of mineral deposit model and identification of mineralization trend in depth using frequency domain of surface geochemical data in Dalli Cu-Au porphyry deposit. Journal of Mining and Environment, 6(2), pp.225-236.
[16] Shahi, H., Ghavami, R., Kamkar Rouhani, A. & Asadi-Haroni, H. (2015). Application of Fourier and wavelet approaches for identification of geochemical anomalies, Journal of African Earth Sciences 106. 118–128.
[17] Shahi, H., Ghavami, R., & Rouhani, A. K. (2016). Detection of deep and blind mineral deposits using new proposed frequency coefficients method in frequency domain of geochemical data. Journal of Geochemical Exploration.
[18] Roberts, R.G., Sheahan, P., Cherry, M.E. (Eds.), (1988). Ore deposit models: Geoscience Canada Reprint Series 3. Geological Association of Canada, Newfoundland. 200 p.
[19] Cox, D.P., Singer, D.A. (Eds.), (1986). Mineral Deposit Models. U.S. Geological Survey Bulletin 1693. U.S. Government Printing Office, Washington. 379 p.
[20] Carranza, E.J.M, Sadeghi, M. (2012). Primary geochemical characteristics of mineral deposits - Implications for exploration, Ore Geology Reviews 45.1–4.
[21] Grigorian, S.V. (1985). Secondary Lithochemical Halos in Prospecting for Hidden Mineralization. Nedra Publishing House, Moscow.
[22] Grigorian, S.V. (1992). Mining Geochemistry. Nedra Publishing House, Moscow.
[23] Hassani, H., Daya, A., Alinia, F. (2009). Application of a fractal method relating power spectrum and area for separation of geochemical anomalies from background. Aust J Basic Appl Sci, 3(4), 3307-3320 [24] Hornak, J. (1997). "The Basic of NMR." http://www.cis.rit.edu/htbooks/nmr/inside.htm.
[25] Dobrin, M. B., Savit, C. H. (1988). Geophysical propecting: McGraw-Hill Book Co., New York, 867 p.
[26] Bhattacharyya, B.K. (1966). Continuous spectrum of the total[1]magnetic-field anomaly due to a rectangular prismatic body. Geophysics, 31(1), 97-121.
[27] Cheng, Q. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration, 122, 55-70.
[28] Cheng, Q. (2014). Vertical distribution of elements in regolith over mineral deposits and implications for mapping geochemical weak anomalies in covered areas. Geochemistry: Exploration, Environment, Analysis, 14(3), 277-289.
[29] Farzamian, M., Rouhani, A. K., Yarmohammadi, A., Shahi, H., Sabokbar, H. F., & Ziaiie, M. (2016). A weighted fuzzy aggregation GIS model in the integration of geophysical data with geochemical and geological data for Pb–Zn exploration in Takab area, NW Iran. Arabian Journal of Geosciences, 9(2), 104. | ||
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