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Joint Inversion Method of Gravity and Magnetic Data with Adaptive Zoning Using Gramian in Both Petrophysical and Structural Domains
Different observation data are utilized to obtain a unified geophysical model based on the correlations of underground geological bodies in joint inversions. By specifying a type of Gramian constraints, Gramian as a coupling term can link geophysical models through relationships of physical properti...
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Published in: | Surveys in geophysics 2024-08, Vol.45 (4), p.1291-1330 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | Different observation data are utilized to obtain a unified geophysical model based on the correlations of underground geological bodies in joint inversions. By specifying a type of Gramian constraints, Gramian as a coupling term can link geophysical models through relationships of physical properties or structural similarities. Considering the complex relationships of physical properties of underground geological bodies, we proposed an adaptive zoning method to automatically divide the whole inversion area into subregions with different relationships of physical properties and to determine the number and range of subregions that utilized correlation between geophysical data before joint inversions. On this basis, we considered the use of a combination of Gramian coupling terms rather than one term to link petrophysical and structural domains during joint inversions. Synthetic tests showed that the algorithm is capable of having a robust estimate of the spatial distribution and relationships between density and magnetization intensity of geological bodies. The idea was also applied to the ore concentration area in the middle and lower reaches of the Yangtze River to obtain the three-dimensional (3-D) distribution model of magnetite-bearing rocks within 5 km underground, which corresponds well with the existing shallow ore sites and demonstrates the existence of available deep resources in the study area. |
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ISSN: | 0169-3298 1573-0956 |
DOI: | 10.1007/s10712-024-09832-0 |