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Cross-Gradient Joint Inversion of Gravity and Seismic Data With Triangular Grid Division by the Second-Order Finite-Difference Method

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Cross-gradient joint inversion of gravity and seismic data can more accurately provide the characteristics of subsurface density and velocity structure. The subsurface is usually divided into triangular grids to accurately… Click to show full abstract

Cross-gradient joint inversion of gravity and seismic data can more accurately provide the characteristics of subsurface density and velocity structure. The subsurface is usually divided into triangular grids to accurately simulate the undulating terrain and irregularity of geological bodies in the inversion, and the existing cross-gradient inversion with triangular grid is finished by the linear trend method. However, the density and velocity changes are not all linear. To better describe both nonlinear and linear variation feature of the physical property, we propose the second-order finite-difference cross-gradient joint inversion method of gravity and seismic data with triangular grid, which can obtain high-resolution results and effectively reflect the linear and nonlinear physical property changes. We also compare the effect of cross-gradient inversion results computed by different order finite-difference method and verify that the second-order finite-difference method is more reasonable according to the computational efficiency and accuracy. To reveal the distribution of the polymetallic minerals in the Lu (Lujiang)-Zong (Zongyang) ore concentration area, we carry out regional gravity and profile seismic measurements in this area. We first obtain the 2-D density and velocity results, and then use the 2-D density as a constraint to compute the 3-D density distribution. The results reveal the distribution of six minerals and that the burial depth of high-density polymetallic ores range from 567 to 959 m, which provides reliable information for subsequent exploitation.

Keywords: cross gradient; order finite; inversion; method

Journal Title: IEEE Transactions on Geoscience and Remote Sensing
Year Published: 2022

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