Three-dimensional gravity inversion based on optimization processing from edge detection

Abstract

Gravity inversion requires much computation, and inversion results are often non-unique. The first problem is often due to the large number of grid cells. Edge detection method, i.e., tilt angle method of analytical signal amplitude (TAS), helps to identify the boundaries of underground geological anomalies at different depths, which can be used to optimize the grid and reduce the number of grid cells. The requirement of smooth inversion is that the boundaries of the meshing area should be continuous rather than jagged. In this paper, the optimized meshing strategy is improved, and the optimized meshing region obtained by the TAS is changed to a regular region to facilitate the smooth inversion. For the second problem, certain constraints can be used to improve the accuracy of inversion. The results of analytic signal amplitude (ASA) are used to delineate the central distribution of geological bodies. We propose a new method using the results of ASA to perform local constraints to reduce the non-uniqueness of inversion. The guided fuzzy c-means (FCM) clustering algorithm combined with priori petrophysical information is also used to reduce the non-uniqueness of gravity inversion. The OpenAcc technology is carried out to speed up the computation for parallelizing the serial program on GPU. In general, the TAS is used to reduce the number of grid cells. The local weighting and priori petrophysical constraint are used in conjunction with the FCM algorithm during the inversion, which improves the accuracy of inversion. The inversion is accelerated by the OpenAcc technology on GPU. The proposed method is validated using synthetic data, and the results show that the efficiency and accuracy of gravity inversion are greatly improved by using the proposed method.

Keywords

• Gravity inversion
• Locally weighted constraint
• Petrophysical constrain
• Fuzzy c-means clustering algorithm
• OpenAcc technology