Seismic structure-constrained inversion of CSAMT data for detecting karst caves

Abstract

Karst cave is a sort of special and buried geological structure that was widely developed in the Permo-Carboniferous coal accumulation area of North China. It brings karst collapse and safety hazard in the mining industry. In this study, we propose a seismic structure-constrained inversion of controlled source audio-frequency magnetotelluric (CSAMT) data on a detailed survey and detection of karst caves. Instead of constrained by seismic impedance, the method in this study directly takes the seismic imaging results as structural constraints, which is different from the cross-gradient technique used by conventional structural constraints. First, the seismic migration section is divided according to the CSAMT inversion grid and applied pixel extraction for each grid. Clustering is carried out according to the structural information interpreted by the seismic migration section and the average pixel value of each cluster is calculated. Then the clustered results were used in the seismic structure-constrained inversion of CSAMT data based on cross-gradient technique. After that, as a karst cave model developed in limestone was established, the study compares the structure-constrained inversions with different clustering strategies shows a much more precision of karst cave detection than the method only applies CSAMT data. Moreover, experimental verification is provided in this study, which is for the detection of a suspected karst collapse column from Shandong Province, China. The comparison results further show that the structure-constrained inversion method proposed in this paper is applicable and may effectively improve the locating accuracy of karst caves.

KEYWORDS:

• Seismic exploration
• controlled source audio-frequency magnetotelluric
• cross-gradient
• clustering
• structure-constrained inversion

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Key Research and Development Program of China: [Grant Number 2017YFC0804105]; Natural Science Foundation of Hebei Province: [Grant Number D2019508160, D2020402013, D2020402032]; Science and Technology Research and Development Program of Handan: [Grant Number 19422121008-40].