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The Alxa area in Inner Mongolia has been selected as a possible site for geological disposal of high-level radioactive waste (HLRW). Based on results of a previous study on crustal stability, the Tamusu rock mass has been chosen as the target. To determine the geological structure of this rock mass, aeromagnetic and gravity data are collected and inverted. Three-dimensional (3D) inversion horizontal slices show that the internal density of the rock mass and the distribution of magnetic properties are not uniform, with fractures and fragmentation being present. To confirm this result, the controlled source audio-frequency magnetotelluric method (CSAMT) was applied to explore the geological structures, the typical CSAMT sounding curve was analyzed, and the response characteristics of the geological structure and surrounding rock are distinguished. The original data were processed and interpreted in combination with data from surface geology and drilling and logging data. It is found that the CSAMT results were consistent with those from 3D inversion of the gravity and magnetic data, confirming the existence of fractures and fragmentation in the exploration area. [ABSTRACT FROM AUTHOR]

Laterally constrained inversion (LCI) has been successfully applied to the inversion of dc resistivity, TEM and airborne EM data. However, it hasn't been yet applied to the interpretation of controlled-source audio-frequency magnetotelluric (CSAMT) data. In this paper, we apply the LCI method for CSAMT data inversion by preconditioning the Jacobian matrix. We apply a weighting matrix to Jacobian to balance the sensitivity of model parameters, so that the resolution with respect to different model parameters becomes more uniform. Numerical experiments confirm that this can improve the convergence of the inversion. We first invert a synthetic dataset with and without noise to investigate the effect of LCI applications to CSAMT data, for the noise free data, the results show that the LCI method can recover the true model better compared to the traditional single-station inversion; and for the noisy data, the true model is recovered even with a noise level of 8%, indicating that LCI inversions are to some extent noise insensitive. Then, we re-invert two CSAMT datasets collected respectively in a watershed and a coal mine area in Northern China and compare our results with those from previous inversions. The comparison with the previous inversion in a coal mine shows that LCI method delivers smoother layer interfaces that well correlate to seismic data, while comparison with a global searching algorithm of simulated annealing (SA) in a watershed shows that though both methods deliver very similar good results, however, LCI algorithm presented in this paper runs much faster. The inversion results for the coal mine CSAMT survey show that a conductive water-bearing zone that was not revealed by the previous inversions has been identified by the LCI. This further demonstrates that the method presented in this paper works for CSAMT data inversion. [ABSTRACT FROM AUTHOR]