Seismic acoustic impedance inversion plays an important role in subsurface quantitative interpretation. Due to the band-limited property of the seismic record and the discretization of the continuous elastic parameters with a limited sampling interval, the inverse problem suffers from serious ill-posedness. Various regularization methods are introduced into the seismic inversion to make the inversion results comply with the prespecified characteristics. However, conventional seismic inversion methods can only reflect fixed distribution characteristics and do not take into account discretization challenges. We have adopted a new poststack seismic impedance inversion method with upsampling and adaptive regularization. The adaptive regularization is constructed with two trained dictionaries from the true model and upsampled model-based inversion result to capture the features of high- and low-resolution details, and a sparsity-based statistical model is proposed to build the relationship between their sparse representations. The high-resolution components can be recovered based on the prediction model and low-resolution sparse representations, and the parameters of the statistical prediction model can be obtained effectively with conventional optimization algorithms. The synthetic and field data tests show that the model-based inversion is dependent on the sample interval, and our method can reveal more thin layers and enhance the extension of the strata compared with conventional inversion methods. Moreover, the inverted impedance variance of our method matches well with the borehole observations. The tests demonstrate that the interpolated model-based inversion result combined with the sparsity-based prediction model can effectively improve the resolution and accuracy of the inversion results.

Underground goaves were left in many mining areas due to the continuous exploitation of coal resources. These mining areas seriously affect the production safety of the mines and the safety of life and property of the surrounding residents. Enormous safety hazards will be generated if the goaf range is not accurately controlled. In this study, we proposed a method for the detection of goaves in coal mines with a complex terrain by combining controlled source audio-frequency magnetotellurics (CSAMT) and an activated-carbon method for radon measurement. The disadvantage of failing to interpret goaf depth for the activated-carbon method for radon measurement was compensated by the advantage of the capability of goaf-depth sounding for CSAMT. Subsequently, the reference for CSAMT data was provided by the immunity of the activated-carbon method for radon measurement to the influences of terrain, earth electricity, and EMF. On this basis, the proposed method was employed to detect the goaf of Houjiagou Coal Mine in Liulin County, China, and obtained reliable detection results. The feasibility of the comprehensive geophysical prospecting method in the complex terrain was verified and it provides a new reference for the detection method of goaves with other conditions.