Joint Inversion of Seismic and Audio Magnetotelluric Data with Structural Constraint for Metallic Deposit

Abstract

Audio magnetotelluric (AMT) and seismic methods are widely used to detect metallic deposits. However, each geophysical method only provides partial information of the underground target. Besides, individual methods have inherent limitations and ambiguity which leads to non-uniqueness when solving the inverse problem. To obtain a more robust and consistent ore deposit model, it is best to integrate different geophysical methods and data types. Towards this effort, we propose a joint inversion algorithm using cross-gradient constraint to build a connection between seismic and AMT data, and simultaneously invert for a resistivity and P-wave velocity model. Compared with separate AMT Gauss–Newton inversion and seismic Full waveform inversion (FWI) method, we can get more detailed and robust inversion results. In addition, frequency domain FWI with the Limited-Memory-Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm provides an effective way to reduce computer memory usage and improve convergence speed. This joint inversion algorithm has been tested using simple synthetic models with two cross targets. The results obtained with separate inversions were compared with those obtained with joint inversion. Then, we applied the algorithm to geophysical models of the Jinchuan sulfide deposit. The AMT results obtained with joint inversion of seismic data were better than those obtained with separate AMT inversion. The joint inversion approach appears more robust than the traditional separate FWI inversion and it is recommended that the proposed algorithm be considered in future projects of real field data.

Obtaining reliable S‐wave velocity depth profile by joint inversion of geophysical data: the combination of active surface‐wave, seismic refraction and electric sounding data

“…Joint inversion strategies may be classified based on how each data set links to each other (Gallardo, 2004). Thus, the structural joint inversion uses a common structural element, for example layer thickness, to link data sets (Hering et al ., 1995; Roy et al ., 2005; Feng et al ., 2018; Mackie, 2019). Here, the quantitative properties of the common structural parameters of the solution (e.g., number of layers and their thickness) are the same for all models.…”