Two-dimensional data-space joint inversion of magnetotelluric, gravity, magnetic and seismic data with cross-gradient constraints
R. Zhang, T. Li, X. Deng, X. Huang and Y. Pak
Journal name: Geophysical Prospecting
Issue: Vol 68, No 2, February 2020 pp. 721 - 731
Info: Article, PDF ( 3.34Mb )
In recent years, joint inversion has been widely used for integrated geological interpretation. We extended a data-space joint inversion algorithm of magnetotelluric, gravity and magnetic data to include first-arrival seismic travel-time and normalized cross-gradient constraints. We describe the main features of the algorithm and apply it to synthetic data generated for hypothetical models. For the synthetic data, we find that the joint inversion with multiple parameters is superior to the joint inversion with two or even three parameters, which can reduce the multisolution of inversion results more effectively. Furthermore, data-space joint inversion involves fewer memory requirements and better calculation speeds than traditional model-space joint inversion. The normalized cross-gradient constraints can better couple model parameters of different magnitudes compared with traditional unnormalized cross-gradient constraints, resulting in higher levels of structural similarity among resistivity, density, magnetic susceptibility and velocity models.