Geology

Inversion (geology)

Image resolution

Mathematics

Algorithm

Statistics

Inverse transform sampling

Artificial intelligence

Seismology

Regularization (linguistics)

Computer science

Pearson product-moment correlation coefficient

Tectonics

Surface wave

Remote sensing

Telecommunications

Geophysics

IEEE Transactions on Geoscience and Remote Sensing

Based on the spatial structure correlation in different geophysical parameters, we propose a new 3-D joint inversion method for frequency-domain airborne electromagnetic (AEM) and airborne magnetic (AirMag) data by incorporating a local Pearson correlation constraint (LPCC). For each iteration, the entire model is separated into multiple subdomains and the Pearson correlation coefficients of resistivity and magnetization in the subdomain are employed as the additional regularization term to do the joint constraint. This new regularization term is continuously updated in the inversion process to ensure that the resistivity and magnetization models in two separated inversions converge to a similar spatial structure. As a statistics technology, the LPCC-based joint inversion scheme not only has the advantages of the conventional joint inversions, but also can implement the structural constraints in different scales by selecting different sizes of the subdomain. This provides the flexibility for solving multiscale problems. Synthetic examples show that the joint inversion can improve the overall inversion resolution by combining the high vertical resolution of the EM method and large exploration depth and high horizontal resolution of the magnetic method. In the application to field survey datasets, the joint inversion delivers better results than those of separate inversions, which further verifies the effectiveness of our method.

3-D Joint Inversion of Airborne Electromagnetic and Magnetic Data Based on Local Pearson Correlation Constraints