Hidden layer imaging using joint inversion of P-wave travel-time and electrical resistivity data

The combination of geophysical surface-based imaging techniques, including seismic and electrical resistivity tomography (ERT), is now common practice to obtain a more accurate characterization of subsurface structures. Due to model non-uniqueness and geological heterogeneity, conventional travel-time tomography cannot solely reveal hidden layers (i.e., low-velocity zones embedded between layers of higher velocities) in the subsurface. Hence, we present a joint inversion algorithm based on a normalized cross-gradients function to detect hidden low-velocity layers. The structural similarity between P-wave velocity (V-p) and resistivity fields is enhanced by incorporating the normalized cross-gradients constraint in the joint inversion algorithm. Improved structural similarity can mitigate the problem of the recovery of a hidden layer. We also take advantage of a priori information derived from borehole geological data to reduce the continuous range of possible solutions (i.e., exact-data non-uniqueness). In both joint and separate inversions, an auxiliary damping factor is used to ensure convergence, and also the smoothness constraints are applied to deal with instability stemming from error in the data. To verify the performance of the joint inversion procedure, the algorithm is tested on synthetic and real data examples with emphasis on hidden low-velocity layer detection. Numerical experiments demonstrate that the joint inversion strategy can produce more reliable and better velocity models of the subsurface structures as compared with those obtained through individual inversions. We conclude that this simultaneous joint inversion of V-p and ERT integrates the best of both schemes and makes it possible to improve resolution, and, hence, reduces uncertainties in hidden low-velocity layer problems.