Modeling the Crust and Upper Mantle Applying an Optimization Method to Multiple Datasets: Surface Wave Dispersion, P-Receiver Function, and S-Waveform

We develop an original algorithm for velocity estimation that incorporates the constraints of three seismic functionals: surface wave dispersion, P-receiver function, and S-waveform. Here, we use a parallelized reflectivity algorithm to generate synthetic seismograms and match the observed functionals by a global optimization scheme called very fast simulated annealing (VFSA). This method also allows us to assess the uniqueness and parameter independence of the resultant models. Synthetic tests using surface wave (SW) dispersion and receiver functions (RF), and then SW, RF, and waveforms windowed around the S arrival, establish that inclusion of the third functional results in the best recovery of the model. We employ the algorithm to model the Kachchh basin in Gujarat, India, because the area is of active interest for monitoring purposes, and prior results of RF and SW modeling are available for comparison. The waveform functionals used here are generated from broadband seismograms of teleseismic, deep (396–609 km), and moderate- to large-magnitude (6–6.8) earthquake events recorded at semipermanent seismograph stations in the Kachchh basin. Joint inversion of SW, RF, and S-waveform improves the velocity structure by revealing layers that were not identified in previous modeling that used SW and RF alone. Our model clearly shows low-velocity zones (LVZs), crustal and lithospheric thinning, and the lithosphere–asthenosphere boundary. We estimate the crustal thickness to be 41 km at all but one station, and the lithosphere to be in the range of 70–80 km. P- and S-velocities from the uppermost crust to the Moho vary from 4.7 to 7.0 km/s, and 2.7–4.1 km/s, respectively.