An Optimum 1D Velocity Model for the Garhwal–Kumaun Himalaya Using Monte Carlo Style Inversion

Abstract

Abstract A Monte Carlo based algorithm is developed to improve the 1D velocity inversion routines and minimize bias due to the choice of a starting model. Using this algorithm, a well-resolved six-layer minimum 1D velocity model, down to ∼24 km depth, is determined for the Garhwal–Kumaun Himalaya. A total of 4765 P- and 4724 S-phase travel times of local earthquakes recorded at 53 broadband seismic stations are used for this purpose. The travel time–distance curves from these carefully analyzed phase data of events are used to subsequently derive a prior 1D velocity model. Forward modeling of the travel time–distance curve yields an average Moho depth of ∼46 km and bulk crustal P- and S-wave velocity values of 7.60 and 4.47 km/s, respectively. To circumvent the subjectivity due to manual intervention in the inversion and automate the process, we propose a Monto Carlo style semirandom generation of initial trial velocity models, guided by the initial values derived from forward modeling. The estimated minimum 1D model reveals P- and S-wave velocities increasing from 5.17 to 6.85 km/s and 3.12 to 3.82 km/s, respectively, from the surface to a depth of 26 km. Subsequently, an optimum model is constructed for the region by incorporating the Moho layer in the minimum 1D model.