Modeling Crustal Structure in the Permian Basin by Waveform‐Matching P Receiver Functions and Autocorrelograms With Particle Swarm Optimization

Abstract

AbstractWe model basin and Moho structure in the Permian Basin region of west Texas and southeastern New Mexico using a method for waveform matching via global optimization of P‐to‐S receiver functions, vertical autocorrelograms, and horizontal autocorrelograms. The algorithm is driven by Particle Swarm Optimization, whose search history can be used to assess the strength of data constraints on model parameters. A common drawback in receiver function modeling is the need to assume a value for Vp before Vs and layer thickness can be estimated. But constraints on Vp can be provided by vertical autocorrelograms of teleseismic arrivals, which detect reverberating P waves, because the phase delay times depend only on Vp and interface depth. P‐to‐S receiver functions and vertical and radial autocorrelograms are computed for M > 5.5 events at 30°–100° epicentral distance, then edited and binned by ray parameter. Synthetic seismograms are computed for layered models using a 1D reflectivity method. The free parameters in the algorithm include basin depth, basin Vp, basin Vp/Vs, and thickness and Vp/Vs of the crystalline crust. Where vertical autocorrelograms prove insufficient for determining basin Vp, Vp is assumed from nearby stations that were modeled successfully. We find an average basin Vp of 4.57 km/s and basin depths to ∼8 km. Moho depths are generally 40–48 km. A basin depth of at least 3.5 km is typically needed to produce sufficient phase separation to allow an average Vp to be determined with confidence. Modeling autocorrelograms jointly with receiver functions therefore improves constraints on deep basin structure.