CANVAS: An Adjoint Waveform Tomography Model of California and Nevada

We present the California-Nevada Adjoint Simulations (CANVAS) model, an adjoint waveform tomography model of the crust and uppermost mantle of the states of California and Nevada. We used WUS256 (Rodgers et al., 2022, ) as the starting model and iteratively decreased the minimum period of CANVAS from 30 to 12 s. CANVAS was iterated in two distinct stages: the first stage with source mechanisms from the Global Centroid Moment Tensor (GCMT) catalog and the second stage with inverted moment tensors (MT) using the CANV_WUS model (Doody et al., 2023, ). We show that updating the MTs with 3D Green's functions improved waveform fits and azimuthal coverage of windowed data used to calculate the gradients. As for the model itself, we improved waveform fits over WUS256, particularly in the dispersed surface waves. CANVAS resolved tectonic features seen in other models and accurately defined the depth to basement of major basins, including the Central Valley and the Ventura Basin. We propose CANVAS as a starting model for crustal tomography models on smaller scales. California sits at the boundary of three tectonic plates and a large portion of the population lives in areas of high seismic hazard. Understanding earthquake ground shaking requires accurate models of the Earth's interior. We present a full waveform inversion (FWI) seismic wavespeed model of California and Nevada. FWI determines Earth structure by modeling wave propagation to optimize the fit of synthetic data to observed data from recorded earthquakes. We ran 161 iterations on a high-performance computing (HPC) system to produce the California-Nevada Adjoint Simulations (CANVAS) model, which used data with a minimum period of 12 s. Using an intermediate version of the model, we inverted for earthquake source parameters and showed that updating sources contribute to accurately modeling waveforms at shorter periods. The final version of CANVAS agrees with previous studies on tectonic structure and resolved the depth to basement of major basins in California. We propose that CANVAS be used as a starting model for other regional studies of California and Nevada. We present an adjoint waveform tomography model of California and Nevada iterated for 161 iterations down to a minimum period of 12 sCANVAS resolves known tectonic structures and can accurately constrain depths to basement of major basins in CaliforniaWe propose CANVAS as a high-quality starting model for smaller-scale regional tomography studies