CANVAS: An Adjoint Waveform Tomography Model of California and Nevada

Abstract

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, https://doi.org/10.1029/2022jb024549) 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, https://doi.org/10.1029/2023jb026463). 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.