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Adjoint waveform tomography (AWT) sits at the cutting edge of seismic tomography on local, regional, and global scales. However, the choice in starting model may have a significant impact on the final inversion results. In this paper, we present 3 AWT models of California that are based on different starting models. We chose three models that were inverted at different scales: SPiRaL, a global travel‐time tomography model (Simmons et al., 2021, 10.1093/gji/ggab277), CSEM_NA, a regional adjoint tomography model of North America and the North Atlantic (Krischer et al., 2018, 10.1029/2017JB015289), and WUS256, a regional adjoint tomography model of the western US (Rodgers et al., 2022, https://doi.org/10.1029/2022JB024549). We then inverted three AWT models using the same source and receiver set. We ran each model over three period bands: 30–100 s, 25–100 s, and 20–80 s. Once the iterations were finalized, we used five methods of testing model similarity in both the model and data space. We conclude that the choice of starting model has a minimal impact on long wavelength models if an appropriate multi‐scale inversion approach is used. Plain Language Summary: Seismic tomography uses earthquake records to resolve three‐dimensional models of seismic wavespeeds below Earth's surface. Full waveform inversion seeks to model the physics of wave propagation to create models that can most accurately reproduce the waveform data that are observed from recorded events. In this paper, we sought to study the effect of the choice of starting model on final inversion results in California using AWT, a type of Full waveform inversion technique; to our knowledge, this type of experiment has not been carried out before with real data at this scale. We began with three starting models that were created on different scales with different datasets. We then used five methods to compare the final model results. We focused on comparisons of the visual similarity of the models, the tectonic structures the models resolve, and how well the synthetic waveforms match the observed data. We find that the choice of starting model does not have a large impact when the model domain has azimuthally distributed events and stations over long length‐scales. Key Points: We present three adjoint waveform tomography models of California that each begin with a different starting modelThe three final models are compared using five metrics that span both the model space and data spaceBased on a conservative multi‐scale inversion approach, we conclude that the choice of starting model has minimal effect on the final model [ABSTRACT FROM AUTHOR]

Reconstructions of Green's functions by ambient noise interferometry enable the imaging of the Earth's subsurface. Coda waves from reconstructed Green's functions can be utilized to perform time‐dependent imaging of processes that vary substantially at daily to monthly time scales in the crust. Time‐lapse Coda‐Wave Imaging (CWI) can detect tiny changes in seismic velocity with high temporal resolution. While previous studies on CWI have mainly focused on the descriptions of coda waves' propagation, little attention has been paid to choosing effective inversion algorithms that are suitable for CWI. Here we address this issue by developing a pragmatic inversion approach based on Voronoi tessellation with mesh cells adapted to coda‐wave sensitivity kernels. Using seismic stations in Central California, we present both synthetic and real data imaging to demonstrate that this approach stabilizes the inversion, is computationally efficient, and provides spatially adaptive resolution. We further propose a heuristic approach for a quantitative assessment of spatial resolution based on multi‐scale checkerboard tests. Plain Language Summary: Seismologists can extract information about the properties and structures beneath the Earth's surface by calculating the interferometry of background seismic vibrations. In recent years, this idea has been widely employed to monitor the time‐varying underground processes associated with tectonic, volcanic, hydrologic, and anthropogenic activities. Coda waves, the waves that are multiply scattered, are often used in monitoring applications because of their high sensitivity and time resolution, but their spatial paths are complicated. In this paper we introduce a new method for improving the inversion in Coda‐Wave Imaging based on Voronoi tessellation. By using mesh cells adapted to the geometry of seismic stations in Central California, we show that this method helps to stabilize the inversion and provide spatially adaptive resolution. Additionally, we propose a straightforward way to assess quantitatively the resolution of the images obtained through this method. Key Points: We present a pragmatic approach to improving coda‐wave imaging of seismic velocity changes based on Voronoi tessellationThis approach stabilizes the spatial inversion and provides adaptive resolution using seismic stations with irregular geometry in Central CaliforniaWe further propose a heuristic approach for a quantitative assessment of the spatial resolution based on multi‐scale checkerboard tests [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) vertical displacements measuring the elastic response of Earth's crust to changes in hydrologic mass have been used to produce terrestrial water storage change (∆TWS) estimates for studying both annual ∆TWS as well as multi‐year trends. However, these estimates require a high observation station density and minimal contamination by nonhydrologic deformation sources. The Gravity Recovery and Climate Experiment (GRACE) is another satellite‐based measurement system that can be used to measure regional TWS fluctuations. The satellites provide highly accurate ∆TWS estimates with global coverage but have a low spatial resolution of ∼400 km. Here, we put forward the mathematical framework for a joint inversion of GNSS vertical displacement time series with GRACE ∆TWS to produce more accurate spatiotemporal maps of ∆TWS, accounting for the observation errors, data gaps, and nonhydrologic signals. We aim to utilize the regional sensitivity to ∆TWS provided by GRACE mascon solutions with higher spatial resolution provided by GNSS observations. Our approach utilizes a continuous wavelet transform to decompose signals into their building blocks and separately invert for long‐term and short‐term mass variations. This allows us to preserve trends, annual, interannual, and multi‐year changes in TWS that were previously challenging to capture by satellite‐based measurement systems or hydrological models, alone. We focus our study in California, USA, which has a dense GNSS network and where recurrent, intense droughts put pressure on freshwater supplies. We highlight the advantages of our joint inversion results for a tectonically active study region by comparing them against inversion results that use only GNSS vertical deformation as well as with maps of ∆TWS from hydrological models and other GRACE solutions. We find that our joint inversion framework results in a solution that is regionally consistent with the GRACE ∆TWS solutions at different temporal scales but has an increased spatial resolution that allows us to differentiate between regions of high and low mass change better than using GRACE alone. Plain Language Summary: Fluctuations in surface water mass cause deformation of the Earth's surface and small changes to the Earth's gravity field. Both of these effects can be observed by satellite‐based observation systems and used to solve for terrestrial water storage change (∆TWS), which is the sum of the changes in groundwater, surface water, soil moisture, ice, and snow over the land surface. These observations are essential for drought monitoring and can be used to inform water management decisions. In this study, we combine deformation data with gravity‐derived ∆TWS to solve for monthly ∆TWS over California and western Nevada, USA. Here, multiyear droughts cause sharp declines in TWS and risk the future productivity of the water‐intensive agricultural sector. Key Points: A new joint inversion framework combining Global Navigation Satellite System and Gravity Recovery and Climate Experiment (GRACE) is put forward to calculate monthly terrestrial water storage change (∆TWS) over California from 2003 to 2016A wavelet‐based approach is used to maintain spectral frequency content, allowing for preservation of multi‐ to sub‐annual signal componentsInversion results show improved isolation of larger magnitude signals over regions where we expect large ∆TWS as compared to GRACE alone [ABSTRACT FROM AUTHOR]