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We propose a new approach capable of measuring local seismic anisotropy from 6C (three‐component translation and three‐component rotation) amplitude observations of ambient seismic noise data. Our recent theory demonstrates that the amplitude ratio of 6C cross‐correlation functions (CCFs) enables retrieving the local phase velocity. This differs from conventional velocity extraction methods based on the travel time. Its local sensitivity kernel beneath the 6C seismometer allows us to study anisotropy from azimuth‐dependent CCFs, avoiding path effects. Such point measurements have great potential in planetary exploration, ocean bottom observations, or volcanology. We apply this approach to a small seismic array at Pin˜ $\widetilde{n}$on Flat Observatory (PFO) in southern California, array‐deriving retrieves rotational ground motions from microseismic noise data. The stress‐induced anisotropy is well resolved and compatible with other tomography results, providing constraints on the origin of depth‐dependent seismic anisotropy. Plain Language Summary: In contrast to the well‐known translational displacement of seismic waves, rotation measures the angle of rotation of a point in a medium as it deforms. Traditionally, the travel time of cross‐correlation functions of translational displacement of ambient seismic noise and its corresponding propagation distance is used to calculate the velocity of the subsurface medium. However, our recent theory shows that the amplitude ratio of translational displacement and rotation allows us to reveal the local velocity of the medium beneath the receiver from ambient seismic noise data and to study anisotropy from earthquake events. This paper points out for the first time that the amplitude observations of 6C cross‐correlation functions of ambient seismic noise provide a new approach to measure the azimuth‐dependent velocity in anisotropic media. Key Points: We show a new approach enabling the extraction of local phase velocity from 6C cross‐correlation functions of ambient noise dataAzimuth‐dependent 6C cross‐correlation functions allow us to study local seismic anisotropy and its depth dependenceLocal anisotropy at Pin˜ $\widetilde{n}$on Flat Observatory is compatible with compression stress, providing constraints on stress‐induced anisotropy [ABSTRACT FROM AUTHOR]

I employ an elasticity‐based method to invert a geodetically derived surface velocity field in the western US using for present‐day surface strain rate fields with uncertainties. The method uses distributed body forces in a thin elastic sheet and allows for discontinuities in velocity across creeping faults using the solution for dislocations in a thin elastic plate. I compare the strain rate fields with previously published stress orientations and moment rates from geological slip rate data and previous geodetic studies. Geologic and geodetic moment rates are calculated using slip rate and off‐fault strain rates from the 2023 US National Seismic Hazard Model (NSHM) deformation models. I find that computed total geodetic moment rates are higher than NSHM summed moment rates on faults for all regions of the western US except the highest deforming rate regions including the Western Transverse Ranges and the northern and southern San Andreas Fault (SAF) system in California. Computed geodetic moment rates are comparable to the moment rates derived from the geodetically based NSHM deformation models in all regions. I find systematic differences in orientations of maximum horizontal shortening rate and maximum horizontal compressive stress in the Pacific Northwest region and along much of the SAF system. In the Pacific Northwest, the maximum horizontal stress orientations are rotated counterclockwise 40–90° relative to the maximum horizontal strain rate directions. Along the SAF system, the maximum horizontal stresses are rotated systematically 25–40° clockwise (closer to fault normal) relative to the strain rates. Plain Language Summary: Geodetic measurements of surface velocities can be converted to strain rate which is a measure of the distortion rate of the crust. Strain rate is an important quantity for earthquake studies because it can be related to the rate of elastic stored energy (called moment) available for future earthquakes. In this paper I employ a new method to invert the surface velocity field in the western US for present‐day surface strain rates. I convert the strain rates to crustal moment rates and compare this with moment release rates computed from previous estimates of fault slip rates from geology as well as with model moment rates from the 2023 US National Seismic Hazard Model (NSHM). I find that computed total geodetic moment rates are higher than NSHM model moment rates on faults for many regions of the western US. I also find systematic differences in present‐day strain rates and total stress in the crust indicating that the total stress in the crust is not simply the accrual of present‐day deformation. Key Points: I implement an elasticity‐based inversion of geodetic data for strain rateSystemic differences in orientation of stress and strain rate in Cascadia, San Andreas Fault, and eastern Basin‐RangeThe total geodetic moment rates are higher than geologic moment rates within most regions of the western US [ABSTRACT FROM AUTHOR]

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]

The generation of strong earthquakes is a long‐debated problem in seismology, and its importance is increased by the possible implications for earthquake forecasting. It is hypothesized that the earthquake generation processes are anticipated by several phenomena occurring within a nucleation region. These phenomena, also defined as preparatory processes, load stress on the fault leading it to reach a critical state. In this paper, we investigate the seismicity preceding 19 moderate (Mw ≥ 3.5) earthquakes at The Geysers, Northern California, aiming to verify the existence of a preparatory phase before their occurrence. We apply an unsupervised K‐means clustering technique to analyze time series of physics‐related features extracted from catalog information and estimated for events occurred before the mainshocks. Specifically, we study the temporal evolution of the b‐value from the Gutenberg‐Richter (b), the magnitude of completeness (Mc), the fractal dimension (Dc), the inter‐event time (dt), and the moment rate (M˙0 ${\dot{M}}_{0}$). Our analysis shows that out of 19 moderate magnitude events considered, a common preparatory phase for 11 events is clearly identified, plus other five events for which we can guess a preparatory phase but with different characteristics from the previous ones. The latter result confirms that even within the same tectonic context different possible activation behaviors may exist. The duration of the preparatory process ranges between about 16 hr and 4 days. We observe that also for the retrieved preparatory process a decrease in b, Mc, and Dc, and an increase of M˙0 ${\dot{M}}_{0}$. Finally, we show a clear correlation between events with a preparation phase and the location of injection's wells, suggesting an important role of fluids in the preparatory process. Plain Language Summary: We investigate the preparatory phase of moderate magnitude‐induced earthquakes at The Geysers geothermal field in California by studying the spatiotemporal evolution and dynamic properties of small magnitude events and using an unsupervised machine learning approach. To this aim, we rely on features extracted from seismic catalog information that are used for a K‐means clustering analysis. Our results highlight changes in the seismicity characteristics before the moderate‐induced earthquakes. We find that most of the analyzed target earthquakes present a preparatory phase, and that most of the cases the latter presents common characteristics in their key features. We show that the seismicity clusters in space and time before moderate events also becoming more energetic. We estimate a duration for the detected preparatory phases that ranges from 16 hr to 4 days. Finally, we show that the presence of a preparatory phase is correlated to the proximity of injection's wells, suggesting a significant role of fluids in the earthquake's nucleation process. Key Points: Moderate‐induced earthquakes at The Geysers can be preceded by a preparatory phase detectable using K‐means clustering on catalog featuresThe detected preparatory phase has common characteristics presenting negative trends of b‐value, Mc, and Dc and a positive trend of M˙0 ${\dot{M}}_{0}$The earthquakes with a preparatory phase are located nearer to the injection's wells in the area than the events without it [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]

A new time series interferometric synthetic aperture radar method is presented based on a strain model. Traditionally, the interferometric phases are optimized based only on a constant set of statistically homogeneous points throughout the observing period, and thus the decorrelated phase cannot be well recovered and possible incoherent (or coherent) phases are (are not) used to optimize the target phase. In the proposed method, a strain model, representing the geophysical relationship of spatially adjacent points' deformations, is employed to optimize the interferometric phases interferogram‐by‐interferogram. The unwrapped phase time series can be estimated from the wrapped interferograms based on the linear iterative weighted least squares method, which is simple and can effectively suppress the decorrelation noise. Both simulations and analysis of data over The Geysers geothermal field (TG), California are conducted, validating that the proposed method can reduce the decorrelations and simultaneously preserve the deformation signals. The displacement results of TG from 2007–2011 L‐band ascending ALOS‐1 data and 2015–2020 C‐band ascending/descending Sentinel‐1 data reveal that (a) the displacement rate of TG is decreasing from 2007–2011 to 2015–2020, and (b) temporally variable displacement signals in addition to steady displacement can be observed from 2015 to 2020 for both the east‐west and the vertical components. Plain Language Summary: The interferometric synthetic aperture radar (InSAR) technique is capable of accurately measuring ground surface displacements at high spatial resolution and large coverage. This paper proposes a strain‐model based InSAR time series method, here referred to as the SIGMA method. The SIGMA method aims to enhance the capability of InSAR to measure displacements and can achieve higher accuracy and density of InSAR displacement measurements. Both simulations and real‐data experiments are conducted to illuminate the benefits of the SIGMA method in the presence of substantial decorrelation noise. Particularly, the SIGMA method is used to investigate the surface displacements of The Geysers geothermal field, California, during 2007–2011 and 2015–2020. These time periods are complementary to those considered in previous studies, providing an important data set to investigate the long‐term evolution of ground surface deformation, as well as the effects of recent fluid injection and production practices in the geothermal field. The new method should be of value for improved geo‐hazards monitoring. Key Points: A new time series interferometric synthetic aperture radar (InSAR) method based on a strain model and iterative weighted least squares is proposedThe method can effectively suppress decorrelation noise and improves the InSAR accuracy and densityThe surface deformation of The Geysers geothermal field, California is investigated with 2007–2011 ALOS‐1 and 2015–2020 Sentinel‐1 data [ABSTRACT FROM AUTHOR]