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Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way. Plain Language Summary: Earthquake nowcasting refers to the determination of hazard for major earthquakes at the present time, the recent past, and the near future. Nowcasting is an idea borrowed from economics, markets, and meteorology, where it has been frequently used. In this paper, we show that there is order hidden within chaotic earthquake seismicity using a very simple transformation of the data. Small earthquakes appear to transition from unstable stick‐slip events that produce seismic waves, to stable sliding where no seismic waves are produced. Our hypothesis is that this transition is due to a material phenomenon called strain‐hardening, that is frequently observed in laboratory rock mechanics experiments. The result is a state variable time series, computed over the last 51 years in California, that strongly resembles the long‐anticipated cycle of stress accumulation and release. Using supervised machine learning techniques, we can optimize the two‐parameter model. From that optimized model, we can rigorously calculate the probability of current hazard from major earthquakes. Extending these methods, we can also compute spatial hazard as well. The result is a new method for assessing earthquake hazard that may be useful for a variety of applications. Key Points: "Chaotic" seismicity contains hidden structure in the form of state variable time seriesStandard data science methods can be used to convert the time series to probabilitiesBoth temporal and spatial probabilities can be computed [ABSTRACT FROM AUTHOR]

Geodetic observations using the Global Positioning System (GPS) and other techniques record a high rate of north-south shortening in an east-southeast-trending, 5-40-km-wide belt in northern metropolitan Los Angeles, California. Downtown Los Angeles is observed to be converging upon the southern San Gabriel Mountains at 6 mm/yr. Aside from the elastic strain that will be released during earthquakes rupturing the San Andreas and San Jacinto faults, east-west lengthening across northern metropolitan Los Angeles is minor,

We present a data‐driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et al., 2013, https://doi.org/10.1785/0220130039). Desired features of the data can be selected for clustering, including some subset of displacement or velocity components, uncertainty estimates, station location, and other relevant information. Based on those selections, the clustering procedure autonomously groups the GNSS stations according to a selected clustering method. We have implemented this approach as a Python application, allowing us to draw upon the full range of open source clustering methods available in Python's scikit‐learn package (Pedregosa et al., 2011, https://doi.org/10.5555/1953048.2078195). The application returns the stations labeled by group as a table and color coded KML file and is designed to work with the GNSS information available from GeoGateway (Donnellan et al., 2021, https://doi.org/10.1007/s12145-020-00561-7; Heflin et al., 2020, https://doi.org/10.1029/2019ea000644) but is easily extensible. We demonstrate the methodology on California and western Nevada. The results show partitions that follow faults or geologic boundaries, including for recent large earthquakes and post‐seismic motion. The San Andreas fault system is most prominent, reflecting Pacific‐North American plate boundary motion. Deformation reflected as class boundaries is distributed north and south of the central California creeping section. For most models a cluster boundary connects the southernmost San Andreas fault with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass. Key Points: Unsupervised clustering methods provide a data‐driven way of analyzing and partitioning Global Navigation Satellite System observations of crustal deformationDeformation is distributed across the San Andreas fault system but is localized at the creeping section in central CaliforniaThe Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system [ABSTRACT FROM AUTHOR]

This paper describes the methods used to estimate positions, velocities, breaks, and seasonal terms from daily Global Navigation Satellite System (GNSS) measurements. Break detection and outlier removal have been automated so that decades of daily measurements from thousands of stations can be processed in a few hours. New measurements are added, and parameters are updated every week. Model parameters allow separation of interseismic, annual, coseismic, and postseismic signals. Tools available through GeoGateway (http://geo-gateway.org) allow rapid visualization and analysis of these terms for results that can be subsetted in time or space. Results show highly variable and nonlinear motion for GPS stations in southern California. The variable motion is related to seasonal motions, distributed tectonic motion, earthquakes, and postseismic motions that can continue for years. In some areas results suggest that additional processes are responsible for the observed motions. In general, following earthquakes, stations return to their long‐term motions after 2–3 years, though some exceptions occur. The use of the tools shows nonlinear motion in the Salton Trough of southern California related to the 2010 M7.2 El Mayor‐Cucapah earthquake, 2012 Brawley earthquake swarm, and a creep event on the Superstition Hills fault in 2017. Key Points: Positions, velocities, breaks, and seasonal terms for thousands of GNSS stations are updated every weekThese results can be used to study interseismic plate motion, coseismic deformation, postseismic deformation, and seasonal variationsThe use of these tools shows highly variable nonlinear motion of GPS stations in southern California [ABSTRACT FROM AUTHOR]

Spatiotemporal coverage gaps and the nature of distant signature sources in Global Navigation Satellite System (GNSS) and The Gravity Recovery and Climate Experiment (GRACE) data require global inversions of data combinations for cross validation and complete and continuous monitoring of surface water mass variations. For spectral completeness, accurate data-based degree-1 surface mass variation coefficient or equivalent geocenter motion estimates are also desired but very difficult to obtain. To improve the reliability of such estimates, we carry out a new effort to combine different space geodetic technique data in a Kalman filter and time series approach to Terrestrial Reference Frame realization using an advanced formulation. With origin at the center of mass of the Earth system, the new parameterization now includes site displacements in the state vector explicitly for realistic assessment of their covariance matrices. More robust geocenter motion results are achieved when the displacement information is combined further with GRACE gravity data in a unified inversion. Previously, various systematic effects in GNSS data resulted in rather large discrepancies in global surface mass change estimates when compared with those from GRACE gravity data. Here, we show that significantly improved GNSS data after reprocessing and the refined Estimating the Circulation and Climate of the Ocean (ECCO) bottom pressure model reveal a global surface mass variation pattern that has been largely reconciled with that from GRACE data. [ABSTRACT FROM AUTHOR]

Seismic shifts occurring before, during and after two earthquakes in southern California were examined using the International Global Positioning System and Geodynamics Service. The first quake occurred near Landers, CA on Jun 28, 1992 followed three hours later by the second quake near Big Bear, CA. The resulting data increased geodetic knowledge of the Johnson Valley and Camp Rock/Emerson faults. In particular, the rupture between the two faults was found to be about three kilometers deep, much shallower than had been thought.

There have been numerous debates about rising authoritarianism and its impact on democracy and human rights. As a human rights funder whose central goal is to strengthen the resilience of the human rights movement, employing a racial justice lens to all our work is critical, now more than ever. Some have argued that highlighting structural racism and discrimination has shifted the focus away from economic inequality and undermined some of the relevance of the contemporary human rights movement. We contend, however, that deep economic inequality is frequently a reflection of deep inequality in the underlying power structures that govern societies. Employing a racial justice lens offers us the opportunity to deconstruct structures that contribute to a host of rights violations and allows us to gain a deeper understanding of how oppressive structures intersect. This essay is a reflection on some of our learning to date. [ABSTRACT FROM AUTHOR]

Uninhabited aerial vehicle synthetic aperture radar (UAVSAR) observations 2009–2017 of the Yuha Desert area and Global Positioning System (GPS) time series encompassing the region reveal a northward migrating pattern of deformation following the 4 April 2010 Mw7.2 El Mayor‐Cucapah (EMC) earthquake. The north end of the EMC rupture exhibits an asymmetric pattern of deformation that is substantial and smooth northeast of the rupture and limited but with surface fracturing slip northwest. The earthquake triggered ~1 cm of surface coseismic slip at the Yuha fault, which continued to slip postseismically. 2.5 cm of Yuha fault slip occurred by the time of the 15 June 2010 Mw5.7 Ocotillo aftershock and 5 cm of slip occurred by 2017 following a logarithmic afterslip decay 16‐day timescale. The Ocotillo aftershock triggered 1.4 cm of slip on a northwest trend extending to the Elsinore fault and by 7 years after the EMC earthquake 2.4 cm of slip had accumulated with a distribution following an afterslip function with a 16‐day timescale consistent with other earthquakes and a rate strengthening upper crustal sedimentary layer. GPS data show broad coseismic uplift of the Salton Trough and delayed postseismic motion that may be indicative of fluid migration there and subsidence west of the rupture extension, which continues following the earthquake. The data indicate that the Elsinore, Laguna Salada, and EMC ruptures are part of the same fault system. The results also suggest that north‐south shortening and east‐west extension across the region drove fracture advancing step tectonics north of the EMC earthquake rupture. Plain Language Summary: Airborne radar and Global Positioning System observations of the Yuha Desert and Salton Trough suggest that the 2010 M7.2 El Mayor ‐ Cucapah earthquake rupture, the Laguna Salada fault in Baja California, Mexico, and the Elsinore fault in California are part of the same fault system. The M5.7 Ocotillo aftershock links these faults and is a result of stress propagation from afterslip on the El Mayor ‐ Cucapah rupture. Further east in the Salton Trough fluid migration also contributes to the observed results. Key Points: The Mw5.7 Ocotillo aftershock of the El Mayor‐Cucapah earthquake extended north from a mainshock surface fracture to the Elsinore faultPostseismic slip on the Yuha fault and Ocotillo section and uplift in the Salton Trough continued for several years following the eventSalton Trough spreading causes left slip on the Yuha fault resulting in a left step that links the Laguna Salada and Elsinore faults [ABSTRACT FROM AUTHOR]

We present and discuss JTRF2014, the Terrestrial Reference Frame (TRF) the Jet Propulsion Laboratory constructed by combining space-geodetic inputs from very long baseline interferometry (VLBI), satellite laser ranging (SLR), Global Navigation Satellite Systems (GNSS), and Doppler orbitography and radiopositioning integrated by satellite submitted for the realization of ITRF2014. Determined through a Kalman filter and Rauch-Tung-Striebel smoother assimilating position observations, Earth orientation parameters, and local ties, JTRF2014 is a subsecular, time series-based TRF whose origin is at the quasi-instantaneous center of mass (CM) as sensed by SLR and whose scale is determined by the quasi-instantaneous VLBI and SLR scales. The dynamical evolution of the positions accounts for a secular motion term, annual, and semiannual periodic modes. Site-dependent variances based on the analysis of loading displacements induced by mass redistributions of terrestrial fluids have been used to control the extent of random walk adopted in the combination. With differences in the amplitude of the annual signal within the range 0.5-0.8 mm, JTRF2014-derived center of network-to-center of mass (CM-CN) is in remarkable agreement with the geocenter motion obtained via spectral inversion of GNSS, Gravity Recovery and Climate Experiment (GRACE) observations and modeled ocean bottom pressure from Estimating the Circulation and Climate of the Ocean (ECCO). Comparisons of JTRF2014 to ITRF2014 suggest high-level consistency with time derivatives of the Helmert transformation parameters connecting the two frames below 0.18 mm/yr and weighted root-mean-square differences of the polar motion (polar motion rate) in the order of 30 μas (17 μas/d). [ABSTRACT FROM AUTHOR]

Using space geodetic observations from four techniques (GPS, VLBI, SLR and DORIS), we simultaneously estimate the angular velocities of 11 major plates and the velocity of Earth's centre. We call this set of relative plate angular velocities GEODVEL (for GEODesy VELocity). Plate angular velocities depend on the estimate of the velocity of Earth's centre and on the assignment of sites to plates. Most geodetic estimates of the angular velocities of the plates are determined assuming that Earth's centre is fixed in an International Terrestrial Reference Frame (ITRF), and are therefore subject to errors in the estimate of the velocity of Earth's centre. In ITRF2005 and ITRF2000, Earth's centre is the centre of mass of Earth, oceans and atmosphere (CM); the velocity of CM is estimated by SLR observation of LAGEOS's orbit. Herein we define Earth's centre to be the centre of mass of solid Earth (CE); we determine the velocity of CE by assuming that the portions of plate interiors not near the late Pleistocene ice sheets move laterally as if they were part of a rigid spherical cap. The GEODVEL estimate of the velocity of CE is likely nearer the true velocity of CM than are the ITRF2005 and ITRF2000 estimates because (1) no phenomena can sustain a significant velocity between CM and CE, (2) the plates are indeed nearly rigid (aside from vertical motion) and (3) the velocity of CM differs between ITRF2005 and ITRF2000 by an unacceptably large speed of 1.8 mm yr−1. The velocity of Earth's centre in GEODVEL lies between that of ITRF2000 and that of ITRF2005, with the distance from ITRF2005 being about twice that from ITRF2000. Because the GEODVEL estimates of uncertainties in plate angular velocities account for uncertainty in the velocity of Earth's centre, they are more realistic than prior estimates of uncertainties. GEODVEL differs significantly from all prior global sets of relative plate angular velocities determined from space geodesy. For example, the 95 per cent confidence limits for the angular velocities of GEODVEL exclude those of REVEL (Sella et al.) for 34 of the 36 plate pairs that can be formed between any two of the nine plates with the best-constrained motion. The median angular velocity vector difference between GEODVEL and REVEL is 0.028° Myr−1, which is up to 3.1 mm yr−1 on Earth's surface. GEODVEL differs the least from the geodetic angular velocities that Altamimi et al. determine from ITRF2005. GEODVEL's 95 per cent confidence limits exclude 11 of 36 angular velocities of Altamimi et al., and the median difference is 0.015° Myr−1. GEODVEL differs significantly from nearly all relative plate angular velocities averaged over the past few million years, including those of NUVEL-1A. The difference of GEODVEL from updated 3.2 Myr angular velocities is statistically significant for all but two of 36 angular velocities with a median difference of 0.063° Myr−1. Across spreading centres, eight have slowed down while only two have sped up. We conclude that plate angular velocities over the past few decades differ significantly from the corresponding angular velocity averaged over the past 3.2 Myr. [ABSTRACT FROM AUTHOR]