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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.

GeoGateway (http://geo-gateway.org) is a web-based interface for analysis and modeling of geodetic imaging data and to support response to related disasters. Geodetic imaging data product currently supported by GeoGateway include Global Navigation Satellite System (GNSS) daily position time series and derived velocities and displacements and airborne Interferometric Synthetic Aperture Radar (InSAR) from NASA's UAVSAR platform. GeoGateway allows users to layer data products in a web map interface and extract information from various tools. Extracted products can be downloaded for further analysis. GeoGateway includes overlays of California fault traces, seismicity from user selected search parameters, and user supplied map files. GeoGateway also provides earthquake nowcasts and hazard maps as well as products created for related response to natural disasters. A user guide is present in the GeoGateway interface. The GeoGateway development team is also growing the user base through workshops, webinars, and video tutorials. GeoGateway is used in the classroom and for research by experts and non-experts including by students.

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 Pythons 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.