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 2-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 timeseries can be 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 5o x 5o in latitude-longitude centered on Los Angeles, a region which we used in previous papers to build similar timeseries using more involved methods (Rundle and Donnellan, 2020; Rundle et al., 2021). Here we show that not only does the state variable timeseries 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.

A document by John B. Rundle. Click on the document to view its contents.

We present a data-driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to their observed velocities, displacements or other selected characteristics. Clustering GNSS stations has the potential for identifying useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et. al., 2013). 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). 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 (Heflin et. al., 2020; Donnellan et al, 2021) but is easily extensible. We focused 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 the southernmost San Andreas fault connects with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass.

We use UAVSAR interferograms to characterize fault slip, triggered by the Mw 7.2 El Mayor-Cucapah earthquake on the southernmost San Andreas Fault in the Coachella Valley providing comprehensive maps of landscape change that complement in situ measurements. Creepmeters and geological mapping of fault offsets on Durmid Hill recorded 4 mm and 8 mm of average triggered slip respectively on the fault, in contrast to radar views that reveal significant off-fault dextral deformation averaging 20 mm. Unlike slip in previous triggered slip events on the southernmost San Andreas fault, dextral shear in 2010 is not confined to transpressional hills in the Coachella valley. Edge detection and gradient estimation applied to the 50-m-sampled interferogram data identify the location (to 20 m) and local strike (to < 4°) of secondary surface ruptures. Transverse curve fitting applied to these local detections provides local estimates of the radar-projected dextral slip and a parameter indicating the transverse width of the slip, which we equate with the depth of subsurface shear. These estimates are partially validated by fault-transverse interferogram profiles generated using the web-based UAVSAR tool of GeoGateway, and appear consistent for radar-projected slip greater than about 5 mm. An unexpected finding is that creep and triggered slip on the San Andreas fault terminate in the shallow subsurface below a surface shear zone that resists the simple expression of aseismic fault slip. We introduce the notion of a surface locking depth above which fault slip is manifest as distributed shear, and evaluate its depth as 6-27 m.