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.

Because cross-polarized radar returns are highly associated with volume scatter, radar polarimetry returns tend to show strong evidence of wildfire scars and recovery in forest and chaparral. We focus on the polarimetry images from UAVSAR line SanAnd_08525, which covers a roughly 20 km wide swath over the Transverse Range including parts of the Santa Monica, San Gabriel and San Bernardino Mountains. We select images from four acquisition dates from October 2009 to September 2020, very roughly four years apart. These are compared to fire perimeters from the national GeoMAC and NIFC databases for years 2003-2020, which shows the areas affected by the major fires (west to east) Springs2013, Woolsey2018, Topanga2005, LaTuna2017, Station2009, BlueCut2016, Pilot2016, Slide2007, Butler2007 and many smaller fires. UAVSAR polarimetry images are shown to be helpful in identifying types and boundaries of fire, fifty-meter scale details of vegetation loss, and variability of vegetation recovery in post-fire years.

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.

The La Conchita Landslide is infamous for its repeated devastation of the coastal community in Southern California. The landslide caused severe damage and loss of life once in 1995 and 2005. In this study we use UAVSAR interferograms of the La Conchita area to identify any residual motion or slope instability associated with the landslides. UAVSAR is NASA’s airborne interferometric synthetic aperture radar (InSAR) platform and is useful for imaging changes in the Earth’s surface. UAVSAR repeat pass interferometry products show disturbances in the image pairs that may correlate with landslides, including where the La Conchita landslides had previously occurred. We used UAVSAR to compare different line-of-sight velocity profiles of the landslide to a stable area to the northeast. UAVSAR pairs show ongoing motion of up to -0.14 cm/day average velocity years after the landslides occurred. More stable areas show less than -0.06 cm/day maximum average velocity. The results imply that damaged rock and soil of a landslide continue to move relative to the surrounding more stable area. UAVSAR image pairs may be useful for identifying unstable areas on slopes that may be associated with landslides or other disturbances.

Tsunami Squares is a computationally lightweight tsunami and inundation simulator which utilizes a unique cellular automata technique. We make modifications to the underlying algorithm which result in increased accuracy and enhanced waveform resolution. These improvements leave Tsunami Squares well suited for machine learning applications where large pre-computed tsunami simulation databases are required. Previous implementations relied heavily on a smoothing algorithm which acts as a moving average applied to the water surface heights and velocities to eliminate anomalies at every time step. Although this allowed the simulation to function properly, it brings several unwanted effects such as reduced wave detail and lowered energy. A solution is found by shifting the location at which the water surface gradient is calculated, reducing the amount of anomalies in the simulation, and thus lowering the amount of smoothing needed by a factor of $\sim 10$. Also introduced is a new method to conserve energy locally, compared to previous methods which reference a simulation-wide energy calculation. We make comparison tests were made using the 2011 Tohoku tsunami along with the 2010 Maule tsunami to demonstrate the improvements made.

Interferometric synthetic-aperture radar (InSAR) interferograms contain valuable information about the fault systems hidden beneath the surface of the Earth. In a new approach, we aim to fit InSAR ground deformation data using a volumetric distribution of multiple seismic point sources whose parameters are found by a genetic algorithm. The resulting source distribution could provide another useful tool in solving the difficult problem of accurately mapping earthquake faults. To test the algorithm, we first apply it to synthetic data, followed by applications to an ALOS-2 InSAR interferogram. We report first results and discuss advantages and disadvantages of this approach.

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries which may correspond to the process of stress accumulation and release. The timeseries is constructed by using Principal Component Analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we then compute the weighted correlation timeseries (WCT) of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper (Rundle and Donnellan, 2020), we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contains information regarding future large earthquakes. For this we compute a Receiver Operating Characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazard.