Toño Bayona, University of Bristol
            The Collaboratory for the Study of Earthquake Predictability (CSEP) is a global community of scientists whose mission is to advance earthquake predictability research though the rigorous and prospective evaluation of probabilistic seismicity forecasts. One of CSEP&#8217;s major international achievements is the development and operation of dozens of time-varying and time-invariant seismicity models for California, including various versions of the well-established Epidemic-Type Aftershock Sequence (ETAS) and Short-Term Earthquake Probability (STEP) models, globally calibrated models, hybrid models and non-parametric models. Over the past ten years, prominent seismic sequences, such as the 2010 Mw 7.2 El Mayor-Cucapah, 2014 Mw 6.8 Ferndale, and 2019 Mw 7.1 Ridgecrest sequences, have been observed in California, providing a unique opportunity to comprehensively evaluate our current ability to forecast earthquakes and thus improve probabilistic seismic hazard assessments. In this talk, I will describe the models, statistical tests and data used in two CSEP forecast experiments, one comparing globally calibrated earthquake forecasting models with regional models and the other evaluating one-day seismicity models for California, and present the results of their respective prospective tests. I will discuss, in particular, the ability of the models to forecast the number and epicentral locations of observed M ≥ 4 earthquakes and compare their short- and long-term performance with that of an time-invariant smoothed seismicity model.

A decade of prospective evaluations of earthquake forecasting models in California: What have we learned and what can we do with it?

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Janet Watt, U.S. Geological Survey
            Investigating the geologic record of shallow megathrust behavior is imperative for estimating the earthquake hazard and tsunamigenic potential along the Cascadia subduction zone. Ship-borne sparker seismic imaging and multibeam mapping is integrated with targeted autonomous underwater vehicle (AUV) bathymetry and sub-bottom data to document along-strike variability in seafloor morphology and deformation mode along the Cascadia subduction zone frontal thrust offshore Oregon and northern California in unprecedented detail. The combined use of high- and ultra-high-resolution bathymetric (30-m to 1-m grids) and seismic imaging (vertical resolution ranging from 2 m to centimeters) allows us to evaluate geologic evidence for co-seismic activation of frontal thrust structures. Multi-scale data synthesis enables investigation of linkages between shallow deformation style and deeper decollement structure and accretionary mode.
The ~580-km-long frontal thrust splay fault system between Astoria and Eel Canyons is divided into seven sections based on along-strike variability in shallow structure and seafloor morphology. Many late Pleistocene to Holocene active fault strands within 10 km of the deformation front exhibit both geomorphic and stratigraphic evidence for coseismic activation. The high degree of variability in detailed shallow structure and morphology along the frontal thrust reflects changes in the crustal-scale frontal thrust fault geometry and décollement level. We present a conceptual model that links the along-strike variability in frontal thrust morpho-tectonics to differences in accretionary mode. Results suggest shallow megathrust rupture including co-seismic activation of frontal thrust splay faults is a common rupture mode along much of the Cascadia margin that should be considered in future earthquake and tsunami rupture models and hazard assessments.

Cascadia’s frontal thrust fault system revealed in unprecedented detail

Jeanne Hardebeck, U.S. Geological Survey
            Aftershock triggering is commonly attributed to static Coulomb stress changes from the mainshock.  A Coulomb stress increase encourages aftershocks in some areas, while in other areas termed &#8220;stress shadows&#8221; a decrease in Coulomb stress suppresses earthquake occurrence.  While the predicted earthquake rate decrease is rarely seen, lower aftershock rates are observed in the stress shadows compared to stress increase regions.  However, the question remains why some aftershocks occur in the stress shadows.  I examine three hypotheses: (1) Aftershocks appear in shadows because of inaccuracy in the computed stress change. (2) Aftershocks in the shadows occur on faults with different orientations than the model receiver faults, and these unexpected fault orientations experience increased Coulomb stress. (3) Aftershocks in the shadows are triggered by other physical processes, specifically dynamic stress changes. For the 2016 Kumamoto, Japan, and 2019 Ridgecrest, California, sequences, the first two hypotheses seem unlikely.  Over many realizations of the stress calculations with different modeling inputs, numerous aftershocks consistently show negative static Coulomb stress changes both on the model receiver faults and the individual event focal mechanisms.  Hypothesis 3 appears more likely, as the spatial and temporal distribution of aftershocks in the stress shadows are consistent with the expectations of dynamic triggering: the aftershocks occur mainly in a burst over the first few days to weeks, and decay with distance like near-field body waves.  The time series of dynamic stress can be modeled, and numerous metrics explored, such as the maximum dynamic Coulomb stress change, and the period and duration of the stressing.  Determining which metrics correspond to aftershock occurrence in the stress shadows may be useful in discriminating between various proposed physical mechanisms of dynamic stress triggering.

Stress Shadows: Insights into the Physics of Aftershock Triggering

Theresa Sawi, U.S. Geological Survey
            Repeating earthquakes sequences are widespread along California&#8217;s San Andreas fault (SAF) system and are vital for studying earthquake source processes, fault properties, and improving seismic hazard models. In this talk, I&#8217;ll be discussing an unsupervised machine learning‐based method for detecting repeating earthquake sequences (RES) to expand existing RES catalogs or to perform initial, exploratory searches. This method reduces spectrograms of earthquake waveforms into low-dimensionality &#8220;fingerprints&#8221; that can then be clustered into similar groups independent of initial earthquake locations, allowing for a global search of similar earthquakes whose locations can afterwards be precisely determined via double-difference relocation. We apply this method to ∼4000 small (⁠Ml 0&#8211;3.5) located on a 10-km-long creeping segment of SAF and double the number of detected RES, allowing for greater spatial coverage of slip‐rate estimations at seismogenic depths. This method is complimentary to existing cross‐correlation‐based methods, leading to more complete RES catalogs and a better understanding of slip rates at depth.

Detecting Repeating Earthquakes on the San Andreas Fault with Unsupervised Machine-Learning of Spectrograms

Andres Pena Castro, University of New Mexico
            The seismicity detected in the Antarctic continent is low compared with other continental intraplate regions of similar size. The low seismicity may be explained by (i) insufficient strain rates to generate earthquakes, (ii) scarcity of seismic instrumentation for detecting relatively small earthquakes, (iii) lack of comprehensive data mining for tectonic seismicity, or a combination of all the aforementioned. There have been ∼ 200 earthquakes in the interior of the Antarctic continent in the past two decades according to the International Seismological Centre (ISC) and other global catalogs. Previous studies in Antarctica have used seismometers installed for relatively short periods of time (∼days to months) to detect icequakes and/or tectonic earthquakes but a thorough integration of temporary and permanent network data is needed. Additionally, most of the reported seismicity was detected using classic earthquake detection techniques such as short-term-average/long-term-average or other energy detectors. State-of-the-art detection techniques, including machine learning, have proven to outperform classic detection techniques in different seismic sequences around the world and enable automated re-analysis of large volumes of data.
Here I will present a new seismic catalog for the southernmost continent. We use a Machine Learning phase picker technique on over 21 years of seismic data from on-continent temporary and permanent networks to obtain the most complete catalog of seismicity in Antarctica to date. The new catalog contains 60,006 seismic events within the Antarctic continent between January 1, 2000 to January 1, 2021, with event magnitudes between −1.0 to 4.5. Most of the detected seismicity occurs near Ross Island, large ice shelves, ice streams, ice-covered volcanoes, or in distinct and isolated areas within the continental interior. Their locations and waveform characteristics indicate volcanic, tectonic, or cryospheric sources. The catalog shows that Antarctica is more seismically active than prior catalogs would indicate. This catalogue provides a resources for more specific targeting with other detection and analysis methods such as template-matching or transfer learning, to further discriminate event types and investigate diverse seismogenic processes across the continent.

(Re)Discovering the seismicity of Antarctica: A new seismic catalog for the southernmost continent

Zachary Smith, University of California Berkley
            Intense dynamic stresses during earthquakes can activate numerous subsidiary faults and generate off-fault damage that alters fault properties and can impact the source processes and rupture dynamics of future earthquakes. Distinguishing how much damage accumulates during a single earthquake versus multiple earthquake cycles and determining how the magnitude of earthquakes impacts off-fault damage remains challenging. We combine geodetic, field, and experimental observations to evaluate the relationship between slip and off-fault deformation during a single earthquake and to assess how deformation evolves through successive earthquake cycles. Our study is focused on distributed faults that ruptured during the 2019 Mw 6.4 and 7.1 Ridgecrest earthquake sequence. Coseismic surface offsets are well imaged by satellite observing systems and the faults cut dikes of the extensive Independence dike swarm which serve as excellent linear cumulative displacement markers and records of near-fault damage exposed at Earth&#8217;s surface. Geodetic observations allow us to constrain slip and off-fault deformation due to a single event and the dikes enable us to constrain cumulative displacements.
We bridge the gap between space geodetic observations of deformation and laboratory scale deformation by collecting sub-millimeter resolution ground-based imagery and LiDAR across offset dikes and along bedrock sections of the major faults. Using these high-resolution scans, we compare fault slip with mesoscale fault-damage properties (e.g., fracture density and fragment size). Optical grain size analysis shows that fault damage is lithology dependent and that asymmetric grain size reduction of bedrock across faults is common. Fault zone asymmetry may result from slip on geometrically complex faults, preferred rupture directivity on subsidiary faults, or by distributed off-fault shearing, as observed in geodetic studies. We performed successive dynamic loading rock mechanics experiments to investigate how deformation may evolve over multiple earthquake cycles leading to the development of damage zone asymmetry and pulverized zones. Integration of geodetic, field, and laboratory observations provides a multiscale view of off-fault deformation to better inform the interpretation of inelastic strain accumulation in geodetic data, damage accumulation along large strike-slip faults, and seismic hazards associated with distributed shallow faulting.

A decade of prospective evaluations of earthquake forecasting models in California: What have we learned and what can we do with it?

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