Earthquake Science Center Seminars U.S. Geological Survey

Zhigang Peng, Georgia Institute of Technology

Earthquakes are not frequent in the Southeastern United States (SEUS), but they do occur in areas with long-term seismic activity and in new regions with no clear seismic history. Most of these earthquakes have relatively small magnitudes (less than 1) and are therefore not well recorded by the current seismic network. Some are extremely shallow, with hypocenters less than a few kilometers deep. In this talk, I will provide an update on our recent efforts to study shallow microearthquakes in several regions of the SEUS using dense nodal seismic networks and advanced processing techniques such as machine learning and template matching. This includes the 2020 magnitude 5.2 Sparta earthquake sequence in North Carolina, the Elgin-Lugoff earthquake swarm in South Carolina that began in December 2021, and the rock exfoliation event at Arabia Mountain in Georgia on July 17, 2023. Studying these extremely shallow events may offer new insights into the physical processes of earthquake nucleation.

Ettore Biondi, California Institute of Technology

Traveltime-based tomographic methods have been extensively explored and employed by researchers since the 80s. Such algorithms have been successfully applied to various geophysical applications, ranging from seismic exploration to global to regional seismological scales. However, given the advancements in computational architectures over the last 20 years, full-waveform methodologies are now dominating most of the subsurface-parameter inversion applications. These workflows seek to match all the waveforms present within active seismic data or synthetic Green’s functions obtained by cross-correlating ambient noise.

Despite this decrease in the popularity of traveltime-based tomographic approaches, these methods have great potential to be successful when applied to distributed acoustic sensing (DAS) data for seismic applications. DAS instruments can operate on existing telecommunication fibers and transform them into large-scale high-resolution seismic arrays. We demonstrate such potential by applying an Eikonal traveltime double-difference tomography algorithm to DAS data recorded in the Long Valley caldera, located in the Eastern Sierra region of California. This active volcanic area has been extensively studied in the last 50 years and its recent unrest remains still poorly understood.

We employ two DAS arrays composed of almost 9000 channels along a 90-km north-south transect across the caldera to characterize the subsurface structures present underneath the area. We use almost 2000 cataloged events and apply a machine-learning algorithm to accurately pick their P and S arrival times necessary for the tomography. The range and spatial resolution of the DAS arrays allow us to retrieve structures that could not be resolved by previous studies that employed only conventional station recordings.

Our results agree well with previous studies and highlight the presence of a low-velocity basin along the Mono-Inyo craters. Both P- and S-wave models also show a low-velocity structure centered below Mono Lake, which agrees with historical gravity surveys. Moreover, the low Vp/Vs ratio inverted below the Long Valley caldera suggests a lack of newly intruded materials at depth above 10 km and a clear separation between the shallow low-velocity basins and the ≥10-km deep magmatic reservoir.

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.

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 “stress shadows” 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.

Theresa Sawi, U.S. Geological Survey

Repeating earthquakes sequences are widespread along California’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’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 “fingerprints” 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 0–3.5) 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.

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.