Geology topics

William L. Yeck

Publications and source records attributed to William L. Yeck.

At least 19 recordsLinked to original sources

USGS2026

Surface-wave relocation and characterization of the 2024–2025 dyking episode along the Fentale–Dofen segment of the Ethiopian rift

From late-December 2024 to mid-March 2025, a 50-km-long dyke intrusion triggered over 300 earthquakes (magnitude 4 to 5.9) between Fentale and Dofen volcanoes along the Northern Main Ethiopian Rift. Dyke intrusions periodically occur along the Fentale–Dofen magmatic segment and are an expression of ongoing rift extension. Preliminary analyses using interferometric synthetic aperture radar revealed extensive ground deformation (up to 60 cm), which closely matched the temporal and spatial evolution of surface manifestations and earthquake locations from global catalogues. While global catalogues are critical for real-time monitoring, the precision of locations in remote and or sparsely instrumented regions can be low. In this investigation, we present surface-wave relocation results of the dyking episode that began near Fentale volcano in December 2024. We estimate relative locations using differential traveltimes measured from regional-to-teleseismic distance surface-wave observations of earthquakes reported by the U.S. Geological Survey. Relative relocations reduce the initial region of diffuse seismicity to a 50-km-long narrow band bounding the strike of surface manifestations and the zone of maximum surface deformation. We demonstrate the precision of surface-wave relocations over incremental time periods, capturing the progression of dyking from seismic onset through seismic migration and caldera subsidence. Results showcase the utility of surface-wave relocations in the characterization of dyking episodes and provide complementary insights into the current understanding of the Fentale–Dofen volcanic plumbing system.

Geophysical Journal International↗

USGS2026

Rapid characterization of the 2025 Mw 8.8 Kamchatka, Russia earthquake

The 29 July 2025 M w 8.8 Kamchatka, Russia, earthquake was the sixth largest instrumentally recorded earthquake. This event was seismically well observed at regional and teleseismic distances, but publicly available near‐source data were sparse at the time of the event, presenting unique challenges for rapid source and impact characterization. The U.S. Geological Survey (USGS) National Earthquake Information Center provides global real‐time monitoring for earthquakes, including rapid response information products that estimate source characteristics, shaking, and the resulting impacts. We describe the USGS rapid response earthquake information products following the Kamchatka event and discuss their implications for ongoing hazards in the region. We describe potential improvements to our response workflows motivated by this event, including more rapid constraints on source geometries and the automated selection of fault geometries for finite‐fault inversions. The rapid response products together support the interpretation of a unilateral southwestward rupture with significant slip on the southwestern end of the rupture extent. The M w 8.8–9.0 event in 1952, which ruptured a comparable extent of the Kuril–Kamchatka subduction interface, has many similarities to the 2025 rupture. This illustrates that slip deficits may remain following great earthquakes and highlights the usefulness of comparative studies between historic and modern events.

Kuril-Kamchatka subduction zone↗

USGS2025

Divide and conquer: Separating the two probabilities in seismic phase picking

There are two fundamental probabilities in the seismic phase picking process – the probability of the existence of a seismic phase (detection probability) and the probability of correctly identifying the phase arrival time (timing probability). The nearly ubiquitous approach in developing deep learning phase picking models is to use a kernel, such as a truncated Gaussian, to mask the labeled phase arrival time, and train a segmentation model. Once a model is trained, the times of the peaks in the output are taken as phase arrival times (picks) and the height of the peaks are taken as “probability” of the picks. Here, we show that this “probability” represents neither the detection nor the timing probabilty because this approach forces the output to follow the shape of the kernel. We introduce an approach using two models to estimate these two distinct probabilities. We use a binary classifier with a calibrated confidence to address the detection probability and a multi-class classifier to obtain a probability mass function to address the timing probability. This new approach makes the deep learning-based phase picking process more interpretable and gives us options to logically control seismic monitoring workflows.

Geophysical Journal International↗

USGS2025

Surface rupture from an aftershock: Remote observations from the January 2024 Wushi (Aykol), China, earthquakes

On 22 January 2024, a moment magnitude ( M w ⁠⁠ ) 7.0 earthquake occurred in the southern Tian Shan region of western China, followed by an M w 5.7 aftershock on 29 January. The M w 7.0 mainshock occurred on a northwest‐dipping fault plane, at a hypocentral depth of 13 km. Both Interferometric Synthetic Aperture Radar (InSAR) and optical satellite imagery observations indicate a lack of surface rupture for the M w 7.0 earthquake. However, mapping and subpixel correlation of high‐resolution optical satellite imagery and InSAR observations spanning the 29 January M w 5.7 thrust‐faulting aftershock support the interpretation of fault surface rupture. We observed ∼4.8 km of discontinuous surface rupture associated with the M w 5.7 aftershock. The surface rupture trace trends subparallel to the northeast–southwest bedding orientation within the limb of a syncline. Subpixel image correlation and digital elevation model (DEM) differencing indicate mean on‐fault offsets of 1 m, with a peak of ∼1.4 m, along the fault. Given these observations, we interpret the M w 5.7 aftershock produced surface rupture on an unmapped flexural‐slip fault within a syncline in the hanging wall of the Maidan fault or on a back thrust associated with the Maidan fault. Pre‐existing fault features along the surface rupture trace in the landscape from pre‐earthquake DEMs indicate that the fault that ruptured in the M w 5.7 aftershock may have ruptured in the past. Surface rupture only from an aftershock in a seismic sequence is a critical observation that highlights the need to understand secondary or distributed faulting in larger earthquakes for seismic and fault displacement hazard analysis.

Tian Shan region↗

USGS2025

Estimating earthquake source depth using teleseismic broadband waveform modeling at the USGS National Earthquake Information Center

The U.S. Geologic Survey National Earthquake Information Center (NEIC) monitors global seismicity, producing a catalog of earthquake source parameters in near-real-time to provide information that can help mitigate the societal impact of earthquakes. The NEIC commonly relies on teleseismic observations to constrain earthquake source parameters (e.g., location, depth, magnitude, and mechanism) due to a lack of local and regional observations. For these ‘teleseismic’ events, depth phase (i.e., pP , sP ) arrival time observations provide the best estimate on source depth. However, depth phases are often difficult to accurately identify and/or pick. Therefore, NEIC relies on waveform modeling, such as those determined from W-phase ( M ww ), body wave ( M wb ), and regional ( M wr ) moment tensor estimations, to provide constraints on source depth. While depth estimates from these approaches are informative, higher frequency observations provide more precise estimates because depth phases are more prominently observed at higher frequencies. Here, we present NEIC’s relatively high-frequency (~0.04 to 1 Hz) teleseismic waveform modeling approach, termed Synthetic Depth Phase Modeling (SynDepth), for determining source depth. SynDepth was developed to provide NEIC with a tool that enables rapid, accurate, and quantifiable estimates of earthquake source depth in cases where locator depths are not reliable. This relatively simple and fast procedure searches over 1 km-incremented source depths and an expanding triangular source-time function to find the best-fitting solution. We compare automatic SynDepth solutions for a dataset of 1,216 earthquakes (M5.5-M7.6) between 2017 and 2021 to NEIC-derived depth estimates from other methods. Our approach provides a robust depth estimate for earthquakes lacking local arrival time data, and it minimizes the need for analyst review of depth-phase picks ( pP , sP ) or using predefined ‘fixed’ depths.

Seismological Research Letters↗

USGS2025

Slow rupture, long rise times, and multi-fault geometry: The 2020 M6.4 southwestern Puerto Rico mainshock

The M 6.4 mainshock of the southwestern Puerto Rico seismic sequence on 7 January 2020, was one of the most impactful modern earthquakes in the northeastern Caribbean. Due to its offshore location and complex aftershock distribution, its source kinematics remain poorly constrained. This active sequence illuminated a complex set of previously unrecognized structures that indicate multiple causative faults may have slipped during its rupture. Here, we utilize seismic and geodetic observations to enhance model resolution, estimate the finite slip of the mainshock, and test a multi-segment, geologically realistic fault geometry. Our refined model finds a lower rupture velocity and longer rise times than typical for an event of this magnitude. This indicates a slow-evolving rupture process that resembles characteristics of a tsunami earthquake. Although this normal/strike-slip faulting event was not tsunamigenic, these qualities, if pervasive for this region, have important implications for future seismic monitoring and hazards in southwestern Puerto Rico.

Puerto Rico↗

USGS2025

An unexplained tsunami: Was there megathrust slip during the 2020 Mw7.6 Sand Point, Alaska, earthquake?

On October 19, 2020, the M w 7.6 Sand Point earthquake struck south of the Shumagin Islands in Alaska. Moment tensors indicate the earthquake was primarily strike-slip, yet the event produced an enigmatic tsunami that was larger and more widespread than expected for an earthquake of that magnitude and mechanism. Using a suite of hydrodynamic, seismic, and geodetic modeling techniques, we explore plausible causes of the tsunami. We find that strike-slip models consistent with the moment tensor orientation cannot produce the observed tsunami. Hydrodynamic inversion of sea surface deformation from deep ocean and tide gauge data suggest seafloor deformation more closely matches a megathrust, rather than a strike-slip, source. Static slip inversions, using sea level and Global Navigation Satellite System data, allow for a portion of co-seismic megathrust slip that can explain tsunamigenesis. Combining all available geophysical datasets to model the kinematic rupture, we show that considerable, relatively slow, megathrust slip is allowable in the Shumagin segment, concurrent with strike-slip faulting. We hypothesize that the slow megathrust rupture does not contribute much seismic radiation allowing it to previously go unnoticed with traditional seismic monitoring.

Alaska↗

USGS2025

Surface-wave relocation and characterization of the October 2023 tsunamigenic seismic unrest near Sofugan volcano, Izu Islands, Japan

A moderate-magnitude earthquake swarm occurred in the remote Izu Islands region of Japan between October 1 and 8, 2023. The swarm included 151 shallow earthquakes cataloged by the U.S. Geological Survey, which notably included a roughly 2.5-hr episode of 15 successive magnitude (M) < 5.5 earthquakes. Origin times were coincident with regionally recorded tsunami waves, but tsunamigenesis for moderate-magnitude earthquakes is uncommon, indicating that volcanic activity generated the ocean displacements. Leveraging a surface-wave relative relocation approach, we estimate precise epicentroid locations for the remote swarm. Final epicentroids and caldera analogs indicate a three-stage model to explain swarm activity: (a) caldera pressurization due to magma intrusion, (b) depressurization via dike propagation away from the caldera, and (c) eruption corresponding with caldera reactivation either by collapse or additional intrusion.

Sofugan volcano, Izu Islands↗

USGS2025

Ultralong, supershear rupture of the 2025 Mw 7.7 Mandalay earthquake reveals unaccounted risk

The 28 March 2025 moment magnitude ( M w ) 7.7 earthquake in Mandalay, Burma (Myanmar), ruptured 475 kilometers of the Sagaing Fault, which was more than twice the length predicted by magnitude scaling relationships. Kinematic slip models and observation of a Rayleigh Mach wave that passed through parts of Thailand confirmed that rupture occurred at supershear velocities of greater than 5 kilometers per second. The anomalous length exposed a vast population to violent near-fault shaking. The Mandalay earthquake is a modern analog for the M w 7.9 1906 San Francisco earthquake, another atypically long and fast rupture. Probabilistic seismic hazard analyses use scaling relations that do not account for such long ruptures at moderate magnitudes. This limitation, in conjunction with a likely increased population and infrastructure exposure for atypically long ruptures, contributes to a potential mischaracterization of seismic risk.

Mandalay↗

USGS2024

Uncertainty and spatial correlation in station measurements for mb magnitude estimation

The body‐wave magnitude (⁠⁠) is a long‐standing network‐averaged, amplitude‐based magnitude used to estimate the magnitude of seismic sources from teleseismic observations. The U.S. Geological Survey National Earthquake Information Center (NEIC) relies on in its global real‐time earthquake monitoring mission. Although waveform modeling‐based moment magnitudes are the modern standard to characterize earthquake size, is important because (1) in many cases, waveform modeling is not possible (e.g., low signal‐to‐noise events), (2) is applicable over a broad range of magnitudes, ∼M 4–7, and (3) there is a many decades‐long history of estimating magnitudes. We use the NEIC Preliminary Determination of Epicenters earthquake catalog to investigate the uncertainty in NEIC station measurements. We show that measurements are spatially correlated, which can bias event ⁠, and we describe an empirical relation between this spatial correlation and station‐to‐station distance. We further describe an approach to mitigate bias from the spatial correlation. Accounting for the spatial covariance of observations can change the event from −0.15 to 0.07 units (10th to 90th percentile) for smaller events (⁠⁠). These smaller events have the largest standard deviations ranging from 0.05 to 0.15 units (10th to 90th percentile).

The Seismic Record↗

USGS2024

Noise constraints on global body‐wave measurement thresholds

Intermediate sized earthquakes (≈ M 4–6.5) are often measured using the teleseismic body‐wave magnitude ( ⁠𝑚b⁠ ). 𝑚b measurements are especially critical at the lower end of this range when teleseismic waveform modeling techniques (i.e., moment tensor analysis) are difficult. The U.S. Geological Survey National Earthquake Information Center (NEIC) determines the location and magnitude of all M 5 and greater earthquakes worldwide within 20 min of the rupture time, and therefore accurate 𝑚b magnitude estimates are essential to fulfill its mission. To better understand how network geometry and noise levels affect the global response capabilities, we developed a method to spatially estimate the minimum measurable 𝑚b⁠ . To do this, we compare expected 𝑚b amplitudes at every station to the station’s background noise level. We find that using NEIC’s current network geometry and these idealized thresholds, NEIC can potentially estimate 𝑚b magnitudes down to M 4.5 globally. Low‐latitude regions in the Southern Hemisphere present the biggest opportunity to improve monitoring capabilities. However, logistically they also present the biggest hurdles for network operators. Finally, to test the resiliency of the network we removed the 20 most important stations and found the 𝑚b threshold remains 𝑚b 4.5. However, the region where only 𝑚b 4.5 and greater can be estimated increases and is again restricted to the Southern Hemisphere.

Bulletin of the Seismological Society of America↗

USGS2024

Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale

The foundation of earthquake monitoring is the ability to rapidly detect, locate, and estimate the size of seismic sources. Earthquake magnitudes are particularly difficult to rapidly characterize because magnitude types are only applicable to specific magnitude ranges, and location errors propagate to substantial magnitude errors. We developed a method for rapid estimation of single‐station earthquake magnitudes using raw three‐component P waveforms observed at local to teleseismic distances, independent of prior size or location information. We used the MagNet regression model architecture ( Mousavi and Beroza, 2020b ), which combines convolutional and recurrent neural networks. We trained our model using ∼2.4 million P ‐phase arrivals labeled by the authoritative magnitude assigned by the U.S. Geological Survey. We tested input data parameters (e.g., window length) that could affect the performance of our model in near‐real‐time monitoring applications. At the longest waveform window length of 114 s, our model (Artificial Intelligence Magnitude [AIMag]) is accurate (median estimated magnitude within ±0.5 magnitude units from catalog magnitude) between M 2.3 and 7.6. However, magnitudes above M ∼7 are more underestimated as true magnitude increases. As the windows are shortened down to 1 s, the point at which higher magnitudes begin to be underestimated moves toward lower magnitudes, and the degree of underestimation increases. The over and underestimation of magnitudes for the smallest and largest earthquakes, respectively, are potentially related to the limited number of events in these ranges within the training data, as well as magnitude saturation effects related to not capturing the full source time function of large earthquakes. Importantly, AIMag can determine earthquake magnitudes with individual stations’ waveforms without instrument response correction or knowledge of an earthquake’s source‐station distance. This work may enable monitoring agencies to more rapidly recognize large, potentially tsunamigenic global earthquakes from few stations, allowing for faster event processing and reporting. This is critical for timely warnings for seismic‐related hazards.

Bulletin of the Seismological Society of America↗

USGS2024

Subduction intraslab-interface fault interactions in the 2022 Mw 6.4 Ferndale, California earthquake sequence

The Mendocino triple junction, the intersection of the Pacific, North American, and Gorda plates, activates a collection of disparate faults that reconcile Cascadia subduction with San Andreas transform motion. The December 20, 2022, Mw 6.4 Ferndale, California earthquake occurred within this complex zone as strike-slip faulting within the subducting Gorda slab. Here, we analyze the seismic and geodetic signatures of the mainshock and aftershock sequence to illuminate its role within complex tectonic surroundings. We find aftershocks on varied fault structures within the uppermost Gorda slab, yet seismicity on the subduction interface itself was notably absent. Nevertheless, we identify small but coherent postseismic deformation that is well modeled by aseismic slip on this interface, likely triggered by stresses generated at the updip limit of coseismic rupture. This sequence demonstrates the potential for interactions between intra-slab earthquakes and slip on the subduction megathrust, highlighting the need to consider this and other subduction zones as coupled systems of interacting faults.

California↗

USGS2023

The 2022 Chaos Canyon landslide in Colorado: Insights revealed by seismic analysis, field investigations, and remote sensing

An unusual, high-alpine, rapid debris slide originating in ice-rich debris occurred on June 28, 2022, at 16:33:16 MDT at the head of Chaos Canyon, a formerly glacier-covered valley in Rocky Mountain National Park, CO, USA. In this study, we integrate eyewitness videos and seismic records of the event with meteorological data, field observations, pre- and post-event satellite imagery, and uncrewed aircraft vehicle imagery to characterize the event and future hazards it may pose. Deformation of the eventual slide mass preceded rapid failure by decades, starting in the early to mid-2000s, accelerating in 2018 (the warmest year on record), and reaching ~ 20 m/year in 2021. The main event, which was preceded by smaller sliding episodes earlier that day, had a volume of ~ 2.1 million m 3 , reached peak velocities of about 5 m/s, slid on a surface up to 80 m deep, and moved up to ~ 245 m downslope in < 2 min. We observed blocks of frozen debris (permafrost) in the landslide deposits. Within ~ 2 weeks, these blocks had melted and became dry, conical debris mounds (molards). We hypothesize that the rapid slide was induced by gradually increasing long-term air temperatures that thawed ice and increased pore pressures. The presence and suspected influence of permafrost on the occurrence of this landslide indicate other slopes in the park, and other moderate-to-low latitude alpine regions may experience similar slope stability issues as temperatures continue to warm.

Colorado↗

USGS2023

Rapid Source Characterization of the 2023 Mw 6.8 Al Haouz, Morocco, Earthquake

The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) estimates source characteristics of significant damaging earthquakes, aiming to place events within their seismotectonic framework. Contextualizing the 8 September 2023, M w 6.8 Al Haouz, Morocco, earthquake is challenging, because it occurred in an enigmatic region of active surface faulting, and low seismicity yet produced significant damage and loss of life. Here, we present the rapid earthquake source products produced by the USGS NEIC, describing how the source model was derived using both seismic and geodetic observations. Our analysis indicates that the earthquake was the result of oblique‐reverse faulting in the lower crust on either a steeply north‐dipping fault or a moderately south‐dipping fault. Finite‐slip models using seismic and geodetic data reveal a compact source, with slip occurring at depths of 15–35 km. The causative fault is not apparent, because the rupture did not break the surface, and it is not possible to definitively attribute the earthquake to a known structure. The earthquake centroid depth of 25 km is noteworthy, because it shows slip extending beyond common estimates of seismogenic depth. This earthquake highlights that the seismogenic processes associated with mountain building in this wide plate boundary region are poorly understood.

The Seismological Record↗

USGS2023

MLAAPDE: A machine learning dataset for determining global earthquake source parameters

The Machine Learning Asset Aggregation of the Preliminary Determination of Epicenters (MLAAPDE) dataset is a labeled waveform archive designed to enable rapid development of machine learning (ML) models used in seismic monitoring operations. MLAAPDE consists of more than 5.1 million recordings of 120 s long three‐component broadband waveform data (raw counts) for P , Pn , Pg , S , Sn , and Sg arrivals. The labeled catalog is collected from the U.S. Geological Survey National Earthquake Information Center’s (NEIC) Preliminary Determination of Epicenters bulletin, which includes local to teleseismic observations for earthquakes ∼ M 2.5 and larger. Each arrival in the labeled dataset has been manually reviewed by NEIC staff. An accompanying Python module enables users to develop customized training datasets, which includes different time‐series lengths, distance ranges, sampling rates, and/or phase lists. MLAAPDE is distinct from other publicly available datasets in containing local (14%), regional (36%), and teleseismic (50%) observations, in which local, regional, and teleseismic distance are 0°–3°, 3°–30°, and 30°+, respectively. A recent version of the dataset is publicly available (see Data and Resources ), and user‐specific versions can be generated locally with the accompanying software. MLAAPDE is an NEIC supported, curated, and periodically updated dataset that can contribute to seismological ML research and development.

Seismological Research Letters↗

USGS2023

Inconsistent citation of the Global Seismographic Network in scientific publications

The highly used Global Seismographic Network (GSN) is a pillar of the seismological research community and contributes to numerous groundbreaking publications. Despite its wide recognition, this survey found that the GSN is not consistently acknowledged in scientific literature and is underrepresented by roughly a factor of 3 in citation searches. Publication tracking is a key metric that factors into operational decisions and funding support for the network; thus, consistent and proper citation of the GSN is important. This study not only serves as a reminder for researchers using GSN observations to cite the network’s digital object identifiers (DOIs) but also promotes a community‐wide conversation among researchers, journal editors, network operators, and other stakeholders regarding more standardized policies and review processes to ensure seismic networks are properly and consistently recognized for their contributions to research.

Seismological Research Letters↗

USGS2023

Rapid characterization of the February 2023 Kahramanmaraş, Turkey, earthquake sequence

The 6 February 2023 M w "> M w 7.8 Pazarcık and subsequent M w "> M w 7.5 Elbistan earthquakes generated strong ground shaking that resulted in catastrophic human and economic loss across south‐central Türkiye and northwest Syria. The rapid characterization of the earthquakes, including their location, size, fault geometries, and slip kinematics, is critical to estimate the impact of significant seismic events. The U.S. Geological Survey National Earthquake Information Center (NEIC) provides real‐time monitoring of earthquakes globally, including rapid source characterization and impact estimates. Here, we describe the seismic characterization products generated and made available by the NEIC over the two weeks following the start of the earthquake sequence in southeast Türkiye, their evolution, and how they inform our understanding of regional seismotectonics and hazards. The kinematics of rupture for the two earthquakes was complex, involving multiple fault segments. Therefore, incorporating observations from rupture mapping was critical for characterizing these events. Dense local datasets facilitated robust source characterization and impact assessment once these observations were obtained and converted to NEIC product input formats. We discuss how we may improve the timeliness of NEIC products for rapid assessment of future seismic hazards, particularly in the case of complex ruptures.

The Seismic Record↗