Geology topics

David Mencin

Publications and source records attributed to David Mencin.

7 recordsLinked to original sources

USGS2026

Detecting earthquakes in noisy real-time GNSS data with deep learning for improved PGD magnitude estimation

To disseminate accurate and useful warnings, earthquake early warning (EEW) systems must quickly determine the size and location of an earthquake to estimate expected shaking. Traditional seismic‐based algorithms tend to underestimate the true magnitudes of large earthquakes, a phenomenon known as magnitude saturation. This limitation motivated the recent inclusion of Global Navigation Satellite Systems (GNSS) data into the U.S. Geological Survey’s ShakeAlert EEW system with the Geodetic First Approximation of Size and Time (GFAST) algorithm because GNSS data do not saturate with large ground motions. However, the noise levels of GNSS data are very high compared with traditional seismic data, which obscures P ‐wave arrivals and can result in less accurate magnitude estimations if displacement amplitudes are low, such as for lower magnitude earthquakes or large source–station distances. In this study, we develop a deep‐learning model that detects earthquakes in GNSS data and use the Ridgecrest, California, earthquake sequence as a case study to demonstrate how the model could act as a filter to reduce the amount of low‐quality data that enters an algorithm like GFAST. To preserve our limited real earthquake data for model inference, we generated a training dataset composed of >700,000 synthetic displacement waveforms. We combined the synthetic waveforms with real‐time GNSS noise to produce realistically noisy training waveforms and then tested our model on additional synthetic data and performed inference using the real data that were held back. We discuss the performance of our trained model on both the unseen synthetic data and real inference data. Our model can be used to selectively filter only high‐quality data where an earthquake signal is observed for input into an algorithm like GFAST (outperforming a simple signal‐to‐noise ratio–based filter) to reduce the error in GFAST’s real‐time earthquake magnitude estimations.

California↗

USGS2025

Characterization and validation of tidally calibrated strains from the Alto Tiberina Near Fault Observatory Strainmeter Array (TABOO-NFO-STAR)

Six horizontal borehole tensor strainmeters (TSM1-6) installed from Fall 2021 to Spring 2022 comprise the Alto Tiberina Near Fault Observatory Strainmeter Array (STAR), providing an unprecedented opportunity to investigate seismic and aseismic deformation from hazardous high- and low-angle normal faults in Italy. Prior to use in tectonic applications, they require in-situ calibration and correction for non-tectonic signals. We tidally calibrate the instruments, characterize the calibration uncertainty, and test the results against environmental and earthquake signals originating from local to teleseismic distances. The STAR sites demonstrably deviate from assumptions common to the standard manufacturer's calibrations, including negative areal coupling at TSM3-6. While the tidally calibrated strains have ~3-56% uncertainty, the calibrated dynamic strains show interstation precision and accuracy to nanostrain levels, and static coseismic offsets in the array footprint are within uncertainty. TSM3 records a complex series of strains that may arise from dynamically triggered near-borehole fracture slip and fluid flow that does not appear to affect its sensitivity to lower strain rate deformation. Future calibration improvement may be afforded with longer stable timeseries, particularly for TSM4. Overall, our analyses demonstrate expanded geodetic capability for detecting deformation in the Alto Tiberina Near Fault Observatory.

Alto Tiberina Near Fault Observatory↗

USGS2020

Kinematics of fault slip associated with the July 4-6 2019 Ridgecrest, Californai earthquakes sequence

The 2019 Ridgecrest, California, earthquake sequence produced observable crustal deformation over much of central and southern California, as well as surface rupture over several tens of kilometers. To obtain a detailed picture of the fault slip involved in the 4 July M 6.4 foreshock and 6 July M 7.1 mainshock, we combine strong‐motion seismic waveforms with crustal deformation observations to obtain kinematic and static slip models of both events. We sample the regional seismic wavefield for both the foreshock and mainshock with three‐component records from 31 stations of the California Integrated Seismic Network. The deformation observations include Global Positioning System (GPS), Interferometric Synthetic Aperture Radar (InSAR), and borehole strainmeter recordings of the dynamic strain field. These data collectively constrain the kinematic coseismic slip distributions of the events, with measurements variously observing coseismic slip from one event (e.g., seismic waveforms, kinematic solutions from continuous GPS, and strainmeter time series) or coseismic slip from both events combined (InSAR). We find that the foreshock ruptured two separate faults, one with left‐lateral strike slip on a northeast–southwest‐trending fault and the other with right‐lateral strike slip on an orthogonal fault, with unilateral rupture propagation along both. The mainshock ruptured a series of northwest–southeast‐trending faults with right‐lateral strike slip concentrated in the uppermost 6 km with exceptionally low‐rupture velocity averaging 1.0 – 1.5    km / s ">

California↗

USGS2019

Regional Global Navigation Satellite System networks for crustal deformation monitoring

Regional networks of Global Navigation Satellite System (GNSS) stations cover seismically and volcanically active areas throughout the United States. Data from these networks have been used to produce high‐precision, three‐component velocity fields covering broad geographic regions as well as position time series that track time‐varying crustal deformation. This information has contributed to assessing interseismic strain accumulation and related seismic hazard, revealed previously unknown occurrences of aseismic fault slip, constrained coseismic slip estimates, and enabled monitoring of volcanic unrest and postseismic deformation. In addition, real‐time GNSS data are now widely available. Such observations proved invaluable for tracking the rapidly evolving eruption of Kīlauea in 2018. Real‐time earthquake source modeling using GNSS data is being incorporated into tsunami warning systems, and a vigorous research effort is focused on quantifying the contribution that real‐time GNSS can make to improve earthquake early warnings as part of the Advanced National Seismic System ShakeAlert system. Real‐time GNSS data can also aid in the tracking of ionospheric disturbances and precipitable water vapor for weather forecasting. Although regional GNSS and seismic networks generally have been established independently, their spatial footprints often overlap, and in some cases the same institution operates both types of networks. Further integration of GNSS and seismic networks would promote joint use of the two data types to better characterize earthquake sources and ground motion as well as offer opportunities for more efficient network operations. Looking ahead, upgrading network stations to leverage new GNSS technology could enable more precise positioning and robust real‐time operations. New computational approaches such as machine learning have the potential to enable full utilization of the large amounts of data generated by continuous GNSS networks. Development of seafloor Global Positioning System‐acoustic networks would provide unique information for fundamental and applied research on subduction zone seismic hazard and, potentially, monitoring.

Seismological Research Letters↗

USGS2016

Geologic and geochemical results from boreholes drilled in Yellowstone National Park, Wyoming, 2007 and 2008

Between 2007 and 2008, seven Earthscope Plate Boundary Observatory (PBO) boreholes ranging in depth from about 200 to 800 feet deep were drilled in and adjacent to the Yellowstone caldera in Yellowstone National Park, for the purpose of installing volcano monitoring instrumentation. Five of the seven boreholes were equipped with strainmeters, downhole seismometers, and tiltmeters. Data collected during drilling included field observations of drill cuttings, stratigraphy within the boreholes, water temperature, and water and drill cuttings samples from selected depths. Six of the seven boreholes encountered rhyolite lavas and tuffs. The rhyolite lavas compose the Canyon flow, the Gardner River flow, the Gibbon River flow, the Hayden Valley flow, the Nez Perce Creek flow, and the West Thumb flow. Boreholes also penetrated a vertical sequence through the Lava Creek Tuff and the Tuff of Bluff Point. In addition, one borehole drilled through a Swan Lake Flat Basalt sequence and terminated in a rhyolite lava flow. After drilling the seven PBO boreholes, cuttings were examined and selected for preparation of grain mounts, thin sections, and geochemical analysis. Major ions and trace elements (including rare earth elements) of selected cuttings were determined by x-ray fluorescence (XRF) and inductively coupled plasma-mass spectrometry (ICP-MS); the ICP-MS provided more precise trace-element analysis than XRF. A preliminary interpretation of the results of geochemical analyses generally shows a correlation between borehole cuttings and previously mapped geology. The geochemical data and borehole stratigraphy presented in this report provide a foundation for future petrologic, geochemical, and geophysical studies.

Wyoming↗

USGS2013

Constraints on the upper crustal magma reservoir beneath Yellowstone Caldera inferred from lake-seiche induced strain observations

Seiche waves in Yellowstone Lake with a ~78-minute period and heights <10 cm act as a load on the solid earth observed by borehole strainmeters with subnanostrain sensitivity throughout the Yellowstone Caldera. The far-field strain induced by the load of the seiche waves calculated with a homogeneous elastic model representing the upper crust is more than an order of magnitude smaller than the measured strain amplitude ~30 km from the lake shore. By contrast, the observed far field strain amplitudes are consistent with the seiche load on a two-layered viscoelastic model representing an elastic upper crust overlying a partially molten body deeper than 3–6 km with Maxwell viscosity less than 10 11 Pa s. These strain observations and models provide independent evidence for the presence of partially molten material in the upper crust, consistent with seismic tomography studies that inferred 10%–30% melt fraction in the upper crust.

Wyoming↗

USGS2010

The Plate Boundary Observatory Permanent Global Positioning System Network on Augustine Volcano before and after the 2006 Eruption

In September of 2004, UNAVCO and the National Science Foundation (NSF) funded EarthScope Plate Boundary Observatory (PBO) installed five permanent Continuous Global Positioning System (CGPS) stations on Augustine Volcano, supplementing one existing CGPS station operated by the Alaska Volcano Observatory. All six CGPS stations proved crucial to scientists for detecting and monitoring the precursory deformation of the volcano beginning in early May 2005, as well as for monitoring the many subsequent small inflationary and deflationary episodes that characterized the 2006 eruption. Following the eruption, in September of 2006, PBO added six additional permanent CGPS stations. The 2006 eruption and its precursors were the first significant activity of the volcano in 20 years and the PBO CGPS network provided an unprecedented opportunity to monitor and detect volcanic ground deformation on an erupting Alaskan stratovolcano. Data from the new CGPS stations coupled with the existing seismic stations provided scientists with the first real opportunity to use geodetic data and real time seismic data to assess the volcanic hazards before, during, and after an Alaskan eruption.

Alaska↗