Geology topics

John Patton

Publications and source records attributed to John Patton.

4 recordsLinked to original sources

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↗

USGS2021

Seismic monitoring during crises at the NEIC in support of the ANSS

Over the past two decades, the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) has overcome many operational challenges. These range from minor disruptions, such as power outages, to significant operational changes, including system reconfiguration to handle unique earthquake sequences and the need to handle distributed work during a pandemic. Our ability to overcome crises is built on the development and implementation of a continuity of operations plan, well‐designed infrastructure, adaptive software systems, experienced staff, and extensive collaboration. The NEIC does not operate in a vacuum but benefits from contributions of United States and international seismic networks. Similarly, the overall resilience of earthquake monitoring in the United States and around the globe benefits from the NEIC’s role as the national center for the Advanced National Seismic System (ANSS). Here, we highlight significant adaptations the NEIC has made in the face of crises. We discuss the COVID‐19 pandemic, which represents the most significant operational crisis to impact the NEIC. The NEIC has maintained continuous operations during the ongoing COVID‐19 pandemic by shifting from a fully onsite operations center to a distributed hybrid of onsite and telework staffing. We then discuss cases in which the NEIC has supported regional monitoring in the face of significant crises. In 2018, the NEIC assisted the Hawaiian Volcano Observatory with the Kīlauea volcano eruption by responding to large events, implementing contingency monitoring procedures, and calculating moment magnitudes for the low‐frequency caldera collapses. Impacts of a crisis extend beyond the immediate response and often require a significant postevent assessment and a rebuilding phase. After the 2017 Hurricane Maria, the NEIC, the USGS National Strong‐Motion Program, and the USGS Albuquerque Seismological Laboratory worked with the Puerto Rico Seismic Network and the Puerto Rico Strong‐Motion program to assess, plan, and implement upgrades at sites that experienced storm damage.

Seismological Research Letters↗

USGS2020

Leveraging deep learning in global 24/7 real-time earthquake monitoring at the National Earthquake Information Center

Machine‐learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real‐time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep‐learning tools into NEIC operations. Automatic seismic arrival detections made from standard picking methods (e.g., short‐term average/long‐term average [STA/LTA]) are fed to trained neural network models to improve automatic seismic‐arrival (pick) timing and estimate seismic‐arrival phase type and source‐station distances. These additional data are used to improve the capabilities of the NEIC associator. We compile a dataset of 1.3 million seismic‐phase arrivals that represent a globally distributed set of source‐station paths covering a range of phase types, magnitudes, and source distances. We train three separate convolutional neural network models to predict arrival time onset, phase type, and distance. We validate the performance of the trained networks on a subset of our existing dataset and further extend validation by exploring the model performance when applied to NEIC automatic pick data feeds. We show that the information provided by these models can be useful in downstream event processing, specifically in seismic‐phase association, resulting in reduced false associations and improved location estimates.

Seismological Research Letters↗

USGS2019

GLASS3: A standalone multi-scale seismic detection associator

The automated global real-time association of phase picks into seismic sources comes with unique challenges when simultaneously monitoring at local, regional and global scales. High spatial variability in seismic station density, transitory seismic data availability, and time-varying noise characteristics of individual stations must be considered in the design of an associator that is fast and accurate with a low false association rate. These challenges are particularly apparent at the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC), which monitors seismicity in near-real time on local, regional, and global scales using seismic data from roughly 2,100 real-time seismic stations. In order to fully leverage this large dataset, NEIC developed a stand-alone, self-configuring seismic phase associator, GLASS3 (GLobal ASSociator 3) that simultaneously processes variably scaled 3D association webs, each with a unique set of nucleation criteria (e.g., nucleation stack threshold). GLASS3 has many useful features for real-time monitoring including its computational efficiency, instantaneous pick processing, and on-the-fly configurability such as the creation and removal of targeted association webs and updates to supporting station metadata. GLASS3 runs both as part of a real-time event processing system, and as a configurable standalone associator that can be applied to a large variety of seismic problems. Here we describe the GLASS3 algorithm and demonstrate (including input data and configuration files) its use in associating phase-ambiguous picks on multiple scales.

BSSA↗