I am a seismologist at the USGS Geologic Hazards Science Center, in Golden, Colorado. Much of my research is focused on operational tools that allow the National Earthquake Information Center to rapidly and accurately detect and model the source characteristics of earthquakes. I use these tools to better understand the seismotectonics of significant events.
Education:
2015 - Ph.D. in Geophysics, University of Colorado at Boulder
2008 - B.S. in Physics, Astronomy-Physics, (Minor in Archeology), University of Wisconsin - Madison
Publications:
Please visit my google scholar page for the most up-to-date list of my publications: Click Here
Science and Products
Improving Earthquake Monitoring with Deep Learning
Release Date: MARCH 12, 2021 On January 20, 2021 at 8:32am light shaking interrupted breakfast customers at a local coffee shop south of downtown Los Angeles, California. Everyone paused briefly while they waited to see if it was going to stop… or start shaking harder.
Waveform Data and Metadata used to National Earthquake Information Center Deep-Learning Models
These data were used to train the Machine Learning models supporting the USGS software release "NEIC Machine Learning Applications Software" (https://doi.org/10.5066/P9ICQPUR), and its companion publication in Seismological Research Letters "Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center" (ht
Spatiotemporal Analysis of the Foreshock-Mainshock-Aftershock Sequence of the 6 July 2017 M5.8 Lincoln, Montana, Earthquake - Data Release
We used matched filter detection and multiple-event relocation techniques to characterize the spatiotemporal evolution of the sequence. Our analysis is from the 14 closest seismic stations to the earthquake sequence, which included seven permanent stations from the Montana Regional Seismic Network, one permanent station from the ANSS backbone network and three temporary seismic stations deployed b
Earthquake Catalogs supporting manuscript "Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho"
This ScienceBase entry contains three seismic catalogs supporting and described by the manuscript - Koper, K. D., Pankow, K. L., Pechmann, J. C., Hale, J. M., Burlacu, R., Yeck, W. L., et al (2018). Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL078196. These are included in three
Aftershock Catalog for the November 2011 Prague, Oklahoma Earthquake Sequence
The dataset contains the catalog of 5446 events and arrival times resulting from subspace detection processing and relocation in the for the 2011 Prague, Oklahoma, aftershock sequence. Lines beginning with "E" contain event information in the following order: event ID, origin year, origin month, origin day, origin hour, origin minute, origin second, latitude, longitude, depth, and magnitude. Lines
Filter Total Items: 23
A global catalog of calibrated earthquake locations
We produced a globally distributed catalog of earthquakes and nuclear explosions with calibrated hypocenters, referred to as the Global Catalog of Calibrated Earthquake Locations (GCCEL). This dataset currently contains 18,782 events in 289 clusters with >3.2 million arrival times observed at 19,258 stations. The term “calibrated” refers to the property that the hypocenters are minimally biased by
Beyond the teleseism: Introducing regional seismic and geodetic data into routine USGS finite‐fault modeling
The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) routinely produces finite‐fault models following significant earthquakes. These models are spatiotemporal estimates of coseismic slip critical to constraining downstream response products such as ShakeMap ground motion estimates, Prompt Assessment of Global Earthquake for Response loss estimates, and ground failure ass
Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations
Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global
Seismotectonic analysis of the 2019–2020 Puerto Rico sequence: The value of absolute earthquake relocations in improved interpretations of active tectonics
We present a new catalog of calibrated earthquake relocations from the 2019–2020 Puerto Rico earthquake sequence related to the 7 January 2020 Mw 6.4 earthquake that occurred offshore of southwest Puerto Rico at a depth of 15.9 km. Utilizing these relocated earthquakes and associated moment tensor solutions, we can delineate several distinct fault systems that were activated during the sequence an
Modeling seismic network detection thresholds using production picking algorithms
Estimating the detection threshold of a seismic network (the minimum magnitude earthquake that can be reliably located) is a critical part of network design and can drive network maintenance efforts. The ability of a station to detect an earthquake is often estimated by assuming the spectral amplitude for an earthquake of a given size, assuming an attenuation relationship, and comparing the predic
A big problem for small earthquakes: Benchmarking routine magnitudes and conversion relationships with coda-envelope-derived Mw in southern Kansas and northern Oklahoma
Earthquake magnitudes are widely relied upon measures of earthquake size. Although moment magnitude (MwMw) has become the established standard for moderate and large earthquakes, difficulty in reliably measuring seismic moments for small (generally MwMwMw. To assess this problem, we apply coda envelope analysis to reliably determine moment magnitudes for a case study of small earthquakes from n
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 cr
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.
National earthquake information center strategic plan, 2019–23
Executive SummaryDamaging earthquakes occur regularly around the world; since the turn of the 20th century, hundreds of earthquakes have caused significant loss of life and (or) millions of dollars or more in economic losses. While most of these did not directly affect the United States and its Territories, by studying worldwide seismicity we can better understand how to mitigate the effects of ea
On the portability of ML-MC as a depth discriminant for small seismic events recorded at local distances
In this paper we show that ML-MC is a viable and regionally portable depth discriminant and therefore may contribute in nuclear test ban treaty verification. A recent study found that the difference between local magnitude (ML) and coda duration magnitude (MC) discriminates shallow seismic events (mining blasts, mining-induced earthquakes, and shallow tectonic earthquakes) from deeper tectonic ear
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 ac
Fault slip associated with the 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake and aftershock sequence
The 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake is one of the largest earthquakes in southern Idaho since the 1983 M 6.9 Borah Peak earthquake. It was followed by a vigorous aftershock sequence for nearly two weeks that included five events above M 4.5. The coseismic and early postseismic deformation was measured with both Interferometric Synthetic Aperture Radar and Global Positioning
neic-machine-learning
NEIC Machine Learning Applications contains various seismic machine learning algorithms developed and used by by the United States Geological Survey, National Earthquake Information Center. These algorithms apply machine learning techniques to seismic processing problems such as seismic phase classification, source-receiver distance classification, and seismic wave arrival time repicking.
neic-glass3
neic-glass3 is an open source and platform independent seismic event detection and association algorithm developed by the United States Geological Survey (USGS) National Earthquake Information Center (NEIC) and Caryl Erin Johnson, PhD, Introspective Systems LLC.
This algorithm converts a time series of seismic phase arrival times, back azimuth estimates from array beams, and cross-correlated de
Science and Products
- Science
Improving Earthquake Monitoring with Deep Learning
Release Date: MARCH 12, 2021 On January 20, 2021 at 8:32am light shaking interrupted breakfast customers at a local coffee shop south of downtown Los Angeles, California. Everyone paused briefly while they waited to see if it was going to stop… or start shaking harder. - Data
Waveform Data and Metadata used to National Earthquake Information Center Deep-Learning Models
These data were used to train the Machine Learning models supporting the USGS software release "NEIC Machine Learning Applications Software" (https://doi.org/10.5066/P9ICQPUR), and its companion publication in Seismological Research Letters "Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center" (htSpatiotemporal Analysis of the Foreshock-Mainshock-Aftershock Sequence of the 6 July 2017 M5.8 Lincoln, Montana, Earthquake - Data Release
We used matched filter detection and multiple-event relocation techniques to characterize the spatiotemporal evolution of the sequence. Our analysis is from the 14 closest seismic stations to the earthquake sequence, which included seven permanent stations from the Montana Regional Seismic Network, one permanent station from the ANSS backbone network and three temporary seismic stations deployed bEarthquake Catalogs supporting manuscript "Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho"
This ScienceBase entry contains three seismic catalogs supporting and described by the manuscript - Koper, K. D., Pankow, K. L., Pechmann, J. C., Hale, J. M., Burlacu, R., Yeck, W. L., et al (2018). Afterslip Enhanced Aftershock Activity During the 2017 Earthquake Sequence Near Sulphur Peak, Idaho. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL078196. These are included in threeAftershock Catalog for the November 2011 Prague, Oklahoma Earthquake Sequence
The dataset contains the catalog of 5446 events and arrival times resulting from subspace detection processing and relocation in the for the 2011 Prague, Oklahoma, aftershock sequence. Lines beginning with "E" contain event information in the following order: event ID, origin year, origin month, origin day, origin hour, origin minute, origin second, latitude, longitude, depth, and magnitude. Lines - Publications
Filter Total Items: 23
A global catalog of calibrated earthquake locations
We produced a globally distributed catalog of earthquakes and nuclear explosions with calibrated hypocenters, referred to as the Global Catalog of Calibrated Earthquake Locations (GCCEL). This dataset currently contains 18,782 events in 289 clusters with >3.2 million arrival times observed at 19,258 stations. The term “calibrated” refers to the property that the hypocenters are minimally biased byBeyond the teleseism: Introducing regional seismic and geodetic data into routine USGS finite‐fault modeling
The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) routinely produces finite‐fault models following significant earthquakes. These models are spatiotemporal estimates of coseismic slip critical to constraining downstream response products such as ShakeMap ground motion estimates, Prompt Assessment of Global Earthquake for Response loss estimates, and ground failure assAchievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations
Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced globalSeismotectonic analysis of the 2019–2020 Puerto Rico sequence: The value of absolute earthquake relocations in improved interpretations of active tectonics
We present a new catalog of calibrated earthquake relocations from the 2019–2020 Puerto Rico earthquake sequence related to the 7 January 2020 Mw 6.4 earthquake that occurred offshore of southwest Puerto Rico at a depth of 15.9 km. Utilizing these relocated earthquakes and associated moment tensor solutions, we can delineate several distinct fault systems that were activated during the sequence anModeling seismic network detection thresholds using production picking algorithms
Estimating the detection threshold of a seismic network (the minimum magnitude earthquake that can be reliably located) is a critical part of network design and can drive network maintenance efforts. The ability of a station to detect an earthquake is often estimated by assuming the spectral amplitude for an earthquake of a given size, assuming an attenuation relationship, and comparing the predicA big problem for small earthquakes: Benchmarking routine magnitudes and conversion relationships with coda-envelope-derived Mw in southern Kansas and northern Oklahoma
Earthquake magnitudes are widely relied upon measures of earthquake size. Although moment magnitude (MwMw) has become the established standard for moderate and large earthquakes, difficulty in reliably measuring seismic moments for small (generally MwMwMw. To assess this problem, we apply coda envelope analysis to reliably determine moment magnitudes for a case study of small earthquakes from nSeismic 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 cr
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.National earthquake information center strategic plan, 2019–23
Executive SummaryDamaging earthquakes occur regularly around the world; since the turn of the 20th century, hundreds of earthquakes have caused significant loss of life and (or) millions of dollars or more in economic losses. While most of these did not directly affect the United States and its Territories, by studying worldwide seismicity we can better understand how to mitigate the effects of eaOn the portability of ML-MC as a depth discriminant for small seismic events recorded at local distances
In this paper we show that ML-MC is a viable and regionally portable depth discriminant and therefore may contribute in nuclear test ban treaty verification. A recent study found that the difference between local magnitude (ML) and coda duration magnitude (MC) discriminates shallow seismic events (mining blasts, mining-induced earthquakes, and shallow tectonic earthquakes) from deeper tectonic earGLASS3: 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 acFault slip associated with the 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake and aftershock sequence
The 2 September 2017 M 5.3 Sulphur Peak, Idaho, earthquake is one of the largest earthquakes in southern Idaho since the 1983 M 6.9 Borah Peak earthquake. It was followed by a vigorous aftershock sequence for nearly two weeks that included five events above M 4.5. The coseismic and early postseismic deformation was measured with both Interferometric Synthetic Aperture Radar and Global Positioning - Software
neic-machine-learning
NEIC Machine Learning Applications contains various seismic machine learning algorithms developed and used by by the United States Geological Survey, National Earthquake Information Center. These algorithms apply machine learning techniques to seismic processing problems such as seismic phase classification, source-receiver distance classification, and seismic wave arrival time repicking.neic-glass3
neic-glass3 is an open source and platform independent seismic event detection and association algorithm developed by the United States Geological Survey (USGS) National Earthquake Information Center (NEIC) and Caryl Erin Johnson, PhD, Introspective Systems LLC. This algorithm converts a time series of seismic phase arrival times, back azimuth estimates from array beams, and cross-correlated de