Eric M Thompson
Eric Thompson is a research geophysicist with the USGS in Golden. He joined the USGS in 2015 where he participates in research, development, and operations of many earthquake hazard products, including near-real-time earthquake hazard products.
Professional Experience
5/2015-Present: Research Geophysicist, USGS, Golden, Colorado.
4/2013-4/2015: Adjunct Professor, Geological Sciences, San Diego State University.
1/2014-3/2015: Lecturer, Department of Civil and Environmental Engineering, University of California, Los Angeles.
9/2010-9/2013: Research Assistant Professor, Civil and Environmental Engineering, Tufts University.
3/2009-8/2010: Postdoctoral Researcher/Lecturer, Civil and Environmental Engineering, Tufts University.
Education and Certifications
2009 Ph.D., Tufts University, Civil and Environmental Engineering.
2002 B.S., University of California at Santa Cruz, Earth Science.
Honors and Awards
2022: Superior Service Award for activities in the planning and development of ground motion processing software named gmprocess.
2019: FEMA Certificate of Appreciation for outstanding contributions in support of national level earthquake exercise.
2018: Western States Seismic Policy Council (WSSPC) Award for Excellence Use of Technology for developing the ShakeMap Scenario Suite.
Science and Products
Filter Total Items: 25
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A global hybrid VS30 map with a topographic slope–based default and regional map insets A global hybrid VS30 map with a topographic slope–based default and regional map insets
Time-averaged shear wave velocity over the upper 30 m of the earth’s surface (VS30) is a key parameter for estimating ground motion amplification as both a predictive and a diagnostic tool for earthquake hazards. The first-order approximation of VS30 is commonly obtained through a topographic slope–based or terrain proxy due to the widely available nature of digital elevation models...
Authors
David C. Heath, David J. Wald, C. Bruce Worden, Eric M. Thompson, Gregory M. Smoczyk
Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence
We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence (Rekoske et al., 2019, 2020) to examine repeatable source‐, path‐, and site‐specific ground motions. A mixed‐effects analysis is used to partition total residuals relative to the Boore et al. (2014; hereafter, BSSA14) ground‐motion model. We calculate the Arias intensity stress drop for the earthquakes and...
Authors
Grace Alexandra Parker, Annemarie S. Baltay Sundstrom, John Rekoske, Eric M. Thompson
Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake
We developed an initial inventory of ground failure features from the November 30, 2018, magnitude 7.1 Anchorage earthquake. This inventory of 153 features is from ground-based observations soon after the earthquake (December 5–10) that include the presence or absence of liquefaction, landslides, and individual crack traces of lateral spreads and incipient landslides. This is not a...
Authors
Alex R. Grant, Randall W. Jibson, Robert C. Witter, Kate E. Allstadt, Eric M. Thompson, Adrian M. Bender
Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence
The 2019 Ridgecrest, California, earthquake sequence, including an Mw 6.4 event on 4 July and an Mw 7.1 approximately 34 hr later, was recorded by 15 instruments within 55 km nearest‐fault distance. To characterize and explore near‐field ground motions from the Mw 6.4 foreshock and Mw 7.1 mainshock, we augment these records with available macroseismic information, including conventional
Authors
Susan E. Hough, Eric M. Thompson, Grace A. Parker, Robert Graves, Kenneth W. Hudnut, Jason Patton, Timothy E. Dawson, Tyler C. Ladinsky, Michael Oskin, Krittanon Sirorattanakul, Kelly Blake, Annemarie S. Baltay Sundstrom, Elizabeth S. Cochran
A machine learning approach to developing ground motion models from simulated ground motions A machine learning approach to developing ground motion models from simulated ground motions
We use a machine learning approach to build a ground motion model (GMM) from a synthetic database of ground motions extracted from the Southern California CyberShake study. An artificial neural network is used to find the optimal weights that best fit the target data (without overfitting), with input parameters chosen to match that of state-of-the-art GMMs. We validate our synthetic...
Authors
Kyle Withers, Morgan P. Moschetti, Eric M. Thompson
The 2019 Ridgecrest, California, earthquake sequence ground motions: Processed records and derived intensity metrics The 2019 Ridgecrest, California, earthquake sequence ground motions: Processed records and derived intensity metrics
Following the 2019 Ridgecrest, California, earthquake sequence, we compiled ground‐motion records from multiple data centers and processed these records using newly developed ground‐motion processing software that performs quality assurance checks, performs standard time series processing steps, and computes a wide range of ground‐motion metrics. In addition, we compute station and...
Authors
John Rekoske, Eric M. Thompson, Morgan P. Moschetti, Mike Hearne, Brad T. Aagaard, Grace Alexandra Parker
Non-USGS Publications**
Thompson, E.M., Baise, L.G., Tanaka, Y. and Kayen, R.E., 2012. A taxonomy of site response complexity. Soil Dynamics and Earthquake Engineering, 41, pp.32-43.
Boore, D.M. and Thompson, E.M., 2012. Empirical improvements for estimating earthquake response spectra with random‐vibration theory. Bulletin of the Seismological Society of America, 102(2), pp.761-772.
Kaklamanos, J., Bradley, B.A., Thompson, E.M. and Baise, L.G., 2013. Critical parameters affecting bias and variability in site‐response analyses using KiK‐net downhole array data. Bulletin of the Seismological Society of America, 103(3), pp.1733-1749.
Kaklamanos, J., Baise, L.G., Thompson, E.M. and Dorfmann, L., 2015. Comparison of 1D linear, equivalent-linear, and nonlinear site response models at six KiK-net validation sites. Soil Dynamics and Earthquake Engineering, 69, pp.207-219.
Moss, R.E., Thompson, E.M., Kieffer, D.S., Tiwari, B., Hashash, Y.M., Acharya, I., Adhikari, B.R., Asimaki, D., Clahan, K.B., Collins, B.D. and Dahal, S., 2015. Geotechnical effects of the 2015 magnitude 7.8 Gorkha, Nepal, earthquake and aftershocks. Seismological Research Letters, 86(6), pp.1514-1523.
Thompson, E.M., Baise, L.G. and Vogel, R.M., 2007. A global index earthquake approach to probabilistic assessment of extremes. Journal of Geophysical Research: Solid Earth, 112(B6).
Zhu, J., Daley, D., Baise, L.G., Thompson, E.M., Wald, D.J. and Knudsen, K.L., 2015. A geospatial liquefaction model for rapid response and loss estimation. Earthquake Spectra, 31(3), pp.1813-1837.
Thompson, E.M., Hewlett, J.B., Baise, L.G. and Vogel, R.M., 2011. The Gumbel hypothesis test for left censored observations using regional earthquake records as an example. Natural Hazards and Earth System Sciences, 11(1), pp.115-126.
Baise, L.G., Lenz, J.A. and Thompson, E.M., 2008. Discussion of “Mapping liquefaction potential considering spatial correlations of CPT measurements” by Chia-Nan Liu and Chien-Hsun Chen. Journal of geotechnical and geoenvironmental engineering, 134(2), pp.262-263.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Filter Total Items: 25
No Result Found
Filter Total Items: 96
A global hybrid VS30 map with a topographic slope–based default and regional map insets A global hybrid VS30 map with a topographic slope–based default and regional map insets
Time-averaged shear wave velocity over the upper 30 m of the earth’s surface (VS30) is a key parameter for estimating ground motion amplification as both a predictive and a diagnostic tool for earthquake hazards. The first-order approximation of VS30 is commonly obtained through a topographic slope–based or terrain proxy due to the widely available nature of digital elevation models...
Authors
David C. Heath, David J. Wald, C. Bruce Worden, Eric M. Thompson, Gregory M. Smoczyk
Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence
We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence (Rekoske et al., 2019, 2020) to examine repeatable source‐, path‐, and site‐specific ground motions. A mixed‐effects analysis is used to partition total residuals relative to the Boore et al. (2014; hereafter, BSSA14) ground‐motion model. We calculate the Arias intensity stress drop for the earthquakes and...
Authors
Grace Alexandra Parker, Annemarie S. Baltay Sundstrom, John Rekoske, Eric M. Thompson
Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake
We developed an initial inventory of ground failure features from the November 30, 2018, magnitude 7.1 Anchorage earthquake. This inventory of 153 features is from ground-based observations soon after the earthquake (December 5–10) that include the presence or absence of liquefaction, landslides, and individual crack traces of lateral spreads and incipient landslides. This is not a...
Authors
Alex R. Grant, Randall W. Jibson, Robert C. Witter, Kate E. Allstadt, Eric M. Thompson, Adrian M. Bender
Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence Near-field ground motions from the July, 2019 Ridgecrest, California, earthquake sequence
The 2019 Ridgecrest, California, earthquake sequence, including an Mw 6.4 event on 4 July and an Mw 7.1 approximately 34 hr later, was recorded by 15 instruments within 55 km nearest‐fault distance. To characterize and explore near‐field ground motions from the Mw 6.4 foreshock and Mw 7.1 mainshock, we augment these records with available macroseismic information, including conventional
Authors
Susan E. Hough, Eric M. Thompson, Grace A. Parker, Robert Graves, Kenneth W. Hudnut, Jason Patton, Timothy E. Dawson, Tyler C. Ladinsky, Michael Oskin, Krittanon Sirorattanakul, Kelly Blake, Annemarie S. Baltay Sundstrom, Elizabeth S. Cochran
A machine learning approach to developing ground motion models from simulated ground motions A machine learning approach to developing ground motion models from simulated ground motions
We use a machine learning approach to build a ground motion model (GMM) from a synthetic database of ground motions extracted from the Southern California CyberShake study. An artificial neural network is used to find the optimal weights that best fit the target data (without overfitting), with input parameters chosen to match that of state-of-the-art GMMs. We validate our synthetic...
Authors
Kyle Withers, Morgan P. Moschetti, Eric M. Thompson
The 2019 Ridgecrest, California, earthquake sequence ground motions: Processed records and derived intensity metrics The 2019 Ridgecrest, California, earthquake sequence ground motions: Processed records and derived intensity metrics
Following the 2019 Ridgecrest, California, earthquake sequence, we compiled ground‐motion records from multiple data centers and processed these records using newly developed ground‐motion processing software that performs quality assurance checks, performs standard time series processing steps, and computes a wide range of ground‐motion metrics. In addition, we compute station and...
Authors
John Rekoske, Eric M. Thompson, Morgan P. Moschetti, Mike Hearne, Brad T. Aagaard, Grace Alexandra Parker
Non-USGS Publications**
Thompson, E.M., Baise, L.G., Tanaka, Y. and Kayen, R.E., 2012. A taxonomy of site response complexity. Soil Dynamics and Earthquake Engineering, 41, pp.32-43.
Boore, D.M. and Thompson, E.M., 2012. Empirical improvements for estimating earthquake response spectra with random‐vibration theory. Bulletin of the Seismological Society of America, 102(2), pp.761-772.
Kaklamanos, J., Bradley, B.A., Thompson, E.M. and Baise, L.G., 2013. Critical parameters affecting bias and variability in site‐response analyses using KiK‐net downhole array data. Bulletin of the Seismological Society of America, 103(3), pp.1733-1749.
Kaklamanos, J., Baise, L.G., Thompson, E.M. and Dorfmann, L., 2015. Comparison of 1D linear, equivalent-linear, and nonlinear site response models at six KiK-net validation sites. Soil Dynamics and Earthquake Engineering, 69, pp.207-219.
Moss, R.E., Thompson, E.M., Kieffer, D.S., Tiwari, B., Hashash, Y.M., Acharya, I., Adhikari, B.R., Asimaki, D., Clahan, K.B., Collins, B.D. and Dahal, S., 2015. Geotechnical effects of the 2015 magnitude 7.8 Gorkha, Nepal, earthquake and aftershocks. Seismological Research Letters, 86(6), pp.1514-1523.
Thompson, E.M., Baise, L.G. and Vogel, R.M., 2007. A global index earthquake approach to probabilistic assessment of extremes. Journal of Geophysical Research: Solid Earth, 112(B6).
Zhu, J., Daley, D., Baise, L.G., Thompson, E.M., Wald, D.J. and Knudsen, K.L., 2015. A geospatial liquefaction model for rapid response and loss estimation. Earthquake Spectra, 31(3), pp.1813-1837.
Thompson, E.M., Hewlett, J.B., Baise, L.G. and Vogel, R.M., 2011. The Gumbel hypothesis test for left censored observations using regional earthquake records as an example. Natural Hazards and Earth System Sciences, 11(1), pp.115-126.
Baise, L.G., Lenz, J.A. and Thompson, E.M., 2008. Discussion of “Mapping liquefaction potential considering spatial correlations of CPT measurements” by Chia-Nan Liu and Chien-Hsun Chen. Journal of geotechnical and geoenvironmental engineering, 134(2), pp.262-263.
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.