Yeck, William
2021-11-18 15:56:42
Here is an exciting research opportunity at the USGS National Earthquake Information Center!
Mendenhall Postdoc 20-22: Leveraging machine learning for next-generation global earthquake monitoring
The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) detects, locates, and characterizes tens of thousands of earthquakes globally each year with a large emphasis placed on both the timeliness and accuracy of event characterization. The rapid analysis of fundamental earthquake properties, including location, depth, magnitude, and other source characteristics, is critical to the NEIC’s rapid estimation of the impact and seismotectonic context of an earthquake. Improving the speed and accuracy of earthquake detection, association, location, and magnitude estimation algorithms is fundamental to improving the NEIC’s capabilities.
Recent research has demonstrated that machine-learning-based event-processing methodologies generalize well, have unprecedented accuracy, are fast, and have the potential to revolutionize seismic monitoring systems. The application of machine learning to NEIC’s operational systems has enormous potential to improve global earthquake monitoring and advance seismological research. Recent machine learning research has focused largely on local and regional datasets, which generally have a smaller variety of source types and higher quality signals as compared to the global case. Therefore, it is necessary to further develop these techniques with a specific focus on global monitoring needs.
The focus of this Mendenhall Research Opportunity is to develop a scientific framework leveraging machine learning that will ultimately provide the rapid and more complete characterization of earthquake properties. This work can be targeted at improving the NEIC’s ability to detect and associate seismic signals, or at estimating other seismic characteristics (e.g., location, magnitude, mechanism, and source-type). While the monitoring component of this project has direct implications for the rapid characterization and understanding of large high-impact earthquakes and the improved detection of smaller events, the research should also improve our understanding of earthquake sources and seismotectonics.
Full project details and contact information:
https://www.usgs.gov/centers/mendenhall/20-22-leveraging-machine-learning-next-generation-global-earthquake-monitoring
Proposed Duty Station: Golden, Colorado;
Research Advisors: William Yeck, Paul Earle, David Shelly, Michelle Guy
Application deadline is January 6, 2022. Potential applicants are strongly encouraged to contact the Research Advisors early and to work with them to develop a suitable proposal.
Please see https://www.usgs.gov/centers/mendenhall for more information on the Mendenhall program and how to apply.
Mendenhall Postdoc 20-22: Leveraging machine learning for next-generation global earthquake monitoring
The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) detects, locates, and characterizes tens of thousands of earthquakes globally each year with a large emphasis placed on both the timeliness and accuracy of event characterization. The rapid analysis of fundamental earthquake properties, including location, depth, magnitude, and other source characteristics, is critical to the NEIC’s rapid estimation of the impact and seismotectonic context of an earthquake. Improving the speed and accuracy of earthquake detection, association, location, and magnitude estimation algorithms is fundamental to improving the NEIC’s capabilities.
Recent research has demonstrated that machine-learning-based event-processing methodologies generalize well, have unprecedented accuracy, are fast, and have the potential to revolutionize seismic monitoring systems. The application of machine learning to NEIC’s operational systems has enormous potential to improve global earthquake monitoring and advance seismological research. Recent machine learning research has focused largely on local and regional datasets, which generally have a smaller variety of source types and higher quality signals as compared to the global case. Therefore, it is necessary to further develop these techniques with a specific focus on global monitoring needs.
The focus of this Mendenhall Research Opportunity is to develop a scientific framework leveraging machine learning that will ultimately provide the rapid and more complete characterization of earthquake properties. This work can be targeted at improving the NEIC’s ability to detect and associate seismic signals, or at estimating other seismic characteristics (e.g., location, magnitude, mechanism, and source-type). While the monitoring component of this project has direct implications for the rapid characterization and understanding of large high-impact earthquakes and the improved detection of smaller events, the research should also improve our understanding of earthquake sources and seismotectonics.
Full project details and contact information:
https://www.usgs.gov/centers/mendenhall/20-22-leveraging-machine-learning-next-generation-global-earthquake-monitoring
Proposed Duty Station: Golden, Colorado;
Research Advisors: William Yeck, Paul Earle, David Shelly, Michelle Guy
Application deadline is January 6, 2022. Potential applicants are strongly encouraged to contact the Research Advisors early and to work with them to develop a suitable proposal.
Please see https://www.usgs.gov/centers/mendenhall for more information on the Mendenhall program and how to apply.
