Closing Date: January 31, 2020
This Research Opportunity will be filled depending on the availability of funds. All application materials must be submitted through USAJobs by 11:59 pm, US Eastern Standard Time, on the closing date.
The 2018 Anchorage, Alaska earthquake (M 7.1) was the largest earthquake to significantly impact the Anchorage area since the great 1964 (M 9.2) earthquake. The 2018 earthquake triggered landslides, liquefaction, and ground cracking over a broad region and caused significant damage to homes, buildings, roads, rail lines, and other infrastructure in southcentral Alaska. The 2018 earthquake provides a unique opportunity to evaluate and improve existing ground-failure models and to develop new approaches for ground-failure prediction for Alaska and elsewhere.
Current USGS near-real-time ground-failure models (https://earthquake.usgs.gov/data/ground-failure) are coarse in resolution and global in scale. As a result, the models for the 2018 earthquake missed important details of the ground failure that occurred. They also were not developed for ground-failure types such as lateral spreading and cracking that are not easily categorized as either landslides or liquefaction; many of these ground failure types occurred in Anchorage. The ground-failure-model accuracy is controlled mostly by the global resolution of the input data sets, including topography, geology, and global proxies for hydrological and geotechnical information. Although the models missed some details in the Anchorage earthquake, they did capture general trends in liquefaction and landslide observations and communicated the overall impact of ground failure through alert levels that are displayed on the USGS earthquake event pages. Acquiring and incorporating higher resolution data at regional—rather than global—scales should result in significant model improvement, as could the incorporation of novel or hybrid modeling approaches.
The objective of this research opportunity is to improve USGS capabilities for modeling earthquake-triggered ground failure and related ongoing hazards in near-real-time. Research as part of this opportunity should involve the development of regional-scale ground-failure model(s) for southcentral Alaska for use in near-real-time ground-failure estimation that could serve as a template for similar regional model development in other regions. Ideally, these models would span multiple ground-failure processes: landsliding, liquefaction and associated lateral spreading, settlement, and extensional cracking, and would also assess the evolving landslide hazards after the earthquake. Models developed as part of this work could be physically or statistically based and should show improvement over existing global models.
Applicants may choose to pursue subprojects related to the primary objective that include: 1) developing geotechnical maps of the region that incorporate existing surficial geologic maps, geotechnical data, remote-sensing products, and statistical techniques; 2) developing models for estimating ongoing postseismic landslide hazard; 3) developing methodologies for updating model probabilities as incoming data from social media and imagery/remote sensing become available; and/or 4) developing a more complete inventory of the ground failure triggered by the 2018 Anchorage earthquake to use for testing models. Because of the difficult field conditions during the earthquake (snow cover and poor lighting), this might require the use of innovative remote-sensing techniques (e.g., INSAR, lidar, image correlation, etc.) combined with field observations. Methods developed for this research could be streamlined for rapid application to assess ground failure from future earthquakes.
Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.
Proposed Duty Station: Golden, Colorado
Areas of PhD: Geophysics, geology, civil engineering or related fields (candidates holding a Ph.D. in other disciplines, but with extensive knowledge and skills relevant to the Research Opportunity may be considered).
(This type of research is performed by those who have backgrounds for the occupations stated above. However, other titles may be applicable depending on the applicant's background, education, and research proposal. The final classification of the position will be made by the Human Resources specialist.)
Human Resources Office Contact: Audrey Tsujita, 916-278-9395, firstname.lastname@example.org