14-27. Integrating Earthquake Ground Failure into Real-time Hazard and Loss Assessment
Landsliding and liquefaction are secondary, shaking-induced hazards that often lead to significant societal losses and significantly impede earthquake response. Assessing their threat in near-real time would be an enormous benefit to society. We seek a postdoctoral fellow to focus on research that will ultimately provide a more complete characterization of two earthquake-related hazards—landslides and liquefaction—in the immediate minutes and hours following a significant domestic or global earthquake. Limited success in this arena in the past is attributed, in part, to the paucity of observational constraints on the shaking input that is fed into statistical models to produce geospatial, probabilistic distributions of the occurrence of ground failure. To overcome these limitations, the candidate must 1) develop a state-of-art secondary shaking-induced hazard case history database, and 2) explore a combination of empirical (statistical) and mechanistic (physical) secondary hazard models in order to develop and implement algorithms needed for near-real-time hazard and loss forecasting.
Although previously developed methods of assessing these hazards exist (e.g., Idriss and Boulanger, 2008; Youd and Perkins, 1987; Knudsen et al., 2009; Jibson, 2007), their use in near-real-time forecasting has not yet proved fruitful in part because these models are not capable of providing robust geospatial, probabilistic estimates of the extent of the region affected. Currently, mechanistically or statistically derived models for landslides and liquefaction for use in rapid response systems have been proposed (e.g., Godt et al., 2009; Holzer et al., 2006; Knudsen and Bott, 2011; Nowicki et al., 2012; Rathje, 2011; Zhu et al., 2013), however, they have not been tested in the near-real time, post-earthquake response environment. To accomplish this goal, the USGS requires proven strategies that incorporate a combination of physical (mechanistic) and empirical (statistical) models capable of automatically ingesting USGS ShakeMaps (Wald et al., 2005) to produce spatial estimates of secondary effects at a variety of scales. Additional effort is also needed to move beyond generalized, global, proxy-based models to those that can be applied locally where high-quality geotechnical data are available. At the global scale, secondary hazard and loss modeling is needed to estimate societal impact for use in the USGS Prompt Assessment of Global Earthquakes for Response (PAGER; Wald et al., 2012) system. At local, site- or facility-specific scales, secondary hazard evaluations are also needed by the users of the USGS ShakeCast system (Lin and Wald, 2008; Wald et al., 2008), for example, by first responders to crises at critical-lifeline facilities. It is anticipated that these models would also be useful for future hazard (susceptibility) assessments in conjunction with ShakeMaps and Probabilistic Seismic Hazard Assessments (PSHA).
The ShakeMap and PAGER teams bring new shaking and landslides and liquefaction data sets to bear on the problem, yet we require expertise and focused attention on these challenges in order to meld our new data with existing or new models for producing probabilistic landslide and liquefaction distributions in near-real time. Proposals that explore and improve upon current methods used in assessing the extent, distribution, severity, and impact of landslides and liquefaction are encouraged. A strategy for implementing these models on a global scale, accounting for the scale-dependence of input variables (e.g., geospatial proxies versus in situ measurements) should be addressed.
Significant starting contributions by the ShakeMap and PAGER teams that will contribute to the success of this project include the very recent compilation of an Atlas of ShakeMaps for many liquefaction and landslide case history events, as well as corresponding high-quality landslide and liquefaction distributions; these provide both hazard input and some critical calibration data sets. However, formidable challenges exist in obtaining additional global data and more importantly, developing these data into predictive models that will provide both landsliding and liquefaction likelihoods, their severity, and their potential impacts. Recent work under the auspices of the PAGER and ShakeCast projects have facilitated the ability to accommodate such models as soon as they are developed and to communicate the severity of ground failure immediately following an earthquake.
The incumbent is also expected to interact with the ShakeMap, ShakeCast, and PAGER teams as well as with some of the technically savvy users of such improved hazard estimates, potentially the California Department of Transportation (Caltrans) and the US Agency for International Development's (USAID) Office of Foreign Disaster Assistance (OFDA). Additional interaction with and guidance of collaborating graduate students working on related problems is encouraged.
Godt, J., B. Şener, K. Verdin, D. J. Wald, P. S. Earle, E. Harp, and R. Jibson (2009). Rapid assessment of earthquake-induced landsliding, Proceedings of the World Landslide Forum, Tokyo, Japan, 4 pp.
Holzer, T. L., J. L. Blair, T. E. Noce, M. J. Bennett (2006). LiqueMap: A Real-Time Post-Earthquake Map Of Liquefaction Probability, Proc. 8th National Conference on Earthquake Engineering, San Francisco, Proceedings, Paper 89, 10 pp.
Idriss, I. M., and R. W. Boulanger (2008). Soil liquefaction during earthquakes. Monograph MNO-12, Earthquake Engineering Research Institute, Oakland, CA, 261 pp.
Jibson, R. W. (2007). Regression models for estimating coseismic landslide displacement. Engineering Geology, 91, 209-218.
Knudsen, K. L., J. D. Bott, M. O. Woods, and T. L. McGuire (2009). Development of a Liquefaction Hazard Screening Tool for Caltrans Bridge Sites, Proc. of the Tech. Lifeline Comm. on Earthq. Eng., Oakland, 12 pp.
Knudsen, K. L. and J. Bott (2011). Geologic and geomorphic evaluation of liquefaction case histories for rapid hazard mapping, 2011, Seismol. Res. Lett., 82, 334.
Lin, K., and D. J. Wald (2008). ShakeCast Manual, U.S. Geol. Survey Open File Rep. 2008-1158, 90 pp.
Nowicki, M. A., M. Hearne, E. M. Thompson, and D. J. Wald (2012). Logistic Regression for Seismically Induced Landslide Predictions: Using Uniform Hazard and Geophysical Layers as Predictor Variables, Abstract NH13A-1586, 2012 AGU Fall Meeting, San Francisco, CA, 3-7 Dec.
Rathje, E. (2011). Validation Of Methodologies Used to Map Earthquake-Induced Landslide Potential, USGS Award No. G09AP00131, Final Technical Report, 124 pp.
Wald, D. J., K. S. Jaiswal, K. D. Marano, E. So, and M. Hearne (2012). Impact-Based Earthquake Alerts with the U.S. Geological Survey’s PAGER System: What's Next?, Proc. 15th World Conf. on Eq. Eng., Lisbon, 11 pp.
Wald, D. J., K. Lin, K. Porter, and L. Turner (2008). ShakeCast: Automating and Improving the Use of ShakeMap for Post-Earthquake Decision-Making and Response, Earthquake Spectra, 24, 533-553.
Wald, D. J., B. C. Worden, K. Lin, and K. Pankow (2005). ShakeMap manual: technical manual, user's guide, and software guide, U. S. Geological Survey, Techniques and Methods, 12-A1, 132 pp.
Youd, T. L., and D. M. Perkins (1987). Mapping of liquefaction severity index, J. Geotech. Engrg. Div., 11, 1374–1392.
Zhu, J., D. Daley, L. G. Baise, E. M. Thompson, D. J. Wald, and K. L. Knudsen (2013). A Geospatial Liquefaction Model for Rapid Response and Loss Estimation. Earthquake Spectra, in review.
Proposed Duty Station: Golden, CO
Areas of Ph.D.: Geophysics, earthquake seismology, or geotechnical, geological, or civil engineering or related fields (candidates holding a Ph.D. in other disciplines, yet with extensive knowledge and skills relevant to the Research Opportunity may be considered).
Qualifications: Applicants must meet one of the following qualifications - Research Geologist, Research Geophysicist, Research Physicist, Research Mathematician, Research Civil Engineer.
(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 theposition will be made by the Human Resources specialist).
Research Advisor(s): David Wald, (303) 273-8441, email@example.com; Keith Knudsen, firstname.lastname@example.org, (650) 329-5154; Jonathan Godt, (303) 273-8626, email@example.com; Eric Thompson (Tufts U), (617) 627-3098, firstname.lastname@example.org; Randall Jibson, (303) 273-8577, email@example.com
Human Resources Office Contact: Robert Hosinski, (916) 278-9397, firstname.lastname@example.org
|Summary of Opportunities|