Closing Date: January 6, 2022
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 National Seismic Hazard Model (NSHM) is an important product of the U.S. Geological Survey and forms the basis for the seismic provisions of U.S. building codes, as well as other engineering-design requirements, risk assessments, and planning decisions (e.g., Petersen et al., 2014; Petersen et al., 2020). The NSHM currently uses ergodic ground-motion models (GMMs). These models are ergodic in the sense that they are empirically derived from data across many regions for a given seismotectonic region, such as for shallow crustal, stable, and subduction regions. There is an increasing need for seismic hazard analysis to incorporate regional variations in ground motion characteristics that arises from variations in geologic processes and structures.
GMMs are currently able to broadly characterize strong ground motions for varying tectonic zones, regions, and style of faulting. These empirical models are a fundamental component of the U.S. National Seismic Hazard Model. However, the ability of the empirical GMM approach to provide substantial innovations is diminishing. Radical improvements can only be overcome through fundamentally new approaches to ground motion modeling. There are several techniques that have attempted to fundamentally rethink ground motion modeling strategies; some efforts are focusing on moving towards nonergodic models (Kuehn et al., 2019; Dawood and Rodriguez-Marek, 2013; Landwehr et al., 2016; Abrahamson et al., 2019; Kuehn and Abrahamson, 2020) that are tailored for application to certain geographic regions. Other studies search for alternative methods to describe specific ground motion trends (e.g., near-source seismic directivity: Spudich et al., 2013). In all these examples, the lack of recorded data is a significant hindrance to adequately understanding new explanations of ground motion phenomena and determining the success of advancements via novel approaches.
This inability to describe ground motion behavior where limited recorded data exists has led researchers to supplement empirical models with simulated ground motions. In some cases, synthetic data focuses on a particular ground motion effect (e.g., hanging wall: Donahue and Abrahamson, 2014; basin effects: Day et al., 2008). In others, a more holistic approach is pursued, to better model the general features of ground motion from the source to receiver stations (Graves et al., 2011) and to help inform future GMMs (as in Wirth et al., 2018; Frankel et al., 2018; Moschetti et al., 2018, Withers et al., 2019a, Withers et al., 2019b).
A typical technique of choice to update and improve ground motion characterization is to use mixed-effects regression on empirical datasets, using guidelines from historical models to best characterize behavior to incorporate into improved GMMs. This method has the benefit of relying upon physical trends to guide the development of ground motion behavior and thus can perform well when extrapolated to areas with limited data. In recent years, there has also been significant progress using data science techniques to complement regression or inversion approaches where data is plentiful (Derras et al., 2014; Khosravikia et al., 2019; Withers et al., 2020). Depending on the setup of the problem, machine learning approaches can avoid implicit biases or assumptions that may be present in other methodologies that rely upon behavior observed in similar environments. In addition, machine learning approaches thrive in situations with large amounts of data, as is typical of synthetic simulations, that may have many combinations of fault ruptures, variation of material complexity, and source-receiver pairs to train upon.
Nonergodic ground motion research is an important component to consider moving forward for the USGS, that may require significant resources and will benefit from multiple avenues of organized dedication to best improve characterization of seismic hazard. There are other ongoing efforts within the USGS that plan towards addressing nonergodic ground motions within the 2023 update to the National Seismic Hazard Maps. These modifications will likely relate to aleatory adjustments that may cause significant changes to hazard curves. Here, we envision this proposed project to have a broader scope and more forward-looking, as part of a longer-term strategy to be implemented in future iterations of the hazard maps.
The Research Opportunity seeks a candidate to conduct research on earthquake ground-motions and probabilistic seismic hazard analyses that may be used to improve the National Seismic Hazard Model. Priority regions for study include Los Angeles, California; San Francisco Bay Area, California; Seattle, Washington; and Salt Lake City, Utah. Multiple approaches to advancing probabilistic seismic hazard analysis through non-ergodic ground-motion models may be employed, including through machine learning approaches such as artificial neural networks, in combination with 3-D ground-motion simulations, as well as modifying empirical ground-motion models with observations or simulated ground-motion data, physically-plausible extrapolations or other novel means. Example research directions that may be addressed by the Fellow are provided below, but other research efforts to improve ground motion characterizations in seismic hazard products are also encouraged.
- 3-D ground-motion simulations: How can 3-D simulations be used in probabilistic seismic hazard assessments? What features of ground-motion simulations are sufficiently characterized for application in probabilistic seismic hazard assessments? How can simulated ground-motions and their variability be validated?
Earthquake ground-motion simulations can constrain the ground-shaking from scenario earthquakes and are inherently non-ergodic; however, simulated values are highly dependent on the details of the earthquake rupture model and require various validation approaches. Research topics may also include the development of simulation-based probabilistic seismic hazard models (i.e., urban seismic hazard models). The Salt Lake City, Utah region is of particular interest for simulation-based seismic hazard modeling.
- Development and implementation of non-ergodic ground-motion models in PSHA: How can ground-motion recordings or ground-motion measurements from simulations be used to develop non-ergodic ground-motion models? How can non-ergodic ground-motion models be implemented in the National Seismic Hazard Model and what are the implications for hazard assessments?
Recordings from small- to moderate-magnitude earthquakes and from ground-motion simulations may further constrain components of empirically based ground-motion models. Potential research questions may address methods to regionalize ground-motion models to account for important geologic structures, such as the sedimentary basins that underlie Los Angeles, California; San Francisco Bay Area, California; Seattle, Washington; and Salt Lake City, Utah, and to implement modified GMMs into the probabilistic seismic hazard assessments of the NSHM.
Interested applicants are strongly encouraged to contact the Research Advisor(s) early in the application process to discuss project ideas.
Abrahamson, N.A., Kuehn, N. M., Walling, M., & Landwehr, N. (2019). Probabilistic Seismic Hazard Analysis in California Using Nonergodic Ground‐Motion Models. Bulletin of the Seismological Society of America; 109 (4), 1235–1249.
Dawood, H. M., & Rodriguez-Marek, A. (2013). A Method for Including Path Effects in Ground-Motion Prediction Equations: An Example Using the Mw 9.0 Tohoku Earthquake Aftershocks. Bulletin of the Seismological Society of America, 103(2B), 1360–1372.
Derras, B., Bard, P. Y., & Cotton, F. (2014). Towards fully data driven ground-motion prediction models for Europe. Bulletin of Earthquake Engineering, 12 (1), 495–516.
Day, S. M., Graves, R., Bielak, J., Dreger, D., Larsen, S., Olsen, K. B., Pitarka, A., Ramirez-Guzman, L. (2008). Model for Basin Effects on Long-Period Response Spectra in Southern California. Earthquake Spectra, 24(1), 257–277.
Donahue, J. L., & Abrahamson, N. A. (2014). Simulation-Based Hanging Wall Effects. Earthquake Spectra, 30(3), 1269–1284.
Frankel, A., Wirth, E., Marafi, N., Vidale, J., and Stephenson, W. (2018) Broadband synthetic seismograms for magnitude 9 earthquakes on the Cascadia megathrust based on 3D simulations and stochastic synthetics (Part 1)—Methodology and overall results. Bulletin of the Seismological Society of America, 108(5A), 2347-2369.
Graves, R., Jordan, T. H., Callaghan, S., Deelman., E., Field, F., Juve, G., Kesselman, C., Maechling, P., Mehta, G., Milner, K., Okaya, D., Small, P., & Vahi, K. (2011). CyberShake: A physics-based seismic hazard model for southern California. Pure and Applied Geophysics, 168(3-4), 367-381.
Khosravikia, F., Clayton, P., & Nagy, Z. (2019). Artificial Neural Network Based Framework for Developing Ground Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas. Seismological Research Letters, 90(2A), 604–613.
Kuehn, N. M., & Abrahamson, N. A. (2020). Spatial correlations of ground motion for nonergodic seismic hazard analysis. Earthquake Engineering & Structural Dynamics, 49(1), 4–23.
Kuehn, N. M., Abrahamson, N. A., & Walling, M. A. (2019). Incorporating Nonergodic Path Effects into the NGAWest2 Ground Motion Prediction Equations. Bulletin of the Seismological Society of America, 109(2), 575–585.
Landwehr, N., Kuehn, N. M., Scheffer, T., and Abrahamson, N. (2016). A nonergodic ground‐motion model for California with spatially varying coefficients. Bulletin of the Seismological Society of America, 106(6), 2574-2583.
Moschetti, M. P., Hartzell, S., Ramírez‐Guzmán, L., Frankel, A. D., Angster, S. J., & Stephenson, W. J. (2017). 3D ground‐motion simulations of Mw 7 earthquakes on the Salt Lake City segment of the Wasatch fault zone: Variability of long‐period (T≥ 1 s) ground-motions and sensitivity to kinematic rupture parameters. Bulletin of the Seismological Society of America, 107(4), 1704-1723.
Moschetti, M. P., Luco, N., Frankel, A. D., Petersen, M. D., Aagaard, B. T., Baltay, A. S., Blanpied, M. L., Boyd, O. S., Gold, R. D., Graves, R. W., Hartzell, S. H., Rezaeian, S., Stephenson, W. J., Wald, D. J., Williams, R. A., & Withers, K. B. (2018). Integrate UrbanScale Seismic Hazard Analyses with the U.S. National Seismic Hazard Model.
Petersen, M.D., Moschetti, M.P., Powers, P.M., Mueller, C.S., Haller, K.M., Frankel, A.D., Zeng, Yuehua, Rezaeian, Sanaz, Harmsen, S.C., Boyd, O.S., Field, Ned, Chen, Rui, Rukstales, K.S., Luco, Nico, Wheeler, R.L., Williams, R.A., and Olsen, A.H. (2014), Documentation for the 2014 update of the United States national seismic hazard maps: U.S. Geological Survey Open-File Report 2014–1091, 243 p.
Petersen, M. D., Shumway, A. M., Powers, P. M., Mueller, C. S., Moschetti, M. P., Frankel, A. D., Rezaeian, S., McNamara, D. E., Luco, N., Boyd, O. S., Rukstales, K. S., Jaiswal, K. S., Thompson, E. M., Hoover, S. M., Clayton, B. S., Field, E. H., & Zeng, Y. (2020). The 2018 update of the US National Seismic Hazard Model: Overview of model and implications. Earthquake Spectra, 36(1), 5–41.
Spudich, P., Bayless, J., Baker, J., Chiou, B., Rowshandel, B., Shahi, S., & Somerville, P. (2013). Final report of the Nga-West2 Directivity Working Group. Pacific Earthquake Engineering Research Center (PEER).
Wirth, E.A., Frankel, A.D., Marafi, N., Vidale, J.E., and Stephenson, W.J. (2018) Broadband synthetic seismograms for magnitude 9 earthquakes on the Cascadia megathrust based on 3-D simulations and stochastic synthetics (Part 2)—Rupture parameters and variability. Bulletin of the Seismological Society of America, 108(5A), 2370-2388.
Withers, K. B., Olsen, K. B., Day, S. M., & Shi, Z. (2019). Ground motion and intraevent variability from 3D deterministic broadband (0-7.5 Hz) simulations along a nonplanar strike-slip fault. Bulletin of the Seismological Society of America, 109(1), 229–250.
Withers, K. B., Olsen, K. B., Shi, Z., & Day, S. M. (2019). Validation of deterministic broadband ground motion and variability from dynamic rupture simulations of buried thrust earthquakes. Bulletin of the Seismological Society of America, 109(1), 212–228.
Withers, K. B., Moschetti, M. P., & Thompson, E. M. (2020). A machine learning approach to developing ground motion models from simulated ground motions. Geophysical Research Letters, 47, e2019GL086690.
Proposed Duty Station: Golden, Colorado
Areas of PhD: Geophysics, seismology, data science, 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: Megan Agy, 303-236-9584, firstname.lastname@example.org