Seismic
Hazard
Hazard curves in PSHA models are used for quantifying the seismic hazards by providing the annual rates of exceedance, the reciprocal of the return period, at various groundmotion levels. Beginning in 2016, the U.S. Geological Survey (USGS) started to produce oneyear PSHA models for the central and eastern United States (CEUS) to account for the elevated seismicity in this region mainly due to the wastewater injection. These models have much shorter return periods (99.5 years) compared to the previous models (e.g. 2475 years for the 2014 model) and consider recent levels of induced seismicity in their construction. However, the nonstationarity in wastewater injection should lead to a change in the rate of induced seismicity, which makes any timeindependent forecast challenging. We assessed the 2016 seismichazard model by comparing the model’s forecast with the observed ground motions during one year period. Moreover, the rapid changes in induced seismicity make it difficult to provide reliable longterm forecasts. Although USGS hazard models influence 1.5 trillion dollars per year in building construction and insurance costs across the United States, these models are presented in terms of average ground motion probabilities, and no information about their confidence intervals is provided. Hence, in a separate study, I estimated the epistemic uncertainties in the 2016 and 2017 models. My results indicate that the ground motion prediction equations are the main source of the uncertainties in probabilistic hazard models.
Related Talks
Related Papers

Mousavi, S. M., and Beroza, G. C., (2018). Evaluating the 2016OneYear Seismic Hazard Model for the Central and Eastern United States Using Instrumental Ground Motion Data, Seismological Research Letters.
Hazard curves in probabilistic seismic hazard assessment (PSHA) models are used to quantify seismic hazard by providing the annual rates of exceedance, the reciprocal of the return period, as a function of ground‐motion levels. Beginning in 2016, the U.S. Geological Survey (USGS) started to produce one‐year PSHA models for the central and eastern United States (CEUS) to account for the elevated seismicity in this region due to the wastewater injection. These models have much shorter return periods (99.5 years) compared to previous models (e.g., 2475 years for the 2014 model) and consider recent levels of induced seismicity in their construction. The nonstationarity in the level and location of wastewater injection, however, should lead to a change in the rate of induced seismicity, which makes any time‐independent forecast challenging. We assess the 2016 seismic hazard model by comparing the model forecast with the observed ground motions during a one‐year period. For this test, we use more than 18,000 instrumental strong‐motion records observed during 2016 by 189 stations in the CEUS. We test the full model by considering the hazard curves in peak acceleration and spectral response acceleration for 1 and 5 Hz over the entire CEUS. Our results indicate that the observed hazard is generally consistent with that forecast by the model for peak ground acceleration (PGA) and 1 Hz (except at 5%g5%g) and 5 Hz spectral accelerations. Although we find that the hazard model is consistent with observed ground motions, this does not necessarily validate the theories and assumptions used in the model development. Our results show that for mapped hazard level (1% probability of exceedance in one year) and using only one year of observation, the power of a statistical test will not be very high unless the actual hazard is grossly larger (>6>6 times) or smaller (<40%<40%) than the forecast hazard. In other words, the data are still unlikely to reveal the inconsistency between the observed and forecasted hazards for one‐year models with high confidence, due to the low amount of data at CEUS.

Mousavi, S. M., Beroza, G. C., Hoover, S. M., (2018). Variabilities in Probabilistic Seismic Hazard Maps for Natural and Induced Seismicity in the Central and Eastern United States, The Leading Edge.
Probabilistic seismic hazard analysis (PSHA) characterizes groundmotion hazard from earthquakes. Typically, the time horizon of a PSHA forecast is long, but in response to induced seismicity related to hydrocarbon development, the USGS developed oneyear PSHA models. In this paper, we present a display of the variability in USGS hazard curves due to epistemic uncertainty in its informed submodel using a simple bootstrapping approach. We find that variability is highest in lowseismicity areas. On the other hand, areas of high seismic hazard, such as the New Madrid seismic zone or Oklahoma, exhibit relatively lower variability simply because of more available data and a better understanding of the seismicity. Comparing areas of high hazard, New Madrid, which has a history of large naturally occurring earthquakes, has lower forecast variability than Oklahoma, where the hazard is driven mainly by suspected induced earthquakes since 2009. Overall, the mean hazard obtained from bootstrapping is close to the published model, and variability increased in the 2017 oneyear model relative to the 2016 model. Comparing the relative variations caused by individual logictree branches, we find that the highest hazard variation (as measured by the 95% confidence interval of bootstrapping samples) in the final model is associated with different groundmotion models and maximum magnitudes used in the logic tree, while the variability due to the smoothing distance is minimal. It should be pointed out that this study is not looking at the uncertainty in the hazard in general, but only as it is represented in the USGS oneyear models.

Mousavi, S. M., Horton, S., Ogwari, P., Langston, C. A., (2017). Spatiotemporal Evolution of FrequencyMagnitude Distribution during Initiation of Induced Seismicity at GuyGreenbrier, Arkansas, Physics of the Earth and Planetary Interiors.
In this study, we carry out detailed analyses of the spatiotemporal variations of seismic bvalue during the onset of the potentially inducedearthquake sequence between Guy and Greenbrier, Arkansas, to investigate correlations with porepressure change. The range of bvalues suggests that the seismicity in the GuyGreenbrier area is mostly a result of the activation of preexisting faults. The spatial distribution of the bvalue correlates with modeled porepressure changes. In the northern segment of the fault, the bvalue increases with depth due to large porepressure changes and opening of new fractures in the deeper part, and stress relaxation in the shallower parts. Whereas, in the southern segment, the shallower part shows higher bvalues due to higher porepressure fluctuations but the deeper part in the crystalline basement has a low bvalue due to higher confining stress. The correlation between the temporal variation of the bvalue and hypocentral depth explains a previously observed temporal drop of the bvalue. This suggests that temporal variations should be interpreted along with spatial variations. An estimation of the seismogenic indices for the GuyGreenbrier fault is provided. Our analysis suggests that monitoring changes in bvalue and seismogenic indices during an injection period might be used to avoid the occurrence of significant events if injection volume is reduced at critical times.

Mousavi, S. M., Omidvar, B., Ghazban, F., Feyzi. R., (2011). Quantitative Risk Analysis for EarthquakeInduced LandslidesEmamzadeh Ali, Iran, Engineering Geology.
Earthquakeinduced landslides are among the most destructive slope movements, not only because of their size and magnitude but also because of the synergistic action with the earthquake the consequences are higher, due to the earthquake shaking and the landslide effect. Studies on landslides triggered by seismic activities are not as extensive as rainfallinduced landslides. In this study, a quantitative risk analysis approach was used to calculate the risk for people exposed to the threat of earthquakeinduced landslide in Emamzadeh Ali, north of Iran. Emamzadeh Ali was selected as it has already experienced some landslides and is located in a tectonically active region. A numerical model was used in this study to carry out the stability analysis of the slope, under both static and dynamic conditions. A quantitative probabilistic approach was developed for determining the frequency and magnitude of the process. In addition, the values of vulnerability for each building were calculated. The highest risks (individual risk to life and societal risk to life) are in the area of the stone mine on the head of the slope and close to the toppling activities northeast of the study area.