SCEC Award Number 15160 View PDF
Proposal Category Individual Proposal (Special Fault Study Area)
Proposal Title Fault Dynamics and Tsunamis in the Ventura Basin
Investigator(s)
Name Organization
David Oglesby University of California, Riverside
Other Participants Eric Geist, Researcher, USGS Menlo Park; Kenny Ryan, Graduate Student, UCR
SCEC Priorities 6b, 4a, 3e SCEC Groups FARM, Seismology, CS
Report Due Date 03/15/2016 Date Report Submitted 04/25/2015
Project Abstract
 The Ventura basin in southern California includes coastal dip-slip faults that can likely produce earthquakes of magnitude 7 or greater, and significant local tsunamis. We have constructed a 3D dynamic rupture model of an earthquake on the Pitas Point and Lower Red Mountain faults to model low- frequency ground motion and the resulting tsunami, with a goal of elucidating the seismic and tsunami hazard in this area. Our model results in an average stress drop of 6 megapascals, an average fault slip of 7.6 meters, and a moment magnitude of 7.7, consistent with regional paleoseismic data. Our corre- sponding tsunami modeling uses final seafloor displacement from the rupture models as initial conditions to compute local propagation and inundation, resulting in large peak tsunami amplitudes northward and eastward due to site and path effects. Modeled inundation in the Ventura area is significantly greater than that indicated by state of California’s current reference inundation line. A modeled earthquake that is otherwise identical, but does not propagate all the way to the free surface, produces similar inundation, implying that the somewhat unconstrained surface penetration of the fault system may not matter tremendously for tsunami generation.
Intellectual Merit Through 3D dynamic rupture modeling, we find that a plausible but severe earthquake on the combined Pitas Point/Lower Red Mountain fault system could produce an earthquake of up to M 7.7. Our tsunami modeling implies that such an earthquake in turn could produce inundation in Ventura and Oxnard that in places exceeds the tsunami inundation previously estimated by the State of California. Whether the earthquake is blind or surface-rupturing does not change the resulting inundation pattern very significantly. These results emphasize the risk that coastal California may experience from local tsunamigenic earthquakes, in contrast to the well-known risk of tsunamis from Japan and Alaska. The probability of such an event in a given time frame is low compared to smaller earthquake events. Nonetheless, it is crucial to investigate the possible effects from such rare but plausible earthquake and tsunami scenarios so that a full hazard assessment can be made. While the details of an actual future event are likely to be more complex, our model likely captures many important aspects for the purposes of tsunami generation.
Broader Impacts Results from these modeling efforts can help reveal potential regions of high tsunami hazard in Southern California. Additionally, further development of this methodology in tsunamigenic regions worldwide can contribute to hazard assessments. This project has supported the training of a graduate student in both dynamic rupture and tsunami modeling—a combination that is not well represented but could be of cru- cial use in the future to SCEC. This project has also fostered long-term collaborations between earth- quake and tsunami scientists.
Exemplary Figure Figure 3. Map (red box shown in figure 1) of localized peak tsunami amplitude, in meters (around Ventura, CA), resulting from slip on the Pitas Point and Lower Red Mountain fault system. The solid black line indicates the coastline. The sold red line is the statewide tsunami inundation map coordinated by the California Emergency Management Agency. Letters indicate example locations (approximate): SB = Santa Barbara; VH = Ventura Harbor; SCRM = Santa Clara River Mouth; MSB = McGrath State Beach; CIHE = Channel Islands Harbor Entrance. Inset shows the map boundary in black. Note that inundation from the model is significantly greater in many places than the statewide estimate. From Ryan et al. [2015].
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