Faulting Process from Top to Bottom Along the San Andreas Fault in the San Juan Bautista Region

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Understanding of the spatially and temporally varying deformation field around fault zones is critically important for understanding active tectonics, fault interaction and the occurrence of large earthquakes. This project is focused on the San Juan Bautista area of California, where the complexity of the fault structure and a variety of time-varying slow-slip (aseismic) phenomena make a perfect natural laboratory to study active faulting processes from the top of the Earth's surface to great depth. The site is also attractive because of the availability of a rich historic data set and the recent deployment of EarthScope instrumentation, including continuous GPS stations, strainmeters and seismometers.The project team is analyzing geodetic and seismic data in this region and integrating them to interpret the underlying architecture and mechanics of the faulting process. Creepmeter, strainmeter, GPS, and InSAR data constrain models of the four-dimensional distribution of fault slip in the upper crust. The geodetic data are complemented by information on aseismic slip deep below the surface inferred from repeating earthquakes. Furthermore, episodes of non-volcanic tremor hint at deformation transients at sub-seismogenic depths. Because fault slip is tied to the mechanical properties of the fault zone rocks and adjoining crustal blocks, the team is also evaluating changes in crustal properties through time using receiver-function analysis.These diverse data sets provide valuable information on the interactions between earthquakes and aseismic slip that occurs in various depth ranges in the fault zone. Results from this work provide insight into the role of aseismic fault slip transients in earthquake occurrence and clustering. Improved understanding of aseismic slip transients, their relation to regional strain anomalies, and improved models of the earthquake cycle may help to improve earthquake forecasts and intermediate to longer-term predictions.