Investigating mechanisms of aseismic and seismic FAult Slip Triggering as a proxy to measure fault strength (FAST)

• Applicant: Dr. Gian Maria Bocchini, Ph.D.
• Subject Area: Geophysics
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 539236286

Subduction forearcs contribute significantly to the seismic hazard of subduction zones because they host complex fault systems that can generate shallow, destructive earthquakes. Recent work combining force-balance modelling and seismic observations suggests that forearcs have low frictional strength, which also makes them ideally suited to study fault frictional strength and mechanisms that initiate slip. This work will use a novel combination of creepmeter, seismic, as well as structural and geological fault parameters to investigate the Atacama Fault System (AFS) in northern Chile and estimate relative fault frictional strength. We will use fault slip speed as a proxy of frictional strength, as frictional strength is not directly observable in situ. Located in the Chilean Coastal Cordillera, the AFS is segmented in several fault strands, some of them exhibiting surface ruptures, and instrumentally monitored remote triggering of slip transients. Upper crustal forearc seismicity is widely distributed but clearly concentrated in specific segments. We will integrate existing creepmeter and seismic observations to identify and quantify slow and fast slip interaction using enhanced signal detection, seismicity clustering and statistical properties and their changes over space and time (for a time span of at least 10 years). Taking into account also geological parameters we will interpret the observations as proxies to frictional strength. The objectives are to integrate observations of slip over a range of aseismic and seismic speeds to answer the question of whether a continuum or bimodal distribution of slip speeds exists on faults, and what geological and structural conditions govern slip speed. The newly synthesized data set will be used to build an enhanced catalog of aseismic and seismic slip events. The analysis will employ machine learning techniques for earthquake and creep event catalog enhancement to quantify and relate earthquake clustering and statistical properties to observed slow deformation transients. We will apply a suite of state-of-the-art approaches to quantify the source-parameters and statistical and clustering properties of small earthquakes.

• DFG Programme: Research Grants
• Co-Investigators: Professorin Rebecca Harrington, Ph.D.; Dr. Pia Victor