Publications by authors named "Srisharan Shreedharan"

Plate motion on shallow subduction megathrusts is accommodated by a spectrum of tectonic slip modes. However, the frictional properties and conditions that sustain these diverse slip behaviors remain enigmatic. Frictional healing is one such property, which describes the degree of fault restrengthening between earthquakes.

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Earthquakes occur in clusters or sequences that arise from complex triggering mechanisms, but direct measurement of the slow subsurface slip responsible for delayed triggering is rarely possible. We investigate the origins of complexity and its relationship to heterogeneity using an experimental fault with two dominant seismic asperities. The fault is composed of quartz powder, a material common to natural faults, sandwiched between 760 mm long polymer blocks that deform the way 10 meters of rock would behave.

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Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab-based studies have used acoustic emission data to predict time-to-failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved.

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Understanding the temporal evolution of foreshocks and their relation to earthquake nucleation is important for earthquake early warning systems, earthquake hazard assessment, and earthquake physics. Laboratory experiments on intact rock and rough fractures have demonstrated that the number and size of acoustic emission (AE) events increase and that the Gutenberg-Richter -value decreases prior to coseismic failure. However, for lab fault zones of finite width, where shear occurs within gouge, the physical processes that dictate temporal variations in frequency-magnitude (/) statistics of lab foreshocks are unclear.

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Tectonic faults fail through a spectrum of slip modes, ranging from slow aseismic creep to rapid slip during earthquakes. Understanding the seismic radiation emitted during these slip modes is key for advancing earthquake science and earthquake hazard assessment. In this work, we use laboratory friction experiments instrumented with ultrasonic sensors to document the seismic radiation properties of slow and fast laboratory earthquakes.

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Machine learning can predict the timing and magnitude of laboratory earthquakes using statistics of acoustic emissions. The evolution of acoustic energy is critical for lab earthquake prediction; however, the connections between acoustic energy and fault zone processes leading to failure are poorly understood. Here, we document in detail the temporal evolution of acoustic energy during the laboratory seismic cycle.

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Slow slip events (SSEs) accommodate a significant proportion of tectonic plate motion at subduction zones, yet little is known about the faults that actually host them. The shallow depth (<2 km) of well-documented SSEs at the Hikurangi subduction zone offshore New Zealand offers a unique opportunity to link geophysical imaging of the subduction zone with direct access to incoming material that represents the megathrust fault rocks hosting slow slip. Two recent International Ocean Discovery Program Expeditions sampled this incoming material before it is entrained immediately down-dip along the shallow plate interface.

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