Earthquake ruptures are complex physical processes that may vary with the structure and tectonics of the region in which they occur. Characterizing the factors controlling this variability would provide fundamental constraints on the physics of earthquakes and faults. We investigate this by determining finite source properties from second moments of the stress glut for a global dataset of large strike-slip earthquakes. Our approach uses a Bayesian inverse formulation with teleseismic body and surface waves, which yields a low-dimensional probabilistic description of rupture properties including spatial extent, directivity, and duration. This technique is useful for comparing events because it makes only minor geometric constraints, avoids bias due to rupture velocity parameterization, and yields a full ensemble of possible solutions given the uncertainties of the data. We apply this framework to all great strike-slip earthquakes of the past three decades, and we use the resultant second moments to compare source quantities like directivity ratio, rectilinearity, stress drop, and depth extent. We find that most strike-slip earthquakes have a large component of unilateral directivity, and many of these earthquakes show a mixture of unilateral and bilateral behavior. We also notice that oceanic intraplate earthquakes usually rupture a much larger width of the seismogenic zone than other strike-slip earthquakes, suggesting these earthquakes consistently breach the expected thermal boundary for oceanic ruptures. We also use these second moments to resolve nodal plane ambiguity for the large oceanic intraplate earthquakes and find that the rupture orientation is usually unaligned with encompassing fossil fracture zones.

Fault zone structures at many scales largely dictate earthquake ruptures and are controlled by the geologic setting and slip history. Characterizations of these structures at diverse scales inform better understandings of earthquake hazards and earthquake phenomenology. However, characterizing fault zones at sub-kilometer scales has historically been challenging, and these challenges are exacerbated in urban areas, where locating and characterizing faults is critical for hazard assessment. We present a new procedure for characterizing fault zones at sub-kilometer scales using distributed acoustic sensing (DAS). This technique involves the backprojection of the DAS-measured scattered wavefield generated by natural earthquakes. This framework provides a measure of the strength of scattering along a DAS array and thus constrains the positions and properties of local scatterers. The high spatial sampling of DAS arrays makes possible the resolution of these scatterers at the scale of tens of meters over distances of kilometers. We test this methodology using a DAS array in Ridgecrest, CA which recorded much of the 2019 Mw7.1 Ridgecrest earthquake aftershock sequence. We show that peaks in scattering along the DAS array are spatially correlated with mapped faults in the region and that the strength of scattering is frequency-dependent. We present a model of these scatterers as shallow, low-velocity zones that is consistent with how we may expect faults to perturb the local velocity structure. We show that the fault zone geometry can be constrained by comparing our observations with synthetic tests.

We present a fully Bayesian inverse scheme to determine second moments of the stress glut using teleseismic earthquake seismograms. The second moments form a low-dimensional, physically-motivated representation of the rupture process that captures its spatial extent, source duration, and directivity effects. We determine an ensemble of second moment solutions by employing Hamiltonian Monte Carlo and automatic differentiation to efficiently approximate the posterior. This method explicitly constrains the parameter space to be symmetric positive definite, ensuring the derived source properties have physically meaningful values. The framework accounts for the autocorrelation structure of the errors and incorporates hyperpriors on the uncertainty. We validate this methodology using a synthetic test and subsequently apply it to the 2019 Mw7.1 Ridgecrest earthquake using teleseismic data. The distributions of second moments determined for this event provide probabilistic descriptions of low-dimensional rupture characteristics that are generally consistent with results from previous studies. The success of this case study suggests that probabilistic and comparable finite source properties may be discerned for large global events regardless of the quality and coverage of local instrumentation.

We present a fully Bayesian inverse scheme to determine second moments of the stress glut using teleseismic earthquake seismograms. The second moments form a low-dimensional, physically-motivated representation of the rupture process that captures its spatial extent, source duration, and directivity effects. We determine an ensemble of second moment solutions by employing Hamiltonian Monte Carlo and automatic differentiation to efficiently approximate the posterior. Our method explicitly constrains the parameter space to be symmetric positive definite, ensuring the derived source properties have physically meaningful values. The framework accounts for the autocorrelation structure of the errors and incorporates hyperpriors on the uncertainty. We validate the methodology using a synthetic test and subsequently apply it to the 2020 Mw 7.7 Caribbean earthquake. The second moments determined for this event indicate the rupture was nearly unilateral and relatively compact along-strike. The solutions from this inverse framework can resolve ambiguities between slip distributions with minimal a priori assumptions on the rupture process.

Fault zone complexities contain important information about earthquake physics. High-resolution fault zone imaging requires high-quality data from dense arrays and new seismic imaging techniques that can utilize large portions of recorded waveforms. Recently, the emerging Distributed Acoustic Sensing (DAS) technique has enabled near-surface imaging by utilizing existing telecommunication infrastructure and anthropogenic noise sources. With dense sensors at several meters’ spacing, the unaliased wavefield can provide unprecedented details for fault zones. In this work, we use a DAS array converted from a 10-km underground fiber-optic cable across Ridgecrest City, California. We report clear spurious arrivals and coda waves in ambient noise cross-correlations caused by surface-to-surface wave scattering. We use these scattering-related waves to locate and characterize potential faults. The mapped fault locations are generally consistent with those in the USGS Quaternary Fault database of the United States but are more precise. We also use waveform modeling to infer that a 35-m wide, 90-m deep fault with 30% velocity reduction can best fit the observed scattered coda waves for one of the identified fault zones. These findings demonstrate the potential of DAS for passive imaging of fine-scale faults in an urban environment.