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.

The 2022 eruption at Mauna Loa, Hawai‘i, marked the first extrusive activity from the volcano after 38 years of quiescence. The eruption was preceded by several years of seismic unrest in the vicinity of the volcano’s summit. Characterizing the structure and dynamics of seismogenic features within Mauna Loa during this pre-eruptive interval may provide insights into how pre- and co-eruptive processes manifest seismically at the volcano. In particular, the extent to which seismicity may be used to forecast the location and timing of future eruptions is unclear. To address these questions, we construct a catalog of relocated seismicity on Mauna Loa spanning 2011-2023. Our earthquake locations image complex, sub-kilometer-scale seismogenic structures in the caldera and southwest rift zone. We additionally identify a set of streaks of seismicity in the volcano’s northwest flank that are radially oriented about the summit. Using a rate-and-state friction model for earthquake occurrences, we demonstrate that the seismicity rate in this region can be modeled as a function of the stressing history caused by magma accumulation beneath the summit. Finally, we observe a mid-2019 step change in the seismicity rate in the Ka‘oiki region that may have altered the stress state of the northeast rift zone in the three years before the eruption. Our observations provide a framework for interpreting future seismic unrest at Mauna Loa.

Coseismic rotations of principal stress axes can provide insights into the strength of the crust, but it is unclear how common this phenomenon is. We use a nearest-neighbor clustering algorithm to identify earthquake sequences in the global ISC-GEM catalog and the regional Southern California catalog. Using an inner-product-based pairwise measure of moment tensor similarity, we demonstrate that, in both catalogs, aftershocks are less similar to their respective mainshocks than foreshocks are. We interpret this effect, which we call moment tensor scattering, as evidence for widespread coseismic stress rotations. Moment tensor scattering is observable for a broad range of mainshock magnitudes in both catalogs. We further demonstrate that mainshock-aftershock similarity recovers logarithmically to pre-mainshock levels on decadal timescales. We conclude that moment tensor scattering is a generally observable feature of seismic sequences which may be useful in future work to discriminate between models of crustal strength.

The 4 July 2019 Mw 6.4 Earthquake and 5 July Mw 7.1 Earthquake struck near Ridgecrest, California. Caltech-Jet Propulsion Laboratory Advanced Rapid Imaging and Analysis (ARIA) project automatically processed synthetic aperture radar (SAR) images from Copernicus Sentinel-1A and -1B satellites operated by the European Space Agency, and products were delivered to the US and California Geological Surveys to aid field response. We integrate geodetic measurements for the three-dimensional vector field of coseismic surface deformation for thee two events and measure the early postseismic deformation, using SAR data from Sentinel-1 satellites and the Advanced Land Observation Satellite-2 (ALOS-2) satellite operated by Japanese Aerospace Exploration Agency. We combine less precise large-scale displacements from SAR images by pixel offset tracking or matching, including the along-track component, with the more precise SAR interferometry (InSAR) measurements in the radar line-of-sight direction and intermediate-precision along-track InSAR to estimate all three components of the surface displacement for the two events together. InSAR coherence and coherence change maps the surface disruptions due to fault ruptures reaching the surface. Large slip in the Mw 6.4 earthquake was on a NE-striking fault that intersects with the NW-striking fault that was the main rupture in the Mw 7.1 earthquake. The main fault bifurcates towards the southeast ending 3 km from the Garlock Fault. The Garlock fault had triggered slip of about 15 mm along a short section directly south of the main rupture. About 3 km NW of the Mw 7.1 epicenter, the surface fault separates into two strands that form a pull-apart with about 1 meter of down-drop. Further NW is a wide zone of complex deformation. We image postseismic deformation with InSAR data and point measurements from new GPS stations installed by the USGS. Initial analysis of the first InSAR measurements indicates the pull-apart started rebounding in the first weeks and the main fault had substantial afterslip close to the epicenter where the largest coseismic slip occurred. Slip on a NE-striking fault near the northern end of the main rupture in the first weeks, in the same zone as large and numerous aftershocks along NE-striking and NW-striking trends shows complex deformation.

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.