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Abstract Large earthquakes rupture faults over hundreds of kilometers within minutes. Finite‐fault models image these processes and provide observational constraints for understanding earthquake physics. However, finite‐fault inversions are subject to non‐uniqueness and uncertainties. The diverse range of published models for the well‐recorded 2011 9.0 Tohoku‐Oki earthquake illustrates this challenge, and its rupture process remains under debate. Here, we comprehensively compare 32 published finite‐fault models of the Tohoku‐Oki earthquake. We aim to identify the most coherent slip features of the Tohoku‐Oki earthquake from these slip models and develop a new method for quantitatively analyzing their variations. We find that the models correlate poorly at 1‐km subfault size, irrespective of the data type. In contrast, model agreement improves significantly with increasing subfault sizes, consistently showing that the largest slip occurs up‐dip of the hypocenter near the trench. We use the set of models to test the sensitivity of available teleseismic, regional seismic, and geodetic observations. For the large Tohoku‐Oki earthquake, we find that the analyzed finite‐fault models are less sensitive to slip features smaller than 64 km. When we use the models to compute synthetic seafloor deformation, we observe strong variations in the synthetics, suggesting their sensitivity to small‐scale slip features. Our newly developed approach offers a quantitative framework to identify common features in distinct finite‐fault slip models and to analyze their robustness using regional and global geophysical observations for megathrust earthquakes. Our results indicate that dense offshore instrumentation is critical for resolving the rupture complexities of megathrust earthquakes. 

Both large and small earthquakes rupture in complex ways. However, microearthquakes are often simplified as point sources and their rupture properties are challenging to resolve. We leverage seismic wavefields recorded by a dense array in Oklahoma to image microearthquake rupture processes. We construct machine-learning enabled catalogs and identify four spatially disconnected seismic clusters. These clusters likely delineate near-vertical strike-slip faults. We develop a new approach to use the maximum absolute SH-wave amplitude distributions (S-wave wavefields) to compare microearthquake rupture processes. We focus on one cluster with earthquakes located beneath the dense array and have a local magnitude range of -1.3 to 2.3. The S-wave wavefields of single earthquakes are generally coherent but differ slightly between the low-frequency (<12 Hz) and high-frequency (>12 Hz) bands. The S-wave wavefields are coherent between different earthquakes at low frequencies with average correlation coefficients greater than 0.95. However, the wavefield coherence decreases with increasing frequency for different earthquakes. This reduced coherence is likely due to the rupture differences among individual earthquakes. Our results suggest that earthquake slip of the microearthquakes dominates the radiated S-wave wavefields at higher frequencies. Our method suggests a new direction in resolving small earthquake source attributes using dense seismic arrays without assuming a rupture model. 

Summary Surface waves are critical in detecting and locating seismic sources that do not produce much high-frequency radiation. For such sources, typical approaches using body waves for detecting and locating earthquakes are less effective. Slow earthquakes and exotic seismic sources often have this seismic radiation characteristic, and array analyses of surface waves recorded on global and regional seismic networks have proven effective in recognizing such sources. Most approaches have relied on Rayleigh waves, whereas Love waves have rarely been used. Here we develop a new approach using multi-scale arrays to detect and locate seismic sources with both Love and Rayleigh surface waves. The method first forms three-station subarrays and then uses three-component records of the stations to independently estimate three sets of surface wave propagation directions and centroid arrival times. The subarray estimates are then assembled to locate seismic sources and their origin times. We find that using multiple, disconnected global networks improves location accuracy and that using both types of surface waves can enhance detection sensitivity and robustness.