Strong ground motion can induce dynamic strains large enough for the Earth's subsurface to respond nonlinearly and to cause permanent, or plastic, damage. The 2011M_w9.0 Tohoku‐Oki earthquake, Japan, generated exceptional and well‐recorded ground motions in the greater Tokyo area. We use continuous records from 234 stations of the dense MeSO‐net seismic network to monitor the temporal evolution of the material properties of the shallow subsurface (upper ∼100 m). We apply the single‐station cross‐correlation method to reconstruct the near‐surface reflectivity response through time. We find that the strong ground motions from the mainshock caused large perturbations in the near‐surface structure, with significant drops in seismic velocities up to 11%. For most sites, we observe a logarithmic and complete recovery of the seismic wave speed, suggesting a relaxation process that can be explained by a viscoelastic rheology. Some sites exhibit an instantaneous and permanent change, which suggests a plastic rheology. Finally, dense seismic measurements allow for statistical inference between seismic velocity drops, recovery time scales, and permanent perturbations and ground motion strength and site conditions. This study highlights the potential for seismic interferometry to assess near‐surface rheology with dense seismic arrays.

We present a deep‐learning approach for earthquake detection using waveforms from a seismic array consisting of multiple seismographs. Although automated, deep‐learning earthquake detection techniques have recently been developed at the single‐station level, they have potential difficulty in reducing false detections owing to the presence of local noise inherent to each station. Here, we propose a deep‐learning‐based approach to efficiently analyze the waveforms observed by a seismic array, whereby we employ convolutional neural networks in conjunction with graph partitioning to group the waveforms from seismic stations within the array. We then apply the proposed method to waveform data recorded by a dense, local seismic array in the regional seismograph network around the Tokyo metropolitan area, Japan. Our method detects more than 97% of the local seismicity catalog, with less than 4% false positive rate, based on an optimal threshold value of the output earthquake probability of 0.61. A comparison with conventional deep‐learning‐based detectors demonstrates that our method yields fewer false detections for a given true earthquake detection rate. Furthermore, the current method exhibits the robustness to poor‐quality data and/or data that are missing at several stations within the array. Numerical experiments using subsampled data demonstrate that the present method has the potential to detect earthquakes even when half of the normally available seismic data are missing. We apply the proposed method to analyze 1‐h‐long continuous waveforms and identify new seismic events with extremely low signal‐to‐noise ratios that are not listed in existing catalogs. We also show the potential portability of the proposed method by applying it to seismic array data not used for the training.

Intense swarm‐like seismicity associated with shallow normal faulting was induced in Ibaraki and Fukushima prefectures, Japan, following the 2011 Tohoku‐Oki earthquake. This seismicity shows a systematic spatiotemporal evolution, but little is known of the heterogeneity in crustal structure in this region, or its influence on the evolution of the seismicity. Here, we elucidate a high‐resolution model of crustal structure in this region and determine precise hypocenter locations. Hypocenters in Ibaraki Prefecture reveal a planar earthquake alignment dipping SW at ~45°, whereas those in Fukushima Prefecture show a more complex distribution, consisting of conjugate sets of aligned small earthquakes. On the north of the hypocenter of the largest earthquake in the sequence (the M7.0 Iwaki earthquake), we imaged a high‐velocity body at shallow depths that lacks aftershock seismicity. Based on fault source models, the large‐slip region of the Iwaki earthquake is situated along a zone that roughly coincides with this high‐velocity body. We delineated a separate low‐velocity anomaly directly beneath the hypocenter of the Iwaki earthquake, indicating crustal fluids in this region. We hypothesize that strong crust underwent structural failure due to the infiltration of crustal fluids into the seismogenic zone from deeper levels, causing the Iwaki earthquake.

We present a complete history of earthquake detection probabilities and detection completeness levels for the Japanese seismic network from 1923 to, and including, 2014 in high resolution over space and time. In our study, we employ the probability‐based magnitude of completeness method developed by Schorlemmer and Woessner (2008). Using this method, for each station we derive detection capabilities over time from empirical data only, namely earthquake information, phase data, station information, and network‐specific attenuation relations. From the entire phase‐data history, we estimate the operational times of each station and then synthesize detection‐probability maps for specific magnitudes or completeness maps. We compute these maps for every month in the period 1923–1987 and for approximately every 10 days in the period 1988–2014. We document the improvement of the Japanese seismic network from its early stage in 1923, when the network reaches completeness levels for shallow seismicity of magnitude 4 only in the wider Tokyo area, to the recent years exhibiting completeness levels of approximately magnitude 1 in all of Japan. We provide detailed completeness estimates for the eight major periods of the Japanese seismic network, defined by the types of seismometers used and the location technique employed, and show the development of completeness levels for selected regions. Finally, we discuss the effects of the 2011 Tohoku‐Oki earthquake on completeness levels in Japan.

Title: The Earthquake in Ōsaka-Fu Hokubu on 18 June 2018 and its Ensuing Disaster | Keywords: northern Ōsaka-fu earthquake, JMA seismic intensity 6 lower, urban earthquake disaster | Author: Naoshi Hirata and Reo Kimura