Numerous studies have reported that surface hydrological loading can seasonally modulate seismicity rates at crustal depths. For example, substantial winter snow accumulation occurs across the Japanese Islands, and these snowy regions appear to have seasonally modulated the occurrence of previous large inland earthquakes. Therefore, it is important to investigate the impact of seasonal stress changes on crustal seismicity to deepen our understanding of earthquake generation. Here we constrain seasonal changes in the surface load across northeastern Japan using Global Navigation Satellite System surface displacements and evaluate the potential relationship between temporal trends in inland seismicity and estimated seasonal stress changes. The spatial distribution of the seasonal surface load is consistent with snow depth along the Sea of Japan. The inland seismicity beneath northeastern Japan is modestly modulated by the seasonal stress changes that are induced by the annual snow load. However, this seasonal response is weaker than that in other regions. This weak modulation may be due to the small surface-load-induced stress perturbation relative to the long-term-averaged stressing rate and/or the limited presence of crustal fluids to trigger seismicity in Japan.

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 $97$ percent of the local seismicity catalogue, with less than $4$ percent 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. Synthetic tests 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-hour-long continuous waveforms and identify new seismic events with extremely low signal-to-noise ratios that are not listed in existing catalogs. (241words)

Short-term slow slip events (S-SSEs) intensively occur at the transition zone along the Nankai subduction zone, southwest Japan. Because crustal deformation due to a single S-SSE is small, the source fault is often represented using a planar uniform single-fault slip model, resulting to little constraint on the spatial heterogeneity in amounts of fault slip. To comprehensively investigate the detailed cumulative spatial distribution of S-SSEs in the entire Nankai subduction zone, we adopted a stacking approach of Global Navigation Satellite System (GNSS) data using low-frequency earthquakes as reference. We extracted cumulative displacements due to a series of S-SSEs from 2004 to 2009; coherent signals in almost opposite direction of plate subduction were obtained. The inverted slip indicated significant slip patches laterally elongated along the transition zone at ~30–35 km depth, and small patches in the shallow portions at ~15–20 km and ~10–15 km depth in eastern Shikoku and in Tokai as well as western Shikoku, respectively. The shallow patches in Shikoku were located on the downdip edge of the coseismic slip area of the 1946 Nankai earthquake, while the Tokai small slip was located on the shallower side of the anticipated source area of a large earthquake. Large slip patches of S-SSEs were complementary to the spatially dense low-frequency earthquake areas; in major S-SSE areas, the number of low-frequency earthquakes is small. This spatial dependence of fault slip style even within the transition zone provides new insights regarding the generation mechanism of slow earthquakes.