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Earthquake size distribution is characterized by the “ b -value” of the power law decay, which exhibits spatiotemporal variations. These variations are sometimes evident before a large earthquake. Understanding spatiotemporal variations is key to developing a model for large-earthquake generation. Previous studies have shown that changes in the b -value are caused by the tectonic stress regime. Furthermore, lab experiments have demonstrated the b -value dependency of acoustic emissions on the criticality of the failure condition. However, the factors controlling the b -values during natural seismic activity are unclear. In this study, changes in the b -value in small earthquake sequences are investigated, focusing on failure criticality. Based on our high-precision focal mechanism dataset, we conclude that the b -value decreases as it nears a critical failure condition, providing a physical explanation for the reduction in b-value before a major earthquake. Our findings elucidate fault failure models, facilitating improvement in earthquake alerts and disaster mitigation. The b -value in earthquake size distribution shows spatiotemporal variations, being key to consider large-earthquake genesis. By high-precision focal mechanism data, we show a physical reason for the reduction in b -value before a major earthquake.

P-wave first-motion polarity is the most useful information in determining the focal mechanisms of earthquakes, particularly for smaller earthquakes. Algorithms have been developed to automatically determine P-wave first-motion polarity, but the performance level of the conventional algorithms remains lower than that of human experts. In this study, we develop a model of the convolutional neural networks (CNNs) to determine the P-wave first-motion polarity of observed seismic waveforms under the condition that P-wave arrival times determined by human experts are known in advance. In training and testing the CNN model, we use about 130 thousand 250 Hz and about 40 thousand 100 Hz waveform data observed in the San-in and the northern Kinki regions, western Japan, where three to four times larger number of waveform data were obtained in the former region than in the latter. First, we train the CNN models using 250 Hz and 100 Hz waveform data, respectively, from both regions. The accuracies of the CNN models are 97.9% for the 250 Hz data and 95.4% for the 100 Hz data. Next, to examine the regional dependence, we divide the waveform data sets according to the observation region, and then we train new CNN models with the data from one region and test them using the data from the other region. We find that the accuracy is generally high (\\[ \\] 95%) and the regional dependence is within about 2%. This suggests that there is almost no need to retrain the CNN model by regions. We also find that the accuracy is significantly lower when the number of training data is less than 10 thousand, and that the performance of the CNN models is a few percentage points higher when using 250 Hz data compared to 100 Hz data. Distribution maps, on which polarities determined by human experts and the CNN models are plotted, suggest that the performance of the CNN models is better than that of human experts.

Seismic phase picking is a fundamental task in seismology that is crucial for event detection and earthquake cataloging; however, manual analysis is impractical given the scale of modern seismic networks. We present SegPhase, a novel seismic arrival time picking model designed to efficiently process large-scale seismic data recorded by dense seismic networks in Japan. In contrast to conventional convolution-based models, SegPhase employs a hierarchical vision transformer structure that utilizes multi-head self-attention to dynamically focus on important waveform features, such as P- and S-wave onsets, noise, and coda waves. Compared to PhaseNet, the most widely used deep learning model, SegPhase improved arrival time match rates by ~ 11% and detected ~ 15% more events in continuous waveform tests, particularly enhancing the detection of small-magnitude events. Benchmark evaluations demonstrated that SegPhase achieved high classification performance in identifying P- and S-waves. We also examined the threshold of the output probability values when applying SegPhase to continuous waveforms for which the optimal threshold was unknown. By lowering the threshold to 0.1, we observed an increase in the number of detected events without noticeable changes in the hypocenter location error and observed–calculated discrepancies. This was achieved by more effectively utilizing high-probability picks, which further improved phase association. Based on these results, we recommend a threshold of 0.1 to enhance event detection while maintaining accurate arrival times. Our findings demonstrate that SegPhase enables robust arrival picking across diverse datasets and supports high-resolution seismic monitoring. Graphical Abstract

We derived a three-dimensional S-wave velocity model for the San-in area of southwest Japan to examine heterogeneous structures such as tectonic faults. Many earthquakes occur in this area, but much of the activity has been relatively recent, so the fault distribution has yet to be fully clarified. Here, we used continuous ambient noise data from a dense seismic network, deployed from November 2009 to extract Rayleigh and Love wave dispersion data between station pairs, and then applied a direct surface wave inversion to the phase velocities of each station pair to determine a three-dimensional S-wave velocity model. In the resulting model, faults and a previously unrecognized tectonic boundary appeared as low-velocity anomalies or velocity boundaries, and the velocity anomalies were also associated with many past earthquake hypocenters. These results contribute to our understanding of heterogeneous structures caused by recent tectonic motion and of possible future tectonic activity, such as intraplate earthquakes. Surface wave tomography using ambient noise recorded in dense seismic networks could also be applied in other parts of the world to reveal new heterogeneous geological structures (i.e., unrevealed tectonic faults) and could contribute to disaster mitigation.

Research interest in the Kinki region, southwestern Japan, has been aroused by the frequent occurrence of microearthquake activity that do not always coincide with documented active fault locations. Previous studies in the Kinki region focused mainly on deep, large-scale structures and could not efficiently resolve fine-scale (~ 10 km) shallow crustal structures. Hence, characterization of the upper crustal structure of this region at an improved spatial resolution is required. From the cross-correlation of the vertical components of the ambient seismic noise data recorded by a densely distributed seismic array, we estimated Rayleigh wave phase velocities using a frequency domain method. Then, we applied a direct surface wave tomographic method for the measured phase velocity dispersion data to obtain a 3D S-wave velocity model of the Kinki region. The estimated velocity model reveals a NE–SW trending low-velocity structure coinciding with the Niigata–Kobe Tectonic Zone (NKTZ) and the active Biwako-seigan Fault Zone (BSFZ). Also, we identified fine-scale low-velocity structures coinciding with known active faults on the eastern side of the NKTZ, as well as sets of low-velocity structures across the Tanba region. Furthermore, sedimentary basins manifest as low-velocity zones extending to depths ranging from ~ 1.5 to 2 km, correlating with those reported in previous studies. Our results therefore contribute towards fundamental understanding of earthquake faulting as well as tectonic boundary and will be useful for hazard assessment and disaster mitigation.

To investigate the dominant deformational patterns and stress conditions in the upper crustal structure of the Kinki region, southern-central Japan, we constructed a high-resolution 3D azimuthal anisotropy model to a depth of ~ 11 km. We used 6-month-long ambient noise data recorded by the densely distributed permanent and temporary stations. From this dataset, cross-correlations were retrieved. We then obtained a 3D isotropic velocity model by inverting Rayleigh wave dispersion data, followed by a direct joint inversion for both 3D azimuthal anisotropy and additional isotropic velocity perturbation. The resolved 3D azimuthal anisotropy reveals significant contrasts of anisotropy across the Kinki region. The predominant fast axes observed in the northwestern Kinki region align with the direction of the maximum horizontal compressional stress and the principal strain rate axes, suggesting that the observed anisotropy is mainly stress-induced. In the southern part of the study area, furthermore, the predominant fast axes trend NE–SW and near E–W, also indicating the presence of stress-induced anisotropy. On the depth profile of the anisotropy, we found depth-dependent variation of azimuthal anisotropy. There exists a significant consistency between the anisotropy observed beneath 3 km depth and the dense distribution of earthquake hypocenters. This interrelationship between anisotropy and seismicity demonstrates that the observed anisotropy could be linked to local crustal stress or fractures relevant to earthquake ruptures. Our 3D anisotropy model therefore contributes towards understanding the locations and features of the seismicity region.

In order to clarify the origin of aftershocks, we precisely analyze the hypocenters and focal mechanisms of the aftershocks following the 2000 Western Tottori Earthquake, which occurred in the western part of Japan, using data from dense seismic observations. We investigate whether aftershocks occur on the mainshock fault plane on which coseismic slip occurred or they represent the rupture of fractures surrounding the mainshock fault plane. Based on the hypocenter distribution of the aftershocks, the subsurface fault structure of the mainshock is estimated using principal component analysis. As a result, we can obtain the detail fault structure composed of 8 best-fit planes. We demonstrate that the aftershocks around the mainshock fault are distributed within zones of 1.0–1.5 km in thicknesses, and their focal mechanisms are significantly diverse. This result suggests that most of the aftershocks represent the rupture of fractures surrounding the mainshock fault rather than the rerupture of the mainshock fault. The aftershocks have a much wider zone compared with the exhumed fault zone in field observations, suggesting that many aftershocks occur outside the fault damage zone. We find that most aftershocks except in and around the large-slip region are well explained by coseismic stress changes. These results suggest that the thickness of the aftershock distribution may be controlled by the stress changes caused by the heterogeneous slip distribution during the mainshock. The aftershock is also distributed within a much wider zone than the hypocenter distribution observed in swarm activity in the geothermal region, which is thought to be caused by the migration of hydrothermal fluid. This result implies a difference in generation processes: Stress changes due to the mainshock contribute primarily to the occurrence of aftershocks, whereas earthquake swarms in the geothermal region are caused by fluid migration within the localized zone. Graphical abstract .

Young faults display unique complexity associated with their evolution, but how this relates to earthquake occurrence is unclear. Unravelling the fine-scale complexity in these systems could lead to a greater understanding of ongoing strain localization in young fault zones. Here we present high-spatial-resolution images of seismic sources and structural properties along a young fault zone that hosted the Tottori earthquake (Mw 6.8) in southwest Japan in 2000, based on data from a hyperdense network of ~1,000 seismic stations. Our precise micro-earthquake catalog reveals conjugate faulting over multiple length scales. These conjugate faults are well developed in zones of low seismic velocity. A vertically dipping seismic cluster of about 200 m length occurs within a width of about 10 m. Earthquake migrations in this cluster have a speed of about 30 m per day, which suggests that fluid diffusion plays a role. We suggest that fine structural complexities influence the pattern of seismicity in a developing fault system. Conjugate faulting and seismic velocity structure reveal the fine-scale complexity of fault growth on a young system that may be facilitated by diffusion of crustal fluids, according to a micro-earthquake catalogue from a hyperdense seismic network.

Inelastic deformation due to seismic activity is an important signal that reflects fault evolution. In particular, aftershock sequences indicate the evolution of damage in a medium that has experienced a large earthquake. Herein, we discuss the inelastic strain rate surrounding the fault that produced the M 7.3 Western Tottori earthquake in 2000 using long-term aftershock analysis. To obtain high-resolution focal mechanisms 18 years after the earthquake occurrence, we conducted dense seismic observations in the focal area. The inelastic strain rate estimated from the aftershock seismic moment tensor data showed spatial variations within a range of 10−7–10−11 per year, 18 years after the main shock. By comparing the inelastic strain rates from immediately after the earthquake and 18 years later, we detected the increase in the spatial variations in the inelastic strain rate; the variations are as small as 102 (= 10−5/10−7) for the early stage but as large as 104 (= 10−7/10−11) for the later period. In addition, the decay of the rate during these two periods varied spatially from spatial bin to bin. Certain bins in the northern segment of the earthquake fault, southern edge of the fault, and surrounding the location of the preceding swarm activity to the M 7.3 event showed slower decay rates than the inverse of the lapse time since the occurrence of the M 7.3 earthquake. We modeled this decay rate change as the relaxation response of a power-law fluid to an elastic strain input from the large earthquake. Most parts of the fault can be explained by this model. However, the areas with low decay rates suggest the presence of a dragging mechanism, such as aseismic slip, at or around these locations.