Stress relaxation arrested the mainshock rupture of the 2016 Central Tottori earthquake

地震の破壊はなぜ止まるのか? --2016年鳥取県中部地震の断層サイズを決めたもの--. 京都大学プレスリリース. 2021-08-12.After a large earthquake, many small earthquakes, called aftershocks, ensue. Additional large earthquakes typically do not occur, despite the fact that the large static stress near the edges of the fault is expected to trigger further large earthquakes at these locations. Here we analyse ~10, 000 highly accurate focal mechanism solutions of aftershocks of the 2016 Mw 6.2 Central Tottori earthquake in Japan. We determine the location of the horizontal edges of the mainshock fault relative to the aftershock hypocentres, with an accuracy of approximately 200 m. We find that aftershocks rarely occur near the horizontal edges and extensions of the fault. We propose that the mainshock rupture was arrested within areas characterised by substantial stress relaxation prior to the main earthquake. This stress relaxation along fault edges could explain why mainshocks are rarely followed by further large earthquakes

Prestate of Stress and Fault Behavior During the 2016 Kumamoto Earthquake (M7.3)

Fault behavior during an earthquake is controlled by the state of stress on the fault. Complex coseismic fault slip on large earthquake faults has recently been observed by dense seismic networks, which complicates strong motion evaluations for potential faults. Here we show the three‐dimensional prestress field related to the 2016 Kumamoto earthquake. The estimated stress field reveals a spatially variable state of stress that forced the fault to slip in a direction predicted by the “Wallace and Bott Hypothesis.” The stress field also exposes the pre‐condition of pore fluid pressure on the fault. Large coseismic slip occurred in the low‐pressure part of the fault. However, areas with highly pressured fluid also showed large displacement, indicating that the seismic moment of the earthquake was magnified by fluid pressure. These prerupture data could contribute to improved seismic hazard evaluations

Prestate of Stress and Fault Behavior During the 2016 Kumamoto Earthquake (M7.3)

Fault behavior during an earthquake is controlled by the state of stress on the fault. Complex coseismic fault slip on large earthquake faults has recently been observed by dense seismic networks, which complicates strong motion evaluations for potential faults. Here we show the three‐dimensional prestress field related to the 2016 Kumamoto earthquake. The estimated stress field reveals a spatially variable state of stress that forced the fault to slip in a direction predicted by the “Wallace and Bott Hypothesis.” The stress field also exposes the pre‐condition of pore fluid pressure on the fault. Large coseismic slip occurred in the low‐pressure part of the fault. However, areas with highly pressured fluid also showed large displacement, indicating that the seismic moment of the earthquake was magnified by fluid pressure. These prerupture data could contribute to improved seismic hazard evaluations

P-wave first-motion polarity determination of waveform data in western Japan using deep learning

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

A physical understanding of large intraplate earthquakes, Earth Planets Space, 54, this issue

We examined several previously proposed models of the process by which large intraplate earthquakes are generated, and found the most plausible to be the 'localized shear model,' in which seismogenic faults have downward extensions in the lower crust and the localized shear deformation on the downward extensions accumulate stress on the seismogenic faults. Further, localized shear deformation may accelerate before a large intraplate earthquake occurs. This model can explain various phenomena related to crustal deformation in the Japanese Islands: preslips, seismicity, distribution of active faults, and stress and strain state. It will play an important role in the forecasting and prediction of large intraplate earthquakes