arXiv subjects

Shiro Hirano

Publications and source records attributed to Shiro Hirano.

3 recordsLinked to original sources

arXiv2026

Co-seismic surface fault displacement captured in videos: image tracking across three earthquakes

Surface displacements, including co-seismic fault slip, have occasionally been captured by closed-circuit television (CCTV) cameras. Such video recordings may provide information unavailable from seismometers located away from the fault. We analyzed videos of surface displacement from three earthquakes---the 2018 Hualien earthquake, the 2025 Mandalay earthquake, and the 2026 Kumamoto earthquake---using a unified methodology. We removed the effects of strong ground motion by aligning each frame so that reference regions in the image remained fixed. This analysis enables the temporal evolution and, in particular, the duration of surface displacement to be estimated. Although the three earthquakes ranged from $M_\mathrm{w}$6.4 to $M_\mathrm{w}$7.7, the tracked motion lasted approximately 2 s in all three cases; in the Hualien case, the motion included compression of the ground rather than fault slip alone. Comparison with field surveys indicates that the maximum velocity was on the order of 1 m/s in each case. The 2026 Kumamoto earthquake, however, exhibited a more complex velocity history than the other two events. The three earthquakes involved different mechanisms of surface fault displacement, suggesting that these differences may also influence the complexity of the displacement time histories.

physics.geo-ph↗

arXiv2025

Supershear-subshear-supershear rupture sequence during the 2025 Mandalay Earthquake in Myanmar

We investigated the rupture dynamics of the 2025 $M_w$7.7 Mandalay, Myanmar earthquake, using a video recording of surface rupture, strong motion recordings, waveform simulation, and satellite imagery. Our assessment, based on the S-wave observation in the video and rupture arrival time at a seismic station 246 km south of the hypocenter, suggests that rupture decelerated to subshear speeds ($\sim$3 km/s) from initial supershear propagation ($\sim$6 km/s) before reaching the camera location. This deceleration is also supported by comparison between the fault-normal acceleration patterns seen in the video and that simulated by kinematic rupture modeling. Additionally, satellite imagery indicated a local minimum in slip (2$-$3 m) approximately 40$-$60 km south of the epicenter, suggesting a region of reduced stress drop that likely caused the temporary deceleration. Beyond this point, the rupture appears to have re-established supershear propagation.

physics.geo-ph↗

arXiv2023

Efficient similar waveform search using short binary codes obtained through a deep hashing technique

A similar waveform search plays a crucial role in seismology for detecting seismic events, such as small earthquakes and low-frequency events. However, the high computational costs associated with waveform cross-correlation calculations represent bottlenecks during the analysis of long, continuous records obtained from numerous stations. In this study, we developed a deep-learning network to obtain 64-bit hash codes containing information on seismic waveforms. Using this network, we performed a similar waveform search for ~35 million moving windows developed for the 30 min waveforms recorded continuously at 10 MHz sampling rates using 16 acoustic emission transducers during a laboratory hydraulic fracturing experiment. The sampling points of each channel corresponded to those of the 5.8-year records obtained from typical seismic observations at 100 Hz sampling rates. Of the 35 million windows, we searched for windows with small average Hamming distances among the hash codes of 16 channel waveforms against template hash codes of 6057 events that were catalogued using conventional autoprocessing techniques. The calculation of average Hamming distances is 1000 times faster than that of the corresponding network correlation. This hashing-based template matching enabled the detection of 23,462 additional events. We also demonstrated the feasibility of the hashing-based autocorrelation analysis, where similar event pairs were extracted without templates, by calculating the average Hamming distances for all possible pairs of the ~35 million windows. This calculation required only 15.5 h under 120 thread parallelisation. This deep hashing approach significantly reduced the required memory compared with locality-sensitive hashing approaches based on random permutations, enabling similar waveform searching on a large-scale dataset.

physics.geo-ph↗