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Bogdan Enescu

Publications and source records attributed to Bogdan Enescu.

2 recordsLinked to original sources

Time-Resolved Surface-Fault Displacement During the 2026 Kumamoto Earthquake From Near-Fault Video

Video recordings can reveal how rapidly fault displacement develops at the Earth's surface, but camera motion and recording artifacts can obscure the ground signal. We analyze a secondary copy of security-camera footage that captured surface displacement during the 28 July 2026 Kumamoto earthquake; the native recording was unavailable and could not be recovered. Two independent image-tracking methods were used. Optical flow follows identifiable image features, whereas normalized cross-correlation (NCC) template matching follows fixed image patches by their similarity. Both measured target-region motion relative to spatially separated reference regions while correcting motion shared by the recording. Image displacement was calibrated to the magnitude of the field-measured offset vector: 1.05 m right-lateral and 0.90 m east-side-up, or 1.383 m in total. We characterize the principal rise by the time required for displacement to progress from 20% to 80% of the selected final level. Across prespecified endpoint choices, optical flow gives 0.866-0.901 s and NCC gives 0.910-0.928 s. These durations correspond to average rates of 0.920-0.958 and 0.895-0.912 m/s, respectively. Checks using independently published tracking windows reproduce the displacement scale, although exact timing is more sensitive in spatially restricted tests. The record also shows an early apparent peak and decline followed by renewed apparent horizontal displacement. Because that later motion may represent either continued ground displacement or the geometry of the secondary recording, neither the permanent endpoint nor physical overshoot can be determined. The most robust conclusion is that the central part of the surface displacement developed in approximately 0.9 s at an average rate near 0.9 m/s.

physics.geo-ph

A computer vision-based approach to clean seismic catalogues

In recent years, seismic data analysis advancements combined with an increasing number of dense seismic networks deployed worldwide, have contributed to the creation of massive seismic catalogs, significantly lowering their magnitude of completeness. However, large automated catalogs are typically released without systematic quality control, and may contain spurious detections, mislocations, or inconsistent magnitudes. In challenging scenarios, such as microseismic monitoring applications, where weak and closely spaced events often overlap in time, pick-based detection and location approaches often fail to reliably associate phases. This leads to missed detections or degraded location accuracy producing seismic catalogues polluted with false or mislocated events. To address this limitation, we present a computer vision based workflow that integrates waveform based seismic location methods with deep learning image classification to discriminate real seismic events from noise directly from coherence matrices. These matrices, computed via waveform stacking, exhibit distinct patterns for real events (single, focused maxima) versus noise (blurred, incoherent patterns) hence the problem of cleaning seismic catalogues can be solved as a binary image classification problem. In addition, the robustness of waveform based location methods allows to obtain an increased resolution in the location of seismic events. We validate our workflow using the publicly available COSEISMIQ dataset.

physics.geo-ph