Emanuele Bozzi | Searcharxiv

arXiv2025

Near real-time channel selection for Distributed Acoustic Sensing technology

Distributed Acoustic Sensing (DAS) technology is advancing seismic monitoring by providing dense observations near earthquake sources. However, the resulting data volumes often limit real-time processing capability, with most seismological applications focusing on retrospective analysis of seismic sequences. To address this challenge, we introduce ORION, a fast and versatile selector of high-quality DAS channels that efficiently reduces the amount of data to analyze. The method first adopts spatial clustering to identify cable segments with similar geometrical attributes (e.g, azimuth), and then performs channel selection within each section using waveform attributes (e.g., signal-to-noise ratio); this approach enables spatial sub-sampling while preserving azimuthal coverage. We demonstrate the flexibility of the selector across several cable geometries. Finally, we analyze a seismic sequence using ORION-selected channels and compare the source locations with those from a more conventional uniform distribution of channels along the cable, showing improvements in hypocenter accuracy.

physics.geo-ph↗

arXiv2025

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↗