Semblance Distance Analysis for Channel Quality Assessment in Distributed Acoustic Sensing
Abstract
Distributed Acoustic Sensing (DAS) enables dense, kilometer-scale measurements for seismic and environmental monitoring, but its performance is limited by spatially heterogeneous noise and fiber to ground coupling. Identifying high-quality sensing channels remains a challenge, particularly in nonstationary and complex noise environments where conventional signal to noise ratio (SNR) or correlation based approaches are insufficient. In this work, we introduce a statistical framework for DAS channel quality assessment based on a semblance distance metric, which quantifies the deviation of individual channels from a locally coherent reference signal. Unlike traditional pairwise similarity measures, the proposed method formulates channel selection as a distance estimation problem in a coherence enhanced signal space, enabling identification of incoherent noise and coupling induced distortions without requiring prior knowledge of installation conditions or noise statistics. The method is validated using multiple independent datasets, including recordings of the 2023 Turkish-Syrian earthquakes, a controlled seismic source experiment, and long-term continuous measurements. Across all scenarios, the approach identifies regions of low noise and strong coupling, with results that correlate with independent SNR and root mean square error (RMSE) metrics. Furthermore, the method detects subtle sub-decibel temporal variations in coupling conditions over seasonal timescales, demonstrating its capability for continuous fiber health monitoring. These results establish semblance distance analysis as a physically interpretable tool for DAS channel selection with direct applications in seismic imaging, structural health monitoring, and noise mitigation in high sensitivity systems such as gravitational wave detectors.
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BibTeX
@misc{rading2026semblance,
title = {{Semblance Distance Analysis for Channel Quality Assessment in Distributed Acoustic Sensing}},
author = {Reinhardt Rading and Markus Hoffmann and Norbert Meyners and Holger Schlarb and Céline Hadziioannou and Oliver Gerberding and Katharina-Sophie Isleif},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30401},
eprint = {2610.30401},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30401},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Semblance Distance Analysis for Channel Quality Assessment in Distributed Acoustic Sensing
AU - Reinhardt Rading
AU - Markus Hoffmann
AU - Norbert Meyners
AU - Holger Schlarb
AU - Céline Hadziioannou
AU - Oliver Gerberding
AU - Katharina-Sophie Isleif
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.30401
UR - https://arxiv.org/abs/2610.30401
AB - Distributed Acoustic Sensing (DAS) enables dense, kilometer-scale measurements for seismic and environmental monitoring, but its performance is limited by spatially heterogeneous noise and fiber to ground coupling. Identifying high-quality sensing channels remains a challenge, particularly in nonstationary and complex noise environments where conventional signal to noise ratio (SNR) or correlation based approaches are insufficient. In this work, we introduce a statistical framework for DAS channel quality assessment based on a semblance distance metric, which quantifies the deviation of individual channels from a locally coherent reference signal. Unlike traditional pairwise similarity measures, the proposed method formulates channel selection as a distance estimation problem in a coherence enhanced signal space, enabling identification of incoherent noise and coupling induced distortions without requiring prior knowledge of installation conditions or noise statistics. The method is validated using multiple independent datasets, including recordings of the 2023 Turkish-Syrian earthquakes, a controlled seismic source experiment, and long-term continuous measurements. Across all scenarios, the approach identifies regions of low noise and strong coupling, with results that correlate with independent SNR and root mean square error (RMSE) metrics. Furthermore, the method detects subtle sub-decibel temporal variations in coupling conditions over seasonal timescales, demonstrating its capability for continuous fiber health monitoring. These results establish semblance distance analysis as a physically interpretable tool for DAS channel selection with direct applications in seismic imaging, structural health monitoring, and noise mitigation in high sensitivity systems such as gravitational wave detectors.
ER -