Automatic Identification of High-Quality Channels in Distributed Acoustic Sensing through Implementation of a Channel Quality Index

Using fiber-optic cables, distributed acoustic sensing (DAS) has emerged as a powerful technology for seismic monitoring, particularly in oceanic regions, but the massive amount of data generated presents challenges for its integration into operational seismic networks. However, since the quality of DAS recordings varies along the fiber due to physical and environmental factors, not all data collected from fiber-optic cables contribute meaningfully to earthquake detection and characterization. Identifying and filtering out low-quality channels reduces unnecessary data processing and optimizes resource use. To address this, we developed a channel quality index (CQI), a machine learning-based approach that automatically identifies high-quality DAS channels based on their ability to record earthquake signals. Trained on data from four offshore DAS experiments in the Spanish Mediterranean and Canary Islands, our model evaluates temporal, frequency, and energy-based features to assign quality scores to individual channels, reliably identifying high-quality channels with high precision (0.92–0.96) and strong performance in terms of area under the receiver operating characteristic curve (ROC-AUC, 0.92–0.98). By prioritizing high-quality data, CQI enhances the efficiency of data transmission and storage in large-scale DAS deployments. It enables the selective retrieval of relevant seismic data from repositories, reducing storage demands and minimizing data transmission time, which is especially critical for large datasets. It also provides a mechanism to quantify information loss in data compression strategies, ensuring critical seismic signals are preserved. These capabilities of CQI facilitate the practical adoption of DAS for both real-time and long-term seismic monitoring, optimizing data quality and resource management. The software for the calculation of the CQI is licensed under GNU LGPL-v3 and is published on GitHub at https://github.com/B-CSI/cqi_das.