Enhancing Data Analysis in Distributed Acoustic Sensing through Implementation of a Channel Quality Index

Managing large datasets in distributed acoustic sensing (DAS) presents significant challenges due to their terabyte-scale or larger size. Effectively addressing these challenges necessitates proactive measures to mitigate the impact of bad data before processing. Moreover, the dynamic nature of data quality in recorded channels requires continuous monitoring for efficient data cleaning.

This study proposes a novel approach building upon prior research to automatically detect and eliminate clusters of flawed channels. Leveraging a metric that identifies channels exhibiting dissimilarity from their neighboring data points, we then employ machine-learning-based anomaly detection techniques to establish a threshold for data exclusion. Our method incorporates user input to accommodate the variability of threshold selection across different use cases, aiming to enhance the likelihood of excluding data considered inadequate for subsequent processing.

This approach is applied to two distinct sets of DAS data collected from the Canary Islands. The first dataset spans two months in 2020, while the second dataset covers a period of six months between 2022 and 2023. While both datasets share substantial location overlap, one main difference is an updated interrogator for the second experiment. We demonstrate the capacity of our method to identify the evolution of channel quality over the acquisition of both datasets, thereby illustrating its efficacy in adapting to dynamic environmental and equipment-related factors.