Item

Abstract

Distributed acoustic sensing (DAS), deployed on dark telecom fiber, is well‐positioned to play a significant role in seismic monitoring networks because of the combination of a large aperture, fine spatial resolution, broadband sensitivity, and the ubiquitous presence of unused telecommunication fibers in many areas of the world. In this study, we explore the feasibility of dark‐fiber array deployed in a noisy environment for detecting small explosions. We test the effectiveness of template matching for the detection of low‐frequency blasts generated by mining activities in the Imperial Valley, California. We first evaluate dark‐fiber detection performance by analyzing the relationship between detection threshold (DT) and the number of DAS channels used. We find that although, as expected, increasing the number of channels yields higher detection significance and lowers DT, the gain in performance is far from linear, with local anomalies across the DAS cable associated with zones of higher noise. We focus on investigating the types of noise affecting template matching and practical approaches mitigating anthropogenic noise that lower detection performance. Using median absolute deviation, we identify two types of noise sources affecting detection performance. Next, we design a voting scheme that selects DAS channels contributing to lowering of the DT and ensures improvement in detection when adding sequential channels. Finally, we compare dark‐fiber detection performance with nearby conventional seismometers and find that a single station can outperform up to ∼10 DAS channels. However, using the full aperture of our dark‐fiber transect allows to obtain ∼10% lower DT and yields fewer false‐positive detections than an array of four seismometers. Methodological solutions for noise assessment and channel selection allow us to fully benefit from the large aperture and dense sampling offered by dark fiber. The findings of this study are a step toward incorporating existing telecom fibers into novel explosion‐monitoring workflows.