Dataset for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"

<p>The dataset contains cryoseismological data recorded in July 2020 on the Rhonegletscher, Switzerland, collected using both Distributed Acoustic Sensing and seismometers.<br>This dataset provides the necessary data to reproduce the results presented in the paper &ldquo;Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data.&rdquo; The corresponding code is available on GitHub, and the paper can be accessed via Authorea.</p> <p>&nbsp;</p> <p>Abstract:&nbsp;</p> <p>One major challenge in cryoseismology is that signals of interest are often buried within&nbsp;the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional&nbsp;sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing&nbsp;(DAS), we can observe seismicity over multiple kilometers. DAS systems turn common&nbsp;fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers&nbsp;to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone,&nbsp;recording seismicity for one month. The highly active and dynamic cryospheric environ&nbsp;ment, in combination with poor coupling, resulted in DAS data characterized by a low&nbsp;Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef&nbsp;fectively denoise this dataset.<br>We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g.,&nbsp;stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare&nbsp;different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of&nbsp;events of interest in cryoseismological DAS data, hence to improve the understanding&nbsp;of processes within Alpine glaciers.</p>