Denoising Cryoseismological Distributed Acoustic Sensing Data Using a Deep Neural Network

One major challenge in Environmental Seismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Those signals potentially stay unnoticed and thus, might not be analyzed further.Distributed acoustic sensing (DAS) is an emerging technology for measuring strain rate data by using common fiber-optic cables in combination with an interrogation unit. This technology enables researchers to acquire seismic monitoring data on poorly accessible terrain with great spatial and temporal resolution. We utilized a DAS unit in a cryospheric environment on a temperate glacier. The data collection took place in July 2020 on Rhonegletscher, Switzerland, where a 9 km long fiber-optic cable was installed, covering the entire glacier from its accumulation to its ablation zone. During one month 17 TB of data were acquired. Due to the highly active and dynamic cryospheric environment, our collected DAS data are characterized by a low signal to noise ratio compared to classical point sensors. Therefore, new techniques are required to denoise the data efficiently and to unmask the signals of interest. Here we propose an autoencoder, which is a deep neural network, as a denoising tool for the analysis of our cryospheric seismic data. An autoencoder can potentially separate the incoherent noise (such as wind or water flow) from the temporally and spatially coherent signals of interest (e.g., stick-slip event or crevasse formation). We test this approach on the continuous microseismic Rhonegletscher DAS records. To investigate the autoencoder’s general suitability and performance, three different types of training data are tested: purely synthetic data, original data from on-site seismometers, and original data from the DAS recordings themselves. Finally, suitability, performance as well as advantages and disadvantages of the different types of training data are discussed.