Deep Learning for Distributed Acoustic Sensing Data Compression
Description:
Distributed acoustic sensing (DAS) is emerging in seismic monitoring due to its ultra-dense spatial sampling, durability to harsh environments, and sensitivity to weak ground vibration. Compared with traditional nodal geophones that are normally sparsely distributed, DAS offers unprecedented detectability for small-magnitude earthquake events, very subtle reservoir dynamics, and other weak signals among various applications. The appealing detectability of weak signals is compromised by the terabyte-scale daily continuous record that causes prohibitive storage problems. The current solution is to save only the segmented data of interest, e.g., a certain length around a target event. Here, we tackle the urgent storage problem of DAS monitoring by designing a deep-learning (DL) based compression algorithm. The compression algorithm can be split into two major components. The first part is the encoder based on the vision transformer architecture, where the input multi-channel DAS dataset goes through an encoding process to output the key features from the input. The second part is the decoder, where the features are optimally combined to reconstruct the data of the original scale. The optimal network parameters are obtained via an unsupervised training process, aiming at minimizing the difference between the reconstructed and input data. In the proposed DL-based compression algorithm, only the decoder's weight parameters and extracted features from the input data through the encoder are saved on the disk, which is sufficient to reconstruct a high-fidelity dataset. The proposed compression algorithm can reach around 50 times the compression rate for a gigabyte-scale DAS dataset without unsatisfactory reconstruction performance.
Session: Fiber-optic Sensing Applications in Seismology - II
Type: Oral
Date: 4/15/2025
Presentation Time: 11:30 AM (local time)
Presenting Author: Yangkang
Student Presenter: No
Invited Presentation:
Poster Number:
Authors
Yangkang Chen Presenting Author Corresponding Author yangkang.chen@beg.utexas.edu University of Texas at Austin |
Omar Saad engomar91@gmail.com King Abdullah University of Science and Technology |
Yunfeng Chen yunfeng_chen@zju.edu.cn Zhejiang University |
Alexandros Savvaidis alexandros.savvaidis@beg.utexas.edu University of Texas at Austin |
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Deep Learning for Distributed Acoustic Sensing Data Compression
Session
Fiber-optic Sensing Applications in Seismology