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Deep Learning for Seismic Data Compression in Distributed Acoustic Sensing
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AI
DOI：10.1109/TGRS.2025.3526933.png)
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Abstract
En 中文
Distributed acoustic sensing (DAS) is emerging in seismic monitoring due to its ultradense 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, for example, 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 multichannel 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 (CR) for a gigabyte-scale DAS dataset without unsatisfactory reconstruction performance.
Keywords:
Feature extraction
Transformers
Decoding
Computer vision
Monitoring
Data mining
Image coding
Band-pass filters
Transform coding
Deep learning
Compression
deep learning
reconstruction
seismic
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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