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Seismic Data Compression Using Deep Learning
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DOI：10.1109/ACCESS.2021.3073090.png)
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Abstract
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The exponential growth of the size of seismic data recorded in seismic surveys and real time data monitoring makes seismic data compression strongly demanded. Furthermore, compression will lead to an efficient use of the bandwidth assigned for the communication link between the seismic stations and the main center. In this paper, two convolutional autoencoders (CAEs) are proposed for seismic data compression. The two algorithms are mainly based on the convolutional neural network (CNN), which has the capability to compress the seismic data into feature representations, thereby allowing the decoder to perfectly reconstruct the input seismic data. The results show that the first model is efficient at low compression ratios (CRs), while the second model improves the signal-to-noise ratio (SNR) from about 3 dB to 12 dB compared to the other benchmark algorithms at moderate and high CRs.
Keywords:
Data compression
Feature extraction
Decoding
Convolution
Deep learning
Image reconstruction
Data models
Convolutional autoencoders (CAE)
deep learning
seismic data compression
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