Seismic Data Compression Using Deep Learning

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.

[1]  S. Z. Gürbüz,et al.  Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Andreas Spanias,et al.  Transform methods for seismic data compression , 1991, IEEE Trans. Geosci. Remote. Sens..

[3]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[4]  Yangkang Chen,et al.  Iterative deblending for simultaneous source data using the deep neural network , 2020 .

[5]  Omar M. Saad,et al.  Automatic waveform-based source-location imaging using deep learning extracted microseismic signals , 2020 .

[6]  Otman Chakkor,et al.  Signal speech reconstruction and noise removal using convolutional denoising audioencoders with neural deep learning , 2019, Analog Integrated Circuits and Signal Processing.

[7]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[8]  Soumik Sarkar,et al.  LLNet: A deep autoencoder approach to natural low-light image enhancement , 2015, Pattern Recognit..

[9]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[10]  Bo Liu,et al.  A Distributed Principal Component Analysis Compression for Smart Seismic Acquisition Networks , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[11]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[12]  Ali Payani,et al.  Advances in Seismic Data Compression via Learning from Data: Compression for Seismic Data Acquisition , 2018, IEEE Signal Processing Magazine.

[13]  Ronald R. Coifman,et al.  Low bit-rate efficient compression for seismic data , 2001, IEEE Trans. Image Process..

[14]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[15]  Anthony A. Vassiliou,et al.  Comparison of wavelet image coding schemes for seismic data compression , 1997, Optics & Photonics.

[16]  Koji Inoue,et al.  Automatic Arrival Time Detection for Earthquakes Based on Stacked Denoising Autoencoder , 2018, IEEE Geoscience and Remote Sensing Letters.

[17]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[18]  Truong Q. Nguyen,et al.  Seismic data compression: a comparative study between GenLOT and wavelet compression , 1999, Optics & Photonics.

[19]  Amr Mohamed,et al.  Convolutional Autoencoder Approach for EEG Compression and Reconstruction in m-Health Systems , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[20]  Xin Tian.  Multiscale Sparse Dictionary Learning With Rate Constraint for Seismic Data Compression , 2019, IEEE Access.

[21]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[22]  Lin Lin,et al.  Aircraft engine fault detection based on grouped convolutional denoising autoencoders , 2019, Chinese Journal of Aeronautics.

[23]  Yangkang Chen,et al.  Deep learning for seismic lithology prediction , 2018 .

[24]  Amir Averbuch,et al.  Lct-Wavelet Based Algorithms for Data Compression , 2013, Int. J. Wavelets Multiresolution Inf. Process..

[25]  Yangkang Chen,et al.  Deep denoising autoencoder for seismic random noise attenuation , 2020 .

[26]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[27]  Oscar Mauricio Reyes Torres,et al.  Seismic Data compression using 2D Lifting-Wavelet algorithms , 2015 .

[28]  Marcelo Bernardes Vieira,et al.  3-D Poststack Seismic Data Compression With a Deep Autoencoder , 2022, IEEE Geoscience and Remote Sensing Letters.

[29]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[30]  Jun Lin,et al.  Compressive Data Gathering With Generative Adversarial Networks for Wireless Geophone Networks , 2021, IEEE Geoscience and Remote Sensing Letters.

[31]  Yingying Wang,et al.  Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder , 2020 .

[32]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[33]  Michael B. Wakin,et al.  Lossy Compression for Wireless Seismic Data Acquisition , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  J.D. Villasenor,et al.  Seismic data compression using high-dimensional wavelet transforms , 1996, Proceedings of Data Compression Conference - DCC '96.

[35]  Yike Liu,et al.  High-Efficiency Observations: Compressive Sensing and Recovery of Seismic Waveform Data , 2019, Pure and Applied Geophysics.

[36]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[37]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[38]  Yangkang Chen,et al.  Convolutional neural networks for microseismic waveform classification and arrival picking , 2020 .

[39]  Omar M. Saad,et al.  Deep Learning Approach for Earthquake Parameters Classification in Earthquake Early Warning System , 2021, IEEE Geoscience and Remote Sensing Letters.

[40]  Peng Jiang,et al.  Deep-Learning Inversion of Seismic Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Abdullatif A. Al-Shuhail,et al.  Processing of Seismic Reflection Data Using MATLAB , 2011, Synthesis Lectures on Signal Processing.

[42]  U. Rajendra Acharya,et al.  An efficient compression of ECG signals using deep convolutional autoencoders , 2018, Cognitive Systems Research.

[43]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[44]  Bo Liu,et al.  Seismic-data compression using autoassociative neural network and restricted Boltzmann machine , 2018, SEG Technical Program Expanded Abstracts 2018.

[45]  Faramarz Fekri,et al.  Deep Neural Networks with Extreme Learning Machine for Seismic Data Compression , 2019, Arabian Journal for Science and Engineering.