A convolutional neural network approach to deblending seismic data
<|reference_start|>A convolutional neural network approach to deblending seismic data: For economic and efficiency reasons, blended acquisition of seismic data is becoming more and more commonplace. Seismic deblending methods are always computationally demanding and normally consist of multiple processing steps. Besides, the parameter setting is not always trivial. Machine learning-based processing has the potential to significantly reduce processing time and to change the way seismic deblending is carried out. We present a data-driven deep learning-based method for fast and efficient seismic deblending. The blended data are sorted from the common source to the common channel domain to transform the character of the blending noise from coherent events to incoherent distributions. A convolutional neural network (CNN) is designed according to the special character of seismic data, and performs deblending with comparable results to those obtained with conventional industry deblending algorithms. To ensure authenticity, the blending was done numerically and only field seismic data were employed, including more than 20000 training examples. After training and validation of the network, seismic deblending can be performed in near real time. Experiments also show that the initial signal to noise ratio (SNR) is the major factor controlling the quality of the final deblended result. The network is also demonstrated to be robust and adaptive by using the trained model to firstly deblend a new data set from a different geological area with a slightly different delay time setting, and secondly deblend shots with blending noise in the top part of the data.<|reference_end|>
@article{sun2024a,
    title={A convolutional neural network approach to deblending seismic data},
    author={Jing Sun, Sigmund Slang, Thomas Elboth, Thomas Larsen Greiner, Steven
  McDonald, and Leiv-J Gelius},
    journal={Geophysics, 2020, vol. 85, no. 4, pp. WA13-WA26},
    year={2024},
    doi={10.1190/geo2019-0173.1},
    archivePrefix={arXiv},
    eprint={2409.07930},
    primaryClass={physics.geo-ph cs.AI}
}