Multichannel sparse spike deconvolution based on dynamic time warping

Zhao, Xiaowei; Wang, Shangxu; Yuan, Sanyi; Cheng, Liang; Cai, Youjun

doi:10.1007/s11600-021-00571-z

Cited by 2 publications

(1 citation statement)

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“…As a common technique for seismic data processing, deconvolution is important for applications like impedance inversion and high-resolution processing [1][2][3][4]. According to the specific implementation, the present deconvolution methods can be generally divided into three categories: non-machine learning methods, supervised machine learning methods, and self-supervised machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Seismic Blind Deconvolution Based on Self-Supervised Machine Learning

Yin,

Xu,

Yang

et al. 2024

Applied Sciences

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Seismic deconvolution is a useful tool in seismic data processing. Classical non-machine learning deconvolution methods usually apply quite a few constraints to both wavelet inversion and reflectivity inversion. Supervised machine learning deconvolution methods often require appropriate training labels. The existing self-supervised machine learning deconvolution methods need a given wavelet, which is a non-blind process. To overcome these issues, we propose a blind deconvolution method based on self-supervised machine learning. This method first estimates an initial zero-phase wavelet by smoothing the amplitude spectrum of averaged seismic data. Then, the loss function of self-supervised machine learning is taken as the error between the observed seismic data and the reconstructed seismic data that come from the convolution of phase-rotated wavelet and reflectivity generated by the network. We utilize a residual neural network with long skip connections as the reflectivity inversion network and a fully connected convolutional neural network as the wavelet phase inversion network. Numerical experiments on synthetic data and field data show that the proposed method can obtain reflectivity inversion results with higher resolution than the existing self-supervised machine learning method without given wavelet.

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Section: Introductionmentioning

confidence: 99%