Elastic Full Waveform Inversion Based on Full-Band Seismic Data Reconstructed by Dual Deconvolution

Chen, Guoxin; Yang, Wayne; Wang, Hanchuang; Zhou, Huamin; Huang, Xingguo

doi:10.1109/lgrs.2022.3178915

Cited by 13 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also has the advantages of high accuracy and low cost, and has been widely used in the geoscience community. Seismic exploration uses the elastic differences of the underground environment to infer the structural distribution of underground rock formations by investigating the propagation of artificially excited seismic waves in the underground medium [1,2]. In the process of acquiring field data, effective waves are inevitably affected by interference waves and external factors, thus the effective signals are usually masked by various types of complex noise [3].…”

Section: Introductionmentioning

confidence: 99%

A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression

Zhao,

Zhou,

Bai

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Seismic data processing plays a key role in the field of geophysics. The collected seismic data are inevitably contaminated by various types of noise, which makes the effective signals difficult to be accurately discriminated. A fundamental issue is how to improve the signal-to-noise ratio of seismic data. Due to the complex characteristics of noise and signals, it is a challenge for the denoising model to suppress noise and recover weak signals. To suppress random noise in seismic data, we propose a multi-scale deformable convolution neural network denoising model based on U-Net, named MSDC-Unet. The MSDC-Unet mainly contains modules of deformable convolution and dilated convolution. The deformable convolution can change the shape of the convolution kernel to adjust the shape of seismic signals to fit different features, while the dilated convolution with different dilation rates is used to extract feature information at different scales. Furthermore, we combine Charbonnier loss and structure similarity index measure (SSIM) to better characterize geological structures of seismic data. Several examples of synthetic and field seismic data demonstrate that the proposed method is effective in the comprehensive results in terms of quantitative metrics and visual effect of denoising, compared with two traditional denoising methods and two deep convolutional neural network denoising models.

show abstract

Section: Introductionmentioning

confidence: 99%

A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression

Zhao,

Zhou,

Bai

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

Multi-scale envelope-based analysis of geomagnetic anomaly field

Chen¹,

Chen²,

Yang³

2022

Acta Geophys.

View full text Add to dashboard Cite

Joint data and model-driven simultaneous inversion of velocity and density

Chen,

Jensen

et al. 2024

Geophysical Journal International

View full text Add to dashboard Cite

Summary Density is an important parameter for both geological research and geophysical exploration. However, for model-driven seismic inversion methods, high-fidelity density inversion is challenging due to seismic wave travel-time insensitivity to density, and crosstalk that density has with velocity. To circumvent the challenge of density inversion, some inversion methods treat density as a constant value or derive density from velocity through empirical equation. On the other hand, deep learning approaches are completely driven by data and have strong target-oriented characteristics, offering a new way to solve multi-parameter coupling problems. Nevertheless, the accuracy of the inversion results of data-driven algorithms is directly related to the amount and diversity of the training data, and thus, they lack the universality of model-driven algorithms. To achieve accurate density inversion, we propose a simultaneous inversion algorithm for velocity and density that combines the advantages of data- and model- driven approaches: A neural network model (U-T), combining the U-net and Transformer architectures, is proposed to construct nonlinear mappings between seismic data as inputs and the velocity and density as predictions. Next, the model-driven inversion algorithm uses the U-T prediction as the initial model to obtain the final accurate solution. In the model-driven module, envelope-based sparse constrained deconvolution is used to obtain full-band seismic data, while a variable dominant frequency full waveform inversion algorithm is employed to perform multi-scale inversion, ultimately leading to accurate inversion results of velocity and density. The performance of the algorithm on the Sigsbee2A and Marmousi models demonstrates its effectiveness.

show abstract

Elastic Full Waveform Inversion Based on Full-Band Seismic Data Reconstructed by Dual Deconvolution

Cited by 13 publications

References 24 publications

A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression

A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression

Multi-scale envelope-based analysis of geomagnetic anomaly field

Joint data and model-driven simultaneous inversion of velocity and density

Contact Info

Product

Resources

About