Envelope-Based Sparse-Constrained Deconvolution for Velocity Model Building

Chen, Guoxin; Yang, Wayne; Liu, Yanan; Luo, Jungang; Jing, Hao

doi:10.1109/tgrs.2021.3063514

Cited by 12 publications

(1 citation statement)

References 88 publications

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“…The forward representation for seismic data is the basis for constructing seismic wave inversion imaging methods, and different forward representations can lead to different inversion targets and methods. The theory of full waveform inversion (FWI) [36][37][38][39][40] highlights that lithology imaging in seismic data requires the effective use of both travel-time and waveform information. In essence, the inclusion of waveform information in seismic inversion results is crucial for advancing seismic inversion methods.…”

Section: Introductionmentioning

confidence: 99%

Waveform Imaging Based on Linear Forward Representations for Scalar Wave Seismic Data

Lu,

Chen,

Chen

2024

Water

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The current reverse-time migration, which is based on wave equations for imaging wavefields, employs an imaging formula derived from Claerbout’s imaging principle. This imaging formula is only valid for plane waves with small incident angles on the perfectly flat reflecting surface. However, the complexity of seismic wave propagation may lead to situations that do not meet this requirement. Therefore, this paper divides the subsurface into local scattering and reflecting bodies. It proposes linear forward representations for scattering and reflection data based on perturbations in the physical parameters and wave impedance, respectively. To further describe the effect on the reflecting body boundary, the local reflection coefficient is defined and the linear forward representation for the reflection data based on it is obtained. After that, the proposed linear forward representations are used as the forward equations for the linear inverse of the seismic data, and the seismic data waveform imaging method is developed based on linear inversion theory. At the same time, the specific waveform imaging calculation formulas for scalar wave scattering data and scalar wave reflection data are provided and validated via numerical experiments. Compared with the current reverse-time migration, waveform migration not only has the correct phase and higher resolution in theory but also does not increase the computational complexity. To some extent, it improves the deficiencies of the current structural imaging and provides a basis for subsurface lithological imaging.

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Section: Introductionmentioning

confidence: 99%

Waveform Imaging Based on Linear Forward Representations for Scalar Wave Seismic Data

Lu,

Chen,

Chen

2024

Water

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Multi-scale envelope-based analysis of geomagnetic anomaly field

Chen¹,

Chen²,

Yang³

2022

Acta Geophys.

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

Chen,

Jensen

et al. 2024

Geophysical Journal International

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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.

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Envelope-Based Sparse-Constrained Deconvolution for Velocity Model Building

Cited by 12 publications

References 88 publications

Waveform Imaging Based on Linear Forward Representations for Scalar Wave Seismic Data

Waveform Imaging Based on Linear Forward Representations for Scalar Wave Seismic Data

Multi-scale envelope-based analysis of geomagnetic anomaly field

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

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