Seismic Imaging of Complex Velocity Structures by 2D Pseudo-Viscoelastic Time-Domain Full-Waveform Inversion

Alaei, Niloofar; Monfared, Mehrdad Soleimani; Kahoo, Amin Roshandel; Bohlen, Thomas

doi:10.3390/app12157741

Cited by 10 publications

(8 citation statements)

References 41 publications

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“…The recurrent neural network, as a special kind of neural network, introduces a time dependence, which is more compatible with the wave field propagation process. In this paper, a recurrent neural network is used to simulate the two-dimensional time-domain finite-difference acoustic wave equation as the forward process using a similar idea of full-waveform inverse forward simulation combined with backpropagation correction, and the velocity is used as a network parameter [26]. The velocity model is updated during the backward propagation of the network.…”

Section: Introductionmentioning

confidence: 99%

Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN)

Lu,

Zhang

2023

Applied Sciences

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Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality into the research of seismic velocity inversion and yielded a wealth of research outcomes. Typically, seismic velocity inversion based on machine learning lacks control over physical processes and interpretability. Starting from wave theory and the physical processes of seismic data acquisition, this paper proposes a method for seismic velocity model inversion based on Physical Embedding Recurrent Neural Networks. Firstly, the wave equation is a mathematical representation of the physical process of acoustic waves propagating through a medium, and the finite difference method is an effective approach to solving the wave equation. With this in mind, we introduce the architecture of recurrent neural networks to describe the finite difference solution of the wave equation, realizing the embedding of physical processes into machine learning. Secondly, in seismic data acquisition, the propagation of acoustic waves from multiple sources through the medium represents a high-dimensional causal time series (wavefield snapshots), where the influential variable is the velocity model, and the received signals are the observations of the wavefield. This forms a forward modeling process as the forward simulation of the wavefield equation, and the use of error back-propagation between observations and calculations as the velocity inversion process. Through time-lapse inversion and by incorporating the causal information of wavefield propagation, the non-uniqueness issue in velocity inversion is mitigated. Through mathematical derivations and theoretical model analyses, the effectiveness and rationality of the method are demonstrated. In conjunction with simulation results for complex models, the method proposed in this paper can achieve velocity inversion in complex geological structures.

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Section: Introductionmentioning

confidence: 99%

Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN)

Lu,

Zhang

2023

Applied Sciences

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“…In recent years, acoustic FWI has successfully inverted industrial-scale real 3D marine data [8] and refracted waves in ocean bottom node data [9]. However, modeling of elastic wave propagation through elastodynamic theory is required for reflection analysis [10,11]. In real cases, when seismic waves propagate in underground media, if they encounter a reflecting interface, energy conversion will occur and converted waves will be generated.…”

Section: Introductionmentioning

confidence: 99%

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Qi,

Huang,

Zhang

et al. 2023

Applied Sciences

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The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method.

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“…To enhance quality and resolution, the source-receiver coordinated seismic records are usually sorted into the common-midpoint (CMP) gathers because of the redundancy source-receiver pairs share. Multiple attenuation in the CMP domain is still a key component, as their occurrence may mislead reflector relocating in migration and destroy seismic quantitative amplitude analysis and interpretation [1,2]. Most of the multiple attenuation methods presented in the CMP domain are based on their periodicity or the difference of velocity stacking between multiples and primaries, such as predictive deconvolution, Radon transform [3][4][5] and velocity stacking [6][7][8], which works at near-or far-offsets, respectively, for surface-related or layer-interbed multiples.…”

Section: Introductionmentioning

confidence: 99%

Inverse Scattering Series Internal Multiple Attenuation in the Common-Midpoint Domain

et al. 2023

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Internal multiple prediction remains a high-priority problem in seismic data processing, such as subsurface imaging and quantitative amplitude analysis and inversion, particularly in the common-midpoint (CMP) gathers, which contain multicoverage reflection information of the subsurface. Internal multiples, generated by unknown reflectors in complex environments, can be reconstructed with certain combinations of seismic reflection events using the inverse scattering series internal multiple prediction algorithm, which is usually applied to shot records in source–receiver coordinates. The computational overhead is one of the major challenges limiting the strength of the multidimensional implementation of the prediction algorithm, even in the coupled plane-wave domain. In this paper, we first comprehensively review the plane-wave domain inverse scattering series internal multiple prediction algorithm, and we propose a new scheme of achieving 2D multiple attenuation using a 1.5D prediction algorithm in the CMP domain, which significantly reduces the computational burden. Moreover, we quantify the difference in behavior of the 1.5D prediction algorithm for the shot/receiver and the CMP gathers on tilted strata. Numerical analysis of prediction errors shows that the 1.5D algorithm is more capable of handling dipping generators in the CMP domain than in the shot/receiver gathers, and it is able to predict the accredited traveltimes of internal multiples caused by dipping reflectors with small inclinations. For more complex cases with large inclination, using the 1.5D prediction algorithm, internal multiple predictions fail both in the CMP domain and in the shot/receiver gathers, which require the full 2D prediction algorithm. To attenuate internal multiples in the CMP gathers generated by large-dipping strata, a modified version is proposed based on the full 2D plane-wave domain internal multiple prediction algorithm. The results show that the traveltimes of internal multiples caused by dipping generators seen in the simple benchmark example are correctly predicted in the CMP domain using the modified 2D prediction algorithm.

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Seismic Imaging of Complex Velocity Structures by 2D Pseudo-Viscoelastic Time-Domain Full-Waveform Inversion

Cited by 10 publications

References 41 publications

Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN)

Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN)

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Inverse Scattering Series Internal Multiple Attenuation in the Common-Midpoint Domain

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