A Consensus Equilibrium Approach for 3-D Land Seismic Shots Recovery

Goyes-Peñafiel, Paul; Vargas, Edwin; Correa, Claudia V.; Agudelo, William; Wohlberg, Brendt; Argüello, Henry

doi:10.1109/lgrs.2021.3082421

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…The DSPRecon method was originally designed for trace reconstruction, nonetheless, for a fair comparison with the DIPsgr method, we transpose the seismic cube to work in the common-receiver-gather domain and recover the shot-gathers for every single receiver line, i.e., recovering the seismic shot-gathers for every single receiver array. The third method is the consensus equilibrium (CE) approach [21] that incorporates several regularizes in the optimization problem for recovering missing shot-gathers. Given that the CE approach outperforms the sparsity-based methods (see [21]), this paper disregards that comparison.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…The third method is the consensus equilibrium (CE) approach [21] that incorporates several regularizes in the optimization problem for recovering missing shot-gathers. Given that the CE approach outperforms the sparsity-based methods (see [21]), this paper disregards that comparison.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…To evaluate the quality of the reconstructed data with theDIPsgrmethod, we used the metrics Peak Signal-to-Noise Ratio (PSNR), and structural similarity index measure (SSIM) recommended by [21], [26]. PSNR is used to quantify the quality in terms of signal amplitude, and with SSIM, we account for the analysis of the structural features related to the shape of the waveforms i.e., hyperbolic and linear events recorded in the shotgathers.…”

Section: Metricsmentioning

confidence: 99%

“…Performance comparison ofDIPsgr (proposed)and IL[20], DSPRecon[8], and CE[21] methods using the average PSNR and SSIM metrics s ability to reconstruct the shot-gather while applying filtering in the FK domain. This behavior is anticipated since DIPsgr method includes the FK domain as a regularization term, thereby facilitating network convergence by producing smoother and more continuous FK spectra.…”

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confidence: 99%

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Shot-gather Reconstruction using a Deep Data Prior-based Neural Network Approach

Rodríguez-López,

León-López,

Goyes-Peñafiel

et al. 2023

Rev. UIS ing.

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Seismic surveys are often affected by environmental obstacles or restrictions that prevent regular sampling in seismic acquisition. To address missing data, various methods, including deep learning techniques, have been developed to extract features from complex information, albeit with the limitation of requiring external seismic databases. While previous works have primarily focused on trace reconstruction, missing shot-gathers directly impact the seismic processing flow and represent a major challenge in seismic data regularization. In this paper, we propose DIPsgr, a seismic shot-gather reconstruction method that uses only the incomplete seismic acquisition measurements to estimate their missing information employing unsupervised deep learning. Numerical experiments on three databases demonstrate that DIPsgr recovers the complete set of traces in each shot-gather, with preserved information and seismic events.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: Simulations and Resultsmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

mentioning

confidence: 99%

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Shot-gather Reconstruction using a Deep Data Prior-based Neural Network Approach

Rodríguez-López,

León-López,

Goyes-Peñafiel

et al. 2023

Rev. UIS ing.

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“…Several research works have proposed to optimize seismic acquisition geometries based on data reconstruction. These traditional model-driven methods include concepts from the area of compressive sensing [2,3,4], the use of low-rank [5,6,7], and sparsity-based [8,9,10] optimization algorithms for the reconstruction of seismic traces (missing receivers) and shots (missing sources). A fundamental requirement to implement model-driven methods in geophysics is the sparsity that presumes the data is sparse under certain transforms or the low-rank that supposes the data contains redundancies.…”

Section: Introductionmentioning

confidence: 99%

3D Geometry Design via End-To-End Optimization for Land Seismic Acquisition

Hernandez-Rojas

Argüello

2022

2022 IEEE International Conference on Image Processing (ICIP)

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Seismic acquisition is important in the exploration of the subsurface to find new petroleum fields, where a regularspaced dense acquisition is critical to obtain high-quality seismic images. However, the acquisition costs and environmental impacts have motivated undersampled acquisition schemes, where several sensing points are removed to decrease the total seismic sources. After the undersampled measurements are acquired, a recovery algorithm reconstructs the missing seismic images (shots). The removed sources are currently selected using random sensing schemes, leading to suboptimal quality in the recovered seismic images. Thus, an optimal design of the removed sources is crucial as it determines the quality of the recovered shots. This work proposes an end-to-end optimization to jointly design undersampled seismic acquisition geometries while preserving the highquality of the reconstructed data. The seismic acquisition geometry is modeled as a deep binary layer to learn the optimal sensing pattern, while a deep neural network is used to recover the underlying removed shots. Extensive simulations were carried out on a realistic-synthetic Foothills model. The results obtained on the reconstructed data validate that the proposed acquisition design outperforms the state-of-the-art random, uniform, and jitter sensing schemes in 4 dB.

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