Accurate velocity estimation from surface-consistent residual statics

This study presents a novel methodology for estimating accurate Normal Moveout (NMO) velocities by utilizing residual time shifts resulting from imperfect NMO corrections. The proposed method uses the residual static component that results from using incorrect NMO velocity to estimate a better velocity that minimizes this component of the statics solution. The proposed algorithm is tested on synthetic and real seismic datasets demonstrating its robustness and high accuracy. When compared with the best velocity obtained by an experienced processor, the velocity estimated via the proposed method shows only a 0.05% deviation from the true model velocity. Furthermore, application to real 2D seismic data from East Texas resulted in an estimated NMO velocity value that differed by only 0.6% from the value derived by an experienced processor. The algorithm minimizes the effects of human error, enhances seismic event flattening, and ultimately improves the signal-to-noise ratio (SNR), making it a valuable tool for subsurface imaging in complex geological environments.

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Publication Details

Journal
Scientific Reports
Published
2025-10-07
DOI
https://doi.org/10.1038/s41598-025-18730-3
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Accurate velocity estimation from surface-consistent residual statics

Abdullatif Al‐Shuhail, Paul Edigbue, Sherif M. Hanafy, Umair bin Waheed et al.
Scientific Reports
Seismic Imaging and Inversion Techniques
article

Accurate velocity estimation from surface-consistent residual statics

Abdullatif Al‐Shuhail, Paul Edigbue, Sherif M. Hanafy, Umair bin Waheed, Ahmad Mazli Muhammad
article en

Abstract

This study presents a novel methodology for estimating accurate Normal Moveout (NMO) velocities by utilizing residual time shifts resulting from imperfect NMO corrections. The proposed method uses the residual static component that results from using incorrect NMO velocity to estimate a better velocity that minimizes this component of the statics solution. The proposed algorithm is tested on synthetic and real seismic datasets demonstrating its robustness and high accuracy. When compared with the best velocity obtained by an experienced processor, the velocity estimated via the proposed method shows only a 0.05% deviation from the true model velocity. Furthermore, application to real 2D seismic data from East Texas resulted in an estimated NMO velocity value that differed by only 0.6% from the value derived by an experienced processor. The algorithm minimizes the effects of human error, enhances seismic event flattening, and ultimately improves the signal-to-noise ratio (SNR), making it a valuable tool for subsurface imaging in complex geological environments.

Scientific ReportsVol. 15(1)
King Fahd University of Petroleum and Minerals (SA)
King Fahd University of Petroleum and Minerals
Openalex Percentile: Top 72%
Seismic Imaging and Inversion Techniques
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