insight - Scientific Computing - # Seismic Data Processing

Recursive Interferometric Surface-wave Suppression for Improved Seismic Reflection Imaging

Q: How might the RISS technique be adapted for use in 3D seismic reflection surveys, and what additional challenges might arise in such applications?

Adapting the RISS technique to 3D seismic reflection surveys involves extending its core principles from a two-dimensional plane to a three-dimensional volume. This presents both opportunities and challenges: Adaptation to 3D: Data Handling and Computation: 3D surveys involve significantly larger datasets compared to 2D. Efficient algorithms and potentially parallel computing would be crucial for handling the increased computational burden of SI and adaptive subtraction in 3D. Source-Receiver Geometry: The success of RISS relies on the proximity of active and virtual sources. In 3D surveys, careful consideration of the acquisition geometry is essential to ensure adequate source-receiver coverage for effective surface wave retrieval and suppression. Surface Wave Behavior: Surface waves in 3D exhibit more complex propagation patterns compared to 2D. Their behavior is influenced by variations in subsurface properties in all three dimensions. This complexity necessitates more sophisticated wavefield separation techniques to isolate surface waves accurately. Challenges in 3D Applications: Computational Cost: The computational cost of 3D SI and adaptive subtraction can be very high, requiring significant computational resources and potentially limiting the feasibility of RISS for large-scale 3D surveys. Wavefield Complexity: Accurately separating surface waves from body waves in 3D is more challenging due to the increased complexity of wave propagation and potential interference from scattered arrivals. Azimuthal Variations: Surface wave characteristics can vary with azimuth, requiring adaptations to RISS to account for these variations and ensure effective suppression across all azimuths.

Q: Could the limitations of RISS in suppressing higher modes of surface waves be mitigated by incorporating a priori information about the subsurface velocity structure, and how would this impact the data-driven nature of the technique?

Yes, incorporating a priori information about the subsurface velocity structure can potentially mitigate the limitations of RISS in suppressing higher modes of surface waves. Here's how: Improved Surface Wave Retrieval: A priori velocity information can be used to construct more accurate velocity models for surface wave analysis. This can lead to better estimation of surface wave dispersion curves, including higher modes, during the SI process. Targeted Adaptive Subtraction: With improved knowledge of higher-mode dispersion characteristics, the adaptive subtraction step can be tailored to target these modes more effectively, leading to more complete surface wave suppression. Impact on Data-Driven Nature: Incorporating a priori information shifts RISS slightly away from being purely data-driven. However, it's important to note that: Data Still Plays a Key Role: Even with a priori information, the success of RISS still heavily relies on the quality and characteristics of the acquired seismic data. Iterative Refinement: The a priori information can be used as an initial model that is iteratively refined based on the actual data during the RISS process. This allows for a balance between data-driven adaptation and the guidance provided by prior knowledge.

Q: Considering the increasing availability of distributed acoustic sensing (DAS) data, how could the principles of RISS be applied to leverage the dense spatial sampling of DAS for even more effective surface wave suppression and enhanced subsurface imaging?

The dense spatial sampling provided by DAS offers significant advantages for applying RISS and achieving more effective surface wave suppression: Enhanced Surface Wave Characterization: The high spatial density of DAS measurements allows for detailed characterization of surface wave propagation, including higher modes, across the entire monitored section. This can significantly improve the accuracy of surface wave retrieval using SI. Improved Adaptive Subtraction: The dense spatial sampling enables the adaptive subtraction process to be applied with greater precision. This is particularly beneficial for suppressing surface waves with complex wavefronts or those propagating in laterally heterogeneous media. Potential for Source-Less Imaging: DAS measurements, often acquired with ambient noise sources, can be used for passive seismic interferometry. This opens up possibilities for applying RISS without the need for active sources, potentially reducing acquisition costs and environmental impact. Enhanced Subsurface Imaging: By effectively suppressing surface waves, RISS applied to DAS data can lead to: Improved Signal-to-Noise Ratio: Removing strong surface wave energy reveals weaker reflections from the subsurface, enhancing the overall signal-to-noise ratio of the data. Better Imaging of Shallow Targets: Effective surface wave suppression is particularly crucial for imaging shallow targets often obscured by strong surface waves. High-Resolution Imaging: The dense spatial sampling of DAS, combined with the improved data quality after RISS, can potentially enable high-resolution imaging of the subsurface.

Core Concepts

Recursive Interferometric Surface-wave Suppression (RISS) is a novel, data-driven technique that effectively suppresses surface waves in seismic reflection data, leading to enhanced visualization of subsurface structures for improved imaging and monitoring.

Abstract

Bibliographic Information: Shirmohammadi, F., Draganov, D., Ghose, R., Verschuur, E., & Wapenaar, K. (2024). Recursive Interferometric Surface-wave Suppression For Improved Reflection Imaging. arXiv preprint arXiv:2411.02620v1.
Research Objective: This paper introduces and evaluates the effectiveness of Recursive Interferometric Surface-wave Suppression (RISS), a novel technique for suppressing surface waves in seismic reflection data, using a 2D dataset from Scheemda, Groningen province, the Netherlands.
Methodology: The RISS technique employs seismic interferometry (SI) to retrieve dominant surface waves, which are then adaptively subtracted from the original data. This process is repeated iteratively, with each iteration using the output of the previous one as input, to enhance surface wave suppression. The authors compare the performance of RISS with conventional techniques like surgical muting and f-k filtering. They further demonstrate the benefits of RISS in conjunction with Marchenko-based isolation for improved subsurface imaging.
Key Findings: The study reveals that RISS effectively suppresses surface waves in the Scheemda dataset, leading to clearer visualization of reflections and subsurface structures compared to conventional methods. Applying RISS with muted deeper reflections in the SI results (RISS-muteR) yielded the most favorable outcome. The integration of RISS with Marchenko-based isolation further enhanced the clarity and continuity of shallow reflectors, suggesting its potential for improved subsurface imaging.
Main Conclusions: RISS offers a promising data-driven approach for effective surface wave suppression in seismic reflection data. The technique proves particularly advantageous when combined with other advanced processing methods like Marchenko-based isolation, highlighting its potential for enhancing subsurface imaging and monitoring in various geophysical applications.
Significance: This research contributes significantly to the field of seismic data processing by introducing a novel and effective technique for surface wave suppression. The improved imaging capabilities offered by RISS hold substantial implications for various applications, including hydrocarbon exploration, induced seismicity monitoring, and subsurface characterization.
Limitations and Future Research: The study acknowledges the challenge of accurately retrieving higher modes of surface waves using SI. Future research could explore modal separation techniques to improve the suppression of these higher modes. Additionally, investigating the optimal parameters for RISS, such as time and space window sizes and filter length, for different datasets would be beneficial.

Customize Summary

Rewrite with AI

Generate Citations

Translate Source

To Another Language

Generate MindMap

from source content

Visit Source

arxiv.org

Stats

The study used a 2D seismic reflection dataset acquired in Scheemda, Groningen province, the Netherlands, in 2022.
The acquisition parameters included 151 source positions with 2 m spacing, 601 three-component geophone nodes with 1 m spacing, and a frequency range of 8-250 Hz for the seismic source.
The authors applied a band-reject filter between 40 Hz and 90 Hz to the common-source gathers to focus on surface wave retrieval.
For adaptive subtraction, a space window of 20 traces, a time window of 0.2 s, and a filter length of 0.05 s were used.
Two iterations of RISS were found to be sufficient for effective surface wave suppression in this dataset.
A constant velocity of 350 m/s was used for normal moveout (NMO) correction based on a comparison of stacked sections with different velocity values.
The Marchenko-based isolation targeted a layer between 30 m and 270 m depth for improved imaging.

Quotes

Key Insights Distilled From

Recursive Interferometric Surface-wave Suppression For Improved Reflection Imaging

by Faezeh Shirm... at arxiv.org 11-06-2024

https://arxiv.org/pdf/2411.02620.pdf

Recursive Interferometric Surface-wave Suppression For Improved Reflection Imaging

Deeper Inquiries

How might the RISS technique be adapted for use in 3D seismic reflection surveys, and what additional challenges might arise in such applications?

Adapting the RISS technique to 3D seismic reflection surveys involves extending its core principles from a two-dimensional plane to a three-dimensional volume. This presents both opportunities and challenges:
Adaptation to 3D:

Data Handling and Computation: 3D surveys involve significantly larger datasets compared to 2D. Efficient algorithms and potentially parallel computing would be crucial for handling the increased computational burden of SI and adaptive subtraction in 3D.
Source-Receiver Geometry: The success of RISS relies on the proximity of active and virtual sources. In 3D surveys, careful consideration of the acquisition geometry is essential to ensure adequate source-receiver coverage for effective surface wave retrieval and suppression.
Surface Wave Behavior: Surface waves in 3D exhibit more complex propagation patterns compared to 2D. Their behavior is influenced by variations in subsurface properties in all three dimensions. This complexity necessitates more sophisticated wavefield separation techniques to isolate surface waves accurately.
Challenges in 3D Applications:

Computational Cost: The computational cost of 3D SI and adaptive subtraction can be very high, requiring significant computational resources and potentially limiting the feasibility of RISS for large-scale 3D surveys.
Wavefield Complexity: Accurately separating surface waves from body waves in 3D is more challenging due to the increased complexity of wave propagation and potential interference from scattered arrivals.
Azimuthal Variations: Surface wave characteristics can vary with azimuth, requiring adaptations to RISS to account for these variations and ensure effective suppression across all azimuths.

Could the limitations of RISS in suppressing higher modes of surface waves be mitigated by incorporating a priori information about the subsurface velocity structure, and how would this impact the data-driven nature of the technique?

Yes, incorporating a priori information about the subsurface velocity structure can potentially mitigate the limitations of RISS in suppressing higher modes of surface waves. Here's how:

Improved Surface Wave Retrieval:  A priori velocity information can be used to construct more accurate velocity models for surface wave analysis. This can lead to better estimation of surface wave dispersion curves, including higher modes, during the SI process.
Targeted Adaptive Subtraction: With improved knowledge of higher-mode dispersion characteristics, the adaptive subtraction step can be tailored to target these modes more effectively, leading to more complete surface wave suppression.
Impact on Data-Driven Nature:
Incorporating a priori information shifts RISS slightly away from being purely data-driven. However, it's important to note that:

Data Still Plays a Key Role: Even with a priori information, the success of RISS still heavily relies on the quality and characteristics of the acquired seismic data.
Iterative Refinement: The a priori information can be used as an initial model that is iteratively refined based on the actual data during the RISS process. This allows for a balance between data-driven adaptation and the guidance provided by prior knowledge.

Considering the increasing availability of distributed acoustic sensing (DAS) data, how could the principles of RISS be applied to leverage the dense spatial sampling of DAS for even more effective surface wave suppression and enhanced subsurface imaging?

The dense spatial sampling provided by DAS offers significant advantages for applying RISS and achieving more effective surface wave suppression:

Enhanced Surface Wave Characterization: The high spatial density of DAS measurements allows for detailed characterization of surface wave propagation, including higher modes, across the entire monitored section. This can significantly improve the accuracy of surface wave retrieval using SI.
Improved Adaptive Subtraction: The dense spatial sampling enables the adaptive subtraction process to be applied with greater precision. This is particularly beneficial for suppressing surface waves with complex wavefronts or those propagating in laterally heterogeneous media.
Potential for Source-Less Imaging: DAS measurements, often acquired with ambient noise sources, can be used for passive seismic interferometry. This opens up possibilities for applying RISS without the need for active sources, potentially reducing acquisition costs and environmental impact.
Enhanced Subsurface Imaging:
By effectively suppressing surface waves, RISS applied to DAS data can lead to:

Improved Signal-to-Noise Ratio: Removing strong surface wave energy reveals weaker reflections from the subsurface, enhancing the overall signal-to-noise ratio of the data.
Better Imaging of Shallow Targets:  Effective surface wave suppression is particularly crucial for imaging shallow targets often obscured by strong surface waves.
High-Resolution Imaging: The dense spatial sampling of DAS, combined with the improved data quality after RISS, can potentially enable high-resolution imaging of the subsurface.