3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering
Abstract
:1. Introduction
- (1)
- Compatibility: The effectiveness of AMHSF reflies solely on the assumption that interpretative features conform to sheet-like structures. Thus, our AMHSF can effectively filter and enhance any sheet-like structure information within attribute images.
- (2)
- Accuracy: As a typical filter, AMHSF accurately isolates sheet-like structures from non-sheet-like ones in a non-iterative and deterministic manner, as long as the underlying assumption of sheet-like behavior is valid.
- (3)
- No Requirement for Additional Data: AMHSF does not necessitate any supplementary data (e.g., strike and dip information) beyond the coherence images, which further contributes to the precision of the filtering.
- (4)
- Simplicity of Implementation: AMHSF involves only three key stages with two post-processing stages, making it straightforward to implement.
- (1)
- AMHSF enables the precise isolation of relevant sheet-like features, such as faults and fractures, from noise-contaminated attribute images. To the best of our knowledge, we are among the first to introduce the sheet-like assumption and the corresponding AMHSF in seismic attribute analysis for the purpose of attribute conditioning in coherence images.
- (2)
- A major innovation of our proposed AMHSF is the development of a novel enhancement function specifically designed to highlight sheet-like structures in coherence images. This function is fundamentally different from the enhancement functions typically used in vascular structure enhancement.
2. Materials and Methods
2.1. Methods
2.1.1. Preliminaries
2.1.2. Proposed Methods
- (1)
- (2)
- (3)
- Blob-like Structures: Blob-like features are typically indicative of noise. The following equation serves to differentiate blob-like structures from other types [43]:
- (4)
- Noisy Structures: In this case, random noise is found in a structureless state, which is represented by half the maximum Frobenius norm [43]:
2.2. Implementation
Algorithm 1 AMHSF |
Require: , , , , . |
Ensure: .
|
2.3. Data and Data Processing
3. Results
3.1. Experimental Results with the Synthetic 3D Dataset
3.2. Experimental Results Using Opunake-3D
3.3. Experimental Results Using Parihaka-3D
4. Discussion
- (1)
- Our proposed method is constructed on the fundamental assumption that seismic data exhibit sheet-like structures. This assumption is satisfied in the majority of cases of faults, as verified in [35]. While it can be readily observed that most features conform to sheet-like structures, a more comprehensive theoretical or experimental investigation is necessary to fully validate this assumption and ensure accurate attribute conditioning. When this sheet-like assumption is not satisfied, our AMHSF will also fail, which is a characteristic of modeling-based methods and a significant drawback.
- (2)
- Our proposed method is sensitive to the selection of parameters. Since the sheetness measure is designed to be maximal at the scale corresponding to the radius of the sheet-like structures, the sheetness index peaks near the center of these features and approaches zero outside. Therefore, to achieve optimal attribute conditioning, the scale parameters must be carefully adjusted throughout the experiment to ensure effective filtering of sheet-like structures, Also, the removal threshold requires fine-tuning, as this parameter directly impacts the suppression of unwanted artifacts in the resulting attribute images. As previously discussed, an excessively high threshold may degrade conditioning performance, while a threshold set too low may result in an excess of artifacts in the final output.
- (3)
- The smoothing ability of AMHSF is limited for various features, leading to difficulties in achieving continuous feature planes as [33,35]. Despite the application of smoothing for post-processing, the final results still exhibit discontinuities. However, as demonstrated in Figure 5, the attribute conditioning of the raw results is already highly effective, with faults being easily identifiable. It is highly probable that applying more advanced smoothing algorithms could lead to improved post-processing outcomes. Therefore, further exploration of smoothing techniques and their impact on the final results presents a promising direction for future work. However, still, attribute conditioning is only an intermediate step of fault detection. A more effective application may be the utilizing of our AMHSF as a pre-processing step for fault detection tasks, such as ant tracking [33], to yield more promising results.
- (4)
- According to other Hessian-based methods [55], the artifacts in AMHSF results may represent theoretical corner cases. As a modeling-based approach, AMHSF shares a common limitation inherent to such methods: the theoretical framework cannot encompass all possible scenarios. Although AHMSF is specifically designed for sheet-like structures, it is inevitably influenced by other non-sheet-like structures that exhibit properties similar to sheet-like structures within our method.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
MHF | multiscale Hessian-based filtering |
AMHF | anisotropic multiscale Hessian-based filtering |
AMHSF | anisotropic multiscale Hessian-based sheet-enhancing filtering |
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Structure Type | * | * | * |
---|---|---|---|
Sheet-like, bright | Low | Low | High negative |
Sheet-like, dark | Low | Low | High positive |
Tubular-like, bright | Low | High negative | High negative |
Tubular-like, dark | Low | High positive | High positive |
Blob-like, bright | High negative | High negative | High negative |
Blob-like, dark | High positive | High positive | High positive |
Noisy | Low | Low | Low |
Dataset | ||||
---|---|---|---|---|
Synthetic | 0.0:0.4:8.0 * | 0.0:0.4:8.0 | 0.0:0.15:3.0 | |
Opunake-3D | 0.0:0.2:4.0 | 0.0:0.2:4.0 | 4.0:0.2:8.0 | |
Parihaka-3D | 0.0:0.1:4.0 | 0.0:0.1:4.0 | 2.0:0.1:6.0 |
Figure 5a * | 0.0:0.4:8.0 | 0.0:0.4:8.0 | 0.0:0.15:3.0 | 0.0015 |
Figure 5b * | 0.0:0.4:8.0 | 0.0:0.4:8.0 | 0.0:0.15:3.0 | 0.0015 |
Figure 5c | 4.0:0.4:12.0 | 0.0:0.4:8.0 | 0.0:0.15:3.0 | 0.0015 |
Figure 5d | 4.0:0.4:12.0 | 0.0:0.4:8.0 | 0.0:0.15:3.0 | 0.0015 |
Figure 5e | 0.0:0.4:8.0 | 4.0:0.4:12.0 | 0.0:0.15:3.0 | 0.0015 |
Figure 5f | 0.0:0.4:8.0 | 4.0:0.4:12.0 | 0.0:0.15:3.0 | 0.0015 |
Figure 5g | 0.0:0.4:8.0 | 0.0:0.4:8.0 | 0.0:0.05:1.0 | 0.0015 |
Figure 5h | 0.0:0.4:8.0 | 0.0:0.4:8.0 | 0.0:0.05:1.0 | 0.0015 |
Figure 5i | 0.0:0.4:8.0 | 0.0:0.4:8.0 | 0.0:0.15:3.0 | 0.0005 |
Figure 5j | 0.0:0.4:8.0 | 0.0:0.4:8.0 | 0.0:0.15:3.0 | 0.0005 |
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Zhao, T.; Yue, Y.; Chen, T.; Qian, F. 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering. Remote Sens. 2025, 17, 278. https://doi.org/10.3390/rs17020278
Zhao T, Yue Y, Chen T, Qian F. 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering. Remote Sensing. 2025; 17(2):278. https://doi.org/10.3390/rs17020278
Chicago/Turabian StyleZhao, Taiyin, Yuehua Yue, Tian Chen, and Feng Qian. 2025. "3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering" Remote Sensing 17, no. 2: 278. https://doi.org/10.3390/rs17020278
APA StyleZhao, T., Yue, Y., Chen, T., & Qian, F. (2025). 3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering. Remote Sensing, 17(2), 278. https://doi.org/10.3390/rs17020278