the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Oct 2025
Enhancing 2D Deep Seismic Reflection Imaging Using Shot Domain Regularization: A Case Study from the Jiangnan Orogenic Belt, South China
Abstract. Deep seismic reflection is a key method for investigating plate tectonics, as it enables detailed imaging of lithospheric structures – particularly within the crust and upper mantle. It plays a crucial role in understanding crustal evolution and identifying mineral enrichment zones. However, during data acquisition, deviations from the planned shot and receiver locations often occur due to surface constraints or other logistical challenges. These deviations result in irregular seismic data that can introduce significant migration artifacts during processing, ultimately reducing data quality and hindering the interpretation of deep geological structures. To address this issue, we evaluated four data regularization strategies based on anti-aliasing Matching pursuit Fourier interpolation using a 2D deep seismic reflection dataset from the central Jiangnan Orogenic Belt. Among these, the method that involves regularizing and infilling shot gathers at 100-meter intervals produced the most effective results. Compared to legacy contractor-processed data, this method achieved a higher signal-to-noise ratio and improved seismic resolution. The superiority of that method was further confirmed through enhanced imaging in the pre-stack time migration results. These findings highlight the importance of shot domain regularization prior to migration in deep seismic reflection surveys.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4554', Anonymous Referee #1, 10 Nov 2025
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RC2: 'Comment on egusphere-2025-4554', Anonymous Referee #2, 17 Nov 2025
The paper presents a 2D reflection seismic case study from Jiangen Orogenic Belt, South China, in which the methodology and importance of regularizing data (using Matching Pursuit Fourier Interpolation in this case) is demonstrated. The results show a clear improvement over unregularized data.
Novelty of the paper lies in investigating the application of regularization to crooked-geometry 2D seismic data, by comparing four different approaches to interpolating shot- and offset-domain data, and assessing the results in prestack and migrated domains.
The authors have done an effective job of outlining the key necessities and benefits of regularizing 2D data prior to prestack migration. However, I believe the paper can be significantly strengthened by discussing some aspects of the method that were unfortunately skipped over, such as limitations and drawbacks of the method, frequency-domain analysis, and some performance metrics. In general, a few of the sections could be fleshed out in more detail and with a bit more clarity.
It is for these reasons that I recommend a minor revision of the manuscript before consideration of publication.
I provide below more detailed commentary and suggestions in the form of: general comments; line-specific comments; figure comments.
General Comments
Some of the tectonic/stratigraphic components of the survey area are difficult to follow due to their absence on the map in Figure 1 (such as the three segments of the JOB, and some important basins like the Xiuwu Basin). Including these in the map will make the geology more accessible to the reader.
Can the authors include how they handled amplitude attenuation? In all the figures with trace gathers, it appears that an amplitude recovery was applied, but neither Table 1 nor accompanying text mentions any amplitude recovery technique.
Can the authors elaborate on and contrast the methods and parameters used for the refraction static corrections and the tomography static corrections in the text? (i.e. how many layers were assumed in the former, what kind of inversion was used in each etc.)
I suggest some more detail be added to section 3.4: typically, deconvolution is used to sharpen reflection signals and suppress multiples. However, the authors motivate their use of deconvolution as a form of noise attenuation (lines 189-190). While this effect can be a byproduct, it is not usually the principal aim of deconvolution. Can the authors elaborate on how the deconvolution enhances S/N? Given the 1 ms gap mentioned in line 196, is it correct to say that the authors are applying a spiking deconvolution operator? This usually introduces more noise rather than suppresses it. Finally, Figure 6 does show a difference in data but, to my eye, it looks more like a difference in amplitude balancing than anything. Are the colour bars identical for the before/after images? I think this image would be more informative with the inclusion of colour bars and the power spectra.
Throughout the paper, there is no textual discussion or figure that considers power spectra of the data. Incorporating these would be useful not only for evaluating the pre-processing steps such as the noise suppression (section 3.3), but especially for evaluating the MPFI regularization, the chief methodology of the paper. I suggest the authors incorporate some images and discussion of the original data in the f-k domain as well as after application of the regularization, specifically focusing on regions of the interpolated traces. What does the frequency content look like before MPFI? What about after? Are there any artefacts introduced? There is a lot of untapped discussion for Figures 5,6,9,10, 11 and their corresponding text if they had power spectra plots and comparisons.
Are the seismic sections in Figures 10 and 11 migrated, as Table 1 implies? If so, I suggest explicitly stating this in the captions to distinguish them from merely stacked sections. Same for Figures 12 and 13, and 14 and 15.
I believe the discussion would be strengthened by discussing assumptions and potential drawbacks/challenges of this regularization. For example, how crooked can the profile be before the regularization underperforms? What, if any, assumptions are being made by the method (such as repositioning shots that were taken with an offset to the line)? What are the implications of out-of-plane signals? Are there any implications/alterations of the amplitude? (this would be important to know for amplitude-based analysis like AVO).
In the final comparisons (Figs. 12-15), the regularized data does show a visual improvement to the imaging capabilities. Are there any other quality controls that can be used to assess the imaging performance? (For example, semblance attributes, spectral amplitude comparison, difference plots etc.)
In section 4, the order of figures does not match the order of those referenced in the text (7; 10; 11; 8; 9). The orders should match to improve readability.
Specific Comments
Ln27: “deep”
Ln41: Panea et al. (2017)
Ln59: “basin”
Ln63: “... in both shallow and deep ___” Signals?
Ln65: “less migration noises”
Ln120: “Receivers”
Ln130: “(c) local position”
Ln149: “... from deep seismic reflection”. First arrivals come from direct and refracted waves, not deep reflections.
Ln208: Should it not be “The seismic data need to be transformed from the space-time domain to the wavenumber-frequency domain” using 2D FFT?
Figure Comments
Figure 2b,c would benefit from the use of a scale bar.
Figure 4: It is very difficult to see any differences in the gathers with the chosen scale, especially between (b) and (c). I suggest either incorporating a zoom window or choosing a scale that better demonstrates the differences in (b) and (c).
Figure 8: The legend shows a Source Number entry, but none are seen on the map.
Citation: https://doi.org/10.5194/egusphere-2025-4554-RC2
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- 1
This manuscript presents a study on improving deep seismic reflection imaging through data regularization techniques. The authors evaluate four anti-aliasing Matching Pursuit Fourier Interpolation (MPFI) strategies and conclude that the approach combining shot-domain regularization and 100 m shot infilling provides the best imaging results. which often leads to migration artifacts and reduced image quality, hindering the interpretation of deep geological structures.
The manuscript addresses a relevant and practical problem in seismic data processing — the degradation of deep seismic reflection images caused by irregular shot and receiver sampling — and proposes a technical solution. The method was applied to a 200 km crooked 2D deep seismic line in the central Jiangnan Orogenic Belt (JOB), South China. The enhanced imaging provides crucial insights into the complex tectonic evolution of this region and aids in identifying major faults and potential mineral enrichment zones.
However, the manuscript would benefit from minor revisions before acceptance. In particular, the reproducibility of the proposed workflow, quantitative evaluation of the improvements, and the geological interpretation of the enhanced images should be strengthened. Please find my comments below:
1、Figure 6 shows the shot gather before and after robust surface consistent deconvolution, however, the significant difference in amplitude scaling between the panels makes it difficult to discern the key improvements brought by the deconvolution.
2、The MPFI workflow (Fig. 7) is clearly presented conceptually, but key implementation details are missing. Please specify: Number of iterations and stopping criteria, frequency band segmentation, construction of the low-frequency prior weights and their influence on alias suppression. These parameters are essential for reproducibility and scientific rigor.
3、Figures 10–11 visually demonstrate improved imaging quality, but the manuscript lacks quantitative measures to support this improvement. Please include metrics such as: Signal-to-noise ratio (S/N) increase (before vs. after regularization), fold enhancement statistics, dominant frequency or resolution changes, migration noise reduction (e.g., variance or coherence measures), etc. Quantitative comparisons would significantly strengthen the validity of the proposed approach.
4、 Figures 10 and 11 are intended to show improvements in the shallow and deep sections, respectively. However, as their vertical axes share an identical time range (0.5-6.5 s), this is confusing. Please clarify the specific time intervals that define the "shallow" and "deep" sections in this context. Additionally, I suggest using boxes or arrows to highlight the key areas of improvement in these figures.
5、 Figure 12, 13, 14, 15 should in the same color to show the structure and progress in the different methods.
6、 The improved sections clearly reveal major fault zones (e.g., F1, F2), but the discussion of their geological significance is rather brief. Please elaborate on how the enhanced imaging contributes to understanding the tectonic evolution of the Jiangnan Orogenic Belt and mineralization processes, citing recent geophysical or geological findings.
I recommend that the manuscript undergo a MINOR REVISION to address the above comments before further consideration for publication.