Quantifiable Elements of Seismic Image Fidelity: A Tutorial Review

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This tutorial review paper addresses an important and often underemphasized aspect of seismic imaging: the quantification of image fidelity. The authors propose a framework based on three quantifiable elements—resolution, artifacts, and position accuracy—and provide a comprehensive discussion on the causes, challenges, and strategies for evaluating seismic image quality. The paper is well-structured, clearly written, and rich in illustrative examples. It serves as a valuable resource for both early-career and experienced geoscientists. However, there are some suggestions:

1. Despite the title, the paper remains largely qualitative in its discussion of "quantifiable elements." The authors should consider including or proposing specific numerical metrics or thresholds for evaluating resolution, artifact levels, or position errors.
2. While the paper mentions the importance of uncertainty assessment, it does not delve into modern probabilistic or Bayesian approaches for quantifying image fidelity. A brief section on this topic would enhance the review.
3. The paper does not mention the emerging role of machine learning in seismic image quality assessment, artifact detection, or velocity model building. Including a few references to recent ML-based QC or imaging studies would improve the review’s completeness.Some relate papers are suggested to cite: Ocean bottom dual-sensor Q-compensated elastic least-squares reverse time migration based on viscoacoustic and   separated-viscoelastic coupled equations. Geophysics. Velocity-adaptive irregular point- spread function deconvolution imaging using X-shaped denoising diffusion filtering operator. IEEE Transactions on Geoscience and Remote Sensing, 61, 5916808. Research progress on seismic imaging technology, Petroleum Science. Q-compensated least-squares reverse time migration with velocity-anisotropy correction based on the first-order   velocity-pressure equations, Geophysics.
4. Some concepts (e.g., poor illumination, velocity model errors) are reiterated across sections. Consolidating these discussions could improve conciseness.
5. “Image fidelity” is used interchangeably with “image quality”. Define the term rigorously and stick to one.
6. Convert imperial units (ft) to SI in Fig. 12.

Author Response

This tutorial review paper addresses an important and often underemphasized aspect of seismic imaging: the quantification of image fidelity. The authors propose a framework based on three quantifiable elements—resolution, artifacts, and position accuracy—and provide a comprehensive discussion on the causes, challenges, and strategies for evaluating seismic image quality. The paper is well-structured, clearly written, and rich in illustrative examples. It serves as a valuable resource for both early-career and experienced geoscientists. However, there are some suggestions:

We thank the reviewer for a very insightful review and suggestions. Below are our responses in blue color following each numbered suggestion by the reviewer.

1. Despite the title, the paper remains largely qualitative in its discussion of "quantifiable elements." The authors should consider including or proposing specific numerical metrics or thresholds for evaluating resolution, artifact levels, or position errors.

We appreciate this suggestion. In writing this tutorial review, we have tried to balance between showing the general concepts and specific details. After considering several cases of numerical metrices and thresholds for quantifying the fidelity elements, we decided to put more emphasis on quantifying image fidelity using the existing examples. The revision has a new Section 4.5. on quantifying seismic image fidelity via machine learning. We have also expanded descriptions on quantitative QC in the following subsections:

2.1.3. Quantifying image similarity for QC image resolution and artifacts.

2.1.5. Quantification of image resolution

2.3.2. Quantifying the absolute accuracy in seismic image position

2.3.3. Quantifying the relative accuracy in seismic image position

2.3.4. Quantifying the uncertainties in imaging velocity models

 

2. While the paper mentions the importance of uncertainty assessment, it does not delve into modern probabilistic or Bayesian approaches for quantifying image fidelity. A brief section on this topic would enhance the review.

We agree. In the revision, we have added a brief description of statistical measures on the errors in image fidelity in subsection 2.1.3. and a very brief review in Section 5 on applying modern statistical approaches for QC seismic image fidelity.

 

3. The paper does not mention the emerging role of machine learning in seismic image quality assessment, artifact detection, or velocity model building. Including a few references to recent ML-based QC or imaging studies would improve the review’s completeness. Some relate papers are suggested to cite: Ocean bottom dual-sensor Q-compensated elastic least-squares reverse time migration based on viscoacoustic and separated-viscoelastic coupled equations. Geophysics. Velocity-adaptive irregular point- spread function deconvolution imaging using X-shaped denoising diffusion filtering operator. IEEE Transactions on Geoscience and Remote Sensing, 61, 5916808. Research progress on seismic imaging technology, Petroleum Science. Q-compensated least-squares reverse time migration with velocity-anisotropy correction based on the first-order velocity-pressure equations, Geophysics.

Thank you for this recommendation and suggested references. In the revision, we have added a new Section 4.5 about machine learning. We have also greatly expanded citations in seismic noises detection, imaging artifact detection and VMB.

 

4. Some concepts (e.g., poor illumination, velocity model errors) are reiterated across sections. Consolidating these discussions could improve conciseness.

In this revision, we have significantly eliminated redundancy and improved the conciseness of the manuscript.

 

5. “Image fidelity” is used interchangeably with “image quality”. Define the term rigorously and stick to one.

We agree. In the revision, we define image fidelity as the unique measure of image quality and eliminate the other terms.

 

6. Convert imperial units (ft) to SI in Fig. 12.

We have converted (ft) to (km) and (m) in Figures 4 and 12, respectively.

Reviewer 2 Report

Comments and Suggestions for Authors

 

 Quantifiable elements of seismic image fidelity: A tutorial review

Dear Editor / Authors,

The submitted manuscript presents a broad compilation of seismological concepts and examples; however, its overall focus and intended audience are not entirely clear. In fact, my main concern relates to the actual purpose and usefulness of the submitted manuscript.

At present, the manuscript seems to fall between two categories: it is not structured as a conventional review, yet it also does not fully qualify as a research article. For readers without a background in seismology, the discussion may be too specialized, while for those already familiar with the field, much of the content reads as a brief overview of well-established concepts. This lack of clear focus makes it difficult to identify the main scientific contribution.

Several statements would require greater precision and contextualization. For example:

- The reference to a “principle of ‘from known to unknown’” is not clear

- The statement “Seismic images are displays of seismic responses of the Earth’s interior properties, such as seismic velocity and reflectivity structures” is semantically debatable and several seismologists would disagree.

The Introduction remains quite general and does not adequately articulate the manuscript’s objectives or target readership. It would help to clearly state whether the paper aims to review existing knowledge, propose new evaluation methods (that would be a research paper), or provide a pedagogical overview.

A key issue is the overly broad scope: the manuscript attempts to cover a wide range of topics—from body-wave tomography to VSP-based shale/gas identification and much more—yet each is treated very briefly. As a result, the depth of discussion is limited. For instance, topics such as seismic tomography are summarized through a short example (Figure 3), where complex challenges like raypath coverage and image artifacts are addressed only qualitatively. Similarly, the section on VSP analysis presents intricate problems using only a single figure and a few sentences, offering limited value.

The whole paper presents relatively basic information that might be too elementary for a specialized audience, while the lack of detail elsewhere makes it less useful for non-specialists seeking a comprehensive overview.

Recommendation:
I would strongly encourage the authors to narrow the scope and develop a smaller number of key topics in greater depth. This would help clarify the purpose of the manuscript and make it more informative for the readers. In its current form, the paper risks being too specialized for general readers and too superficial for experienced seismologists. A clearer definition of goals, along with more precise and technically grounded discussion, would substantially improve its overall quality and impact.

Comments on the Quality of English Language

The manuscript would benefit from minor language refinement, especially at the semantic level (see Comments for the Authors).

Author Response

First of all, we thank the reviewer for a great effort in reviewing our manuscript and providing advices for improving the writing. Below we have copied each of the review comments and followed with our responses.

 

The submitted manuscript presents a broad compilation of seismological concepts and examples; however, its overall focus and intended audience are not entirely clear. In fact, my main concern relates to the actual purpose and usefulness of the submitted manuscript.

We agree. In the revision, we clarify in the Introduction that the intended readers are early-career geoscientists using seismic images and seismic interpreters in exploration and crustal geophysics. This manuscript was inspired by our observations of numerous cases of miss-interpretations of seismic images in exploration and crustal seismology.

 

At present, the manuscript seems to fall between two categories: it is not structured as a conventional review, yet it also does not fully qualify as a research article. For readers without a background in seismology, the discussion may be too specialized, while for those already familiar with the field, much of the content reads as a brief overview of well-established concepts. This lack of clear focus makes it difficult to identify the main scientific contribution.

Our manuscript is intended as a tutorial review, to help early career users of seismic images and seismic interpreters fully understand major elements of seismic image fidelity and their quantification in practice. We have highlighted the targeted reader in the last paragraph of the Introduction section.

 

Several statements would require greater precision and contextualization. For example:

- The reference to a “principle of ‘from known to unknown’” is not clear

- The statement “Seismic images are displays of seismic responses of the Earth’s interior properties, such as seismic velocity and reflectivity structures” is semantically debatable and several seismologists would disagree.

We agree that the original writing is insufficient in precision and needs further clarification. We have considered your notion seriously in our revision. Specifically, we have rephrased the first sentence on seismic images in the Introduction, and we now use “the rule of ‘from known to unknown’”, which is useful in seismic interpretation.

 

The Introduction remains quite general and does not adequately articulate the manuscript’s objectives or target readership. It would help to clearly state whether the paper aims to review existing knowledge, propose new evaluation methods (that would be a research paper), or provide a pedagogical overview.

This tutorial review is a pedagogical overview. We have revised the Introduction by defining the purpose of this review and the intended readers.

 

A key issue is the overly broad scope: the manuscript attempts to cover a wide range of topics—from body-wave tomography to VSP-based shale/gas identification and much more—yet each is treated very briefly. As a result, the depth of discussion is limited. For instance, topics such as seismic tomography are summarized through a short example (Figure 3), where complex challenges like raypath coverage and image artifacts are addressed only qualitatively. Similarly, the section on VSP analysis presents intricate problems using only a single figure and a few sentences, offering limited value.

We appreciate this feedback. Indeed, it is challenging to achieve an optimal level of conciseness for a tutorial review. However, our focus is on illustrating the problematic uses of seismic images due to poor understanding of image fidelity, by illustrating their symptoms in a wide range of applications.

In writing this tutorial review, we have debated how detail we should elaborate on quantification for a general audience. Though we have considered some cases with numerical metrices and thresholds to quantify image fidelity, the results deviate too far from the main flow, hence deleted. In the end, we revised the manuscript with more emphasis on quantifying image fidelity using the existing examples. We have expanded descriptions on quantitative QC in subsections below:

2.1.3. Quantifying image similarity for QC image resolution and artifacts.

2.1.5. Quantification of image resolution

2.3.2. Quantifying the absolute accuracy in seismic image position

2.3.3. Quantifying the relative accuracy in seismic image position

2.3.4. Quantifying the uncertainties in imaging velocity models

We have also added Section 4.5. on quantifying seismic image fidelity via machine learning.

 

The whole paper presents relatively basic information that might be too elementary for a specialized audience, while the lack of detail elsewhere makes it less useful for non-specialists seeking a comprehensive overview.

We agree that this manuscript presents very basic information due to the nature as a tutorial review. We have purposely limited the use of equations and technical detail in order to allow geologic interpreters to capture the fundamental concepts of seismic image fidelity.

 

Recommendation: I would strongly encourage the authors to narrow the scope and develop a smaller number of key topics in greater depth. This would help clarify the purpose of the manuscript and make it more informative for the readers. In its current form, the paper risks being too specialized for general readers and too superficial for experienced seismologists. A clearer definition of goals, along with more precise and technically grounded discussion, would substantially improve its overall quality and impact.

We appreciate the concern by the reviewer. In the Introduction, we have clearly stated that the purpose of this tutorial review is to promote a better understanding of seismic image fidelity by early career users of seismic images. We have limited the scientific fields to exploration and crustal seismology. We have also substantially improved the writing.

 

Comments on the Quality of English Language: The manuscript would benefit from minor language refinement, especially at the semantic level (see Comments for the Authors).

We have carefully revised the writing and submitted a copy of annotated revision marking all corrections in yellow color.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

After the revision, the manuscript has been significantly improved. I recommend acceptance.