Review Reports

Round 1

Reviewer 1 Report

A Spatially Self-Adaptive Multiparametric Anomaly Identification Scheme Based on Global Strong Earthquakes:

·        Add some of the most important quantitative results to the Abstract.

·        Discuss more the MCCs of AIRS multiparameter anomalies using optimal parameters.

·        Discuss the most important reasons for the variations of the spatial distributions and histograms of MCCs obtained using the optimal parameters of anomaly recognition criteria at the test stage for 2006–2010 based on training data within 2011–2020.

·        “The identified optimal values indicate that most anomalies associated with impending earthquakes are characterized by short-term and subtle signals with a low occurrence frequency.”. Explain.

·        Focus on the advantages/disadvantages of the proposed method concerning the obtained results.

·        At the end of the manuscript, explain the implications and future works considering the outputs of the current study.

 

 The quality of the language needs to be improved for grammatical style and word use.

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

The paper presents a novel spatially adaptive multi-parameter anomaly detection scheme that utilizes surface and atmospheric parameters obtained from satellite thermal infrared observations to capture seismic precursory signals for improving earthquake prediction accuracy and reliability. The paper makes interesting contributions to the field of earthquake prediction, and its content is substantial, effectively addressing the research problem. It provides valuable insights for future research on earthquake disasters. I recommend publication.

However, before finalizing the paper, there are a few issues and suggestions that I would like the authors to consider for making minor revisions or clarifications:

1. In the introduction section, it would be beneficial to expand the discussion on the limitations associated with existing methods. By elaborating on these limitations, the authors can establish a stronger rationale for employing satellite thermal infrared observations and multi-parameter anomaly detection as a promising solution.

2. Within the results section, it is advisable to provide a more comprehensive analysis of the characteristics of seismic precursory signals and establish a clear connection between these signals and seismic activity. Offering additional explanations in this context will enhance the readers' understanding of the research findings.

3. It is advised to further explore the possibilities and challenges of applying this method to practical earthquake prediction in the discussion section. How can these challenges be addressed? It would be beneficial for the authors to provide an explanation.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

The manuscript introduces an innovative spatially self-adaptive multiparameter anomaly identification scheme, aiming to enhance the capability of earthquake forecasting through the establishment of an integrated framework. By evaluating the positive MCC values of five geophysical parameters from AIRS products based on global M>6 earthquakes, the authors quantitatively assess the improvements achieved by their proposed scheme. The results reveal significant enhancements in MCC values, ranging from 0.143 to 0.186 on average, and an increased number of earthquake-prone regions with MCC values surpassing 0.5. These findings are very interesting and they provide compelling evidence of the effectiveness of the proposed methodology. I recommend this paper for publication in remote sensing after minor revisions as follows.

 

1. In Section 3.1, can you explain the basic statistical principles behind the z-score normalization method (Equation 1)?

2. The discussion section should be expanded, authors should add the limitations of the current method, encompassing factors such as data availability, the quality of the utilized AIRS products in the analysis, and the underlying assumptions made during the analysis.

3. Why did the authors opt for a 3-fold cross-validation instead of 5 or 10? It should be explained in the text.

4. In Figure 4, the full names of ST, AT, COLR, OLR, CWV and MCC should be provided, and similarly, other figure titles would also require clarification.

5. In Line 362, “reveals” instead of “reveal”.

Minor editing of English language required.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Acceptable in its current form.