## 5 Discussion and Conclusions
[PERSON] et al. [37] utilized a 3D finite-element model to simulate the spatial distribution features of rock fractures and small earthquakes preceding strong earthquakes. Their findings suggested that randomly distributed cracks surrounding inclusions played a crucial role in the formation of small seismic gaps, belts, and clusters of earthquakes. [PERSON] et al. [38] proposed that \"creep sliding\" along the fault zone before a large earthquake could be the potential mechanism for seismic quiescence based on experimental results of acoustic emission during rock deformation. Thus, seismic gaps are interpreted as regional complex fault systems or seismogenic regionsexisting in an unstable or critical state. As stress accumulates, the likelihood of a strong earthquake occurring in these gaps in the future increases. Identifying seismic gaps commonly involves the utilization of epicenter distribution maps and monitoring temporal changes in earthquake frequency, particularly the interval time. The above are only retrospective statistical analysis results of earthquake records. Hence, merely basic statistical understanding is presented here. The associated physical mechanisms (or models) and quantitative analyses require further studies. Whether the research results can be effectively adopted in actual earthquake prediction has to be further validated. The spatio-temporal images of the six seismic gaps are very similar, and the gaps show a high degree of spatial overlapping, which indicate remarkable statistical and predictive significance. In recent years, artificial intelligence (AI) tools, such as machine learning, are employed in earthquake prediction [39; 40; 41; 42]. A key point is to establish sample datasets and feature parameters for machine learning to train and test the earthquake prediction models. The proposed parameters of the seismic gap images and their spatio-temporal characteristics can, to a certain extent, provide useful sample data for the research and application of AI in related fields. Conceptualization, Y.S. and X.Z. ; methodology, X.Z. and Y.S. ; software, X.Z. and G.W. ; validation, X.Z. ; formal analysis, X.Z. ; investigation, Y.S. and X.Z. ; resources, X.Z. ; data curation, X.Z. and Y.S. ; writing--original draft preparation, X.Z. and Y.S. ; writing--review and editing, X.Z. and G.W. ; visualization, G.W. and X.Z. ; supervision, Y.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (2021 YFC3000705-08). Not applicable. Informed Consent Statement:Informed consent was obtained from all subjects involved in the study. The catalog used in this study is The National Small Earthquake Catalog published by the China Earthquake Networks Center. We thank [PERSON] et al. for the Generic Mapping Tools (GMT) for plotting some of the figures [43]. We also thank three anonymous reviewers for their thoughtful and constructive comments on an earlier draft. All the authors listed certify that they have no involvement in any organization or entity with any financial interests (including honoraria; educational grants; participation in speakers' membership, bureaus, consultancies, stock ownership, employment, or other equity interest; and patent-licensing arrangements or expert testimony) or nonfinancial interests (including professional or personal relationships, knowledge or beliefs, and affiliations) in the subject materials or matter discussed.