Zhao Zaibo, Li Yaoxi, Zhang Yongwen
Data Science Research Center, Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China.
Entropy (Basel). 2025 Mar 27;27(4):347. doi: 10.3390/e27040347.
Earthquake activity poses significant risks to both human survival and economic development. However, earthquake forecasting remains a challenge due to the complex, poorly understood interactions that drive seismic events. In this study, we construct an earthquake percolation model to examine the relationships between earthquakes and the underlying patterns and processes in Southern California. Our results demonstrate that the model can capture the spatiotemporal and magnitude characteristics of seismic activity. Through clustering analysis, we identify two distinct regimes: a continuous increase driven by earthquake clustering, and a discontinuous increase resulting from the merging of clusters dominated by large, distinct mega-earthquakes. Notably, in the continuous increase regime, we observe that clusters exhibit a broader spatiotemporal distribution, suggesting long-range and long-term correlations. Additionally, by varying the magnitude threshold, we explore the scaling behavior of earthquake percolation. The robustness of our findings is confirmed through comparison with multiple shuffling tests.
地震活动对人类生存和经济发展都构成了重大风险。然而，由于驱动地震事件的相互作用复杂且难以理解，地震预测仍然是一项挑战。在本研究中，我们构建了一个地震渗流模型，以研究南加州地震与潜在模式和过程之间的关系。我们的结果表明，该模型能够捕捉地震活动的时空和震级特征。通过聚类分析，我们识别出两种不同的状态：由地震聚类驱动的持续增加，以及由大型、独特的特大地震主导的聚类合并导致的不连续增加。值得注意的是，在持续增加状态下，我们观察到聚类呈现出更广泛的时空分布，这表明存在长程和长期相关性。此外，通过改变震级阈值，我们探索了地震渗流的标度行为。通过与多次重排测试进行比较，证实了我们研究结果的稳健性。
