Abstract:

Examining the spatial and temporal distribution of seismic activity holds significant importance for seismic risk assessment, particularly in regions prone to frequent and intense earthquakes such as the Sichuan-Yunnan region in China. It is widely recognized that earthquakes exhibit non-random patterns in both spatial and temporal dimensions.

Early scientists endeavored to predict earthquakes using statistical principles, leading to the development of various forecasting methods. Among these, the Relative Intensity(RI)and Pattern Informatics(PI)methods emerged as statistical approaches to earthquake prediction modeling. Essentially, both methods fall under the category of smoothing seismic activity models. They employ techniques to quantify temporal changes in seismic activity graphs, generating maps that highlight areas(hot spots)where earthquakes may occur during specific future periods. While the RI algorithm’s theory is straightforward, its forecasting efficacy is robust, particularly notable in predicting major earthquakes, demonstrating similar advantages to the PI algorithm. Widely adopted globally for proactive predictions across diverse tectonic systems, it has shown commendable results in seismic forecasting practices both domestically and internationally. Over years of development, its predictive performance has gained prominence. However, further research is needed to assess its suitability for predicting minor seismic events in low-seismicity zones. Additionally, its successful application hinges on background seismic activity and the selection of target magnitudes.

To aid seismic activity prediction in the Sichuan-Yunnan region and identify potential future seismic source areas, a comprehensive parameter analysis was conducted using the Relative Intensity(RI)algorithm with the parameter traversal test(PTT). The RI algorithm operates on the premise that the predicted intensity of future earthquakes in a given region closely mirrors the intensity of past earthquakes. While it may not explicitly consider the “active” and “quiet” characteristics of seismic activity, as a fundamental prediction algorithm, it often yields improved prediction outcomes when applied to assess seismic probability in regions with high seismic activity, such as the Sichuan-Yunnan region.

In this study, the statistical-based Relative Intensity(RI)algorithm is employed to calculate the relative intensity of earthquakes based on quantitative earthquake characteristics. The study involves gridding the investigated area and statistically analyzing historical earthquake occurrences within each grid unit under specific magnitude conditions to inform predictions of future earthquake frequencies. The research focuses on evaluating the influence of four key model parameters: grid size, length of the anomalous learning window, starting point of the prediction window, and length of the prediction window, on the algorithm’s prediction efficiency. Furthermore, the study investigates the applicability of the RI algorithm to the Sichuan-Yunnan regions in China. The results yield two significant findings:

(1)The integration of the Relative Intensity(RI)algorithm with the Parameter Traversal Test(PTT)yielded significantly improved results compared to random guessing, primarily due to its optimized parameter selections. These parameters include the grid size, length of the anomalous learning time window, starting time of the prediction time window, and length of the prediction time window.

(2)The parameters of the prediction model exhibit a degree of stability and demonstrate predictive capability for seismic activity in the Sichuan-Yunnan region over the next 1-5 years. The study revealed specific rules and effective parameter intervals applicable to earthquake-prone areas in Sichuan-Yunnan.

The findings suggest that the integration of the Relative Intensity(RI)algorithm with the Parameter Traversal Test(PTT)holds promise for predicting seismic activities in the Sichuan-Yunnan region. This approach enhances the pool of references available for predicting earthquake trends in regions prone to frequent and intense earthquakes. Further research on the RI algorithm is anticipated to yield a more refined numerical model for earthquake trend prediction, contributing to enhanced forecasting accuracy and preparedness in earthquake-prone areas.

Key words: statistical method, relative intensity algorithm, parameter traversal test, retrospective statistical test

摘要：

探索地震活动的时空分布对于地震风险评估, 尤其是对于中国川滇地区这样的地震频发区和强震危险区而言具有重要意义。相对强度算法(RI)基于统计学理论, 使用过去的地震强度评估预测同一地点的未来地震强度。其原理简单, 已多次在国内外强震预测的实践应用中取得了良好的效果。目前, 经过多年的发展完善, 该方法的预测性能愈加突出。为了辅助川滇地区的地震活动性预测工作, 文中使用相对强度算法(RI)和参数遍历试验(PTT)进行了全面的参数分析, 深入研究了RI算法在中国川滇地区的适用性, 结果表明: 由于参数选择合理(包括网格大小、 异常学习时间窗长度、 预测时间窗起始时间和预测时间窗长度), RI和PTT的组合研究表现出了明显优于随机猜测的预测效果, 揭示了川滇地震危险区地震预测的有效参数区间。相对强度算法能够对川滇地区的地震活动进行预测, 文中成果丰富了地震危险区地震趋势预测的参考依据。

关键词: 统计学, 相对强度方法, 参数遍历试验, 回溯性检验