A Comparative Study of Four Commonly Used Declustering Algorithms
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摘要: 合理的开展地震目录除丛，即确定地震序列中的背景事件和丛集事件，是地震序列分析和模型构建的重要环节。本文针对常用的时空窗法、伪随机时空窗法、最邻近法和随机除丛法进行对比分析，并通过K-S和变化系数等方法在华北地块5条地震带中检验除丛效果。总体而言，时空窗法以及最邻近法有效删除了所有地震带的丛集事件，除丛结果均服从泊松分布，但除丛比例相对较高，尤其对小震的删除比例偏高，使得b值变小；随机除丛法有效去除了序列中丛集事件，同时保留了大多数事件，对震级的依赖性较弱，没有显著地改变统计特性（b值）；相比之下，伪随机时空窗法仅在2个地震带中有效地去除了丛集事件，其余地震带未通过泊松检验，对高震级档地震的删除比例偏高，使得b值增加。本文的对比分析结果可为区域选择合适的除丛算法提供参考，为地震活动性分析和地震危险性评估提供数据支撑。Abstract: Seismic catalog declustering is a crucial step in earthquake sequence analysis and model construction, as it distinguishes background seismic events from clustered events. This study conducts a comparative analysis of four commonly used declustering algorithms—time-space window, time-space link window, nearest neighbor, and stochastic declustering—across five seismic belts in the North China Block. The effectiveness of declustering is evaluated using the Kolmogorov-Smirnov (K-S) test and the coefficient of variation (CV) of interevent times. The results indicate that the time-space window and nearest neighbor algorithms effectively remove clustered events in all seismic zones, yielding declustered catalogs that follow a Poisson distribution. However, these methods tend to remove a high proportion of small earthquakes, leading to a reduction in the b value. In contrast, the stochastic declustering algorithm successfully eliminates clustered events while preserving most seismic events, demonstrating minimal magnitude dependence and negligible impact on statistical properties such as the b value. The time-space link window algorithm, however, is only effective in two seismic zones, while the remaining zones fail the Poisson test. Moreover, this method disproportionately removes high-magnitude events, resulting in an increased b value. The findings of this study provide a valuable reference for selecting appropriate declustering algorithms in the region and offer data support for seismicity analysis and probabilistic seismic hazard assessment.
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Key words:
- Seismic belts /
- Declustering algorithms /
- Declustering rate /
- Poisson test
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图 1 华北地块不同地震带的地震活动特征
Figure 1. The seismicity characteristics of different seismic zones in the North China block
图 2 华北地块1970年以来地震目录完整性分析
Figure 2. Completeness analysis of earthquake catalogs in the North China block since 1970
图 3 华北地块不同地震带序列活动的除丛特征
Figure 3. The declustering characteristics of seismicity sequences in different seismic zones of the North China block
图 4 四种除丛算法在华北地块不同地震带的移除比例
Figure 4. Fraction of earthquake removed by declustering algorithms in different seismic zones of the North China block
图 5 华北地块不同地震带2.5级以上地震事件的b值计算结果
Figure 5. Calculation results of b-values with M ≥ 2.5 in different seismic zones of the North China block
表 1 4种除丛算法的对比分析结果
Table 1. Comparative analysis of results by four declustering algorithms
构造区域 除丛算法 背景数目/个 丛集数目/个 移除比例/% 变化系数Cv K-S检验 b P值 α=0.01，是否接受原假设 长江下游—黄海地震带 原始目录 2769 0 0 1.3463 6.8 e-09 否 0.78±0.01 时空窗法 1427 1342 48.47 1.0115 0.2037 是 0.72±0.02 伪随机时空窗法 2260 509 18.38 1.1821 0.0367 是 0.83±0.02 最邻近法 1350 1419 51.25 0.9388 0.2755 是 0.72±0.02 随机除丛法 2007 762 27.52 1.1228 0.4704 是 0.78±0.02 郯庐地震带 原始目录 6160 0 0 1.4072 1.9 e-11 否 0.83±0.01 时空窗法 1910 4250 68.99 1.0309 0.4654 是 0.79±0.02 伪随机时空窗法 4485 1675 27.19 1.1513 6.7 e-06 否 0.98±0.01 最邻近法 1900 4260 69.16 0.9461 0.2989 是 0.81±0.02 随机除丛法 3109 3051 49.53 1.0937 0.0818 是 0.88±0.02 华北平原地震带 原始目录 5391 0 0 1.4685 2.1 e-17 否 0.75±0.01 时空窗法 1399 3992 74.05 1.0070 0.1792 是 0.78±0.02 伪随机时空窗法 3817 1574 29.20 1.1768 1.9 e-05 否 0.88±0.01 最邻近法 1763 3628 67.30 0.9454 0.1907 是 0.80±0.02 随机除丛法 2779 2612 48.45 1.0811 0.0403 是 0.79±0.02 汾渭地震带 原始目录 3994 0 0 1.3014 6.3 e-08 否 0.89±0.01 时空窗法 1390 2604 65.20 0.9973 0.2515 是 0.78±0.02 伪随机时空窗法 3191 803 20.11 1.1097 2.2 e-04 否 1.03±0.02 最邻近法 1938 2056 51.48 0.9110 0.9293 是 0.89±0.02 随机除丛法 2817 1177 29.47 1.0993 0.0653 是 0.96±0.02 银川—河套地震带 原始目录 3317 0 0 1.3332 1.3 e-07 否 0.77±0.01 时空窗法 1123 2194 66.14 0.9893 0.3021 是 0.69±0.02 伪随机时空窗法 2720 597 18.00 1.1816 0.0130 是 0.85±0.01 最邻近法 1542 1775 53.51 0.9868 0.0997 是 0.73±0.02 随机除丛法 2637 680 20.50 1.1238 0.0201 是 0.78±0.02 
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