用逐步代价最小决策法识别地震与爆破

张博; 边银菊; 王婷婷

doi:10.3969/j.issn.0253-3782.2014.02.008

用逐步代价最小决策法识别地震与爆破

中国北京 100081 中国地震局地球物理研究所

基金项目: 地震行业科研专项基金(200808003)资助.

详细信息

通讯作者:
边银菊, E-mail: bianyinju@Yahoo.com.cn

中图分类号: P315.3⁺1
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- 文章访问数: 566
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出版历程
- 收稿日期: 2012-12-16
- 修回日期: 2013-11-14
- 发布日期: 2014-02-28

Discrimination of earthquakes and explosions by SAMC decision method

Institute of Geophysics, China Earthquake Administration, Beijing 100081, China

摘要

摘要: 在动态时间规整法的基础上，建立了逐步代价最小决策法(SAMC). 该方法中的代价函数可以很好地反映特征归属，对较差的特征具有一定的“容忍度”、稳定性好，还可用全程代价函数评判识别结果的可信度. 用SAMC方法对北京及其周边地区33次地震和29次爆破中提取的5个分类特征量进行识别，识别率为90%；从该5个特征量中选择较好的3个特征量进行识别，识别率为92%；在上述地区另选13次事件作为检验样本进行U检验， 5个分类特征量和3个分类特征量的识别率分别为92%和100%，识别效果很好. 这表明SAMC是识别地震与爆破的有效方法.
- 动态时间规整 /
- 逐步代价最小决策法 /
- 地震和爆破的识别 /
- 识别判据
Abstract: Based on dynamic time warping (DTW) algorithm, this paper proposes a novel recognition algorithm, stepwise accumulating minimal cost (SAMC) decision method. By this method, each cost function can well reflect the tendency of event’s features; moreover, SAMC is unsusceptible to the quality of features. The absolute value of overall cost function can be also served as the reliability of the results. Five features were extracted for recognition from 62 events of earthquakes (33) and explosions (29) which occurred in Beijing and its peripheral regions. The result respectively reached 90% recognition rate for five features and 92% for three features which are better features from the five ones. In another testing by U-test, another 13 events (eight earthquakes and five explosions) were randomly chosen from the same area, and the recognition rate was 92% for five features and 100% for three ones. These suggest that SAMC method can be useful to discriminate earthquakes and explosions effectively.
- dynamic time warping /
- stepwise accumulating minimal cost decision method /
- recognition of earthquakes and explosions /
- feature

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图 1 选取事件的位置分布图

Figure 1. Location of events and stations

下载: 全尺寸图片幻灯片

图 2 人工爆破与天然地震识别量的阈值分析

(a) P波初动方向; (b) A_P₁/A_{S_max} ； (c) A_{P_max}/A_{S_max}; (d) A_{P_max}/t_{m_e}; (e) A_{S_max}/t_{m_e} 红色圆圈和叉号分别表示学习样本集中的爆破与地震事件；正方形和棱形分别表示学习样本集中爆破与地震各项特征的重心；蓝色圆圈和六角星表示检验样本集中的爆破与地震事件

Figure 2. Thresholds for discriminating the explosions and earthquakes

(a) P-wave first motion; (b) The ratio of P-wave first motion amplitude to the maximum amplitude of S-wave; (c) P/S maximum amplitude ratios; (d) The ratio of P-wave maximum amplitude to duration; (d) The ratio of S-wave maximum amplitude to their duration. The red circles and crosses represent explosions and earthquakes for learning, respectively; the squares and the diamonds represent template of each feature belonging to explosions and earthquakes, respectively; the blue circles and the hexagrams separately represent explosions and earthquakes for testing

下载: 全尺寸图片幻灯片

图 3 5次爆破的3个分类特征量测试模板的最优代价路径

图中非垂直虚线表示局部约束，实线表示选择的最优路径

Figure 3. Overall optimal cost paths of five explosive test samples with three features

Non-perpendicular dashed lines represent local constraint, and solid lines represent optimal path

下载: 全尺寸图片幻灯片

图 4 8次地震的3个分类特征量测试模板的最优代价路径

Figure 4. Overall optimal cost paths of eight seismic test samples with three features

下载: 全尺寸图片幻灯片

表 1 每一步权重值σ

Table 1 Weight σ at each step

下载: 导出CSV

表 2 3个分类特征量的C检验结果

Table 2 Results of C-test on learning sample set with three features

下载: 导出CSV

表 3 5个分类特征量的C检验结果

Table 3 Results of C-test on learning sample set with five features

下载: 导出CSV

表 4 测试事件的U检验结果

Table 4 Results of U-test on all test sample set

下载: 导出CSV

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施引文献(8)

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