A technique for earthquake and karst collapse recognition based on wavelet packet fractal and neural network
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摘要: 本文以近年来广西地震台网中心记录的天然地震和岩溶塌陷为例,尝试利用基于小波包的分形和径向基函数神经网络技术对这两类事件的波形进行识别,以期有效地识别地震与岩溶塌陷。结果表明,基于小波包分形与神经网络相结合的事件识别方法对天然地震和岩溶塌陷事件的识别率高达89.5%,可作为识别天然地震与岩溶塌陷的一个有效方法。Abstract: The focal mechanism and propagation path of natural earthquakes and karst collapse are different, so the frequency characteristics of their waveforms are different, too. The wavelet packet fractal method can effectively extract the natural earthquake and karst collapse waveform characteristics, and the radial basis function (RBF for short) neural network can well identify two kinds of events, therefore by using RBF neural network based on wavelet packet this paper takes the natural earthquake and karst collapse recorded by Guangxi Earthquake Networks Center in recent years as an example to try to identify two kinds of event waveforms. The results show that the recognition rate of natural earthquake and karst collapse event is up 89.5%, suggesting it is an effective method to identify natural earthquakes and karst collapse.
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表 1 地震和岩溶塌陷事件的小波包分形维数均值和方差
Table 1 Mean and variance of fractal dimension of earthquake and collapse events
小波包
分解系数训练集(地震) 训练集(塌陷) 测试集(地震) 测试集(塌陷) 维数均值 维数方差 维数均值 维数方差 维数均值 维数方差 维数均值 维数方差 1 1.379 6 0.010 0 1.463 0 0.009 2 1.389 0 0.010 4 1.440 8 0.008 5 2 1.341 8 0.008 2 1.412 2 0.010 0 1.337 6 0.007 7 1.438 0 0.006 5 3 1.354 4 0.013 3 1.435 1 0.014 0 1.358 0 0.010 1 1.466 3 0.010 2 4 1.344 9 0.009 7 1.400 4 0.013 2 1.333 9 0.008 1 1.444 3 0.009 1 5 1.416 3 0.017 9 1.483 0 0.018 5 1.426 5 0.017 2 1.505 5 0.011 0 6 1.388 8 0.016 0 1.438 4 0.016 0 1.395 4 0.012 8 1.478 4 0.010 2 7 1.368 3 0.014 0 1.450 2 0.019 9 1.364 6 0.012 0 1.471 0 0.008 8 8 1.370 0 0.014 5 1.451 4 0.016 6 1.370 1 0.012 6 1.462 8 0.010 3 9 1.411 5 0.011 8 1.539 9 0.015 9 1.414 9 0.013 4 1.557 0 0.009 4 10 1.438 5 0.017 3 1.508 4 0.010 2 1.443 9 0.015 7 1.524 9 0.011 6 11 1.423 4 0.017 0 1.493 3 0.010 0 1.433 3 0.015 5 1.518 4 0.007 7 12 1.429 8 0.017 4 1.486 3 0.010 5 1.440 5 0.016 2 1.513 1 0.009 6 13 1.422 9 0.019 2 1.530 1 0.009 8 1.438 5 0.017 2 1.539 2 0.007 7 14 1.422 4 0.018 8 1.520 0 0.009 5 1.433 2 0.017 8 1.536 9 0.006 0 15 1.421 1 0.018 4 1.497 6 0.010 0 1.425 9 0.016 6 1.516 1 0.008 6 16 1.420 3 0.017 6 1.501 0 0.016 2 1.429 7 0.017 4 1.524 8 0.007 1 表 2 小波包分形维数判据的识别结果
Table 2 The recognition result of the criteria based on wavelet packet fractal
事件类型 测试样本数 正确识别样本数 错误识别样本数 识别率 地震(训练集) 100 100 0 100% 塌陷(训练集) 100 100 0 100% 地震(测试集) 100 87 13 87% 塌陷(测试集) 100 92 8 92% 表 3 错误识别事件的相关信息
Table 3 The directory of false recognition
序号 发震时间
年-月-日事件类型 ML 地点 东经/° 北纬/° 台站 震中距/km 1 2010-08-26 地震 2.2 钦州 22.24 109.47 LNS 26 2 2010-09-29 地震 2.7 河池 25.24 106.99 TE 36 3 2010-09-29 地震 2.7 河池 25.24 106.99 YIZ 188 4 2011-02-28 地震 2.9 百色 24.42 108.11 YAX 101 5 2011-02-28 地震 2.9 百色 24.42 108.11 PGX 122 6 2011-02-28 地震 2.9 百色 24.42 108.11 GAL 136 7 2011-10-03 地震 2.9 百色 24.61 105.40 YTT 143 8 2012-03-28 地震 2.5 来宾 23.64 108.90 PGX 136 9 2013-03-28 地震 2.2 崇左 21.88 107.12 PGX 128 10 2013-06-17 地震 2.3 崇左 23.42 107.50 SHX 115 11 2014-02-06 地震 2.5 百色 23.38 107.51 SHX 118 12 2014-02-06 地震 2.5 百色 23.38 107.51 QIP 122 13 2015-01-09 地震 2.3 钦州 22.25 108.04 LNS 112 14 2010-06-01 塌陷 1.9 防城港 23.71 109.23 LIB 34 15 2015-01-14 塌陷 1.0 百色 23.14 106.39 JXX 25 16 2015-09-08 塌陷 2.4 来宾 23.88 108.89 XCT 35 17 2015-09-08 塌陷 2.1 来宾 23.85 108.89 DHX 82 18 2015-09-08 塌陷 2.1 来宾 23.85 108.89 TAP 95 19 2015-09-08 塌陷 2.1 来宾 23.85 108.89 JL 200 20 2015-09-08 塌陷 2.1 来宾 23.88 108.89 TAP 95 21 2015-09-09 塌陷 1.6 河池 25.24 107.00 GAL 90 -
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