Automatic picking up earthquake's P waves using signal-to-noise ratio under a strong noise environment
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摘要:
大数据量、强噪声环境给地震P波到时的自动提取带来很大挑战.针对此问题，本文通过构建特殊的特征函数，建立SNR与STA/LTA的内在联系，提出两种基于SNR的地震P波到时自动提取方法，即基于SNR的STA/LTA方法与基于SNR的综合方法.这两种方法分别是运用SNR概念对传统STA/LTA方法和STA/LTA与AIC综合方法的改进.仿真分析结果表明：对于弱噪声环境（10 dB）和一般噪声环境（6 dB），本文方法较传统STA/LTA方法对地震P波到时提取的准确度更高；而对于强噪声环境（3 dB），本文方法仍能准确提取地震P波到时，而传统STA/LTA方法则出现了较大的误判率（10%）与漏判率（65%）.本文方法为STA/LTA赋予了明确的物理意义，使其阈值的选取建立在严密的数学推导之上.另外，本文方法在进行地震P波到时自动提取的同时，兼具数据预处理功能，无需额外的基线校正或高通滤波，因而具有较好的实时性.
Abstract:Big data and strong noise environments bring great challenges to pick up P waves automatically. In order to solve this problem, a special characteristic function is constructed with the conception of Signal-to-Noise Ratios (SNR). By using this function, an internal relationship between the SNR and the Short-Term Average and Long-Term Average ratio (STA/LTA) is built. And two novel SNR-based P waves' picking methods are proposed, namely the SNR-based STA/LTA method and the SNR-based comprehensive method which are respectively the improvements of the traditional STA/LTA method and the comprehensive method of STA/LTA and Akaike Information Criteria (AIC) by using the SNR conception. The simulation analysis shows that under a weak noise circumstance (10 dB) and normal noise circumstance (6 dB) the two proposed methods have higher accuracy than the traditional STA/LTA method. And under a strong noise circumstance (3 dB), both the methods can accurately pick up the seismic P waves without any regulations, whereas the traditional STA/LTA method has a big error ratio (10%) and a large missing ratio (65%). The proposed methods give STA/LTA a clear physical meaning, and their thresholds can be obtained based on rigorous mathematical derivation. In addition, the proposed methods have favorable real-time performances because they can process data without requirement of additional baseline correction or high-pass filtering.
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图 2
信噪比与长短窗相对功率比的关系框图
Figure 2.
Relation block diagram of the SNR and the relative power ratio of long and short sliding windows
图 4
一组强噪声环境下地震P波自动提取的结果
Figure 4.
Automatic picking of seismic P waves under strong background noise
表 1
不同背景噪声下地震P波自动提取统计表
Table 1.
Automatic picking results of seismic P waves under different background noise
方法 SNR/dB 平均偏差/s 标准偏差/s 误判率/% 漏判率/% 传统STA/LTA 10 0.113 0.039 0 0 6 0.240 0.122 5 15 3 0.227 0.049 10 65 基于SNR的STA/LTA 10 0.072 0.035 0 0 6 0.138 0.036 0 0 3 0.238 0.091 0 0 基于SNR的综合法 10 0.020 0.007 0 0 6 0.012 0.020 0 0 3 0.036 0.061 0 0 
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表 2
汶川余震P波自动提取结果
Table 2.
Automatic P waves′ picking results for the Wenchuan aftershocks
序号 台站编码 人工结果/s 基于SNR的STA/LTA方法/s 基于SNR的综合方法/s 1 JMG 4.997 5.05 5.02 2 PWU 5.001 5.01 5.01 3 QCH 5.000 4.98 4.96 4 JMG 5.001 5.02 4.99 5 MXI 4.999 5.01 5.01 6 PWU 4.998 5.03 5.02 7 QCH 5.001 5.03 5.02 8 JJS 4.999 5.27 4.94 9 JMG 4.997 4.93 4.91 10 PWU 5.002 5.01 5.01 11 QCH 5.005 5.05 5.03 12 JJS 5.002 5.23 4.98 13 JMG 5.003 5.07 4.97 14 PWU 5.002 5.06 4.95 15 QCH 5.003 5.09 4.94 16 JMG 4.999 5.23 4.62 17 PWU 4.998 5.01 5.00 18. QCH 4.997 5.20 5.01 19 QCH 4.999 5.10 5.03 20 QCH 4.996 5.02 5.01 21 JMG 5.004 5.06 5.03 22 JMG 4.996 5.01 4.98 23 JMG 4.996 5.02 5.00 24 JMG 5.006 5.07 5.01 25 PWU 5.004 5.03 5.02 26 PWU 5.003 5.03 5.02 27 PWU 5.001 5.02 5.01 28 PWU 5.004 5.03 5.02 29 QCH 4.998 5.01 5.01 30 QCH 5.004 5.00 4.99 平均偏差/s 0.056 -0.017 标准偏差/s 0.078 0.075
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表 3
汶川余震P波的SNR估计
Table 3.
Estimated SNRs for Wenchuan aftershocks′ P waves
序号 台站编码 基于SNR的STA/LTA最大值 P波的SNR最大值/dB 1 JMG 9.402 21.5 2 PWU 9.996 43.5 3 QCH 9.901 29.5 4 JMG 9.860 28.0 5 MXI 9.991 40.0 6 PWU 9.791 26.2 7 QCH 9.873 28.4 8 JJS 7.126 13.3 9 JMG 9.859 28.0 10 PWU 9.933 31.2 11 QCH 9.647 23.9 12 JJS 5.432 9.9 13 JMG 6.772 12.5 14 PWU 8.336 16.4 15 QCH 7.844 15.0 16 JMG 6.229 11.4 17 PWU 9.965 34.1 18. QCH 8.314 16.4 19 QCH 9.034 19.2 20 QCH 9.916 30.3 21 JMG 9.980 36.5 22 JMG 9.301 20.7 23 JMG 9.880 28.7 24 JMG 8.644 17.5 25 PWU 9.955 33.0 26 PWU 9.231 20.3 27 PWU 9.984 37.5 28 PWU 9.703 24.7 29 QCH 9.995 42.6 30 QCH 9.995 42.6
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