强噪声环境下基于信噪比的地震P波到时自动提取方法

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(10dB)和一般噪声环境(6dB),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(3dB),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.

Automatic picking up earthquake's P waves using signal-to-noise ratio under a strong noise environment

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.