【摘要】 针对井中微地震监测数据由于信噪比低、震源强度小、信号弱等原因造成的有效信号难于识别的问题,本文将多尺度形态学理论应用于弱信号分析、识别中。有效信号与噪声在振幅和延续时间上具有一定的差异,因此可以在形态上进行数字信号分析。该方法基于波形形态的细节差异进行分析,对数据的形态特征进行分解。利用形态学中多个尺度的结构元素与原始数据进行运算,可以得到不同尺度的分量。通过分析不同尺度下的信号特征,估计并检测出微弱信号和噪声。模型数据测试和野外实际微地震资料处理结果均表明,本文方法可有效地识别较弱的信号并对噪声进行压制,验证了该方法的有效性和实用性。更多还原

【Abstract】 Data acquired by borehole microseismic monitoring is characterized by low signal-to-noise ratio and weak energy.So it is very difficult to identify signals.We propose in this paper a multi-scale morphological approach for weak signal identification.There are some small difference in amplitude and duration between noise and signal,Therefore it can be carried out in digital analysis based on morphology.This approach decomposes data morphological characteristics,and analyzes waveform shape variance details.With different-scale structural elements,the original data can be decomposed into different scales.Then characteristics of weak signals and noise in different scales are identified and noise would be eliminated.Examples of both synthetic and real data show that the proposed approach can identify weak signals and suppress noise,which proves the effectiveness and practicability of the proposed approach.更多还原