论文详情
井中微地震监测过程中因压裂施工、井筒噪声和仪器噪声等因素可能产生持续的强能量背景干扰信号。这些干扰信号严重影响了有效微地震事件的识别与初至拾取,它们具有时变非平稳随机信号特点,常规时域或频域滤波的预处理方法对其压制效果并不理想,过度滤波则可能对有效微地震信号造成损害。针对上述问题,以井中微地震监测三分量记录信号之间的同步与相关分析为基础,提出了基于多维经验模态分解的微地震监测记录中背景干扰信号的识别与压制方法。该方法通过对三分量微地震监测记录进行多维经验模态分解,获得不同阶次的本征模态函数分量,然后根据偏振属性的变化从中识别并去除与背景干扰信号对应的成分,以达到净化监测记录的目的。实际资料处理结果表明,与常规预处理方法相比,此方法能够有效识别和压制持续背景干扰信号,显著降低监测记录的噪声水平,有助于改善弱信噪比微地震信号的识别及初至拾取的效果。
Strong background interferences may occur during microseismic monitoring,owing to factors such as fracturing construction,wellbore disturbance,and instrument noise,which may seriously affect the identification of microseismic events and arrival time picking.These signals are generally non-stationary and time-varying;therefore,conventional time-domain or frequency-domain filtering preprocessing methods are not appropriate for suppressing them.Moreover,over-treatment may cause the microseismic signals to be distorted.In order to overcome these issues,in the framework of a synchronization and correlation analysis between the three-component recording signals of microseismic monitoring,a method based on multivariate empirical mode decomposition is proposed.In this method,three-component microseismic monitoring recordings are decomposed to obtain the intrinsic mode function,and the components corresponding to the background interference signal are identified and removed based on the variation of polarization properties.An application to actual data revealed that continuous background interference signals can be effectively identified and compressed.Compared with a conventional preprocessing method,the proposed method can significantly reduce the noise level of monitoring recordings,thereby improving the identification and arrival time picking of microseismic signals with low signal-to-noise ratios.
国家科技重大专项课题“MEMS技术及工业化试验”(2017ZX05008-008)资助。