Wang Chunlu, Fan Yanqing, He Renjie, Li Jiwu, Zhao Fa, Zhou Xiaohua, Chen Zubin
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China.
Key Lab of Geo-Exploration Instrumentation of Ministry of Education, Jilin University, Changchun 130026, China.
Rev Sci Instrum. 2025 Apr 1;96(4). doi: 10.1063/5.0239346.
Microseismic (MS) monitoring, which captures signals generated during rock mass fractures, can monitor changes in underground reservoir characteristics. It is of significant importance for the guidance and evaluation of hydraulic fracturing and prediction of geological disasters. However, the signals recorded by seismic detectors often contain various types of noise, especially in surface monitoring with more complex environments. Extracting effective MS signals and accurately picking up their arrivals serves as the foundation for subsequent positioning and other inversion processes. Given the unknown frequency distribution of effective MS signals, it is difficult to achieve signal-to-noise separation through simple filtering methods. In this paper, we propose a novel automatic arrival picking method based on variational mode decomposition (VMD) and Akaike information criterion (AIC). First, VMD is utilized to decompose the original signal into several intrinsic mode functions (IMFs). Then, the Pearson correlation coefficient (CC) and peak-to-average power ratio (PAPR) are combined to determine the effective components. Finally, we reconstruct the signal and employ the AIC method to pick up the arrival of MS events. Applying this method to synthetic tests based on Ricker wavelet, the results demonstrate that it can accurately distinguish effective signals from noise components, exhibiting superior robustness to noise compared to other arrival picking methods. Furthermore, the processing results of field MS signals during the fracturing process of a shale gas well in Sichuan Province also validate the advantages and application potential of the proposed method.
微地震（MS）监测能够捕捉岩体破裂过程中产生的信号，可用于监测地下储层特征的变化。这对于水力压裂的指导与评估以及地质灾害的预测具有重要意义。然而，地震探测器记录的信号通常包含各类噪声，尤其是在环境更为复杂的地表监测中。提取有效的微地震信号并准确拾取其初至波是后续定位及其他反演过程的基础。鉴于有效微地震信号的频率分布未知，难以通过简单的滤波方法实现信噪分离。本文提出了一种基于变分模态分解（VMD）和赤池信息准则（AIC）的新型自动初至波拾取方法。首先，利用VMD将原始信号分解为若干本征模态函数（IMF）。然后，结合皮尔逊相关系数（CC）和峰均功率比（PAPR）来确定有效分量。最后，对信号进行重构，并采用AIC方法拾取微地震事件的初至波。将该方法应用于基于雷克子波的合成测试，结果表明它能够准确地从噪声分量中区分出有效信号，与其他初至波拾取方法相比，对噪声具有更强的鲁棒性。此外，对四川省某页岩气井压裂过程中的现场微地震信号的处理结果也验证了该方法的优势及应用潜力。
