Microseismic monitoring aims at detecting as weak events as possible and providing reliable locations and source mechanisms for these events. Surface monitoring arrays suffer from significant variations of noise levels across receiver lines. When using a large monitoring array, we use a stacking technique to detect microseismic events through maximizing the signal-to-noise ratio (S/N) of the stack. But some receivers with a high noise level do not contribute to improving the S/N of the stack. We have derived a theoretical concept for the proper selection of receivers that best contribute to the stack for a constant strength of a signal across the array. This receiver selection criterion, based on the assumption of constant signal amplitude, provides a robust estimate of the noise threshold level, which could be used to discard or suppress contribution from the receivers that do not improve the S/N of the stack. We found that limiting the number of receivers for stacking improves the location accuracy and reduces the computational cost of data processing. Although the assumption of a constant signal never holds in real-life seismic applications, the noise level varies across the surface receivers in a significantly wider range than the signal amplitude. These noise variations can also increase the uncertainty of the source mechanism inversion and should be accounted for. Synthetic and field data examples show that weighted least-squares inversion with receiver weighting according to the noise level produces more accurate estimates for source mechanisms compared to the inversion that ignores information about noise.

中文翻译：

---

表面微震监测中噪声变化较大的检测，定位和震源机制确定

微地震监测的目的是尽可能地检测弱事件，并为这些事件提供可靠的位置和来源机制。表面监测阵列的噪声在整个接收器线路上变化很大。当使用大型监视阵列时，我们使用堆栈技术通过最大化堆栈的信噪比（S / N）来检测微地震事件。但是某些具有高噪声水平的接收器不会有助于提高堆栈的信噪比。我们已经得出了一个理论概念，用于正确选择接收器，该接收器对于整个阵列中信号强度恒定的堆栈最有帮助。基于恒定信号幅度的假设，此接收器选择标准可提供对噪声阈值水平的可靠估计，可以用来丢弃或抑制来自接收器的贡献，这些贡献不会改善堆栈的信噪比。我们发现，限制用于堆叠的接收器的数量可以提高定位精度，并降低数据处理的计算成本。尽管恒定信号的假设在现实地震应用中永远不成立，但整个地面接收器的噪声水平在比信号幅度大得多的范围内变化。这些噪声变化也会增加源机制反转的不确定性，因此应予以考虑。合成和现场数据示例显示，与忽略噪声信息的反演相比，根据噪声水平进行加权的最小二乘反演与接收器权重可对源机制产生更准确的估计。