Microseismic source mechanisms in underground mines can provide information about the rock mass response to mining. Conventional approaches to such studies rely upon moment tensor solutions that are susceptible to modeling assumptions and need reliable information about source locations and high-resolution velocity models. We propose the application of unsupervised clustering to group microseismic events into different classes directly from the waveform data such that the events in a specific class have similar source mechanisms. Our method has three main steps, first using spectral decomposition to separate the source terms from the path-receiver contributions in the observed amplitude spectra of events occurring in spatially dense clusters. Second, reducing the number of features from the source spectra using independent component analysis (ICA). Third, applying a Gaussian mixture model (GMM) to the reduced feature matrix to obtain event clusters. To test our method, we generate synthetic waveform data using the receiver network and the recorded microseismic event locations in an underground potash mine in Saskatchewan. Results show the ability of our method to separate events into different classes corresponding to differences in source mechanisms. Application to field data recorded in the mine during February 2021 successfully discriminates between blasts and microseismic events. The data recorded between 1 March and 30 June 2021 that contain microseismic events only are divided into two dominant classes. Using known moment tensors (MT) of some of these events for labeling, we interpret one of the two classes as having dominant double-couple mechanisms as compared to the other which most likely corresponds to the linear dipole-tensile mechanisms. Our method, combined with some expert knowledge such as MT of some larger magnitude events, can offer an assessment of source types of large microseismic populations as often encountered in induced seismicity.

中文翻译：

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采矿诱发的微震活动的无监督聚类提供了对震源机制的见解


地下矿井中的微震源机制可以提供有关岩体对采矿响应的信息。此类研究的传统方法依赖于矩张量解，这些解容易受到建模假设的影响，并且需要有关源位置和高分辨率速度模型的可靠信息。我们提出了应用无监督聚类，直接从波形数据中将微震事件分组为不同的类别，以便特定类别中的事件具有相似的源机制。我们的方法有三个主要步骤，首先使用频谱分解将源项与观测到的空间密集集群中发生的事件的振幅谱中的路径接收器贡献分开。其次，使用独立成分分析 （ICA） 减少源光谱中的特征数量。第三，将高斯混合模型 （GMM） 应用于约化特征矩阵以获得事件簇。为了测试我们的方法，我们使用接收器网络和萨斯喀彻温省地下钾矿中记录的微震事件位置生成合成波形数据。结果表明，我们的方法能够将事件分成不同的类，这与源机制的差异相对应。应用于 2021 年 2 月在矿井中记录的现场数据，成功区分了爆炸和微震事件。2021 年 3 月 1 日至 6 月 30 日期间记录的仅包含微震事件的数据分为两个主要类别。使用其中一些事件的已知矩张量 （MT） 进行标记，我们将两类中的一类解释为具有占主导地位的双耦合机制，而另一类很可能对应于线性偶极子拉伸机制。 我们的方法结合一些专业知识，例如一些较大震级事件的 MT，可以评估诱发地震中经常遇到的大型微震群的震源类型。