A k-mean characteristic function for optimizing short- and long-term-average-ratio-based detection of microseismic events

Event detection is an essential component of microseismic data analysis. This process is typically carried out using a short-and long-term-average-ratio (STA/LTA) method, which is simple and computationally efficient but often yields inconsistent results for noisy data sets. We have aimed to optimize the performance of the STA/LTA method by testing different input forms of 3C waveform data and different characteristic functions (CFs), including a proposed k-mean CF. These tests are evaluated using receiver operating characteristic (ROC) analysis and are compared based on synthetic and field data examples. Our analysis indicates that the STA/LTA method using a k-mean CF improves the detection sensitivity and yields more robust event detection on noisy data sets than some previous approaches. In addition, microseismic events are detected efficiently on field data examples using the same detection threshold obtained from the ROC analysis on synthetic data examples. We recommend the use of the Youden index based on ROC analysis using a training subset, extracted from the continuous data, to further improve the detection threshold for field microseismic data.

事件检测是微震数据分析的一个重要组成部分。这个过程通常使用短长时平均比（STA/LTA）方法来进行，该方法简单且计算效率高，但对于有噪声的数据集往往会产生不一致的结果。我们旨在通过测试三分量波形数据的不同输入形式以及不同的特征函数（CF），包括一种提出的k - 均值CF，来优化STA/LTA方法的性能。这些测试使用受试者工作特征（ROC）分析进行评估，并基于合成数据和现场数据实例进行比较。我们的分析表明，使用k - 均值CF的STA/LTA方法比一些先前的方法提高了检测灵敏度，并在有噪声的数据集上产生更稳健的事件检测结果。此外，使用从合成数据实例的ROC分析中获得的相同检测阈值，在现场数据实例上能够有效地检测到微震事件。我们建议使用基于从连续数据中提取的训练子集进行ROC分析的约登指数，以进一步提高现场微震数据的检测阈值。