A k-mean characteristic function for optimizing STA/LTA 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 datasets. Here, we aim to optimize the performance of the STA/LTA method by testing different input forms of three-component waveform data and different characteristic functions (CFs), including a proposed k-mean CF. These tests are evaluated using receiver operating characteristic (ROC) analysis and compared based on synthetic and field data examples. Our analysis shows that the STA/LTA method using a k-mean CF improves the detection sensitivity and yields more robust event detection on noisy datasets 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 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.