Event detection is one of the time-consuming parts in microseismic processing. Different automated event detection algorithms have been proposed, such as the short-time average over the long-time average (STA/LTA), power spectral density (PSD), and subspace detection (SD). However, these approaches are not convenient for big data sets. In this study, we introduce a fast matched filter (MF) algorithm, which can solve the efficiency challenge for these detectors. The proposed fast MF is built based on a fast normalized cross-correlation (NCC) technique. This method detects events in the data based on their similarity with template events by comparing the NCC coefficients between the template events and the data with a specific user-defined threshold. The detection workflow consists of six steps, namely data preconditioning, selecting template waveforms, multiplexing, fast NCC computation, extracting potential events, and quality control of the detection results. We have implemented the MF algorithm on a microseismic data set with over 19,000 events detected on two monitoring wells. The MF detection results are compared with the results from STA/LTA. The MF algorithm is more efficient in event detection with fewer false alarms, higher detection probability, and shorter processing time than STA/LTA, especially when dealing with big, noisy data sets.
Presentation Date: Monday, October 12, 2020
Session Start Time: 1:50 PM
Presentation Time: 4:20 PM
Location: 360A
Presentation Type: Oral
