Event detection using a fast matched filter algorithm – An efficient way to deal with big microseismic data sets
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".