Automated seismic waveform location using Multichannel Coherency Migration (MCM)—II. Application to induced and volcano-tectonic seismicity
Bibliographic record
Abstract
Locating microseismic events is essential for many areas of seismology including volcano and earthquake monitoring and reservoir engineering. Due to the large number of microseismic events in these settings, an automated seismic location method is required to perform real time seismic monitoring. The measurement environment requires a precise and noise-resistant event location method for seismic monitoring. In this paper, we apply Multichannel Coherency Migration (MCM) to automatically locate microseismic events of induced and volcano-tectonic seismicity using sparse and irregular monitoring arrays. Compared to other migration-based methods, in spite of the often sparse and irregular distribution of the monitoring arrays, the MCM can show better location performance and obtain more consistent location results with the catalogue obtained by manual picking. Our MCM method successfully locates many triggered volcano-tectonic events with local magnitude smaller that 0, which demonstrates its applicability on locating very small earthquakes. Our synthetic event location example at a carbon capture and storage site shows that continuous and coherent drilling noise in industrial settings will pose great challenges for source imaging. However, automatic quality control techniques including filtering in the frequency domain and weighting are used to automatically select high quality data, and can thus effectively reduce the effects of continuous drilling noise and improve source imaging quality. The location performance of the MCM method for synthetic and real microseismic datasets demonstrates that the MCM method can perform as a reliable and automatic seismic waveform analysis tool to locate microseismic events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".