Locating Microseismicity from Surface Monitoring Arrays Using Grid Search and Hypocenter Inversion - A Case Study
Bibliographic record
Abstract
Summary In a previous numerical study ( Huang et al., 2013 ), we developed a new grid search algorithm to automatically locate microseismic (MS) events from streaming data recorded by surface monitoring survey. The symmetric nature of the semblance with respect to the origin time was used to identify the MS location. In this paper, we form a comprehensive workflow by extending this algorithm to include arrival time refinement using cross-correlation and nonlinear optimization similar to Geiger’s method to update the location and origin time in order to minimize the residual between the observed and the theoretical arrivals. We demonstrate the efficiency of our workflow on seismograms continuously recorded by surface monitoring stations. We show that the automated grid search identified a significant amount of potential signals indicating the level of local seismic activities. The nonlinear optimization is applied to events with clear arrivals, and the vertical uncertainty due to the surface acquisition geometry and the coarse grid effect in the grid search are reduced. This algorithm can be directly applied to both natural earthquakes and reservoir stimulation monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".