Toward a near real‐time system for event hypocenter and source mechanism recovery via compressive sensing
Bibliographic record
Abstract
The simultaneous inversion for source location and mechanism of microseismic events is a significant problem in the fields of applied geophysics and petroleum engineering. Methodologies for real-time source moment tensor estimation may become of significant importance for the monitoring of hydraulic fractures. We examine an algorithm for a quasi-real time implementation of source location estimation and seismic moment tensor inversion of microseismic events. The algorithm requires the inversion of a dictionary of Green functions containing the multi-component response of each potential seismic source in the subsurface. The problem entails the inversion of a prohibitively large matrix that, at a first glance, does not appear amenable to a real-time implementation. Compressive sensing, however, can be used to reduce the size of the dictionary of Green functions to up to 90% of its original size, making a near real-time execution computationally tractable. The algorithm is validated with a small magnitude earthquake (18 June 2002, Caborn, Indiana) with a well-studied source mechanism and hypocenter location.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".