Continuous hypocenter and source mechanism inversion via a Green's function-based matching pursuit algorithm
Bibliographic record
Abstract
The hydraulic fracturing of rock formations bearing oil and gas is a process that aims to improve well productivity. The breaking of rocks releases seismic energy that is recorded by stations usually located in nearby wells. By processing these recordings, the hypocenters of induced microseismic events are retrieved and interpreted to estimate the fractured volume. Several techniques have been developed to determine hypocenters; the most common are based in the inversion of P- and S-wave time picks from multiple stations (Pujol, 2004). There are also procedures that involve, for example, the back propagation in time of the recorded wavefields (Gajewski and Tessmer, 2005). Besides the hypocenter location of microseismic events, there is additional information embedded in the seismic recordings: the source mechanism, which is expressed via the seismic moment tensor (SMT). Not as common as the estimation of the hypocenter location, the SMT of induced microseismic events is also estimated from monitoring records (e.g., Nolen-Hoeksema and Ruff, 2001; Jechumtalova and Eisner, 2008). The SMT provides an additional source of information for understanding the fracturing process. A quasi real-time system for simultaneous event location and SMT inversion can reduce the processing and interpretation turnaround times. More notably, such a system could facilitate a realistic integration of microseismic geophysical data to significant decisions that are made during engineering operations such as fracturing.
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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".