What moved where?: The impact of velocity uncertainty on microseismic location and moment-tensor inversion
Bibliographic record
Abstract
With the rise of unconventional resources, microseismic monitoring is becoming increasingly important because of its cost-effectiveness. This has led to significant research activity on how best to locate events and characterize their moment tensors. Locations tell us where fracturing is occurring, allow the tracking of fluid movement, and fracture propagation. Moment tensors help to determine the type of failure occurring, which is beneficial in planning and interpreting the results of hydraulic-fracturing jobs and in monitoring production. The rising number of methods to determine parameters raises important questions about how uncertainties in the input parameters are translated into uncertainties in the final locations and moment tensors. We present a framework for assessing these uncertainties and use it to demonstrate how velocity uncertainty — as well as uncertainties in arrival times and amplitudes — translates into uncertainties on the recovered quantities of location and moment-tensor parameters.
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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 | 0.001 | 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".