Numerical modeling of low-frequency distributed acoustic sensing signals for mixed-mode fracture activation
Bibliographic record
Abstract
In recent years, the development of distributed acoustic sensing (DAS) technology has enabled direct monitoring of subsurface strain during hydraulic fracturing operations. Most low frequency (<1Hz) DAS signals (LFDAS) exhibit strain or strain-rate patterns that are characteristic of propagating Mode-I (tensile) hydraulic fractures. However, in cases where pre-existing natural fractures or faults exist in proximity to operations, mixed-mode failure is possible, consisting of shear slip (Mode-II) plus dilation. Yet, the characteristics of mixed-mode LFDAS signals are poorly understood. We present a numerical simulation approach based on the Displacement Discontinuity Method to perform forward modeling of the mixed-mode LFDAS signals during the initial reactivation stages for a critically stressed fault. Our novel workflow provides a practical tool to perform fast forward modeling to characterize fault reactivation during hydraulic fracturing operations.
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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) | 0.001 | 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 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".