Inversion for Shear-Tensile Focal Mechanisms Using an Unsupervised Physics-Guided Neural Network
Bibliographic record
Abstract
Abstract We present a novel physics-guided neural network to estimate shear-tensile focal mechanisms for microearthquakes using displacement amplitudes of direct P waves. Compared with conventional data-driven fully connected (FC) neural networks, our physics-guided neural network is implemented in an unsupervised fashion and avoids the use of training data, which may be incomplete or unavailable. We incorporate three FC layers and a scaling and shifting layer to estimate shear-tensile focal mechanisms for multiple events. Then, a forward-modeling layer, which generates synthetic amplitude data based on the source mechanisms emerging from the previous layer, is added. The neural network weights are iteratively updated to minimize the mean squared error between observed and modeled normalized P-wave amplitudes. We apply this machine-learning approach to a set of 530 induced events recorded during hydraulic-fracture simulation of Duvernay Shale west of Fox Creek, Alberta, yielding results that are consistent with previously reported source mechanisms for the same dataset. A distinct cluster characterized by more complex mechanisms exhibits relatively large Kagan angles (5°–25°) compared with the previously reported best double-couple solutions, mainly due to model simplification of the shear-tensile focal mechanism. Uncertainty tests demonstrate the robustness of the inversion results and high tolerance of our neural network to errors in event locations, the velocity model, and P-wave amplitudes. Compared with a single-event grid-search algorithm to estimate shear-tensile focal mechanisms, the proposed neural network approach exhibits significantly higher computational efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".