InSAR- and PCA-Based Inversion Reveals the Surface Deformation and Earthquake Sequence in the Weiyuan-Rongxian Shale Gas Field
Bibliographic record
Abstract
In recent years, the rapid expansion and development of the shale gas industry in the Sichuan Basin has coincided with a series of unexpected moderate-sized earthquakes. Given that the Sichuan Basin is situated within a stable interior block, the focal mechanism of the 2019 earthquake sequence (ML4.7, ML5.4, and ML5.2) in the Weiyuan-Rongxian area remains a subject of debate. In this study, we propose a joint InSAR- and PCA- based inversion method utilizing the distributed Mogi model to investigate the spatial-temporal characteristics of a gas reservoir and evaluate the induced Coulomb stress change. The surface deformation derived from Sentinel-1 data between 2015 and 2021 was consistent with the spatial distribution of production wells, and it correlated with the temporal changes in reservoir volume associated with the shale gas operating process. The Coulomb stress loading on the regional faults suggests that human activities associated with shale gas operation likely triggered the three moderate earthquakes. Furthermore, our results indicate Coulomb stress loadings of 10 kPa, 15 kPa, 5 kPa, 3 kPa, and 87 kPa on the Dongxingchang fault, Gaoqiao fault, Dayaokou fault, Niujingao fault, and Lijiachang fold, respectively. Consequently, fluid injection and extraction during shale gas development could be contributing to the elevated seismic activity in the Weiyuan-Rongxian area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".