Inversion Study of Hydrogeological Parameters for Metro Foundation Pit Confined Aquifiers Based on Surrogate Modeling
Bibliographic record
Abstract
[Objective] To enhance the safety of metro station foundation pit construction, it is essential to accurately determine the groundwater hydrogeological parameters, improving the accuracy of permeability and storage coefficient in confined aquifers particularly. This is a critical prerequisite for formulating dewatering schemes. Therefore, a more in-depth study of the hydrological parameters of confined aquifers in metro foundation pits is required. [Method] Based on the dewatering test at Sports Center Station on Ningbo Rail Transit Line 7, a three-dimensional transient groundwater seepage model is developed with Modflow6 software called via the Flopy module in Python language. An LSTM (long- and short-term memory) deep learning model is introduced to build a surrogate model of confined aquifer water level variations. Combined with a particle swarm optimization algorithm and based on field-measured data, an inverse analysis of the confined aquifer permeability and storage coefficients is conducted. Thereby a method for hydrogeological parameter inversion in metro foundation pits based on surrogate modeling and optimization algorithms is proposed. [Result & Conclusion] The obtained inverted vertical permeability coefficient is 0.76×10-5 m/s, the horizontal permeability coefficient is 1.38×10-5 m/s, and the storage coefficient is 6.42×10-5 m-1. When these parameters are input into the numerical seepage model, the calculated data closely matches the measured data in all process, including the stage of rapid water level drop in the initial pumping, the stage of gradual change during the stabilization period, and the stage of water level gradual recovery after pumping stops, validating the feasibility of the inversion method. The use of deep learning-based surrogate modeling combined with optimization algorithms enables efficient and accurate inversion analysis of groundwater parameters.
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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) | 0.000 | 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 | 0.001 |
| 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 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".