Interpretation of subsurface stratigraphic variations from limited boreholes using Dual Bi-LSTM
Bibliographic record
Abstract
The determination of stratigraphic delineation is the core of geotechnical design, significantly influencing the safety and serviceability of infrastructures. Accurately inferring subsurface stratigraphic variations from sparse borehole data still presents a considerable challenge due to the complicated spatial correlation of soils. In this paper, the dual bidirectional long short-term memory (Bi-LSTM) neural network is developed for accurate stratigraphic delineation by using limited boreholes. Based on a novel cross-shaped sampling system, the Dual Bi-LSTM efficiently captures intricate spatial dependencies. Moreover, numerical and one-hot encoding methods are compared to explore different ways of representing stratigraphic features. The proposed model is validated and implemented in three practical projects collected from Australia, Hong Kong, and the Netherlands, respectively, compared with the Markov random field (MRF) and IC-XGBoost method. Furthermore, the effects of borehole density, neighborhood scale, and sampling scheme are investigated based on a nonlinear and non-homogeneous synthetic case. The proposed model achieves an accuracy of 60.35% in boundary predictions of the Australia case, surpassing the MRF and the IC-XGBoost model by around 6% and 23%, respectively. The proposed Dual Bi-LSTM is highlighted to provide a user-friendly alternative for involving an accurate and reasonable soil profile using sparse site-specific boreholes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".