Innovative subsurface stratigraphy interpretation by integrating electrical resistivity tomography and borehole data
Bibliographic record
Abstract
Subsurface stratigraphy interpretation often relies on sparse borehole data. Due to the inherent spatial variability and limited borehole data, uncertainty inevitably exists in the interpreted subsurface stratigraphy. Geophysical approaches such as electrical resistivity tomography (ERT) provide continuous subsurface data in the concerned cross-section. Though the reliability of the geophysical data derived is much lower than that of the solid borehole data, the continuous subsurface data obtained from geophysical investigations might be taken as a complement to the sparse borehole data in the subsurface stratigraphy interpretation. This paper proposes an innovative subsurface stratigraphy interpretation approach, which takes advantage of the high reliability of sparse borehole data and the abundance of ERT data. This approach is partially developed based on the random field approach recently advanced by the authors, and the relationship between ERT data and borehole stratigraphies is mapped with the algorithm of the support vector machine. The uncertainty of the interpreted subsurface stratigraphy arising from the random selection of the training and testing datasets (for training the mapping relationship between ERT data and stratigraphies) is analyzed by a “ bootstrapping” method. Furthermore, field tests of site investigation at four benchmark stratigraphic cross-sections are conducted, and high-quality ERT and borehole data are collected. Based on the high-quality site investigation data obtained, illustrative applications of the proposed approach are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".