The effect of clay seams in borehole GPR attenuation tomography
Bibliographic record
Abstract
Shallow borehole GPR measurements can be of precious aid in geotechnical and environmental studies when the topmost soils show high conductivity and prevent penetration of EM energy from the surface. However, in many sedimentary environments, clay seams or lenses are likely encountered along the borings. The presence of such seams affects the radiation pattern of the antennas, thus corrupting the attenuation tomography results. The influence of small scale clay units on attenuation tomographic results are appraised through numerical modeling and comparison with field data. Borehole GPR measurements were performed to complement the site characterization of a planned expansion of a cement plant. A basic geological model is built from interpretation of these data. Forward modeling is performed with clay lenses of various sizes successively inserted along the transmitting borehole. The results show that the travel times are almost unaffected by the presence of clay. On the other hand, amplitudes are more severely distorted. It is found that the presence of clay can either reduce or amplify the first arrivals, depending on the position of the transmitter and receiver pair relative to the clay seam. The synthetic amplitudes and travel times are inverted, thus allowing the construction of a synthetic conductivity map for the various seam models. These maps show that whatever the size of the lens, the quality of the reconstruction is affected. Therefore, conclusive observations made from attenuation tomography based on amplitude inversion are hazardous in the presence of small scale heterogeneities near the 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".