Integrating Geophysics and Soil Sampling for Site Characterization: A Kernel Approach
Bibliographic record
Abstract
ABSTRACT Reconstructing the variation of contaminant concentration with a limited number of soil samples is more or less the norm, even though it fails more often than not for problems of even moderate complexity. To overcome the limits inherent to discrete measurements, we propose to integrate soil sampling with continuous surface geophysical measurements in a geostatistical framework. We present this integrated analysis for a PAH contaminated site in France. For the study site, two 3D surveys were acquired: an electrical resistivity tomography survey and a seismic travel time tomography survey. Those two surveys permitted us to infer two spatially continuous physical properties on the whole volume, namely the electrical resistivity and P-wave velocity. The probability density function relating the velocity-resistivity pairs with each of the 75 lab measurements of PAH concentration was modeled using a Gaussian kernel. This probability density function combined with the 3D volumes of resistivity and P-wave velocity provided a means to translate the latter into a 3D map of PAH concentration. This 3D map of concentration was then used as a secondary variable in a cokriging simulation of the 75 lab samples, thus reintroducing the spatial correlation of the initial dataset. Comparing this final 3D PAH concentration model with the simple kriging of the PAH samples, the geophysical integrated model reproduce much better the distribution of measured concentration, shows a much more realistic spatial pattern of the contamination, and lowers the estimated contaminated volume.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".