Simulation of porosity field using wavelet bayesian inversion of crosswell GPR and log data
Bibliographic record
Abstract
In this paper, we present a novel approach to simulate porosity fields constrained by borehole radar tomography images. The cornerstone of the method is the bayesian analysis of the approximation wavelet coefficients of a petro-physical analogue. The method is tested with a two-dimensional porosity field generated from a digital picture of a real sand deposit. The porosity field is translated into electrical properties and a cross-hole tomography synthetic survey is modeled using a finite-difference modeling algorithm. In parallel, an analogue deposit is created based on the geological knowledge of the area under study. The analogue porosity field is converted into electrical property fields using the same equations as previously. A synthetic GPR tomography is also computed from the latter. Wavelet decomposition of both measured and analogue tomograms and porosity analogue fields is then calculated. Based on the assumption that geophysical data carry only the large-scale information about the geological model, statistical analysis of the approximation coefficients of each variable is carried out. From the measured tomogram approximation coefficients and the cross statistics evaluated on the analogues, the approximation of the real porosity field is inferred using bayesian inference. Finally, based on the geostatistical relationships between wavelet coefficients across the different scales, all the porosity wavelet detail coefficients are simulated using a standard geostatistical simulation algorithm. The wavelet coefficients are then back transformed in the porosity space. The final simulated porosity fields contain the large wavelengths of the measured radar tomogram and the texture of the analogue porosity field.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".