Capturing Geological Realism in Stochastic Simulations of Rock Systems with Markov Statistics and Simulated Annealing
Bibliographic record
Abstract
Abstract Simulated annealing is a numerical algorithm that can be used to impose statistical structures on numerical grids representing heterogeneous rock or sediment. In this paper, we use the flexibility of simulated annealing to generate grids with Markov statistical structures. Our purpose is to transmit the rich geological information captured in Markov statistics into stochastic grids while maintaining the flexibility of annealing to honor field data. Performance issues that compromise annealing grids imbued with Markovian properties include scales of bedding or rock bodies relative to grid size, and the amount of geological complexity in the embedded Markov structures. The remedies to these issues include proper selection of grid size, careful choice of annealing type, and consideration of an alternative annealing stopping rule based on a chi-squared test statistic. If performance issues are overcome, complex stratal patterns such as higher-order dependency, cyclicity, and directionality can be replicated in grids by this method. In addition, accounting for variations in depositional rate allows for transference of Markov structures obtained from vertical boreholes to the horizontal dimension when other information is lacking. A field example using borehole data collected at the Gloucester special waste site near Ottawa, Canada, as well as synthetic examples, demonstrate the technique and performance issues.
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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 | 0.000 |
| 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) | 0.001 | 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 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".