Inference of 2D and 3D locally varying anisotropy fields for complex geological formations
Bibliographic record
Abstract
Many geostatistical techniques perform better when anisotropy is considered to be locally varying, but there are few techniques available for inferring the necessary locally varying anisotropy (LVA) field. All geostatistical modeling methodologies assume a form of stationarity; one common assumption is second order stationarity where anisotropy is considered to be globally constant. This assumption is often geologically inappropriate and requires geostatisticians to subdivide the modeling area into stationary domains, thereby increasing the required professional time for modeling and potentially producing disjointed domains that are globally inconsistent. Existing algorithms can be modified to relax the assumption of second order stationarity, for example: multiple point statistics with local pattern reorientation; local kriging search reorientation, and; kriging with LVA. These techniques require an exhaustive model of the local orientation and magnitude of anisotropy, termed an LVA field. In practice there are rarely direct measurements of the LVA field, even local dipmeter data can be unreliable due to the discrepancy between the measurement of small scale anisotropy and the larger block scale anisotropy required for modeling. The focus of this work is to develop a suite of methodologies for LVA field inference from various 2D or 3D data sources. No single methodology is appropriate for all deposits because local geology and data availability vary. Methodologies presented for inference of 2D LVA fields include (1) manual feature interpolation (2) structure tensor, and (3) polylines. Methodologies for inference of 3D LVA fields are similar but require additional considerations for visualization and checking. Techniques are demonstrated with real data when available, examples include: a porphyry deposit and a uranium role front deposit. Recommendations to aid in technique selection are provided and are largely determined by the nature of the data available and the geometry of the formation under study.
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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) | 0.001 | 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".