Using multi-attribute transforms to predict log properties from seismic data
Bibliographic record
Abstract
In this paper, a new method for predicting well log properties from seismic data is described. The analysis data consists of a series of target logs from wells which tie a 3-D seismic volume. The objective is to derive a multi-attribute transform, linear or nonlinear, between a subset of the attributes and the target log values. The selected subset is determined by a process of forward step-wise regression, which derives increasingly larger subsets of attributes. In the linear mode, the transform consists of a series of weights, which are derived by least-squares minimisation. In the non-linear mode, an artificial neural network is used. Cross-validation is used to estimate the reliability of the derived multi-attribute transform.This method is applied to a real data set. We see a continuous improvement in prediction power as we progress from singleattribute regression to linear multi-attribute prediction to neural network prediction. This improvement is evident not only on the training data, but more importantly, on the validation data. In addition, the neural network shows a significant improvement in resolution over that from linear regression.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".