Geological and geophysical integrated interpretation and modelling techniques
Bibliographic record
Abstract
Exploration is becoming harder, at depth or under cover and decisions need to be made in model rather than data space; supported by multiple data sets. Geophysics plays an ever increasing role and integration of information from various geophysical data sets in tight collaboration with geological control is required to maximise the return from the individual data sets.In terms of integrating geological and geophysical data, the essential goal is to interpret the available geophysical data in terms of geological domains. The process requires a common sense approach to interpretation that is flexible, adaptive and objective driven. It is not an exact formula or procedure; particularly when multiple geophysical surveys are involved. Understanding the relationships between geology, geophysical responses and rock properties is the key to develop a geological basis for your integrated interpretation. Following this, rapid 3D geological modelling and geologically based forward modelling and inversion are essential for model validation and quantitative integration of data. An integrated interpretation is not necessarily the simplest approach, but does provide answers to geoscientific questions that are stronger than individual elements interpreted on their own.This paper presents a review of the mechanics involved in integrated interpretation and demonstrates the results with selected case study examples.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".