Visualization of 3D Geologic maps: an example using volumetric clipping with hardware
Bibliographic record
Abstract
The geological map is an essential means of convey-ing information about a geological domain. However, quantitative applications and precise depth mapping call for a 3D representation of geological units. On such 3D maps, the 2D visualization of rock units along a section remains essential to understand and check the consistency of a 3D model. We propose a fast and simple tool to slice in real-time a sealed geological model by one or several arbitrary planes. The intersection polygon between a section plane and the geological model is never explicitly computed in three-dimensions, but filled by graphics hardware in screen space using the stencil buffer. Edge detec-tion algorithms can be run on the section to draw sharp unit boundaries. The polygon is filled either by one single color corresponding to the legend, or by a two-dimensional texture image corresponding to the interpolation of a rock property inside the geolog-ical unit. The method is applied to the imaging of a continuous velocity model in an oil reservoir without generating a large velocity cube, and to the visualiza-tion of complex Precambrian units in the Canadian
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".