Analysing spatialized data: applications of geo-datasciences in geophysics and petroleum industry
Bibliographic record
Abstract
For centuries, the classical theories of the earth-sciences were developed based on the observations and the hypotheses to justify them. Since 20th century, the digitized observations (data) as well as the progress of statistical methods and numerical capacities paved the road for the “geo-datasciences” (§ 1).This manuscript reviews the application of the data-sciences to divers’ domains within the earth-sciences. Most publications are about the petrophysical well-log interpretation to characterize petroleum reservoirs, using data fusion algorithms: estimation, classification, and cluster analysis (§ 2).However, the recent publications are mainly about the application of geostatistics in modelling spatialized data in mapping contaminated soil by caesium-137. Other presented case studies are related to a geostatistical-geotechnical study about soil liquefaction, as well as mapping total magnetic field while decreasing artefacts related to the trajectory (go-and-return) during the acquisition (§ 2).Three themes are presented as current research subjects which constitute the perspective too: (i) application of local anisotropy as a geostatistical solution, (ii) developing fuzzy pairing in variographical analysis, and (iii) developing deconvolution relations for geophysical measurements for the purpose of sampling design (§ 3 and § 4).Though chapters § 2 and § 3 address the experts in the geo-datasciences, chapters § 1 and § 4 are composed for a larger pubic, interested in the evolution of the earth-sciences, as well as some general views regarding researchers’ typology and research values shared in § 4.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.015 |
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".