Applying principal component analysis to seismic attributes for interpretation of evaporite facies: Lower Triassic Jialingjiang Formation, Sichuan Basin, China
Bibliographic record
Abstract
Abstract In China and elsewhere, it is important to predict different lithologies and lithofacies for hydrocarbon exploration in a mixed evaporite-carbonate-siliciclastic system. The lower section of the second member of the Jialingjiang Formation (T1j2L) is mainly composed of anhydrite, dolostone, limestone, and siliciclastic rocks, providing a rare opportunity to reconstruct detailed facies in a 2500 km2 3D seismic survey with 31 wells. Wireline logs (sonic, density, and gamma ray) calibrated by core analysis are essential in distinguishing anhydrite, siliciclastics, and carbonates. Although different lithologies are characterized by different acoustic impedance (AI), with certain overlapping, it is still difficult to predict lithology by any single seismic attribute because of the limited seismic resolution in a thinly interbedded formation of multiple lithologies. In our study, principal component analysis (PCA) was applied to extract lithologic information from selected seismic attributes; the first two principal components were used to predict the content of anhydrite, siliciclastics, and carbonates. Content maps of anhydrite, siliciclastics, and carbonates — created by mixing the represented color — were used to reconstruct lithofacies of the T1j2L submember. It is quite difficult, even with the PCA approach, to uniquely resolve the three lithologies due to the overlapped AI and the limited resolution of the seismic data. However, the workflow that we evaluated dramatically improved the prediction accuracy of lithology and lithofacies. Facies transition during the deposition of the T1j2L submember in the study area was inferred from a paleo-uplift in the southwest to a restricted lagoon and then to an open marine setting in the northeast.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".