“Texas in Australia? Imaging channel sands in the Cooper Basin”
Bibliographic record
Abstract
This paper is inspired by an image of a time slice from the prolific hydrocarbon state of Texas showing a stacked channel system. Similar channel systems also exist in Australia’s Cooper Basin but are often difficult to see with legacy sparse seismic acquisition geometries. Due to reasons of cost and environmental and cultural heritage protection, relatively wide (‘sparse’) line intervals have been used, though these also allow coverage of larger areas than would otherwise be achieved. These sparse designs can combine low environmental impact with reasonable images at target. Lack of traces at medium and near offset ranges may result in strong amplitude artefacts in the final image - “acquisition footprint”. This case study is from the 2012 acquisition where, to reduce these artefacts, we deployed sources in a smooth “wavy” sinusoidal pattern, modified as necessary to follow natural features in the terrain. This methodology results in acquisition with a minimal visual and environmental impact and provides significant benefits in reducing the acquisition footprint. Innovative survey design and data processing techniques which accommodate non-uniform sampling resulted in the dataset where channel features are now clearly visible on the migrated volumes. The acquisition technique also features broadband point- source vibroseis using a non-linear Maximum Displacement sweep of 2 to 100 Hz, broadband digital point-receivers and dense sampling along both the source and receiver lines. Using the described technologies the acquired survey not only met, but by far exceeded, the initial expectations, and within the specified time frame. This survey has shown that exploration seismic surveys can be tailored to minimize environmental and acquisition foot print, and provide a high quality seismic dataset suitable for seismic attributes extraction.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".