Contourites—A Paleovelocity Meter from Geologic Analogues Created from 3D Seismic Data
Bibliographic record
Abstract
Summary We introduce a method to infer the direction and velocity of paleoocean bottom currents from seismic data using the concept of geologic analogues and integration with modern oceanographic measurements. Colour-processed seismic data provide erosional features at the ocean bottom that can be correlated with oceanographic measurements to validate the modern direction and velocity of the bottom current. We use this calibrated model as the modern analogue to infer paleoocean bottom characteristics of erosional contourite features from subsurface seismic data. We have discarded traditional wavelet-based seismic attributes because of their limited vertical resolution and vertical spatial averaging, which often hides geologic features indicative of paleocurrents. The comparison of a modern analogue from the northern Gulf of Mexico with an ancient analogue from the lower Paleogene of Flemish Pass offshore east Canada shows such level of similarity that we consider the sedimentary processes largely similar, thus enabling us to infer not only the direction of flow but also its velocity. Future analysis will consider contourite depositional features under lower energy conditions and the combination of the depositional and erosional features would help us to better understand the modern day and the ancient formation of contourite depositional systems (CDS). Measurements of paleo-ocean bottom currents impact the understanding of deepwater contourites or reworked turbidites for the reconstruction of the paleoclimate as an input to present day climate change models and for geosciences applied to energy transition (C02 sequestration and hydrocarbon exploration).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".