Illustrated Workflow for Automatic Data Reduction and Quantification of X-ray Computed Tomography Images of Sedimentary Cores
Bibliographic record
Abstract
The developed workflow reduces information contained in 3-Dimensional single energy X-ray computed tomography (XR-CT) images of sedimentary rocks into sub-mm scale resolution core scalar and core image logs. The resulting log views provide a quantitative and compact (data volume reduction of \(\sim\) 90%) description of the XR-CT images easing core-log-seismic integration efforts. Density-related outputs are calibrated using automatic integration with continuous digital visual core description (VCD) and discrete moisture and density (MAD) property index measurements of selected samples. Following the conversion of the X-ray attenuation coefficients into density values based on lithology, quantitative presentations include (1) histogram of the distribution of density values and its related statistical parameters, (2) radial and angular distributions of core density values, (3) volume, average density and mass contributions of three core fractions defined by density thresholds corresponding to voids or vugs (VV, density \(\le\) \(\sim\) 1 g.cm-3), and a break in the histogram of distribution of the density values showing the limit between the damaged (DM) and non-damaged (ND) fractions of the core material, and so, (4) provide a sub-mm scale bulk density core log free of any drilling disturbance. The method is demonstrated using data from the 365 m deep Hole C9001C drilled off-shore Shimokita (northeast coast of Honshu, Japan) and early stage of the Nankai Trough Seismological zone (NanTroSEIZE, Center for Deep Earth Exploration-Jamstec, International Ocean Discovery Program). The outputs can be used for a variety of purposes, such as (1) derivation of sub-mm scale porosity core log, (2) correction of volume sensitive measurements in case of poor core quality and partially filled core liner, and (3) seismic modeling and well ties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.020 |
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".