Wavelet Analysis of Well-Logging Data from Oil Source Rock, Egret Member, Offshore Eastern Canada
Bibliographic record
Abstract
Abstract Wavelet analysis is a sensitive method for automatically detecting and distinguishing abrupt discontinuities (i.e., faults, unconformities), cyclicity, and gradual changes in sedimentation rate by transforming depth-related sedimentary signals (i.e., gamma-rays) into wavelengths at distinct depth intervals. We used wavelet analysis for evaluation of the spatio-temporal distribution of oil source rocks and for estimating accumulation rates in a sedimentary basin having high resolution. The method was applied to 16 gamma-ray logs from the Jurassic Egret Member (an oil-source rock succession 55 m to 227 m in thickness), offshore eastern Canada. Dominant gamma-ray cycles having wavelengths varying from 2.8 m (western margin of the basin) to 24 m (eastern part of the basin) have been detected by wavelet analysis. The coincidence of the ratio of predominant gamma-ray cycles with the ratio of Milankovitch spectra (about 400, 100, 40, 20 k.y.) suggests that climatic cycles are an important factor controlling sedimentary cyclicity in the Egret Member. Dominant wavelengths likely represent ~100 k.y. eccentricity, giving accumulation times of ~1.9 m.y. for stratigraphically complete sections having 19 successive 2.8 m gamma-ray cycles and giving accumulation times of ~600 k.y. for incomplete successions having only 6 cycles. Up to four discontinuities occur in gamma-ray log cyclicity and separate the Egret Member into subunits. We interpret the discontinuities as unconformities or faults and as related to sediments having low petroleum potential. The stratigraphic completeness of the Egret Member is correlated to total mass of organic carbon and decreasing thickness of non-source rock intervals, having correlation coefficients of r = 0.8 and r = -0.76, respectively.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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 teacher head, 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".