Lineament Detection over Shale Gas Play of Horn River Basin Using Monogenic Phase Congruency of Magnetic Data
Bibliographic record
Abstract
Summary Mapping magnetic lineaments especially those attributed to faults and fractures is vital for oil and gas exploration in general and for shale gas in particular. Traditionally, we use magnitude-based gradient filters to delineate lineaments. These filters are vary with direction, magnitude and scale; thus lineaments are often not mapped correctly. In this abstract we are introducing a new technique based on the phase of the magnetic data since phase carries most of the structural information in the data. This new technique is called ‘monogenic phase congruency’ and is able to map lineaments more accurately because it is invariant with direction, magnitude and scale. We applied the monogenic phase congruency to high resolution aeromagnetic (HRAM) data flown over shale gas play of Horn River Basin in Canada. The preliminary results are very interesting and we were able to detect various lineaments in a more coherent fashion than is typical when derivative and gradient based filters are used.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".