An automatic network-extraction algorithm applied to magnetic survey data for the identification and extraction of geologic lineaments
Bibliographic record
Abstract
Lineament analysis is commonly undertaken by interpreting a wide range of geoscientific data to delineate geologic structures. These structures include faults, fractures, dykes, and lithological contacts, which provide information for geologic mapping and mineral and energy exploration. We offer a simple automatic lineament analysis method that combines the principles of peak-identification algorithms typically used in geophysical data interpretation and a GIS drainage “network-extraction” algorithm commonly applied to a topographic surface. We apply this network-extraction process to a magnetic surface (grid) rather than a topographic one. The GIS approach calculates the curvature of a surface to determine whether a specific coordinate is at a minimum (trough). A simple quadratic surface is computed for a moving 3 × 3 window to determine if the local surface has the form of a dipping plane (or a trough). Continuity of troughs between adjacent kernels defines lineaments that typically correspond to streamflow pathways when analysis is carried out on a topographic surface. On a magnetic anomaly map surface, network extraction identifies magnetic lows that may represent faults that have undergone magnetite (depletion) alteration, or dykes with predominantly reversed polarity remanence. As network extraction is designed to locate troughs, it is possible to isolate normally magnetized dykes by inverting the values of a magnetic data set by to produce ridges. This modified ridge analysis method is successfully applied to three synthetic data sets, showing that network extraction offers the principal benefits of continuity in solutions to produce polylines (over isolated ridge solutions), automation for consistency and reliability, and optional amplitude thresholding.
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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 | 0.002 | 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) | 0.000 | 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".