Interpolation and gridding of aliased geophysical data usingconstrained anisotropic diffusion to enhance trends
Bibliographic record
Abstract
Abstract Geophysical data are frequently collected with a fine sample interval along traverse lines but with a coarser sampling in the direction perpendicular to the traverses. This disparity in sampling intervals is particularly evident when magnetic data are collected simultaneously with airborne electromagnetic data. Interpolating this traverse data onto an evenly spaced 2D grid can result in aliasing artifacts. For example, narrow linear structures that trend at acute angles to the traverse lines are imaged as a thick/thin/thick feature, looking like a boudinage or string of beads. Applying the anisotropic diffusion process to the resulting grids of data removes the artifacts, but the grid values close to the traverses are altered significantly from their initial values. The altered values are therefore not faithful to the original traverse data. The anisotropic diffusion algorithm can be modified to constrain values close to the original traverses. This modification removes the aliasing artifacts and produces a data grid faithful to the original traverse data. Some small artifacts along the traverse lines in the final data grid become more evident when grids containing derivative data (such as the analytic signal) are generated from the new data grid. However, these small traverse-line artifacts can be removed with standard microleveling procedures. The constrained anisotropic diffusion process is iterative, and some experimentation is required to determine the appropriate number of iterations.
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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 | 0.001 |
| 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".