Open-source software for two-dimensional Fourier processing of gridded magnetic data
Bibliographic record
Abstract
Abstract Magnetic data are widely available and useful in many exploration and applied-geophysics projects. The magnetic data are usually processed, imaged, and interpreted in commercial software packages. The algorithms used in these packages are sometimes difficult to check or tune, and the code is not available for review. However, these packages often have an application programming interface (API) for people to access data and undertake their own processing and data enhancement. In many cases, these APIs use the Python programming language. In the course of developing a new method for transforming magnetic data called reduction to pole and vertical dip (RTPVD), the initial test code was written in Python. This initial code was then rewritten and incorporated into GAMS, an open-source software package capable of using a Python API to read from and then write transformed (or enhanced) data to a commercial database. In addition to RTPVD, the other enhancements GAMS can generate are the zeroth-order analytic-signal amplitude (ASA0), tilt, spatial derivatives of ASA0, the zeroth-order local wavenumber, the first-order analytic-signal amplitude, and the apparent susceptibility. These transformations require that the data be transformed to the wavenumber domain using a fast Fourier transform (FFT), operated on, and then transformed back to the space domain. The FFT and some of the preprocessing steps can be done with a number of built-in Python tools. For the preprocessing steps, some of the available Python options are fast, but they can occasionally introduce unwanted artifacts. Our open-source tool allows users to test the different options and check the intermediate steps to ensure the result is appropriate.
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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 | 0.001 | 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 | 0.001 | 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".