Fast inversion of large-scale magnetic data using wavelet transforms and a logarithmic barrier method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper wavelet transforms and a logarithmic barrier method are applied to the inversion of large-scale magnetic data to recover a 3-D distribution of magnetic susceptibility. The fast wavelet transform is used, along with thresholding the small wavelet coefficients, to form a sparse representation of the sensitivity matrix. The reduced size of the resultant matrix allows the solution of large problems that are otherwise intractable. The compressed matrix is used to carry out fast forward modelling by performing matrix-vector multiplications in the wavelet domain. The reduction in CPU time is directly proportional to the compression ratio of the matrix. A second important feature of the algorithm used here is the use of an interior-point method of optimization to enforce positivity constraints. In this approach, the positivity is incorporated into the inversion by a sequence of non-linear optimizations approximated by truncated Newton steps. At the heart of the algorithm, a linear system of equations is solved. The conjugate gradient technique has been used as the basic solver to take the advantage of the efficient forward modelling offered by the sparse matrix representation. Overall, the combination of wavelet transforms, interior point optimization and conjugate gradient solutions readily allows us to solve magnetic inverse problems that have a few hundred thousand parameters and tens of thousands of data.
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.
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 | 0.001 |
| Open science | 0.001 | 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 it