Magnetic forward modelling and inversion for high susceptibility
Bibliographic record
Abstract
We develop an algorithm to invert geophysical magnetic data to recover 3-D distributions of subsurface magnetic susceptibility when the bodies have complicated geometry and possibly high magnetic susceptibility. For the associated forward modelling problem, a full solution to Maxwell's equations for source-free magnetostatics is developed in the differential equation domain using a finite volume discretization. The earth region of interest is discretized into many prismatic cells, each with constant susceptibility. The resulting system of discrete equations is solved using an ILU-preconditioned Bi-Conjugate Gradient Stabilized (BiCGStab) algorithm. Formulations for total and secondary field computations are developed and tested against analytic solutions and against a solution in the integral equation domain. The finite volume forward modelling method forms the foundation for a subsequent inversion algorithm. The underdetermined inverse problem is solved as an unconstrained optimization problem and an objective function composed of data misfit and a regularization term is minimized using a Gauss—Newton search. At each iteration, the CGLS algorithm is used to solve for the search direction. The inversion code is tested on synthetic data from both geometrically simple and complicated bodies and on field survey data collected over a planted ferrous shipping container.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".