3‐D INVERSION OF FREQUENCY‐DOMAIN CSEM DATA BASED ON GAUSS‐NEWTON OPTIMIZATION
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Quantitative interpretation of large‐scale controlled‐source electromagnetic (CSEM) data in frequency domain requires efficient and stable 3D forward modeling and inversion codes. In this work, we present an efficient approach to 3D inversion of CSEM data, which is based on Gauss‐Newton (GN) optimization in combination with a direct solver for the forward modeling. In order to avoid computing and storing sensitivity matrix explicitly, a preconditioned conjugate gradient solver (PCG) is used to solve the system of the normal equations resulted from linearization at each GN iteration. This scheme only requires matrix‐vector products of Jocabian and its transpose with vectors, which are equivalent to one forward and one adjoint problem. Therefore the matrix factorization obtained when solving forward problem can be used in subsequent PCG process, which dramatically speeds up PCG iterations and reduces overall computational cost. Numerical experiments on synthetic data from land and marine CSEM surveying configurations show that our inversion scheme exhibits excellent convergence rate and only ten‐odd to tens of iterations are needed to reach desired data misfit, demonstrating its efficiency and stability.
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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 | 0.001 | 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