A Fourier Neural Operator Surrogate Model for nonlinear Electrical Resistivity Tomograph
Bibliographic record
Abstract
Original Article Electrical resistivity tomography (ERT) is used to infer the subsurface resistivity structure. ERT requires solving a nonlinear inverse problem that is often approximated as linear to reduce computational time. However, the approximation requires assumptions that cause limitations for the data analysis. Most of the computational time is due to the forward problem that requires solving the Poisson equation. Recently, similar forward problems have been shown to be replaceable with a surrogate model of lower computational cost. We present a geoelectric surrogate based on Fourier Neural Operators (FNO) and demonstrate a successful application in nonlinear inversion. The standard deviation of Surrogate prediction errors for unseen samples are <5%. Furthermore, the surrogate reduces computational time by over two orders of magnitude, enabling ERT for previously intractable settings. We apply the surrogate in Markov chain Monte Carlo (MCMC) inversion of simulated data. The results resolve sharp resistivity changes with plausible uncertainties.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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 | 0.000 |
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 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".