Three dimensional inversion of multisource time domain electromagnetic data
Bibliographic record
Abstract
ABSTRACT We present a 3D inversion methodology for multisource time-domain electromagnetic data. The forward model consists of Maxwell’s equations in time where the permeability is fixed but electrical conductivity can be highly discontinuous. The goal of the inversion is to recover the conductivity-given measurements of the electric and/or magnetic fields. The availability of matrix-factorization software and high-performance computing has allowed us to solve the 3D time domain EM problem using direct solvers. This is particularly advantageous when data from many transmitters and over many decades are available. We first formulate Maxwell’s equations in terms of the magnetic field, H→. The problem is then discretized using a finite volume technique in space and backward Euler in time. The forward operator is symmetric positive definite and a Cholesky decomposition can be performed with the work distributed over an array of processors. The forward modeling is quickly carried out using the factored operator. Time savings are considerable and they make 3D inversion of large ground or airborne data sets feasible. This is illustrated by using synthetic examples and by inverting a multisource UTEM field data set acquired at San Nicolás, which is a massive sulfide deposit in Mexico.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".