3D Inversion of Large Scale Marine Controlled-Source Electromagnetics
Bibliographic record
Abstract
Three-dimensional controlled-source electromagnetic (CSEM) surveys can be a useful technique for oil and gas hydrate detection in marine environments. Electromagnetic waves are emitted from sources, and the ensuing electric and/or magnetic fields are recorded at one, or more receivers. The number, frequency, and position of sources and the placement of receivers depends on the particular application. The solution of an inverse problem is required to recover the earth’s conductivity, which can be either isotropic or anisotropic in nature.A major issue with either an isotropic or anisotropic CSEM inversion is the computational cost associated with the solution of many linear systems of equations. This is a result of a large spatial domain potentially containing complicated bathymetry, as well as the existence of thousands of source and frequency combinations. Overall, there could be thousands or even millions of systems of equations to solve on expansive meshes. To assist with these numerical issues, we use ideas developed for airborne electromagnetic inversions. First, we incorporate a locally refined mesh for the forward problem, specifically optimized for a source and set of receivers. Second, we use stochastic programming techniques to solve the CSEM problem with many sources and receivers. These methods dramatically reduce the numerical cost of each forward model as well as the total number of simulations. In this work we describe the methods used to overcome these computational difficulties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".