Marine downhole to seafloor dipole-dipole electromagnetic methods and the resolution of resistive targets
Bibliographic record
Abstract
Abstract Most active marine electromagnetic surveys use an array of transmitting and receiving electric dipoles located on the sea-floor. There are many reports of successful detections of buried resistors with these arrays. Nevertheless, the concept has a significant drawback: Large offsets between the transmitter and receiver with respect to the target depth are needed. A logical alternative is the use of a vertical electric-dipole transmitter in a borehole deeper than the target, combined with receivers located at the seafloor. Because current must pass through the target, even data recorded at short offsets with respect to target depth should be sensitive to the target. We derived the equations for the electric and magnetic field components produced by the vertical electric dipole in a 1D layered medium. The equations are used to model the fields for resistive layers. An eigenparameter analysisis applied to the different field components, and the resolution is compared with the standard seafloor inline array. The analyses show that data for the downhole transmitter are more sensitive to the resistance between the source and the receiver than the sea-floor array. However, model geometry such as depth to the target is resolved less easily. The resolution of the model geometry is improved significantly when a long bipole transmitter is used, extended from below the target layer to a level above it. We carried out 3D modeling for the downhole transmitter through the use of a finite-difference algorithm. Results indicate that these data are very sensitive to lateral changes within the target layer. Differences between electric fields simulated at the seafloor for 3D models and a reference model show a correlation to lateral resistivity differences. This finding suggests that the array is suitable for mapping and monitoring resistivity anomalies outward from the borehole.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".