An Application of Coast Effect Correction to Magnetotelluric Data from Jailolo Geothermal Prospect Area, on the Island of Halmahera
Bibliographic record
Abstract
Abstract Several geothermal prospect areas are situated near coastal regions. One of them is Jailolo geothermal prospect area located on the island of Halmahera. Magnetotelluric (MT) data obtained in the vicinity of the coast may be suffered from coast effect. Boundaries between ocean and land may induce severe distortion of electrical fields due to its extremely high conductivity contrast. The effect mostly distorted MT data in low-frequency and may produce some artifacts in deep parts of resistivity inversion model. In order to get a reliable subsurface resistivity model in Jailolo, a 3-D inversion of acquired MT data was carried out by including an oceanic model, which was set as a prior model, to overcome coast effect distortion. Before that, to examine coast effect influence on acquired MT data, 3-D forward modeling of a simple synthetic model was performed using a similar 3-D inversion mesh grid. Conductive seawater around survey area was also built in the mesh grid and adjusted to bathymetry data. Furthermore, synthetic MT data were then inverted in two schemes with and without oceanic model. Based on inversion results, use of oceanic model can significantly improve inversion result and give a more comparable inversion model with a synthetic model. Meanwhile, 3-D inversion of real MT data, which was carried out with a similar approach, successfully produces a representative subsurface resistivity model to describe geothermal system in study area.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".