Novel ways to process and model GEOTEM and MEGATEM data
Bibliographic record
Abstract
The high‐frequency (i.e. near‐surface) information of GEOTEM and MEGATEM data is contained in the data recorded during the transmitter pulse, which can be difficult to model with conductivity‐depth algorithms. Data processing methods originally developed for the TEMPEST system allow GEOTEM and MEGATEM half‐sine data to be transformed to GEOTEM and MEGATEM square‐wave data, respectively. The advantages of the transformed square‐wave data are that they refer to a standardised waveform that does not vary through a survey, the high‐frequency information in the received signal can be readily utilised and the data can be easily corrected for variations in the transmitter height, pitch, roll and receiver offset. Modelling results from MEGATEM data acquired at the Reid Mahaffy test site indicate that the transformation works well for survey data. An alternate way to make use of the GEOTEM and MEGATEM on‐time data for conductivity‐depth modelling is their inclusion in the modelled transient response. Model resolution is further improved by the inversion of multicomponent data sets. In highly conductive terrain such as above seawater where system parameters such as the bird position are hard to derive reliably from the primary field estimate, the joint inversion of multicomponent data helps to correctly resolve layered‐ earth parameters. Jointly inverting the 3‐component on‐ and offtime data of a GEOTEM bathymetry survey in the Torres Strait showed that the data fit can be greatly improved by allowing the inversion to determine the receiver offset and attitude. This results in greater confidence in the derived conductivity‐depth values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".