Data Fusion by a Supervised Learning Method for Orientation Estimation Using Multi-Sensor Configuration Under Conditions of Magnetic Distortion and Shock Impact
Bibliographic record
Abstract
Accurate subsurface sensing during directional drilling is critical in the mining and energy extraction industries. One challenge is to measure the azimuth accurately. Azimuth measurements are hindered by magnetic disturbances such as iron debris, especially when magnetometers are used. Moreover, gyroscopes are susceptible to shocks during drilling surveys. To overcome these challenges, we developed a supervised learning filter (SLF) using a multi-sensor configuration (MSC) to accurately estimate the azimuth. The MSC consists of micro-electro-mechanical systems (MEMS) based magnetometers, gyroscopes, and accelerometers into two set of sensors, and the groups are separated by a known distance D to acquire additional rotational information using a dual acceleration difference (DAD) method. Also, D can reduce the negative effect of magnetic disturbances. A Kalman filter (KF) with known a priori noise information removes white noise; however, it is difficult to deal with unknown magnetic and shock disturbances. To reduce the effect of unknown magnetic and shock disturbances, we use the SLF to estimate orientation information. First, the SLF employs an adaptive neuro network fuzzy inference system (ANFIS) to build error models of each sensor; then the SLF calculates the proper weights of the sensors using the error models. Lab-scale experiments are performed on a test rig where the SLF is evaluated using one case with training and verified using two cases without training. The results showed an improvement in azimuth estimation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".