Real-Time Joint Filtering of Gravity and Gravity Gradient Data Based on Improved Kalman Filter
Why this work is in the frame
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Bibliographic record
Abstract
Gravity and gravity gradient data are widely used in geodesy, geodynamics, oil and mineral exploration, and aided navigation. The measured gravity and gravity gradient data include high-frequency noise caused by instrument system error, environmental conditions, and human factors. Separating noise from the measured gravity and gravity gradient data is one of the most challenging tasks in processing the measured data. Traditional low-pass digital filters can remove the noise of an individual component in real-time, which cannot realize the joint filtering of gravity and gravity gradient data. As a postprocessing method, the inversion-based methods can combine gravity and all the gradient components to remove the noise constrained by the Laplace equation. However, a real-time filter method that combines gravity and all gradient components is needed for some special applications, such as submarine gravity and gravity gradient-aided navigation. In this study, gravity and gravity gradient data are combined in establishing system equation and measurement equation of the standard Kalman filter, and denoised in real-time by the improved Kalman filter (IKF). Based on the model test, this method can simultaneously remove the noise in gravity and gravity gradient data in real-time, and ensure denoising performance. Finally, we apply this method to real gravity and gravity gradient data in St. George’s Bay, Canada, acquired by Bell Geospace, and compared the denoised results by full tensor noise reduction (FTNR) and Gaussian low-pass filter, which verified that the performance of IKF is well in real-time joint filtering of gravity and gravity gradient data.
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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 | 0.001 | 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 it