Nonparametric Array Manifold Calibration for Ice Sheet Tomography
Bibliographic record
Abstract
Manifold calibration improves parametric angle estimator accuracy and resolution performance by reducing the mismatch between the model of an array’s response to directional sources and truth. This article presents nonparametric array manifold calibration for a multichannel ice-penetrating synthetic aperture radar (SAR) sounder used for imaging subglacial morphology with parametric angle estimation in tomography. In this study, we outline a methodology for identifying scatterers at known angles from multichannel imagery by aligning our measurements to an independent fine-resolution satellite-derived digital elevation model of the Arctic that extends beyond the swath of the SAR. We adopt a support statistic based on our partial knowledge of the array response to identify approximately single-source measurements in our scenes. This technique is a departure from traditional approaches to the sounder array characterization problem that require measurements of flat, specular surface reflections from a maneuvering platform. We aggregate observations of single sources and measure manifold corrections relative to our nominal model from the principal eigenvector of our array covariance. We demonstrate the application of three measured manifolds in tomography and compare performance to a nominal manifold that assumes isotropic radiators and known array geometry. We present radar-derived topography of exposed rock and sea ice in the Canadian Arctic Archipelago under the measured and nominal manifolds and report improved vertical accuracy realized with a measured manifold model assumed by the MUltiple SIgnal Classification angle estimators in 3-D image formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".