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Diffraction Suppression for Ground Penetrating Radar Data Using F-X Domain Variational Mode Decomposition

2022· article· en· W4312477139 on OpenAlex
Haoqiu Zhou, Xuan Feng, Zejun Dong, Cai Liu, Wenjing Liang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
FundersMinistry of Natural Resources
KeywordsGround-penetrating radarGeologyDiffractionRadarFrequency domainInterference (communication)Filter (signal processing)Surface waveTime domainSeismologyOpticsPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Diffracted waves on ground penetrating radar (GPR) radargrams will severely affect the positioning of subsurface anomalies and stratigraphic division of subsurface. I this study, we proposed an f-x domain VMD dip filter to suppress the diffracted waves on GPR radargrams. A simple model and complex model tests are performed. The results indicate that the f-x domain VMD dip filter can suppress the interference of diffraction well. The radargrams after processing can highlight the reflected signals generated by the surface of anomalies and subsurface strata, which is beneficial to further object positioning and stratigraphic division.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.338
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it