Learning data distribution of three-dimensional ocean sound speed fields via diffusion models
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel diffusion model approach for generating 3D sound speed fields (3D SSFs) in oceans. The method effectively learns complex SSF distributions, aiding acoustic inversion and transmission loss characterization.
Area Of Science
- Oceanography
- Acoustics
- Machine Learning
Background
- Three-dimensional sound speed fields (3D SSFs) are crucial for understanding ocean variations and acoustic propagation.
- Learning the probability distribution of 3D SSFs is challenging due to their high dimensionality and complexity.
Purpose Of The Study
- To develop a deep generative model for learning 3D SSF probability distributions.
- To address limitations in existing 3D SSF datasets and model architectures for generative tasks.
Main Methods
- Proposed a diffusion model adapted for 3D SSF generation.
- Introduced the 3DSSF dataset for training and evaluation.
- Developed a high-capacity neural architecture and utilized continuous-time optimization with a predictor-corrector scheme.
Main Results
- Demonstrated the diffusion model's capability to learn 3D SSF data distributions effectively.
- Validated the model's performance in assisting sound speed field inversion tasks.
- Showcased the utility in characterizing underwater acoustic transmission loss.
Conclusions
- The diffusion model is effective for generating 3D SSF data.
- The proposed method enhances SSF inversion and acoustic transmission loss analysis.
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