Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

500
As with waves on a string, the speed of sound or a mechanical wave in a fluid depends on the fluid's elastic modulus and inertia. The two relevant physical quantities are the bulk modulus and the density of the material. Indeed, it turns out that the relationship between speed and the bulk modulus and density in fluids is the same as that between the speed and the Young's modulus and density in solids.
The speed of sound in fluids can be derived by considering a mechanical wave...
500
Speed of Sound in Solids and Liquids00:51

Speed of Sound in Solids and Liquids

2.9K
Most solids and liquids are incompressible—their densities remain constant throughout. In the presence of an external force, the molecules tend to restore to their original positions, which is only possible because the constituents interact. The interactions help the constituents pass on information about external disturbances, like sound waves. Therefore, sound waves travel faster through these media. Compared to solids, the constituents in a liquid are less tightly bound. Thus, sound...
2.9K
Distribution of Molecular Speeds01:27

Distribution of Molecular Speeds

3.9K
The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...
3.9K
Sound Waves01:01

Sound Waves

9.1K
Sound waves can be thought of as fluctuations in the pressure of a medium through which they propagate. Since the pressure also makes the medium's particles vibrate along its direction of motion, the waves can be modeled as the displacement of the medium's particles from their mean position.
Sound waves are longitudinal in most fluids because fluids cannot sustain any lateral pressure. In solids, however, shear forces help in propagating the disturbance in the lateral direction as well....
9.1K
Distribution and Dispersion00:54

Distribution and Dispersion

21.7K
To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
21.7K
Sound as Pressure Waves01:17

Sound as Pressure Waves

2.4K
Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
2.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Periodic extension phase unwrapping for direction-of-arrival estimation using sparse undersampled arrays: Method and field validation.

The Journal of the Acoustical Society of America·2026
Same author

Continuous forecasting of range-dependent ocean sound speed field: Diffusion model meets multi-output Gaussian process.

The Journal of the Acoustical Society of America·2026
Same author

Direction-of-arrival estimation for weak underwater targets via flexible sparsity-aware modeling.

The Journal of the Acoustical Society of America·2026
Same author

Hankel-FNO: Fast underwater acoustic charting via physics-encoded Fourier neural operator.

The Journal of the Acoustical Society of America·2025
Same author

Hybrid data- and model-driven three-dimensional ocean sound speed field super-resolution: Diffusion model meets low-rank tensor.

The Journal of the Acoustical Society of America·2025
Same author

Active control of target scattered sound fields in ocean environments using virtual sensing.

The Journal of the Acoustical Society of America·2025

Related Experiment Video

Updated: Jun 25, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.5K

Learning data distribution of three-dimensional ocean sound speed fields via diffusion models.

Siyuan Li1, Lei Cheng1, Jun Li2

  • 1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.

The Journal of the Acoustical Society of America
|May 23, 2024
PubMed
Summary

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.

More Related Videos

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

8.6K
Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
04:54

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging

Published on: June 16, 2023

2.9K

Related Experiment Videos

Last Updated: Jun 25, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.5K
Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing
08:54

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

8.6K
Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
04:54

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging

Published on: June 16, 2023

2.9K

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.