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

Traveling Waves: Lossless Lines01:27

Traveling Waves: Lossless Lines

158
The provided content explores the behavior of traveling waves on single-phase lossless transmission lines. It begins with a single-phase two-wire lossless transmission line of length Δx, characterized by a loop inductance LH/m and a line-to-line capacitance C F/m. These parameters result in a series inductance LΔx  and a shunt capacitance CΔx.
158
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

343
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured...
343
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K
Plane Electromagnetic Waves I01:30

Plane Electromagnetic Waves I

3.7K
The existence of combined electric and magnetic fields that propagate through space as electromagnetic (EM) waves is the most significant prediction of Maxwell's equations. As Maxwell's equations hold in free space, the predicted electromagnetic waves do not require a medium for their propagation. An EM wave comprises an electric field, defined as the force per charge on a stationary charge, and a magnetic field, which is the force per charge on a moving charge.
The EM field is assumed...
3.7K
Space-Time Curvature and the General Theory of Relativity01:17

Space-Time Curvature and the General Theory of Relativity

2.8K
In 1905, Albert Einstein published his special theory of relativity. According to this theory, no matter in the universe can attain a speed greater than the speed of light in a vacuum, which thus serves as the speed limit of the universe.
This has been verified in many experiments. However, space and time are no longer absolute. Two observers moving relative to one another do not agree on the length of objects or the passage of time. The mechanics of objects based on Newton's laws of...
2.8K
Distribution of Molecular Speeds01:27

Distribution of Molecular Speeds

4.0K
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...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Multimodal Non-Extensive Frequency-Magnitude Distributions and Their Relationship to Multi-Source Seismicity.

Entropy (Basel, Switzerland)·2025
Same author

Can ephapticity contribute to brain complexity?

PloS one·2024
Same author

Cycling reduces the entropy of neuronal activity in the human adult cortex.

PloS one·2024
Same author

Recurrence microstates for machine learning classification.

Chaos (Woodbury, N.Y.)·2024
Same author

Quantifying population dynamics via a geometric mean predator-prey model.

Chaos (Woodbury, N.Y.)·2023
Same author

Generalized statistics: Applications to data inverse problems with outlier-resistance.

PloS one·2023

Related Experiment Video

Updated: Jul 21, 2025

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

10.8K

A Graph-Space Optimal Transport Approach Based on Kaniadakis κ-Gaussian Distribution for Inverse Problems Related to

Sérgio Luiz E F da Silva1,2, João M de Araújo3, Erick de la Barra4

  • 1Department of Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new Full-Waveform Inversion (FWI) method using Kaniadakis κ-Gaussian distributions and optimal transport theory. The novel approach effectively addresses non-Gaussian noise and cycle-skipping issues in seismic data.

Keywords:
Wasserstein metriccycle skippinginverse problemsnon-linear optimizationoptimal transportseismic imagingwave propagationκ-Gaussian distribution

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.9K

Related Experiment Videos

Last Updated: Jul 21, 2025

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

10.8K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.9K

Area of Science:

  • Geophysics
  • Inverse Problems
  • Data Science

Background:

  • Full-waveform inversion (FWI) is crucial for inferring subsurface properties from seismic data.
  • Standard FWI methods struggle with non-Gaussian noise and cycle-skipping, limiting model accuracy.
  • Robust objective functions are essential for overcoming these FWI challenges.

Purpose of the Study:

  • To develop a novel FWI objective function resilient to non-Gaussian noise and phase ambiguity.
  • To mitigate cycle-skipping issues inherent in traditional FWI.
  • To enhance the convergence and resolution of FWI models.

Main Methods:

  • Utilized Kaniadakis κ-Gaussian distribution and optimal transport (OT) theory.
  • Constructed a κ-objective function via probabilistic maximum likelihood.
  • Integrated the κ-objective function within the Kantorovich-Rubinstein metric (a well-posed OT formulation).
  • Represented data in graph space to meet probability axioms for the Kantorovich-Rubinstein framework.

Main Results:

  • The proposed κ-Graph-Space Optimal Transport FWI (κ-GSOT-FWI) effectively circumvents non-Gaussian noise and cycle-skipping problems.
  • Kaniadakis κ-statistics significantly improve FWI objective function convergence.
  • Achieved higher-resolution subsurface models compared to classical FWI techniques, particularly with κ=0.6.

Conclusions:

  • The κ-GSOT-FWI offers a robust solution for challenging FWI scenarios.
  • The integration of Kaniadakis statistics and OT theory enhances FWI performance.
  • This approach leads to more accurate and detailed geophysical models.