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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

297
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
297
Transformations of Functions III01:20

Transformations of Functions III

92
Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...
92
Discrete Fourier Transform01:15

Discrete Fourier Transform

723
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
723
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

617
The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
617
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

801
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
801
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

618
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
618

You might also read

Related Articles

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

Sort by
Same author

Compensatory coupling between leak and HCN conductances defines a low dimensional solution manifold in GPe neuron subtypes.

Scientific reports·2026
Same author

180th Anniversary of Ludwig Boltzmann.

Entropy (Basel, Switzerland)·2025
Same author

Twenty Years of Kaniadakis Entropy: Current Trends and Future Perspectives.

Entropy (Basel, Switzerland)·2025
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 journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Dec 17, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.4K

Full-waveform inversion based on Kaniadakis statistics.

Sérgio Luiz E F da Silva1, Pedro Tiago C Carvalho, João M de Araújo

  • 1Departamento de Física Teórica e Experimental, Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil.

Physical Review. E
|June 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new seismic Full-Waveform Inversion (FWI) method using a generalized Gaussian distribution to improve subsurface imaging. The proposed κ-FWI method enhances parameter estimation, especially with noisy seismic data containing outliers.

More Related Videos

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.1K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.6K

Related Experiment Videos

Last Updated: Dec 17, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.4K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.1K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.6K

Area of Science:

  • Geophysics
  • Seismic Imaging
  • Inversion Methods

Background:

  • Full-Waveform Inversion (FWI) estimates subsurface parameters using wave equations.
  • Classical FWI minimizes least-squares misfit, assuming Gaussian error distribution.
  • Real-world seismic data often exhibit non-Gaussian errors and outliers.

Purpose of the Study:

  • To develop a novel misfit function for FWI based on non-Gaussian error laws.
  • To introduce κ-FWI, utilizing the κ-generalized Gaussian probability distribution and Kaniadakis statistics.
  • To evaluate the performance of κ-FWI against traditional least-squares FWI.

Main Methods:

  • Formulation of a new misfit function derived from the κ-generalized exponential function.
  • Implementation of κ-FWI based on κ-generalized Gaussian probability distribution.
  • Numerical simulations on a realistic acoustic velocity model with Gaussian and outlier-corrupted noisy data.

Main Results:

  • κ-FWI demonstrates superior performance compared to least-squares FWI in parameter estimation.
  • The proposed method shows significant improvements, particularly with noisy seismic data containing outliers.
  • The κ-parameter influences convergence speed but not the overall quality of the results.

Conclusions:

  • κ-FWI offers a robust alternative to traditional FWI for subsurface imaging.
  • The method is particularly advantageous in scenarios with significant data noise and outliers.
  • κ-FWI provides more accurate subsurface physical parameter estimation, enhancing geophysical interpretations.