Jove
Visualize
Contact Us

Related Concept Videos

Deconvolution01:20

Deconvolution

255
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
255

You might also read

Related Articles

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

Sort by
Same author

Analysis of the Association Between Weight Status and Myopia in Children and Adolescents and Development of a Screening Model Based on Body Fat Percentage.

Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)·2026
Same author

Chromatin and genomic instability in the cochlea contributing to age-related hearing loss: Insights from in vitro and in vivo models.

Hearing research·2026
Same author

Transoral single-port robotic surgery for benign or early stage malignant tumors of pharynx and larynx - a prospective real-world study from mainland China.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2026
Same author

Hemorrhagic fever with renal syndrome complicated by reversible secondary hyperparathyroidism during renal recovery: a case report.

Frontiers in medicine·2026
Same author

Folate metabolism-based risk stratification identifies CYP27B1 as a determinant of tumor progression in HNSCC.

Frontiers in medicine·2026
Same author

Specific associations between heart rate variability and motor domains in children with attention-deficit/hyperactivity disorder: a comparative study.

Frontiers in psychiatry·2026
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 Experiment Video

Updated: Sep 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

636

Seismic data denoising based on attention dual dilated CNN.

Haixia Hu1,2,3, Youhua Wei4,5, Hui Chen1,3

  • 1Geomathematics Key Lab of Sichuan Province, Chengdu University of Technology, Chengdu, 610059, China.

Scientific Reports
|August 1, 2025
PubMed
Summary

This study introduces an Attention Dual-Dilated Convolutional Neural Network (ADDC-Net) for seismic data denoising. ADDC-Net effectively suppresses random noise while preserving crucial seismic signals for better subsurface analysis.

Keywords:
Deep learningDenoisingDilated convolutionSeismic data

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Related Experiment Videos

Last Updated: Sep 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

636
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Area of Science:

  • Geophysics
  • Signal Processing
  • Machine Learning

Background:

  • Seismic data denoising is crucial for accurate seismic exploration and interpretation.
  • Traditional noise suppression methods can degrade essential subsurface signal data.
  • Effective random noise reduction remains a challenge in seismic data processing.

Purpose of the Study:

  • To develop an advanced deep learning model for enhanced seismic data denoising.
  • To address the limitations of existing methods in preserving signal integrity.
  • To improve the characterization of subsurface structures through noise reduction.

Main Methods:

  • Introduction of the Attention Dual-Dilated Convolutional Neural Network (ADDC-Net).
  • Utilizing expanded model width for complementary feature extraction.
  • Incorporating dilated convolution to increase receptive field and attention mechanisms for signal preservation.

Main Results:

  • ADDC-Net demonstrated superior performance compared to DnCNN and DudeNet.
  • Achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) by 2.8905 dB and 0.6410 dB, respectively.
  • Showcased faster processing speeds than comparable convolutional networks.

Conclusions:

  • ADDC-Net offers a robust solution for random noise suppression in seismic data.
  • The proposed network effectively preserves critical seismic signals, aiding subsurface interpretation.
  • ADDC-Net represents a significant advancement in seismic data processing techniques.