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

Computed Tomography01:10

Computed Tomography

7.9K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.9K

You might also read

Related Articles

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

Sort by
Same author

A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.

IEEE access : practical innovations, open solutions·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

Sensor beampattern and equivalent aperture in a distributed acoustic sensing system.

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

Evaluating Gaussian processes for matched-field processing localization using minimum mean squared error criterion.

JASA express letters·2025
Same author

Differentiable physics for sound field reconstruction.

The Journal of the Acoustical Society of America·2025

Related Experiment Video

Updated: Jan 5, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.5K

High-resolution seismic tomography of Long Beach, CA using machine learning.

Michael J Bianco1, Peter Gerstoft2, Kim B Olsen2,3

  • 1NoiseLab, University of California San Diego, La Jolla, California, USA. mbianco@ucsd.edu.

Scientific Reports
|October 20, 2019
PubMed
Summary

A new machine learning tomography method reveals detailed underground structures in Long Beach, California. This technique accurately maps the Silverado aquifer using seismic noise data from a large geophone array.

More Related Videos

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

1.0K
Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
10:40

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

1.4K

Related Experiment Videos

Last Updated: Jan 5, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.5K
Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

1.0K
Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
10:40

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Published on: August 12, 2025

1.4K

Area of Science:

  • Geophysics
  • Machine Learning
  • Seismology

Background:

  • Ambient noise processing on large seismic arrays generates dense subsurface sampling.
  • Traditional tomography methods can be limited in resolving small-scale geophysical features.

Purpose of the Study:

  • To apply a novel machine learning-based tomography method for high-resolution subsurface imaging.
  • To generate a detailed 1 Hz Rayleigh wave phase speed map of Long Beach, CA.
  • To assess the capability of the locally sparse travel time tomography (LST) method in learning small-scale geophysical features directly from data.

Main Methods:

  • Utilized a "large-N" array of 5204 geophones to record seismic noise.
  • Employed unsupervised machine learning within the locally sparse travel time tomography (LST) framework.
  • Processed approximately 13.5 million travel times to learn a dictionary of local geophysical features.

Main Results:

  • Generated a high-resolution 1 Hz Rayleigh wave phase speed map of the Long Beach area.
  • Successfully isolated the Silverado aquifer, demonstrating improved resolution compared to previous studies.
  • The LST method effectively learned and represented small-scale patterns of Earth structure relevant to the imaging scenario.

Conclusions:

  • The locally sparse travel time tomography (LST) method shows significant promise for detailed geophysical structure imaging.
  • Machine learning-based tomography can enhance the resolution of subsurface features from dense seismic noise data.
  • This approach offers a powerful tool for understanding complex geological settings and resource mapping.