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

Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

449
The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
449
Errors in Taping01:18

Errors in Taping

290
Errors in taping arise from multiple factors that can significantly impact measurement accuracy in surveying. Misalignment of the tape, often due to human error, is one primary source. A skilled rear tapeman, using a telescope, can help correct alignment by guiding the head tapeman; however, human limitations still lead to small inaccuracies. These errors may include misplacement of pins or inaccurate tape readings due to common visual confusions, such as mistaking a six for a nine. Such...
290
Quality Assurance01:19

Quality Assurance

929
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
929
Quality Control01:05

Quality Control

1.2K
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
1.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.8K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.8K
Common Leveling Mistakes and Errors01:17

Common Leveling Mistakes and Errors

373
A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
373

You might also read

Related Articles

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

Sort by
Same author

ApoE impairs microglial efferocytosis by targeting Gas6/MerTK in a mouse model of acute seizure.

Acta pharmacologica Sinica·2026
Same author

Continued overexpression of <i>EPSPS</i> transgene enhances fitness in multigeneration crop-wild rice hybrids and its long-term environmental impact.

Frontiers in plant science·2026
Same author

Genomic insights into stepwise selection reshaping fruit traits and male-biased selection driving hermaphroditism in papayas.

Molecular plant·2025
Same author

Sri Lankan cassava mosaic virus Silencing Suppressor AC4 Mediates Autophagic Degradation of SGS3/RDR6 Bodies in Plants.

Plant, cell & environment·2025
Same author

Comparative transcriptome analysis provides insights into ABA alleviating postharvest physiological deterioration of cassava.

Plant physiology and biochemistry : PPB·2025
Same author

Pearl Bodies as a Potential Source of Secondary Transmission of Papaya Leaf Distortion Mosaic Virus in <i>Carica papaya</i>.

Plant disease·2025

Related Experiment Video

Updated: Jan 9, 2026

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

9.9K

Test-Time Augmentations and Quality Controls for Improving Regional Seismic Phase Picking.

Bingyao Han1, Lin Tang2, Li Ma3

  • 1Key Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

This study enhances seismic phase picking accuracy for regional earthquakes using deep learning and test-time augmentation. Filter-bank augmentation significantly improves Pn phase picking, aiding Earth structure and earthquake studies.

Keywords:
deep learningseismic phase pickingtest-time augmentation

More Related Videos

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.3K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

8.2K

Related Experiment Videos

Last Updated: Jan 9, 2026

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
07:58

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

Published on: August 7, 2017

9.9K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.3K
Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

8.2K

Area of Science:

  • Geophysics
  • Seismology
  • Machine Learning

Background:

  • Regional seismic phases are crucial for understanding Earth's structure.
  • Automatic seismic phase picking using deep learning shows promise but struggles with distant phases like Pn.
  • Vast amounts of seismic data remain underutilized due to picking limitations.

Purpose of the Study:

  • To systematically evaluate test-time augmentation strategies for improving Pn phase picking accuracy.
  • To assess the effectiveness of different augmentation methods (filter-bank, shift, rotation) on deep learning models (PickNet, PhaseNet).
  • To propose quality control measures for reliable phase picking without ground truth.

Main Methods:

  • Utilized the Seis-PnSn dataset for worldwide Pn phase picking.
  • Applied PickNet and PhaseNet models with filter-bank, shift, and rotation test-time augmentations.
  • Implemented quality control based on the standard deviation of augmentation results.

Main Results:

  • Filter-bank augmentation outperformed shift and rotation, increasing picks within ±0.5/1.0s error.
  • PickNet improved from 48.98%/66.94% to 53.87%/70.82% baseline; PhaseNet from 46.32%/64.28% to 48.45%/67.06%.
  • Quality control further boosted PickNet to 67.39%/78.53% and PhaseNet to 57.99%/74.72%.

Conclusions:

  • Test-time augmentation, particularly filter-bank, significantly enhances deep learning-based Pn phase picking.
  • Proposed quality control methods yield reliable results even without ground truth.
  • The study provides practical scripts, improving regional seismic phase picking accuracy and accessibility for geophysical research.