Automated design of a convolutional neural network with multi-scale filters for cost-efficient seism...
Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
About this item
Full title
Author / Creator
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning methods for image recognition. However, complex reflections of seismic waves tend to form high-dimensional and multi-scale signals, making traditional...
Alternative Titles
Full title
Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_47df02517dab422f98ad99c5a7c03762
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_47df02517dab422f98ad99c5a7c03762
Other Identifiers
ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-020-17123-6
