Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Xinming Wu, Zhicheng Geng, Yunzhi Shi, Nam Pham, Sergey Fomel, Guillaume Caumon

DOI: 10.1190/geo2019-0375.1
Published: 2020-07-01
Container: Geophysics
Publisher: Not recorded
Open access: yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

cautionDOI registered: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
cautionDOI resolves: No matching Crossref record was present in this response. Source: Crossref; license: CC0 metadata
not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility. Source: DOAJ; license: CC0
not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredROR affiliation: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete. Source: Retraction Watch; license: CC BY 4.0
supportingOpen access status: Normalized open-access status: open. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
not scoredPublication license: Not checked or no result supplied; no credibility inference made. Source: No authority result supplied; license: Unknown
not scoredPublication version: A publication version was supplied but is not scored. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer
cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty. Source: Normalized work metadata; license: Caller-provided; provenance license not supplied to scorer

Cite this work

BibTeX

@article{allodium:10.1190/geo2019-0375.1,
  title = {Building realistic structure models to train convolutional neural networks for seismic structural interpretation},
  author = {Xinming Wu and Zhicheng Geng and Yunzhi Shi and Nam Pham and Sergey Fomel and Guillaume Caumon},
  year = {2020},
  journal = {Geophysics},
  doi = {10.1190/geo2019-0375.1},
  url = {https://doi.org/10.1190/geo2019-0375.1}
}

RIS

TY  - JOUR
TI  - Building realistic structure models to train convolutional neural networks for seismic structural interpretation
AU  - Xinming Wu
AU  - Zhicheng Geng
AU  - Yunzhi Shi
AU  - Nam Pham
AU  - Sergey Fomel
AU  - Guillaume Caumon
PY  - 2020
JO  - Geophysics
DO  - 10.1190/geo2019-0375.1
UR  - https://doi.org/10.1190/geo2019-0375.1
ER  -

APA

Wu, X., Geng, Z., Shi, Y., Pham, N., Fomel, S., & Caumon, G. (2020). Building realistic structure models to train convolutional neural networks for seismic structural interpretation. Geophysics. https://doi.org/10.1190/geo2019-0375.1

Source records

hal · retrieved 2026-11-10T11:39:30.977Z