Standard
Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data. / Qu, Dongfang; Mosegaard, Klaus; Feng, Runhai; Nielsen, Lars.
In:
Interpretation, Vol. 11, No. 2, 1M-T456, 01.05.2023.
Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
Qu, D
, Mosegaard, K, Feng, R & Nielsen, L 2023, '
Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data',
Interpretation, vol. 11, no. 2, 1M-T456.
https://doi.org/10.31223/X5QD2T
APA
Qu, D.
, Mosegaard, K., Feng, R., & Nielsen, L. (2023).
Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data.
Interpretation,
11(2), [1M-T456].
https://doi.org/10.31223/X5QD2T
Vancouver
Qu D
, Mosegaard K, Feng R, Nielsen L.
Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data.
Interpretation. 2023 May 1;11(2). 1M-T456.
https://doi.org/10.31223/X5QD2T
Author
Qu, Dongfang ; Mosegaard, Klaus ; Feng, Runhai ; Nielsen, Lars. / Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data. In: Interpretation. 2023 ; Vol. 11, No. 2.
Bibtex
@article{18225aafdd0e4ce698c3cf6bc4eba3fb,
title = "Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data",
author = "Dongfang Qu and Klaus Mosegaard and Runhai Feng and Lars Nielsen",
year = "2023",
month = may,
day = "1",
doi = "10.31223/X5QD2T",
language = "English",
volume = "11",
journal = "Interpretation",
issn = "0020-9643",
publisher = "Union Theological Seminary",
number = "2",
}
RIS
TY - JOUR
T1 - Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data
AU - Qu, Dongfang
AU - Mosegaard, Klaus
AU - Feng, Runhai
AU - Nielsen, Lars
PY - 2023/5/1
Y1 - 2023/5/1
U2 - 10.31223/X5QD2T
DO - 10.31223/X5QD2T
M3 - Journal article
VL - 11
JO - Interpretation
JF - Interpretation
SN - 0020-9643
IS - 2
M1 - 1M-T456
ER -