Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Kou-Yuan Huang"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 5915-5928 (2020)
Perceptron is adopted to classify the Ricker wavelets and to detect the seismic anomaly in a seismogram. Three learning rules are used in the training of perceptron to solve the decision boundary. The optimal learning-rate parameter is derived. The l
Externí odkaz:
https://doaj.org/article/c576d94494634d77b6cac9b5f94f9bc8
Autor:
Kou-yuan Huang
The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain
Autor:
Kou-Yuan Huang, Dar-Ren Leu
Publikováno v:
IGARSS
In a seismogram, there exist many kinds of wavelets. The reflected wavelet from the gas sand zone has a different shape with other layers. Usually, the information of each wavelet is weak and unknown, and the unsupervised classification method is app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2500eb9674caa135459c8c457d122237
https://doi.org/10.1109/jstars.2019.2908690
https://doi.org/10.1109/jstars.2019.2908690
Autor:
Kou-Yuan Huang, Fajar Abdurrahman
Publikováno v:
IGARSS
In a seismogram, there exist many kinds of wavelets. It can be classified into two classes. One class is normal, the other is abnormal. The abnormal may be the bright spot pattern caused from the gas sand zone. It has the properties of high amplitude
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da68f5e2c620abfd4f647631493996f1
https://doi.org/10.1109/igarss.2019.8900579
https://doi.org/10.1109/igarss.2019.8900579
Autor:
Wen Hsuan Hsieh, Kou-Yuan Huang
Publikováno v:
SEG Technical Program Expanded Abstracts 2018.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4f0740bff5f69b259fca64f81f4cb54
https://doi.org/10.1190/segam2018-2997758.1
https://doi.org/10.1190/segam2018-2997758.1
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7:4849-4859
Sequential pattern detection with simulated annealing (SA) is adopted to estimate parameters and detect lines, ellipses, hyperbolas type by type, and patterns by patterns in each type. The motivation of the sequential detection method is to deal with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65d773ca2c1a4ed7e7c3e53b9ed2ee26
https://doi.org/10.1109/jstars.2014.2344756
https://doi.org/10.1109/jstars.2014.2344756
Autor:
Wen-Hsuan Hsieh, Kou-Yuan Huang
Publikováno v:
IGARSS
Cellular neural network is adopted for seismic pattern recognition. We design cellular neural network to behave as associative memory according to the stored patterns, and finish the training process of the network. Then we use this associative memor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::69980a352a10734a21dae8f9bdb8cda4
https://doi.org/10.1109/igarss.2017.8127805
https://doi.org/10.1109/igarss.2017.8127805
Autor:
Kou-Yuan Huang, Jia-Rong Yang
Publikováno v:
IGARSS
The Hopfield neural network (HNN) is adopted for velocity picking in the time-velocity semblance image of seismic data. A Lyapunov function is generated from the velocity picking problem. We use the gradient descent method to decrease the Lyapunov fu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::434087c1f75cf2d80613cc8f719e5544
https://doi.org/10.1109/igarss.2016.7729825
https://doi.org/10.1109/igarss.2016.7729825
Publikováno v:
IGARSS
In the multilayer perceptron (MLP), there was a theorem about the maximum number of separable regions (M) given the number of hidden nodes (H) in the input d-dimensional space. We propose a recurrence relation to prove the theorem using the expansion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba7a107b0be32ee452243e0f8a141dcf
https://doi.org/10.1109/igarss.2016.7729721
https://doi.org/10.1109/igarss.2016.7729721