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Ditemukan 3 dari pencarian Anda melalui kata kunci: author=Naihao Liu
cover
Automated stratigraphic correlation of well logs using Attention Based Dense …
Komentar Bagikan
Yang YangJinghuai GaoNaihao LiuJingyu WangZhuo LiRongchang LiuTao Wei

The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs. However, it suffers from a small amount of training data and expensive computing time. In this work, we propose the Attention Based Dense Network (ASDNet) for the stratigraphic correlation of well logs. To implement the suggested model, we first employ the attention mechanism to the input…

Edisi
Vol.4, December 2023
ISBN/ISSN
2666-5441
Deskripsi Fisik
9 hlm PDF, 4.161 KB
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
cover
Irregularly sampled seismic data interpolation via wavelet-based convolutiona…
Komentar Bagikan
Zhiguo WangNaihao LiuYihuai LouLukun WuLin LiuKai YuWei Wang

Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task for seismic processing and subsequent interpretation. Recently, with the development of machine learning and deep learning, convolutional neural networks (CNNs) are applied for interpolating irregularly sampled seismic data. CNN based approaches can address the apparent defects of traditional inter…

Edisi
Vol.3, December 2022
ISBN/ISSN
2666-5441
Deskripsi Fisik
11 hlm PDF, 5.141 KB
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
cover
Sparse inversion-based seismic random noise attenuation via self-paced learning
Komentar Bagikan
Zhiguo WangYang YangJinghuai GaoNaihao LiuZhen Li

Seismic random noise reduction is an important task in seismic data processing at the Chinese loess plateau area, which benefits the geologic structure interpretation and further reservoir prediction. The sparse inversion is one of the widely used tools for seismic random noise reduction, which is often solved via the sparse approximation with a regularization term. The ℓ1 norm and total vari…

Edisi
Vol.2, December 2021
ISBN/ISSN
2666-5441
Deskripsi Fisik
11 hlm PDF, 3.665 KB
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551