Name
Abstract | ||
|---|---|---|
Seismic attributes comprise an effective method for oil and gas reservoir characterization and prediction. Hundreds of seismic attributes have been introduced in the last 30 years. Among the seismic attributes targeting different reservoir features, the autoencoder (AE) receives a significant amount of attention, as it extracts deep attributes of seismic data, providing more details of seismic lateral features than other seismic waveform data and seismic attributes. However, data-driven deep attributes bring new challenges to interpretation as they lack the support of intrinsic physical mechanisms. Hence, a shared AE (S-AE) method is proposed in this article, which can extract seismic deep attributes and fuse traditional seismic attributes simultaneously. An S-AE is a revised version of an AE, which consists of an encoder and decoder. An S-AE takes the seismic waveform as the input of the encoder and obtains the deep attribute, and the decoder then transforms the deep attribute to reconstruct the seismic waveforms and attributes. In an S-AE, the network in front of the decoder is shared, while the networks after the decoder consist of independent layers. Such a network structure ensures the effect of reconstruction and associates seismic attributes with the extracted deep attribute, so as to achieve the purpose of attribute fusion and deep attribute extraction. The proposed S-AE method is compared with conventional seismic data fusion methods, such as RGB and principal component analysis, and the superiority of the S-AE is demonstrated in both synthetic and field applications. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2021.3113075 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Decoding, Feature extraction, Principal component analysis, Data mining, Reservoirs, Neural networks, Image color analysis, Deep learning, seismic attribute | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kunhong Li | 1 | 0 | 0.34 |
| Jingjing Zong | 2 | 0 | 1.01 |
| Yifeng Fei | 3 | 0 | 0.34 |
| Jiandong Liang | 4 | 0 | 0.34 |
| Guang-min Hu | 5 | 87 | 19.78 |