Name
Title | ||
|---|---|---|
Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint |
Abstract | ||
|---|---|---|
Deep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed to learn both structural similarity and resistivity–velocity relationships according to prior knowledge. During the inversion, the unknown resistivity and velocity are updated alternatingly with the Gauss... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TGRS.2020.3032743 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
Training,Deep learning,Knowledge engineering,Conductivity,Data models,Numerical models,Space exploration | Journal | 59 |
Issue | ISSN | Citations |
9 | 0196-2892 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Guo | 1 | 1 | 2.04 |
| He Ming Yao | 2 | 0 | 0.34 |
| Maokun Li | 3 | 6 | 4.00 |
| Michael K. Ng | 4 | 395 | 42.26 |
| Lijun Jiang | 5 | 17 | 8.49 |
| Aria Abubakar | 6 | 20 | 7.58 |