TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal With Signal-to-Image Transformation
University of Electronic Science and Technology of China
Abstract
The considerable prospecting depth and accurate subsurface characteristics can be obtained by the transient electromagnetic method (TEM) in geophysics. Nevertheless, the time-domain TEM signal received by the coil is easily disturbed by environmental background noise, artificial noise, and electronic noise of the equipment. Recently, deep neural networks (DNNs) have been used to solve the TEM denoising problem and have achieved better performance than traditional methods. However, the existing denoising method with DNN adopts fully connected neural networks and is therefore not flexible enough to deal with various signal scales. To address these issues, a novel denoising framework with deep convolutional…
Citation impact
- FWCI
- 36.65
- Percentile
- 100%
- References
- 57
Authors
6- KCKecheng ChenCorresponding
University of Electronic Science and Technology of China
- XPXiaorong Pu
University of Electronic Science and Technology of China
- YRYazhou Ren
University of Electronic Science and Technology of China
- HQHang Qiu
University of Electronic Science and Technology of China
- FLFanqiang Lin
Topics & keywords
- Computer science
- Noise reduction
- Artificial intelligence
- Convolutional neural network
- Noise (video)
- SIGNAL (programming language)
- Deep learning
- Transformation (genetics)
- Life in Land