BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals

BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals

Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferenc...

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Bibliographic Details
Main Authors: Weijie Wang, Xuben Wang, Xiaodong Yu, Debiao Luo, Xinyue Liu, Kai Yang, Wen Yang, Xiaolan Yang, Ke Hu, Wenyi Hu
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
deep learning
airborne transient electromagnetic method
multi-head self-attention mechanism
Bayesian learning
signal denoising
Online Access:https://www.mdpi.com/1424-8220/25/1/77
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