6 papers
OpenEM: Large-scale multi-structural 3D datasets for electromagnetic methods
Shuang Wang, Xuben Wang, Fei Deng +3
Electromagnetic (EM) methods, owing to their efficiency and non-invasive nature, have become one of the most widely used techniques in geological exploration. Nevertheless, data pr…
3-D Magnetotelluric Deep Learning Inversion Guided by Pseudo-Physical Information
Peifan Jiang, Xuben Wang, Shuang Wang +4
Magnetotelluric deep learning (DL) inversion methods based on joint data-driven and physics-driven have become a hot topic in recent years. When mapping observation data (or forwar…
DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal
Shuang Wang, Ming Guo, Xuben Wang +4
The semi-airborne transient electromagnetic method (SATEM) is capable of conducting rapid surveys over large-scale and hard-to-reach areas. However, the acquired signals are often…
Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion
Shuang Wang, Xuben Wang, Fei Deng +3
The extraction of geoelectric structural information from airborne transient electromagnetic (ATEM) data primarily involves data processing and inversion. Conventional methods rely…
SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction
Shuang Wang, Fei Deng, Peifan Jiang +2
Due to limitations such as geographic, physical, or economic factors, collected seismic data often have missing traces. Traditional seismic data reconstruction methods face the cha…
SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote Sensing
Bin Wang, Fei Deng, Shuang Wang +3
Semantic segmentation of remote sensing (RS) images is a challenging yet essential task with broad applications. While deep learning, particularly supervised learning with large-sc…