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Updated: Jun 27, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Deep learning for high-resolution seismic imaging.

Liyun Ma¹, Liguo Han², Qiang Feng¹

¹Jilin University, College of Geoexploration Science and Technology, Changchun, 130026, China.

Scientific Reports

|May 5, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning seismic imaging method using neural networks for high-resolution subsurface interpretation. The efficient approach accurately reconstructs geological structures, overcoming limitations of traditional techniques.

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Area of Science:

Geophysics
Artificial Intelligence
Deep Learning

Background:

Seismic imaging is vital for subsurface geological interpretation.
Traditional seismic methods struggle with high resolution due to theoretical and computational limits.

Purpose of the Study:

To develop a deep learning framework for high-resolution seismic imaging.
To directly map seismic data to reflection models, bypassing post-processing.

Main Methods:

Integration of Transformer and Convolutional Neural Network (CNN) architectures.
Enhancement using Adaptive Spatial Feature Fusion (ASFF).
Direct mapping of seismic data to reflection models.

Main Results:

Accurate inference of subsurface geological structures.
High-fidelity reconstruction of subsurface features demonstrated by RMSE, CC, and SSIM metrics.
Reliable performance even with noise injection.

Conclusions:

The proposed deep learning method achieves high-resolution seismic imaging.
This approach overcomes limitations of traditional seismic interpretation.
Deep learning shows significant potential for advancing seismic imaging techniques.