Integrating Forward Modeling and Deep Learning for Precise Detection of Geological Anomalies in Coal Seams Using the Radio Imaging Method

Integrating Forward Modeling and Deep Learning for Precise Detection of Geological Anomalies in Coal Seams Using the Radio Imaging Method

The radio imaging method (RIM) is a non-destructive detection technique widely used in geophysical surveys and monitoring. However, it currently faces challenges in accurately detecting geological anomalies. To enhance detection accuracy, we propose a novel approach that integrates forward modeling...

Full description

Saved in:
Bibliographic Details
Main Authors: Shun Yang, Yanqing Wu, Yinan Zhao, Mingwei Liu, Peng Lu, Zhifang Liu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep learning
forward modeling
geological anomaly prediction
precise detection
radio imaging method
Online Access:https://ieeexplore.ieee.org/document/10685413/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The radio imaging method (RIM) is a non-destructive detection technique widely used in geophysical surveys and monitoring. However, it currently faces challenges in accurately detecting geological anomalies. To enhance detection accuracy, we propose a novel approach that integrates forward modeling and the deep learning UNet semantic segmentation architecture, termed the FM-UNet method. We first construct a forward model that reflects the geological characteristics of the target exploration area and validate it using field RIM survey data. Next, we use the RIM data generated from the forward model for inversion and construct the training dataset. Finally, we apply the deep learning UNet architecture to accurately predict the locations of geological anomalies within the exploration region. Our results demonstrate that the FM-UNet method effectively reduces the false geological anomaly (FGA) areas and significantly improves detection accuracy compared to traditional methods. Additionally, we compared the performance of four UNet architectures, with the Transformer UNet achieving the best results: the average ratio of FGA decreased by 37.37%, and the mean intersection over union (IoU) and average F1 score increased by 24.26% and 23.78%, respectively, confirming the feasibility of our approach.
ISSN:2169-3536

Similar Items