Workshop on Muon Physics at the Intensity and Precision Frontiers (MIP 2024)

Joint muography and machine learning detection method for fractured zones inside mountain slopes

Not scheduled

20m

Peking University

Oral Talks Plenary-2

Speakers

Ning Qiu (South China Sea Institute of Oceanology, Chinese Academy of Sciences) Chunwu Pan (South China Sea Institute of Oceanology,, Chinese Academy of Sciences)

Description

The creep collapse and high-level dangerous rock collapse disasters in the internal fractured zone of mountain slopes are common and serious problems. However, traditional monitoring methods have shortcomings in real-time and accuracy. Therefore, it is necessary to develop a reliable evaluation model to verify the effectiveness of the joint muography and machine learning recognition method for the fractured zone inside mountain slopes.

This study aims to evaluate the feasibility and accuracy of the joint Muography and machine learning recognition method for the internal fracture zone of mountain slopes through theoretical simulation numerical calculations.

We established a theoretical model based on the geological characteristics of the internal fractured zone of mountain slopes and simulated the density structure changes of the fractured zone using numerical calculation methods. Then, we apply joint Muography and machine learning recognition methods to process and analyze the simulated data to evaluate its accuracy and feasibility in identifying fractured zones.

The theoretical simulation numerical calculation results show that the joint Muography and machine learning recognition method for the fractured zone inside mountain slopes have shown high accuracy and feasibility in model evaluation. The simulation results match the expectations of the theoretical model, verifying the effectiveness and reliability of this method in identifying fractured zones.

The feasibility and accuracy of the joint muography and machine learning recognition method for the internal fracture zone of mountain slopes have been demonstrated through theoretical simulation and numerical calculations. Although lacking support from measured data, this method has potential application value in disaster monitoring and early warning. It is recommended to further study and apply this method, combined with on-site monitoring data, to verify its accuracy and practicality in the assessment of internal fractured zone disasters in mountainous slopes.