Identification of the Damages and Abnormal Objects in Tibetan Stone Walls Based on GPR Data Analysis

ABSTRACT

Based on the amplitude attribute analysis of ground penetrating radar (GPR) data, this paper applies GPR to the identification of internal damages and abnormal objects in stone walls of Tibetan ruins. The corresponding relationship between the characteristics of internal damages and abnormal objects and the radar data is explored through the Tibetan stone walls (TSWs) simulation test. The identification of location and size of the damages and abnormal objects in TSWs is realized based on the Mahalanobis distance abnormal data discrimination method. Using the normal distribution and the noncentral chi-square distribution, the identification law of types of the damages and abnormal objects is constructed, which takes root mean square (RMS) amplitude and interface reflection coefficient as the characteristic values. The field detection results of TSWs at three Tibetan sites are taken as an extension and supplement to the simulation results, and a set of identification atlas for the location, size and type of the internal damages and abnormal objects in TSWs is established. The application results of the identification atlas were verified by the field detection and the results show that the atlas has a high accuracy, which can significantly improve the efficiency of the identification, and can provide a basis for the performance research, protection and maintenance of TSWs.

KEYWORDS:

• GPR
• identification of internal damages and abnormal objects
• interface reflection coefficient
• mahalanobis distance
• non central chi square distribution
• RMS amplitude
• Tibetan ruins stone wall

Acknowledgments

The authors would like to thank the financial support from the Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China and 111 project.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities under Grant number 2022JBZY008; National Natural Science Foundation of China under Grant number 51878034; and 111 project of the Ministry of Education and the Bureau of Foreign Experts of Chinaunder Grant number B13002