Karst geological hazards pose a significant challenge for the urban construction and underground space development and utilization in the Guangdong-Hong Kong-Macao Greater Bay Area

especially in Guangzhou and Shenzhen. Karst exploration generally involves identifying and assessing caves through a combination of drilling and geophysical information. In recent years

the cross-hole computed tomography (CT) geophysical method has been widely used in karst exploration in the Greater Bay Area due to its ease of operation and strong ability to obtain geological information. However

the accuracy of this method in identifying caves still needs to be quantitatively evaluated. This paper collected a large amount of data on karst drilling and exploration

and the accuracy of cross-hole CT karst identification was statistically analyzed using the model factor method. The results showed that this method could accurately detect the buried depth of the cave roof

floor and height

with an average error less than 5%. The predictive accuracy of the buried depths of the cave roof and floor has very low variability

only 5%

while the predictive accuracy of the cave height has medium variability

exceeding 35%. The accuracy stability of the cross-hole CT karst identification method is satisfactory and not affected by the factors such as CT method type

cave filling condition

emission and reception point distance

drilling type

cave roof thickness

drilling distance

and verification hole distance. This paper also conducted a simple correction of the current cross-hole CT method

which increased the average accuracy of the model by 4% and reduced the variability by 3% without increasing the computational complexity. Finally

the analysis confirmed that the model factors for predicting cave height follow a Weibull distribution. The research results can provide theoretical support for karst cave exploration and risk assessment in karst areas