Quantitative Prediction And Evaluation Of Geothermal Anomaly Area Along Sichuan-Tibet Railway And Adjacent Areas

The Sichuan-Tibet Railway is not only a landmark infrastructure project for the great rejuvenation of the Chinese nation,but also a century-old project related to the national plan and a national unity project.It has very important strategic,economic and political significance.The Sichuan-Tibet Railway needs to pass through the most complex geological and geomorphological areas in the world.The special geological environment breeds various geological disasters and adverse geological phenomena,which are characterized by complete types,numerous quantities and huge scale.In addition to the most common engineering geological problems such as active fault,earthquake,landslide,collapse,debris flow and so on,the high ground temperature of deep buried tunnel also poses a severe challenge to railway engineering planning,especially the geological route selection work.Due to the large length and burial depth of railway tunnel,the construction and maintenance process of railway tunnel is greatly affected by high temperature and heat damage.The phenomenon of high temperature geothermal will seriously affect the health and safety of workers and the efficiency of mechanical equipment in the process of railway construction,and reduce the durability of building materials,thus threatening the stability and safety of deep-dug tunnels.Therefore,it is particularly important to use modern space detection technology and space analysis method to make quantitative prediction and evaluation of geothermal high temperature anomaly area.In order to predict the geothermal high temperature anomaly area,this paper constructed the geothermal anomaly area prediction method based on the weighted information model for the Sichuan-Tibet railway and its adjacent areas.As a scientific and effective geological information evaluation tool,information model can comprehensively analyze the influence and relationship of multiple factors on the research target,accurately predict the distribution of geothermal anomaly area,and provide geological information support for the design and construction of Sichuan-Tibet railway.Compared with the current prediction work of geothermal anomaly area,we fully consider the geological factors,natural environment factors and inducing factors of geothermal high temperature anomaly phenomenon,and finally select 6 index factors closely related to geothermal,reducing the one-sided analysis of single factor or small factor.The selected original factor data are respectively Landsat-8 image data,stratigraphic lithology data,fault distribution data,drainage distribution data,aeromagnetic anomaly data and seismic data.They are respectively converted into surface temperature map,formation combination entropy map,fault line density map,drainage buffer distance map,aeromagnetic anomaly map and Gutenberg-Liszt B value distribution map.Will introduce a method of information entropy theory and evidence information model,based on Arc GIS and python build index weights,the amount of information entropy model,information model and evidence information model for quantitative prediction research area geothermal anomaly area,and through natural breakpoint method could be divided into high anomaly areas,predicted results in the abnormal area,low anomaly area and no abnormal area 4level.The accuracy of the evaluation results was verified by success index analysis,area ratio analysis and ground validation.The success index and area ratio of the high anomaly region of the index coverage information model reached 0.0046% and 0.853,respectively.The success index and area ratio of the high anomaly region in the entropy weight information model are 0.0040%and 0.843 respectively.The success index and area ratio of the high anomaly region in the weight of evidence information model reached 0.0052% and 0.865,respectively.The results show that the overall accuracy of the three models is high,and the differences between the three models are small.The accuracy of the weight of evidence information model is slightly higher than the other two models,and the spatial distribution of geothermal hotspots is basically consistent with the evaluation results.