Hebei State Key Laboratory of Mine Disaster Prevention,North China lnsttute of Science and Technology College of Geosiences and Surveying Engineering,China University of Mining and Technology(Beijing)

为了预测岩溶陷落柱的发育可能性、发育靶区或空间位置,依据岩溶陷落柱形成主控因素,建立了包含2个一级指标和10个二级指标的综合预测指标体系,并根据各指标的重要性,运用模糊层次分析法对其赋以权重;运用所建立的预测体系分别对华北煤田3个典型煤矿进行了预测评价。结果表明:Ⅰ煤矿发育程度综合评价评分为84.9,可判断陷落柱发育可能性大;Ⅱ煤矿基于GIS平台进行了陷落柱位置预测,预测Ⅱ煤矿有3个发育区、2个较发育区,经工程勘察揭露,有1个陷落柱位于预测区;Ⅲ煤矿基于预测的岩溶强径流带进行了陷落柱位置预测,预测Ⅱ煤矿有8个较发育靶区,其中有1个靶区内已揭露有陷落柱。所建立的预测方法体系能够实现陷落柱位置的定量或半定量预测,预测结果与实际情况比较吻合。

in order to predit accurately the development possibilitythe target area or the locaton of paleo-.arst s nMkhnoles,the factor prediction system,including wo indexes at the frst class.geological structure and karst development and 10 indexes at the second, was esablished on the basis of man conrol actors for formationof sinkholes.According to the importance of ideves,each index was given diferent weights by using fuzzy comprehensive evaluation metod.Based on thestablishetprediction system,three typieal mine areas in Northen China ad been predited and evaluated respectivly.The development probatiliy of paleo-karst sinkholes in Mine I was prediced, and the comprehensive evaluation score was 84.9 which meant the pale -.arst sinikholes was likely to be found.Locations of pales-vkarst nknoles in Mine Ⅱ were predicted on the basis of cls paform,and the resuts showed tat there were tre most developed locaton.two relatively developed locaion. Nhatis more,one pale-kart sinihnole was discovered in the most developed location by thelate project Locations of paleo-kast siMkhnoes in Mine Ⅲwere predicted on the basis of the predicted intensive runoff zones.and the resulis showed that there were most developed faget area and one palec-kast sinkhole was discovered in he most developed area.The evauatio results indicate tat the estabished prediction system can be used to predict locations of paleb-.arst sinkhnoles and the resultsaccord with facts.

国家自然科学基金资助项目(51074075,41072188)；“十二五”国家科技支撑计划资助项目(2012BAK04B04)；河北省自然科学基金联合资助项目(E2012508001)；