基于微震参数的岩体稳定性评价方法及其在Spark平台的实现

现场微震监测产生了大数据体现了开采诱发岩体损伤及其演化过程。基于大数据技术可以对有效数据进行挖掘,寻求微震数据与岩体损伤之间的定量关系,并同时进行岩体稳定性的动态评价和对潜在失稳灾害的预警。本项目搭建了可与云端进行交互的本地Spark平台,利用在Spark平台上编写的局部异常检测模型及模糊统计程序化分级模型,分别从时间序列和空间定位的角度对石人沟铁矿的微震数据进行了分析,并综合这2种模型的分析结果对石人沟铁矿岩体稳定性进行了评价。研究结果表明:以Spark和云服务器为基础编写的岩体稳定性评价算法可以较好地检测出石人沟铁矿19#~21#线之间大型断层向深部破裂发展,以及16#线附近境界顶柱被完全贯通所引发的岩体性质急剧劣化。以上研究工作是云计算、大数据平台在矿山应用的一次有益尝试,研究成果可以为矿山的安全生产提供参考。

Rock Mass Stability Evaluation Method Based on Microseismic Parameters and Its Implementation on Spark Platform

The on-site microseismic monitoring results in large data， which shows the damage and evolution of rock mass because of excavation. On the basis of big data technology， the quantitative relation between the microseismic data and the rock mass damage is sought through the excavation of the effective data， and the dynamic evaluation of the stability of the rock mass and the early warning of the potential instability disaster are carried out at the same time. A local Spark platform which can interact with the cloud is built in this paper， and it is used as microseismic monitoring data analysis system. Based on the local anomaly detection model and fuzzy statistical programming classification model written on Spark platform， the microseismic data of Shirengou iron mine are analyzed from the views of time series and spatial location， respectively. The stability of the rock mass in Shirengou iron ore is evaluated by synthesizing the results of the two models. The results show that the evaluation algorithm of rock mass stability based on Spark and cloud server， can detect that the large faults between 19# and 21# in Shirengou Iron Mine to develop into deep rupture， and the degradation of rock mass properties caused by the complete penetration of the boundary top column near 16#. The above research work is a beneficial attempt of cloud computing and big data platform application in mines， which can provide a reference for mine safety production.