The Monitor And Prediction Of TONG TING Square Surface Coal Industry Settlement

In recent years, shafts in mines of Huainan and Huaibei, as well as Xuzhou, have been destroyed in varying degrees.When the industry square surface subsidence occurs, Building (Structure) on the surface and wellbore have also been a certain amount of damage,which posed a great threat to mine production and safety. Against TONG TING square surface coal industry sedimentation monitoring, build predictive models.The paper describes the basics of subsidence monitoring and established industrial square surface subsidence monitoring system based on the example of TONG TING coal mine. According to the National Class Leveling, the reference network and network heads conducted3comprehensive observations, The secondary network for the17settlement observation. Using rank defect free network adjustment method based on the center of gravity reference network benchmarks,and the stability of the reference point are been analyzed. The classical adjustment is used for primary control network and the joint points are tested for diversity. After rigorous adjustment processing and analysis results, In the observation period:Industry Square cumulative average surface subsidence is12.4mm; Main well derrick average cumulative basis for settlement12.6mm; the maximum tilt deformation of bunkerl, bunker2and bunker3is0.12mm/m,0.15mm/m,0.13mm/m. Accordance to relevant regulations, the main shaft headframe foundation and three bunker are all in a safe condition throughout the whole observation period.The paper introduces the modeling principles and methods of gray system forecasting model and time series models,and design TONG TING square surface coal industrySubsidence monitoring and forecasting systems based on the development environment of Visual Basic6.0. Based on gray system theory, select the best dimension, then establish a corresponding GM (1,1) gray model, Finally, we can get the six feature points (ZN2, ZJK,1#, E8, W2, E3) average residual value is0.2mm, Model accuracy rating is more than2, the period18(December2014) predictive value of each feature points are:-11.5mm,-3.7mm,-10.8mm,-13.9mm,-13.3mm,-11.6mm, but prediction accuracy need to be further verified. Based on the theory of time series, established the AR (p) model, then we can get six feature points (ZN2, ZJK,1#, E8, W2, E3) average residual value is0.4mm,which have higher accuracy. Acorrding to the existing settlement monitoring data we can drawn that:the accuracy of the two models all can meet the requirements, but the gray system model own smaller average residual and error, so gray system model is slightly better than the time series model.