Applied Research For Spatial Data Processing And3D Solid Modeling In Mine

Under the background of the general tendency of “digital mine”, study on3D entitymine modeling has become an increasingly important research in the field of digital mine. Inthis dissertation, it mainly studies the mine entity modeling based on data preparation, dataprocessing and3D digital display. This research achievement has an important guidingsignificance to the theory studies of “digital mine”, mineral resources exploration,miningdecision-making management,mine production and mine safety etc.The main researches ofthis dissertation are given as follows:1、The data source of the3D mine modeling is obtained mainly from the drilling data andlogging information which is established as the primary data of modeling from the aggregateanalysis. However, there exist gross error and the error inevitably in the process of the dataacquisition due to the influence of external factors and human factors. Based on the wavelettheory, it primarily deals with the gross error detecting and eliminating in the acquired data inthis dissertation. By taking advantage of the characteristic of multi-resolution analysis ofwavelet, it illustrates the decomposition of the original information and discusses the effectsof the wavelet function and the decomposition level on the gross error detection, and finallydecides that the original data signal is decomposed into the four scales by using db2wavelet.Furthermore, it explores the methods on the gross error elimination by using threshold-basedwavelet. The results show that the gross error detection of spatial data in mining based on thewavelet theory proves to be more effective and reliable.2、The acquired data for underground mines is limited due to its high costs, therefore, inorder to make the data meet the needs of modeling, the interpolation methods are researchedin the dissertation. Kriging interpolation method is widely used in the methodology, and theparameter of variation function model is key for the kringing method. In the dissertation, thisparameter is optimized by use of combination of neural network and particle swarm, whichavoids the defects of neural network falling into local minimum and makes best of searchadvantage in global optimization of the particle group method. As a result, a new interpolation method, the particle swarm Kriging neural network, is put forward, which has the smallestinterpolation error and reliable results with good effect compared with the Krigingmethod,neural network and particle swarm neural network interpolation, and will helpimprove the interpolation of the different strata interface in the research area.3、In the dissertation, it makes a thorough research on the3D modeling of strata, coalseam and laneway based on TIN and multi-DEM. Surface patches of different strata, coalbody, laneway were constructed by use of point by point method for interpolation data; then,the different levels of the TIN are sutured by using the shortest diagonal method andmulti-DEM to simulate real space distribution of strata, coal seam and laneway. The modelingspeed is boosted through building a grid index for points and improving optimization methodof TIN. In addition, laneway smoothing is further studied by use of circular curve processingmethod on the road research.3D visualization for the strata, coal seam and laneway of a mineis better achieved by using these methods.4、By use of the gross error detection method, interpolation method and3D entitymodeling method, strata, coal body and tunnel of mine was better constructed.