Application Of Parallel Algorithm In Ore Reserve Estimation

With the increasing scale of the problem,the speed of data operation is getting higher and higher in the modern scientific research and engineering.It is more and more important to study the parallel operation,because the traditional serial algorithm can not meet the requirement of problem solution.Mining engineering and mining production object have strong spatial attributes.In addition,integrating the original geologic materials obtained by computer and estimating resource reserves,which has become an important step by modern mineral exploration.However,in the actual engineering,the contradiction between the demand of data storage and computational efficiency is caused due to the fact that the mineral deposit and its surrounding rocks is huge scale,and the range of extension is often measured in kilometers.In recent years,people can use GPU for general computing due to the rapid development of computer graphics processor,which can improve effectively the speed of computing.Therefore,this dissertation uses GPU parallel computing technology to solve this problem.We mainly use the parallel algorithm to construct the ore body model in the dissertation.The main contents are as follows:(1)Based on the theory of mine complex field,the problems are analyzed in the construction of mineral mineralization model,and the possibility of parallel calculation is demonstrated based on its characteristics.(2)Aiming at the key problems in the process of mineralization modeling,the serial algorithm of grade interpolation technique(including Tyson polygon interpolation algorithm and distance power inverse interpolation algorithm)is analyzed,and the parallel computation of two algorithms is designed.(3)The CUDA parallel computing technique is used to implement the parallelization of the Tyson polygon interpolation algorithm and the distance power inverse interpolation algorithm on the GPU,and the temporal and spatial complexity of the two algorithms is analyzed.(4)Through a project example,the grade attribute of the original borehole data is used as the known data in the project,and two kinds of interpolation algorithms are used to calculate the attribute of ore body grade,The operation time,error and acceleration ratio of the algorithm are tested for random data of different sizes.The results show that the parallel algorithm does not realize the effective acceleration when the data size is relatively small due to the increase of the access delay.The scale of data is increasing,the acceleration ratio is getting bigger and the acceleration effect is obvious.In this dissertation,it mainly deals with the feasibility of cross-validation parallel algorithm for two kinds of interpolation algorithms and the body mineralization is accelerated.However,for further algorithm optimization and algorithm adaptive sampling the case of uniformity is the next step to study the problem.