The Study Of Nonlinear Inversion Method In High-density Resistivity Method Inversion

As is known by us all, the electrical prospecting method is gaining a wide application prospect and playing an increasingly important role especially in advanced warning and detection of water disaster in coal mines. However, the inversion result always shows a significant deviation compared with the reality due to the linear processing method towards the original data, so the interpreting accuracy can hardly meet the requirements of the mining production. As an important branch of electrical prospecting, the data processing methods that are most commonly used for the high density resistivity method include the finite difference method, the finite element method, the conjugate gradient method as well as the least square method, among which the two dimensional inversion software RES2DINV and the three dimensional software RES3DINV developed by M. H. Loke are the most frequently used ones, however, the result is also beyond satisfaction. Therefore, research on the promotion of inversion accuracy of the high density electrical method would not only enrich the data processing theory of mining geophysical exploration, but also provide a theoretical significance as well as practical utility to the safety in mine production and some problems in engineering geology.Although nonlinear inversion method had already been applied in the geophysical inversion field, there are fairly few applications of nonlinear method in electrical exploration. Many researchers had attempt the nonlinear joint inversion technology with the intensively research in the nonlinear method in recent years, however, there are still no substantial progress. As a result, the application in geophysical inversion field especially in electrical prospecting method of four representational nonlinear method including neural network algorithm (BP), simulated annealing method (SA), genetic algorithm (GA) and ant colony optimization (ACO) are stated in this thesis, besides, the joint nonlinear algorithm is also introduced in according to the limitations of the four algorithms, so the majorization and combination of the four above mentioned algorithms is fulfilled, from the utilization of which the requirements of interpreting accuracy can be met in the inversion of high density electrical method.Based on the characteristics of different kinds of nonlinear inversion method, the joint inversion between neural network algorithm and other three kinds of algorithms is carried out, in which the overall search advantage of SA, GA and ACO is introduced in optimizing the original weight and weight matrix of the BP neural network algorithm, by doing that, the calculation time of the inversion could be shorten, so the efficient of the inversion of the BP neural network algorithm could be enhanced to a large extent, from which the inversion accuracy could eventually be promoted.In this thesis, the overall framework and procedures of joint optimized algorithm including SA-BP algorithm, GA-BP algorithm and ACO-BP algorithm is firstly worked out, after which computer programming is used to fulfill the inversion on the three typical models and eventually the nonlinear inversion of the high density resistivity method. Through the result graph of the three joint inversion method and the single BP algorithm generated by SURFER, a conclusion can be drawn that the inversion result of the three joint algorithms are significantly better than the single BP algorithm, and the occurrence form of abnormal body from using the nonlinear inversion method is more close to the theoretical model.In order to display the advantage of the joint algorithm over the single algorithm, the comparison between those two algorithm is also stated in this thesis, the result shows that joint algorithm could overcome the disadvantage to avoid the optimal explain phenomena and to save the training time through reduce training time, besides, higher inversion accuracy could be obtained through the joint algorithm, apart from which, the stability and similarity with the model of joint algorithm are better than the single algorithm.Through the comparison between the three different joint algorithm in the inversion results from judge coefficient and mean square error, advantages of different method could be concluded that the ACO-BP method has a advantage of short time consumption, high inversion accuracy, low E value, a suitable judge coefficient close to 1 and good suitability to the model, while the GA-BP method has a relatively higher time consumption due to the non-parallel optimal way, however, the inversion accuracy is high. As to the SA-BP method, it hardly has advantages over the other two methods. Lastly, the inversion method of ACO-BP and GA-BP is applied to the data processing of the detection of the goaf filled with water in panjiayao coal mine in Shanxi province, the inversion result of which is more highly closed to the reality compared with the results obtained from the traditional inversion method, which illustrates the utility and feasibility of joint nonlinear inversion method in the processing of the data of the high density resistivity method.The conclusion of this thesis illustrates the efficiency of the joint nonlinear inversion method, which would provide some experience to the inversion technology used in other kinds of electric method in future.