New Models Of Metallogenic Diagnosis Based On Coupling Of Multi-source Information

In the process of modern mineral exploration, the massive special information is produced from geology, geophysics, geochemistry, remote sensing and so on, therefore, metallogenic diagnosis based on multi-source information holds important status. It has been ever since one of geo-science research focuses and difficulties to effectively combine specialized geological theory knowledge with mathematical methods so as to fully extract the useful metallogenic information from multi-source data to establish the integrated finding mineral model. Shitai region of South Anhui province, which is located at the western section of South of Yangtze River transitional zone and the south side of Cu-Fe-S-Au mineralization belt of the middle and lower reaches of Yangtze River, owns transitional features of Cu-Fe-S-Au mineralization belt along the Yangtze River and polymetallic mineralization belt in South Anhui province with promising metallogenic prospects. However, up to now, it has been difficult for the mineralization prediction because of poor geological exploration and few mineralization points. Therefore, it is of great significance to make the mineral prediction for the copper-polymetallic deposits from the views of multi-source information on the basis of mentioned above.Taking the example of copper-polymetallic deposits prediction, the dissertation deals with metallogenic diagnosis coupled by multi-source metallogenic information by selecting Dingxiang district of Shitai county in South Anhui province on the basis of the geological exploration special project of Anhui province (AHGTT 2006-4).Firstly, the metallogenic diagnosis is made by a variety of conventional prediction models (integrated finding mineral model, expert weight of evidence model, prospecting-information contents, BP artificial neural networks). Secondly, two new models that can cope with complicated relationships among variables are used to do that.The main contents and achievements obtained in the dissertation are as follows:1. An integrated finding mineral model of copper-polymetallic deposits is constructed by synthetically studying regional multi-source information from geology, geophysics, geochemistry and so on, and analyzing the metallogenic laws of mineralization points found. Seven forecast variables (stratum, fault, magmatic rock, adjoining rock, aeromagnetic, geochemical exploration, remote sensing) are selected to make the thematic layers of variables. According to expert knowledge, thematic layers of variables are given the corresponding weights by means of their importance in metallogenic diagnosis, and then comprehensive mineralization potential information of the research area can be obtained by using GIS. On the basis of prediction data, topic chart of mineralization forecast is obtained by dividing metallogenic favorability area into four grades and enclosing perspective zones. By the analysis of enclosed perspective zones, the prediction result shows the model is suitable and effective. In addition, the method is a convenient, effective forecast technique and can provide the basis for further exploration work.2. On the basis of data used by the integrated finding mineral model and DPIS software, expert weight of evidence model is used to carry out metallogenic prediction, which can overcome the shortage of data-driven weight of evidence model that needs more data. Firstly, documents of variables for the shp form are made and unit grid meshes are produced. Secondly, weights of variables according to the expert knowledge are set. Finally, topic chart of mineralization forecast is obtained on the basis of the posterior probability of every unit grid mesh and the curve of accumulation posterior probability with metallogenic favorability area divided into four grades and perspective zones enclosed. The prediction result shows that the result of model is basically consistent with that of integrated finding mineral model.3. The objective model of metallogenic diagnosis-prospecting-information contents is used in mineralization forecast to avoid the subjective influence from experts. Its computation result indicates that the mineralization contribution descending order is geochemical exploration, stratum, magmatic rock, fault, remote sensing, adjoining rock, aeromagnetic. In the light of distribution characteristic of prospecting-information contents, the corresponding topic chart can be obtained with metallogenic favorability area divided into four grades and perspective zones enclosed. The prediction result shows that the result of model is basically consistent with that of integrated finding mineral model and expert weight of evidence model.4. A new method of metallogenic diagnosis is proposed in the dissertation; that is, forecast technique based on the coupling of prospecting-information contents-expert weight of evidence. To make full use of knowledge from the subjective and objective aspects, it can obtain new comprehensive weights by fusing subjective weights which come from experts and objective weights which come from prospecting-information contents on the basis of D-S evidence theory. Subsequently, expert weight of evidence is used according to comprehensive weights. The forecasting result indicates that this method can obtain more satisfactory effect. 5. The prediction is carried out by artificial neural network that has strong ability of treating the non-linear relationship among variables in the paper. According to the above data, 128 samples to train BP artificial neural network are produced, considering the analysis result of prospecting-information and the variables'weight information of integrated finding mineral model. The training samples are used according to 1, 0 two types which express geological condition's existence and no existence in the units respectively. MATLAB software is employed to realize the training and forecasting of BP artificial neural networks by programming. According to the distributed characteristics of forecasting values, topic chart of mineralization forecast is obtained with metallogenic favorability area divided into four grades and perspective zones enclosed. Analysis shows forecasting result is basically consistent with that of former models. In addition, aimed at the fault of easily falling into the local extremum in the training, the dissertation uses swarm intelligence optimization algorithm-particle swarm algorithm to carry out optimization of the weight and the threshold values of the neural network so as to promote its generalization ability, and makes the comparison on the prediction effect.6. The dissertation for the first time applies a new method-projection pursuit interpolation model which deals with the complex relationship among variables and reduces the dimensions into mineralization forecast. On the basis of 128 samples, an improved ant colony algorithm is presented to compute the best projection direction, by which the unknown projection values of every unit can be computed. According to the distributed characteristics of projection values, topic chart of mineralization forecast is obtained with metallogenic favorability area divided into four grades and perspective zones enclosed. Analysis shows forecasting result is effective and basically consistent with that of former methods.7. Studying the course of metallogenic prediction, it can be regarded as a pattern recognition problem. Therefore, a kind of new pattern recognition method-support vector regression model is used for the first time to mineralization forecast. Training samples are the same as the above models, the best model parameters are investigated through the grid method and swarm intelligent optimization algorithm-particle swarm algorithm, by which predicted values of units can be computed. According to their distributed characteristics, topic chart of mineralization forecast is obtained with metallogenic favorability area divided into four grades and perspective zones enclosed. The result indicates that this model has its own typical characteristics, besides, is consistence with former forecasting results for most locations.8. To test the validity of three models that can treat complex nonlinear relationship among variables, 16 samples out of 128 ones are selected and modeled in equidistance as sub-samples. The result indicates projection pursuit model is better, support vector regression model moderate, and neural network worse.