Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

NUMERICAL GEODYNAMIC MODELLING OF COUPLED MECHANO-THERMO-HYDROLOGICAL PROCESSES AND ITS APPLICATION IN PREDICTIVE EXPLORATION IN THE FENGHUANGSHAN ORE FIELD, TONGLING

Mineralisation is the result of the coupled multi-geodynamic processes in the crust. The coupled mechano-thermo-hydrological （MTH） processes are the basic physical processes that govern the location of the hydrothermal mineralization, which can be simulated in the computer by using of the numerical codes, such as FLAC. The numerical modeling results can be used not only to explain the features of existing ore deposits, but also to predict the fhvorable mineralization locations. This paper has summarized the basic equations describing coupled MHT processes in the water-saturated porous rocks, the principles of FLAC, and its application to the MHT processes related to copper mineralization in the Fenghuangshan ore field. We used the FLAC to simulate the syn-deformation cooling and fluid flowing evolution after the intrusion was emplaced and solidified. The modeling results suggest a most prospective exploration area where the subsequent exploration supported the prediction and the test bore hole disclosed the high quality copper ore bodies in the target, demonstrating a positive role of the numerical MTH modeling in facilitating predictive ore discovery.

Metallogenic Correlations for the Fe-Nb-REE Mineralization in the West Mine of the Bayan Obo Deposit, Inner Mongolia, China

The West Mine of the Bayan Obo deposit, located in the northern-central part of Inner Mongolia, China, is enriched in Nb, rare earth elements and iron （Nb-REE-Fe） mineral resources. This paper presents a combined method to explore metallogenic correlation of the Nb-REE-Fe mineralization at the Bayan Obo West Mine. The method integrates factor analysis and Back Propagation （BP） neural network technology into processing and modeling of geological data. In this study, the Nb and REE contents of samples were transformed into discrete values to analyze the correlations among the metallogenic elements. The results show weak mineralization correlations between Nb and REEs. Nb and U are closely related in the geochemical patterns, while Fe is closely related to both Th and Mn. LREEs are an important factor for the mineralization of the Bayan Obo deposit, while Fe and Nb can be considered as the results of passive mineralization. On the basis of a metallogenic correlation analysis, the factors affecting the Fe-REE-Nb mineralization were extracted, and the Nb mineralization model was established by the BP neural network. Based on the BP neural network data computing, the variability of the Nb concentration displays a coupled multi-factor nonlinear relationship, which can be used to reveal the inherent metallogenic elemental regularities and predict the degree of element mineralization enrichment in the mining area.