مدل واریوگرام

Calculation of variograms and spatial continuity is one of the first and most important processes in geostatistical modeling, which is a long and experience-oriented process. Due to the complexities of calculating experimental variograms, interpretation and fitting the appropriate model are always the main challenges in this field. This article presents an intelligent variogram modeling method using deep learning that can increase the speed of variogram modeling and also prevent common errors in manual variogram model fitting. In this method, two convolutional neural networks are used. The first CNN network converts the initial data into a 2D simulated map based on various variogram models. For this purpose, it is necessary to train the first network with initial data and their corresponding simulations. The output of this model is entered into the second convolutional neural network as input, and the variogram parameters (including range, azimuth, ratio, and nugget effect) are predicted. In this article, the proposed algorithm is implemented on synthetic 2D data and the parameters of the CNN models are optimized. The accuracy of the proposed model was 97 %, and then the proposed algorithm was used for variogram modeling of Nouchon area geochemical data, which included the elements Cu, Zn, and Pb. the accuracy of the obtained model compared to manual fitting was 90%.

Mining operations and waste resulting from it can be considered as one of the most important sources of hazardous elements in the environment. Identification of the spatial distribution of toxic elements in the mine waste dump systems requires the assessment of environmental hazards and strategies. To achieve the goal, the sampling was conducted on a waste dump located in the east of Angouran mine at an area of 300,000 square meters. For this purpose, 38 soil samples were selected for study and modeling. Statistical studies of the data set for As, Cd, Cu and Co elements were performed. In addition, Q-Q plot was used to remove outlier from the data, which resulted in an increase in the correlation coefficient of the results. In the next step, statistical analyses were performed to determine variogram parameters for the studied elements and the distribution of toxic elements was modeled on the basis of sequential Gaussian simulation (SGS) method, which results in high concentrations As in the south, west, and north of the dump. Elevated concentration of Cd can be seen in the southwest and northeast parts of the dump. Also, high concentrations of Co and Cu accumulate in the western, southern and northern parts of the dump. In order to validate variograms, cross-validation method was employed which is a fundamental method for comparing the effect of different variogram models and simulation methods on interpolation results. Correlation coefficients for As, Cd, Cu and Co elements were 0.885, 0.8056, 0.6867 and 0.9792, which represent the validity of simulation results.