Two parameter optimization methods of multi-point geostatistics

Multi-point geostatistics has been widely used, but it involves many parameters when applying the method. These modeling parameters are usually sensitive to the quality of the model, such as the template size of the simpat algorithm, the number of nodes in the neighborhood searched of the Snesim algorithm, and the multigrid in common use. What kind of evaluation criteria for selecting the value and ranges of the parameters has become an urgent problem to be solved. Some methods to optimize these modeling parameters have been proposed. These methods either statistics the pattern feature distribution of training image, or use some certain spatial structure as the index to quantitatively evaluate the quality of training image reconstruction of stochastic realizations, or use cross-validation to compare the sample matching degree between the realization and the training image. However, these methods have their limitations in optimizing modeling parameters. In this paper, two methods to optimize the modeling parameters of multi-point geostatistics were proposed based on gray level co-occurrence matrix and convolutional neural network. Both methods can optimize the appropriate parameters by analyzing the influence of the parameters on modeling quality. Taking the frequently used parameters of Snesim and simpat as an example, the parameter optimization results of the GLCM-based method and the Deep Learning-based method are very close, which indicates that both methods can effectively extract and distinguish the spatial features of the model. In addition, the relationship between modeling parameters and evaluation indexes accords with the visual features of stochastic models. Therefore, the method proposed in this paper will be helpful to solve the difficulty of parameter setting in multi-point geostatistical modeling.