Study on the Influence of Sand Core Compactness on Surface Entropy considering Engineering Disturbance

Abstract

In order to explore the correlation between the compactness of sand core samples and its surface image features and to provide the basis for rapid identification and recognition of core samples in engineering investigation, a typical image data set of sand core samples disturbed by drilling construction in practical engineering has been established, using Python language to compile algorithm to calculate one-dimensional entropy and two-dimensional entropy of 60 groups of sand core samples with different densities. The influence of different sand core compactness on surface entropy characteristics was discussed, and the following conclusions were obtained: (1) Affected by drilling construction and disturbance, the looser the sand core surface particles are, the worse the sorting is and the more irregular the shape characteristics are. There is a close relationship between grain texture and compactness. (2) The calculation results of sand image entropy of one-dimensional entropy and two-dimensional entropy showed that the entropy value of loose, slightly dense, and medium dense sand images is positively correlated with the compactness of sand. (3) The maximum variance of two-dimensional entropy of loose, slightly dense, and medium dense sand image in the same borehole is less than 0.09, and the data variance amplification effect of two-dimensional entropy of image is mainly between different boreholes. (4) The dense feature of core sample structure forms an ordered structure with a gray change boundary, which increases the roughness of the image and leads to the increase of entropy. The two-dimensional entropy reveals the internal correlation mechanism of the influence of the engineering state on the surface structure of sand more clearly than the one-dimensional entropy and more effectively characterizes the dense degree of sand particles. (5) Using two-dimensional entropy to judge the compactness of sand image in the same borehole, the data fluctuation is small, and the algorithm is stable and reliable. The research results have reference values for the detection and analysis of sand sample density in geotechnical engineering investigation.

Research on Prediction Model of Rock and Soil Layer Information Based on Adjacent Boreholes

“…For example, Huang Handong et al [2] classified the soil based on the optimization method of grey correlation, Gao Lihua et al [3] quantitatively classified the sandy debris flow reservoir, and Wei Yinghui et al [4] analyzed and classified the heavy metals in the soil based on the positive definite matrix factor model, Dong Jun et al [5] adopted the analytic hierarchy process weight calculation optimization method, and Zhang Wei [6] applied the combination of multi-point geostatistics and phase controlled modeling to carry out geological modeling and mathematical statistics. The application of traditional statistical and analytic hierarchy process methods to soil classification requires a large number of survey data as the basis [7][8][9].…”