The Di models method: geological 3-D modeling of detrital systems consisting of varying grain fractions to predict the relative lithological variability for a multipurpose usability

The coexistence of a wide variety of subsurface uses in urban areas requires increasingly demanding geological prediction capacities for characterizing the geological heterogeneities at a small-scale. In particular, detrital systems are characterized by the presence of highly varying sediment mixtures which control the non-constant spatial distribution of properties, therefore presenting a crucial aspect for understanding the small-scale spatial variability of physical properties. The proposed methodology uses the lithological descriptions from drilled boreholes and implements sequential indicator simulation to simulate the cumulative frequencies of each lithological class in the whole sediment mixture. The resulting distributions are expressed by a set of voxel models, referred to as Di models. This solution is able to predict the relative amounts of each grain fraction on a cell-by-cell basis and therefore also derive a virtual grain size distribution. Its implementation allows the modeler to flexibly choose both the grain fractions to be modeled and the precision in the relative quantification. The concept of information entropy is adapted as a measure of the disorder state of the clasts mixture, resulting in the concept of “Model Lithological Uniformity,” proposed as a measure of the degree of detrital homogeneity. Moreover, the “Most Uniform Lithological Model” is presented as a distribution of the most prevailing lithologies. This method was tested in the city of Munich (Germany) using a dataset of over 20,000 boreholes, providing a significant step forward in capturing the spatial heterogeneity of detrital systems and addressing model scenarios for applications requiring variable relative amounts of grain fractions.

Application of a new seismic reflection imaging system TSP-SK in tunnel advanced geological prediction

When there are water rich-karst caves or large faults in front of the tunnel face, blind construction may easily cause safety accidents such as water inrush, mud outburst and collapse. Using advanced geological prediction methods to predict the spatial distribution of unfavorable geological bodies can greatly reduce construction risks. This paper introduces a new seismic reflection imaging system TSP-SK, and discusses the features, working mode and data processing methods of the prediction system, and verifies the reliability of TSP-SK through practical tunnel engineering prediction case. The result shows that TSP-SK can collect high signal-to-noise ratio seismic reflection data with little interference from clean first arrival when the water sealing effect of explosives in the borehole is good. Good reflection wave pickup effect can be obtained when the maximum gain of inverse Q filter and Q value are taken in the range of 15 to 30. The prediction results of TSP-SK are in good agreement with the actual excavation results, that is, the areas with low P-wave velocity indicate poor rock mass quality, and the area of low S-wave velocity is more likely to produce water from construction.

Application of comprehensive advanced geological prediction technology in Da-puling tunnel

For the Da-puling tunnel of Puqing Expressway in Guangxi, the advanced geological prediction is carried out by combining TSP long-distance forecast method with short distance geological radar method. This paper describes the principle of seismic wave propagation in elastic medium, as well as the key points of data processing and analysis, some requirements that should be paid attention to the field test and scientific way of image interpretation put forward to improve the accuracy of prediction; When TSP is deployed, it should be sharp angle with potential joint surface. P-wave reacts surrounding rock properties, the shear wave is closely related to the transverse skeleton of medium. In data interpretation, it is necessary to focus on the analysis of the characteristics of P-wave and S-wave, weakening Poisson’s ratio and Young’s modulus. TSP and GPR can achieve the mutual complement and improve the detection accuracy.

Accuracy Evaluation of Advanced Geological Prediction Based on Improved AHP and GPR

Ground penetrating radar (GPR) is widely used in advanced geological prediction. It is necessary to choose a scientific and effective evaluation method to give a reasonable evaluation of the accuracy of prediction. In this paper, a method based on improved analytic hierarchy process (AHP) and GPR is proposed to evaluate the accuracy of advanced geological prediction. Based on the analysis and induction of the factors that affect the accuracy of GPR prediction, an improved AHP is proposed, in which a new measure of “numerical weight” is added and the principle of maximum membership degree is integrated, and an improved AHP model is established for GPR prediction accuracy classification and evaluation. The engineering application of Xiaobeishan Tunnel of Jie-Hui Highway is taken as a case study, and it is proved that the evaluation indices are easy to obtain and the evaluation results are accurate and reliable. The improved AHP-GPR method can be further used for other tunnel engineering.