The inverse-distance weighting interpolation is widely used in 3D geological modeling and directly affects the accuracy of models. With the development of “smart” or “intelligent” geology, classical inverse-distance weighting interpolation cannot meet the accuracy, reliability, and efficiency requirements of large-scale 3D geological models in these fields. Although the improved inverse-distance weighting interpolation can basically meet the requirements of accuracy and reliability, it cannot meet the requirements of efficiency at the same time. In response to these limitations, the adaptive inverse-distance weighting interpolation method based on geological attribute spatial differentiation and geological attribute feature adaptation was proposed. This method takes into account the spatial differentiation of geological attributes to improve the accuracy and considers the first-order neighborhood selection strategy to adaptively improve efficiency to meet above requirements of large-scale geological modeling. The proposed method was applied to an area in eastern China, and the results of the proposed method, compared to the results of classical inverse-distance weighting interpolation and improved inverse-distance weighting interpolation, suggest that the problems encountered above in large-scale geological modeling can be solved with the proposed method. The method can provide effective support for large-scale 3D geological modeling in smart geology.
Urban geological information management systems based on a B/S structures are an inevitable trend in the development of smart geology and big data. Such systems are important platforms for promoting the sharing of data and services among different city entities. It is necessary to overcome the limitations of C/S or partial B/S structures and establish an urban geological information management system based on a comprehensive B/S structure, which is currently rare. According to the above questions, this paper focuses on three types of technical tasks to construct an urban geological information management system based on a B/S structure. In terms of data management, we leave many functions to the server, and group data by type. In terms of data application and sharing, we quickly query and graphically display the data from massive data sets, and perform collaborative analyses of different professional data. In terms of 3D geological modelling and analysis, in order to efficiently provide large-scale 3D geological model analysis in the browser, we explore a new data structure and algorithm for optimization modelling to reduce the amount of model and the number of calculations. Using the above technical methods, we develop a geological information management system for a city in eastern China. The system realizes the ''One Map'' function for land and resources, creates a platform for the sharing of geological data and services among different entities, and provides technical support for the construction of ''smart cities''. INDEX TERMS B/S structure, large-scale geological data management, data sharing, 3D geological modelling and analysis, urban geological information management system.
Simulation of a geostratigraphic unit is of vital importance for the study of geoinformatics, as well as geoengineering planning and design. A traditional method depends on the guidance of expert experience, which is subjective and limited, thereby making the effective evaluation of a stratum simulation quite impossible. To solve this problem, this study proposes a machine learning method for a geostratigraphic series simulation. On the basis of a recurrent neural network, a sequence model of the stratum type and a sequence model of the stratum thickness is successively established. The performance of the model is improved in combination with expert-driven learning. Finally, a machine learning model is established for a geostratigraphic series simulation, and a three-dimensional (3D) geological modeling evaluation method is proposed which considers the stratum type and thickness. The results show that we can use machine learning in the simulation of a series. The series model based on machine learning can describe the real situation at wells, and it is a complimentary tool to the traditional 3D geological model. The prediction ability of the model is improved to a certain extent by including expert-driven learning. This study provides a novel approach for the simulation and prediction of a series by 3D geological modeling.To understand the geological structure, many techniques and methods have been developed to describe, simulate, and model strata [1][2][3][4][5][6]. With the introduction of the Glass Earth [7] concept and geological data, interdisciplinary theoretical integration and application research is being carried out. The most representative traditional method of simulating the stratum structure is three-dimensional (3D) geological modeling, such as that with the B-rep model [8], octree model [9], tri-prism model [10] and geochron concepts [11][12][13][14]. However, the traditional method relies on the guidance of expert knowledge and experience in the selection of assumptions, parameters, and data interpolation methods, which are subjective and limited [15]. Assumptions about the borehole data distribution must be made, and it is difficult to effectively evaluate the stratum simulation results.Machine learning [16][17][18] has been widely used in various fields of geology. The machine learning method does not make too many assumptions about the data but selects a model according to the data characteristics. Then, the machine learning method divides the data into a training set and a test set and constantly adjusts the parameters to obtain better accuracy. Machine learning is more concerned with the predictive power of models [19]. In the fields of geology and engineering, there have been numerous research and application examples in different fields [20-25]. Rodriguez-Galiano et al. conducted a study on mineral exploration based on a decision tree [26]. Porwal et al. used radial function and neural network to evaluate potential maps in mineral exploration [27]. Zhang studied the relationships between ch...
Red beds are Meso–Cenozoic continental sedimentary strata that are mainly composed of gravel stone, sandstone, siltstone, mudstone, and shale and occasionally have interlayers of limestone, halite, and gypsum. As a typical rock mass, red beds are widely distributed throughout South China. In a typical tropical and subtropical continental environment, red beds are the product of multiple sedimentary cycles, which have resulted in complicated rock mass structures that play an important role in rock mass stability. It is thus of great significance to investigate the influence of different rock mass structures on the stability of red-bed slopes. In this paper, the geological formation history of red beds in South China is described. The main features of red-bed rock mass slopes in South China are discussed. The main combinations of inner geomechanical structures comprise: (1) mega-thick soft rock structures; (2) mega-thick hard rock structures; (3) thick hard rock structures with weak intercalation; and (4) soft–hard interbedded structures. In addition, the features of slope failure are analyzed, and four common failure modes are identified from the statistical data: (a) weathering spalling and scouring; (b) rock falls; (c) landslides; and (d) tensile dumping.
The purpose of this article is to investigate the rheological deformation behavior of soft rocks subject to the combination of externally applied compressive pressure and water-softening effects. To achieve this goal, a series of mechanical tests on soft rocks were performed by using a customized meso-mechanical triaxial test system consisting of a bidirectional servo confining pressure loading subsystem and a water pressure chamber. The system has the capability of simulating the actual compressive stress and water environment of soft rocks in engineering practice. The experimental results show that, under compressive stresses, water-softening effects could significantly increase the deformation rate of the soft rocks, ultimately lead to a larger deformation of the rocks. To further understand the combination of compressive pressure and water-softening effects on the deformation behavior of the soft rocks, an elastoplastic damage model was developed. It shows that the model can reproduce the experimentally observed deformation behavior of soft rocks. In addition, it reveals that, with the rock–water interaction, the deformation process of the compressed soft rocks can be described as the change from the attenuation state to the steady state of rheological deformation.
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