3D Geological modeling from DEM, boreholes, cross-sections and geological maps

Qiao, Jinhai; Mao, Pan; LI, Zhaoliang; Jin, Y.

doi:10.1109/geoinformatics.2011.5980941

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Three-dimensional modeling methods are roughly divided into two categories, which are explicit modeling and implicit modeling [4,5]. In the explicit modeling, the contour lines are drawn manually from geological exploration data, and then the contour lines are spliced to realize a 3D reconstruction of the geological bodies [6][7][8]. However, in the explicit modeling, the model cannot be updated automatically.…”

Section: Related Workmentioning

confidence: 99%

Repair of Geological Models Based on Multiple Material Marching Cubes

Zhong

Wang

2021

Mathematics

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In this paper, we present a multi-domain implicit surface reconstruction algorithm for geological modeling based on the labeling of voxel points. The improved algorithm sets a label for each voxel point to represent the type of its geological domain and then obtains all the voxel points in the void areas. After that, the improved algorithm modifies the labels of the voxel points in the void areas and finally reconstructs the geological models through the Multiple Material Marching Cubes (M3C) algorithm. The improved algorithm solves the problems of some unexpected overlaps and voids in geological modeling by setting and modifying the labels of the voxel points. Our key contribution is proposing a labeling processing method to repair the overlap and void defects generated in the geological modeling and realizing the improved M3C algorithm. The experimental results of some geological models show the performance of the improved method. Compared with the original method, the improved method can repair the overlap and void defects in geological modeling to ensure the raw structural adjacency relationships of the geological bodies.

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Section: Related Workmentioning

confidence: 99%

Repair of Geological Models Based on Multiple Material Marching Cubes

Zhong

Wang

2021

Mathematics

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“…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.…”

mentioning

confidence: 99%

A Stratigraphic Prediction Method Based on Machine Learning

et al. 2019

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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...

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A Convolutional Neural Network Approach for Stratigraphic Interface Detection

Zhang

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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3D Geological modeling from DEM, boreholes, cross-sections and geological maps

Cited by 4 publications

References 9 publications

Repair of Geological Models Based on Multiple Material Marching Cubes

Repair of Geological Models Based on Multiple Material Marching Cubes

A Stratigraphic Prediction Method Based on Machine Learning

A Convolutional Neural Network Approach for Stratigraphic Interface Detection

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