3D Geological Modeling for Zhouan Cu-Ni-Pt Sulfide Deposit, Henan, China

2013 ◽  
Vol 734-737 ◽  
pp. 257-264 ◽  
Author(s):  
Jin Xiang Miao ◽  
Hong Wang ◽  
Ke Jin Song ◽  
Jin Qu ◽  
Ming Yun Zhang ◽  
...  
Keyword(s):  
Mineral Resources ◽  
Geological Structure ◽  
Modeling Technique ◽  
Sulfide Deposit ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
Layered Model ◽  
Existing Data ◽  
Ore Grade

3D Geological modeling technique can apply the existing data comprehensively, representing geological structure intuitively, so further the research has significant meaning on this basis. This article constructs the 3D layered model and ore grade model of the sulfide deposit based on exploration data and geological knowledge. In order to estimate precisely the amount of mineral resources depending on the ore grade model, which has been built by the constraint layer, this method can improve the calculation.

3D Geological Modeling in Complex Reservoir with Fractures – A Case of Biandong Oilfield

2014 ◽  
Vol 556-562 ◽  
pp. 4116-4119
Author(s):  
Jing Rui Xu ◽  
Xue Li
Keyword(s):  
3D Visualization ◽  
Geological Structure ◽  
Quantitative Prediction ◽  
Geological Model ◽  
Geological Modeling ◽  
3D Display ◽  
3D Geological Modeling ◽  
3D Geological Model ◽  
Fast Development ◽  
Visualization Technology

With the fast development of computer technology and 3D visualization technology, geological modleing has made great progress in recent years. The aim of geological modeling is to realize the integrated and quantitative prediction of underground geological bodies, and provide researchers with 3D display of geological characteristics, consequently. So, 3D geological modeling has become an important tool for people to carry out related studies in every oilfield of in China. This paper analyzes the complexity and diversity of geological bodies and geological structure, because these are the main factors that control the distribution and spread of sandboied and reservoir parameters. Based on these previous analysis, the 3D geological model is established with proper modeling method, and a certain 3D visualization of geological bodies are realized by through-well profiles and fence models. Also, the 3D geological model can provide a reliable scientific tools for decision-making for geological researchers.

scholarly journals Resource-environment joint forecasting using big data mining and 3D/4D modeling in Luanchuan mining district, China

2021 ◽  
Vol 44 (3) ◽  
pp. 219-242
Author(s):  
Gongwen Wang ◽  
Shouting Zhang ◽  
Changhai Yan ◽  
Zhenshan Pang ◽  
Hongwei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sustainable Development ◽  
Big Data ◽  
Mineral Resources ◽  
Geological Model ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
Multi Scale ◽  
Mine Environment ◽  
3D Geological Model

The Fourth generation industrial age and 5G + intelligent communication in the "Fourth Paradigm of Science" in the 21st century provide a new opportunity for research on the relationship between mining development and environmental protection. This paper is based on the theory of metallogenic geodynamics background, metallogenic process and quantitative evaluation and chooses the Luanchuan district as a case study, using deep-level artificial intelligence mining and three/four-dimensional (3D/4D) multi-disciplinary, multi-parameter and multi-scale modeling technology platform of geoscience big data (including multi-dimensional and multi-scale geological, geophysical, geochemical, hyperspectral and highresolution remote sensing (multi-temporal) and real-time mining data), carrying out the construction of 3D geological model, metallogenic process model and quantitative exploration model from district to deposit scales and the quantitative prediction and evaluation of the regional Mo polymetallic mineral resources, the aim is to realize the dynamic evaluation of highprecision 3D geological (rock, structure, hydrology, soil, etc.) environment protection and comprehensive development and utilization of mineral resources in digital and wisdom mines, it provides scientific information for the sustainable development of mineral resources and mine environment in the study area. The research results are summarized as follows: (1) The geoscience big data related to mineral resource prediction and evaluation of district include mining data such as 3D geological modeling, geophysics interpretation, geochemistry, and remote sensing modeling, which are combined with GeoCube3.0 software. The optimization of deep targets and comprehensive evaluation of mineral resources in Luanchuan district (500 km2, 2.5 km deep) have been realized, including 6.5 million tons of Mo, 1.5 million tons of W, and 5 million tons of Pb-Zn-Ag. (2) The 3D geological modeling of geology, mineral deposit, and exploration targeting is related to the mine environment. The data of exploration and mining in the pits of Nannihu – Sandaozhuang – Shangfang deposits and the deep channels of Luotuoshan and Xigou deposits show a poor spatial correlation between the NW-trending porphyryskarn deposits and the ore bodies. The NE-trending faults are usually tensional or tensional-torsional structures formed in the post-metallogenic period, which is the migration pathway of hydrothermal fluid of the related Pb-Zn deposit. There is a risk of groundwater pollution in the high-altitude Pb-Zn mining zones, such as the Lengshui and Bailugou deposits controlled by NE-trending faults are developed outside of porphyry-skarn types of Mo (W) deposits in the Luanchuan area. (3) Construction of mineral resources and environmental assessment and decision-making in intelligent digital mines: 3D geological model is established in large mines and associated with ancient mining caves, pit, and deep roadway engineering in the mining areas to realize reasonable orientation and sustainable development of mining industry. The hyperspectral database is used to construct three-dimensional useful and harmful element models to realize the association of exploration, mining, and mineral processing mineralogy for the recovery of harmful elements (As, Sb, Hg, etc.). 0.5 m resolution Worldview2 images are used to identify the distribution of Fe in the wastewater and slag slurry of important tailings reservoirs, so as to protect surface runoff and soil pollution.

An Implementation of 3D Landslide Geological Modeling and Visualization

2012 ◽  
Vol 594-597 ◽  
pp. 2338-2343
Author(s):  
Ting Yao Jiang ◽  
Le Le Cui ◽  
Jia Heng Li
Keyword(s):  
Slip Surface ◽  
3D Visualization ◽  
Three Dimensional ◽  
Geological Structure ◽  
Landslide Monitoring ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
Visualization Process ◽  
Hybrid Data ◽  
Landslide Terrain

Three-dimensional (3D) geological modeling and visualization of landslide is very important for landslide monitoring and stability evaluation. Unfortunately there have not been very efficient methods to realize this modeling and visualization process currently. An implementation of 3D landslide geological modeling and visualization based on a hybrid data structure of TIN and GTP is introduced in this paper. The proposed implementation method includes three sections: pre-processing terrain data for known or history data; 3D modeling of landslide terrain surface, slip surface and geological structure surfaces; construction of 3D landslide geological model and 3D visualization of landslide model through java 3D API. The introduced method contributes to a new approach to landslide research.

3D Geological Modeling-While-Drilling for Geosteering

2005 ◽  
Author(s):  
N.F. Najjar ◽  
T. Jerome ◽  
M. Alshammery
Keyword(s):  
Geological Modeling ◽  
3D Geological Modeling

A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China)

2021 ◽  
pp. 104754
Author(s):  
Ran Jia ◽  
Yikai Lv ◽  
Gongwen Wang ◽  
EmmanuelJohnM. Carranza ◽  
Yongqing Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Geological Modeling ◽  
3D Geological Modeling

scholarly journals A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 816
Author(s):  
Mohammad Jooshaki ◽  
Alona Nad ◽  
Simon Michaux
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Raw Materials ◽  
Relevant Literature ◽  
Geological Structure ◽  
Subject Area ◽  
Ore Deposits ◽  
Machine Learning Algorithms ◽  
Global Demand ◽  
Ore Grade

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

scholarly journals Quantitative and Qualitative Research on the Waste from the Mining of Rock Raw Materials in Lower Silesia

Minerals ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 375 ◽  
Author(s):  
Urszula Kaźmierczak ◽  
Jan Blachowski ◽  
Justyna Górniak-Zimroz ◽  
Herbert Wirth
Keyword(s):  
Raw Materials ◽  
Mineral Resources ◽  
Geological Structure ◽  
Mining Activity ◽  
Road Engineering ◽  
Quantitative And Qualitative Research ◽  
Sw Poland ◽  
Natural Aggregates ◽  
Graphical Presentation ◽  
Lower Silesia

The Lower Silesia area in SW Poland is characterized by a geological structure that is conducive to mining activity. The exploitation of rock raw materials plays an important role in this sector of the economy. By the end of 2017, there were in total approximately 400 current concessions for the exploitation of rock raw materials in the analysed area (Polish Geological Institute, MIDAS database—Management and Protection System of Polish Mineral Resources). The conducted mining activity results in waste, which in the greatest amount occurs in the process of obtaining crushed road and construction aggregates, natural aggregates, carbonate raw materials for the cement and lime industry, as well as stone elements for construction and road engineering. At the end of 2016, the mining plants accumulated 26,569,600 Mg of waste. As part of the European Regions Toward Circular Economy (CircE) project, research was conducted on the volume and composition of the mining waste of rock raw materials in the years 2010–2016 within Lower Silesia. This research used the methods of statistical, descriptive and spatial analysis to identify mining plants with the highest potential for using their wastes. In the course of this study, 6 mining plants with the highest potential of using their waste for industrial production purposes were selected. In order to objectively select these plants, the methodology of qualitative multi-criteria analysis was developed, and 7 criteria were selected for assessing the economic potential of using waste from the mining of rock raw materials. An additional result of this research is a database and graphical presentation of changes in the spatial distribution of generated waste in the Lower Silesia region in the years ranging from 2010 to 2016.

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