Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this ana...Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.展开更多
The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper...The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.展开更多
Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric featur...Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.展开更多
As the profundity of open-pit mining operations has increased,so has the frequency of geological disasters.The complex interaction of factors causing these disasters presents technical challenges for early warning and...As the profundity of open-pit mining operations has increased,so has the frequency of geological disasters.The complex interaction of factors causing these disasters presents technical challenges for early warning and control systems.However,emergent technologies such as the internet,5G networks,and artificial intelligence provide new opportunities for constructing integrated digital early warning platforms that synthesise multifaceted monitoring data to predict and mitigate open-pit mine hazards.Using efficient Internet-mediated information integration,data from various sources can be consolidated for enhanced disaster management.This paper reviews the current state of digital early warning platforms for open-pit mines using a Web of Science database search for pertinent literature.The framework,data layer,technology layer,and application layer of these platforms are investigated in order to identify associated technologies and obstacles.Important results include:(1)Inconsistent data formats and monitoring software diminish platform workflow efficiency.Robust data exchange protocols and feature-rich software could increase efficiency.(2)Platforms rely on limited data types as opposed to intelligent algorithms that integrate diverse monitoring inputs into global disaster predictions.The underutilization of advanced technologies such as artificial intelligence,the internet of things,and cloud computing.Mining calamity mechanisms and rock mechanics require additional study.展开更多
基金Supported by the National Natural Science Foundation of China(No.51379006 and No.51009106)the Program for New Century Excellent Talents in University of Ministry of Education of China(No.NCET-12-0404)the National Basic Research Program of China("973"Program,No.2013CB035903)
文摘Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.
文摘The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.
基金financially supported by the Youth Innovation Promotion Association CAS(No.2021325)the National Natural Science Foundation of China(Nos.52179117,U21A20159)the Research project of Panzhihua Iron and Steel Group Mining Co.,Ltd.(No.2021-P6-D2-05)。
文摘Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.
基金supported by the National Natural Science Foundation of China(No.52178393,No.51578447)the Science and Technology Innovation Team of Shaanxi Innovation Capability Support Plan(No.2020TD005)the Special Project of Shaanxi Provincial Education Department(No.20JK0709).
文摘As the profundity of open-pit mining operations has increased,so has the frequency of geological disasters.The complex interaction of factors causing these disasters presents technical challenges for early warning and control systems.However,emergent technologies such as the internet,5G networks,and artificial intelligence provide new opportunities for constructing integrated digital early warning platforms that synthesise multifaceted monitoring data to predict and mitigate open-pit mine hazards.Using efficient Internet-mediated information integration,data from various sources can be consolidated for enhanced disaster management.This paper reviews the current state of digital early warning platforms for open-pit mines using a Web of Science database search for pertinent literature.The framework,data layer,technology layer,and application layer of these platforms are investigated in order to identify associated technologies and obstacles.Important results include:(1)Inconsistent data formats and monitoring software diminish platform workflow efficiency.Robust data exchange protocols and feature-rich software could increase efficiency.(2)Platforms rely on limited data types as opposed to intelligent algorithms that integrate diverse monitoring inputs into global disaster predictions.The underutilization of advanced technologies such as artificial intelligence,the internet of things,and cloud computing.Mining calamity mechanisms and rock mechanics require additional study.
