Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
The method of building 3D model was discussed at first. Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN (triangular irregular network) was put forward, and the...The method of building 3D model was discussed at first. Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN (triangular irregular network) was put forward, and the data structure and visualization of vacant place were discussed. Then some crucial technologies of realizing function in 3D-GIS were proposed. In addition,the software about special 3D mapping and assaying was introduced.展开更多
In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as real...In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as reality borehole due to the multi-information check and validation in展开更多
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
文摘The method of building 3D model was discussed at first. Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN (triangular irregular network) was put forward, and the data structure and visualization of vacant place were discussed. Then some crucial technologies of realizing function in 3D-GIS were proposed. In addition,the software about special 3D mapping and assaying was introduced.
文摘In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as reality borehole due to the multi-information check and validation in
