| Citation: |
CAI Huihui, XU Yongyang, LI Zixuan, CAO Haohao, FENG Yaxing, CHEN Siqiong, LI Yongsheng. The division of metallogenic prospective areas based on convolutional neural network model: A case study of the Daqiao gold polymetallic deposit[J]. Geological Bulletin of China, 2019, 38(12): 1999-2009.
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The division of metallogenic prospective areas based on convolutional neural network model: A case study of the Daqiao gold polymetallic deposit
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Abstract
Big data and high performance computing make it possible for geology to break through the limitations of various subjective and objective factors and transform from the traditional qualitative description and uncertainty to a more comprehensive quantitative development stage, that is, geology pays more attention to exploring the geological genesis process by mining the correlation between complex and multiple geoscience data. In order to clarify the diversity of geological data in the study area and divide the metallogenic prospective area, the authors aimed to help the geoscientists to make decisions intelligently and efficiently by combining the new methods and technologies of modern informatization. With the Daqiao gold deposit in Gansu Province as the study area, the authors proposed to use one-dimensional convolutional neural network instead of traditional manual calculation and, through training the geochemical and geophysical element data in the study area, excavated the comprehensive metallogenic information in the study area, and then recognized four types of metallogenic prospective areas based on the training results. The results show that the geological mineralization process is complex, and each element of metallogenic prediction plays an important role in the geological mineralization process. On a large scale, the deep learning network model can objectively reflect the nonlinear characteristics of diversified geological data, identify the spatial characteristics of geological elements, extract and excavate the information of mineralization anomalies, and realize the intelligent prediction and evaluation of mineral resources.
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Keywords:
- convolutional neural network /
- model /
- metallogenic prospective area /
- Daqiao /
- orefield
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References
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CAI Huihui, XU Yongyang, LI Zixuan, CAO Haohao, FENG Yaxing, CHEN Siqiong, LI Yongsheng. The division of metallogenic prospective areas based on convolutional neural network model: A case study of the Daqiao gold polymetallic deposit[J]. Geological Bulletin of China, 2019, 38(12): 1999-2009.
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Figure 1.
Structural diagram of one-dimensional convolutional ore-prospecting prediction neural network
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Figure 2.
Schematic diagram of activation function
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Figure 3.
The sample learning point of neural network classification model
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Figure 4.
Trend diagram of model loss function under different iteration rounds of network model
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Figure 5.
Prediction accuracy diagram at different learning rates
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Figure 6.
Prediction accuracy of different data structures when the learning rate is 0.002
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Figure 7.
Classification network model comprehensive information prediction diagram
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Figure 8.
Comparison between prediction results of geochemical data network model and F3 anomaly
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Figure 9.
Results before and after geochemical data overlaid with geophysical data (c1 and c3: before superposition; c2 and c4: after superimposition)
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Figure 10.
Results before and after geochemical data overlaid with geological data (d1: before the superposition; d2: superimposed)
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Figure 11.
Prediction diagram of deep convolutional neural network model (dotted box with 9 perspective areas)