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Application of Wavelet Denoising Neural Network in Digital Core
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Tian-su HE1, Wei LI1, 2, *, Jing-ming GAI1, Lin-hao ZOU1, Huan ZHAO1, 2, Sheng-jie JIAO1, Xiao-rui XIE1
Science Technology and Engineering | 2025, 25(1) : 270 - 277
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Science Technology and Engineering | 2025, 25(1): 270-277
• Papers·Automation and Computational Technology •
Application of Wavelet Denoising Neural Network in Digital Core
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Tian-su HE1, Wei LI1, 2, *, Jing-ming GAI1, Lin-hao ZOU1, Huan ZHAO1, 2, Sheng-jie JIAO1, Xiao-rui XIE1
Affiliations
  • 1. School of Petroleum Engineering, Northeast Petroleum University, Daqing 163318, China
  • 2. Oil and Gas Drilling Completion Technology National Engineering Research Center, Daqing 163318, China
Published: 2025-01-08 doi: 10.12404/j.issn.1671-1815.2308335
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Based on the sand-mud interlayer core of a block in Ordos Basin, denoising neural network based on wavelet transformation (DWTNet) was used to denoise the core image. The evaluation of this method was carried out by comparing the peak signal-to-noise ratio (PSNR) and the post-denoising image outcomes. The investigation reveals that by applying the DWTNet denoising algorithm to the test sets YX1 and YX2, and contrasting it with other denoising algorithms such as EGDNet, the PSNR values at noise levels of 25, 50, and 75 dB are respectively 0.527, 0.418, and 1.1 dB higher than those achieved by the EGDNet algorithm. The proposed algorithm surpasses others in terms of metrics including peak signal to noise ratio(PSNR), and visually, the resulting images processed by it exhibit enhanced clarity. The introduction of this method holds substantial significance for the calculation of parameters like porosity, mean specific surface area, mean curvature, among other rock properties, thereby advancing the capabilities in digital core technology, CT scanning analysis, and understanding of rock characteristics.

digital core technology  /  core samples  /  CT scanning  /  rock properties  /  neural network  /  noise reduction
Tian-su HE, Wei LI, Jing-ming GAI, Lin-hao ZOU, Huan ZHAO, Sheng-jie JIAO, Xiao-rui XIE. Application of Wavelet Denoising Neural Network in Digital Core[J]. Science Technology and Engineering, 2025 , 25 (1) : 270 -277 . DOI: 10.12404/j.issn.1671-1815.2308335
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Article Info
doi: 10.12404/j.issn.1671-1815.2308335
  • Receive Date：2023-10-25
  • Online Date：2025-07-29
  • Published：2025-01-08
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  • Received：2023-10-25
  • Revised：2024-10-08
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    1. School of Petroleum Engineering, Northeast Petroleum University, Daqing 163318, China
    2. Oil and Gas Drilling Completion Technology National Engineering Research Center, Daqing 163318, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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