Deep convolutional neural network target detection algorithm for coal mine fault detection
-
摘要: 断层解释技术在煤矿安全开采领域起着至关重要的作用。随着神经网络技术的发展，许多基于神经网络算法的智能化地震资料解释处理方案被提出。首先通过对比不同的深度卷积神经网络目标检测算法，选择适合于识别断层的Faster R-CNN目标检测算法；其次建立具有多种地质特征的地震正演模型，分别对AlexNet、残差网络ResNet50和ResNet101三种特征提取网络进行测试，优选得出ResNet101特征提取网络在断层检测方面具有更加优异的表现；最后基于优选的ResNet101特征提取网络和Faster R-CNN目标检测算法构建断层检测模型，对实际地震数据进行断层检测。结果表明：基于深度卷积神经网络的目标检测算法在断层检测上具有很好的泛化能力，提高了断层的解释效率，在实际应用上具有巨大潜力。Abstract: Fault interpretation plays an important role in the field of coal mine safety. The development of neural network gives rise to many intelligent seismic data interpretation and processing schemes based on neural network algorithm. This study ①selected the Faster R-CNN target detection algorithm more suitable for fault recognition by comparing different deep convolutional neural network target detection algorithms. ②tested AlexNet, residual network ResNet50 and ResNet101 feature extraction networks through seismic forward modeling with various geological characteristics. It is found that ResNet101 feature extraction network has better performance in fault detection. ③constructed a fault detection model based on the preferred ResNet101 feature extraction network and Faster R-CNN target detection algorithm, and detected the actual seismic data. Results show that the object detection algorithm based on deep convolutional neural network shows satisfactory generalization ability in fault detection. It could improve the fault interpretation efficiency, and has potential in application.
-
图 16 C2煤层方差体属性沿层切片平面
Figure 16. Plan view of C2 coal seam variance volume attributes along the layer
表 1 网络训练参数
Table 1. Network training parameters
名称 参数 trainingOptions sgdm Momentum 0.9 MiniBatchSize 1 InitialLearnRate 1e-4 LearnRateSchedule piecewise LearnRateDorpFactor 0.1 LearnRateDorpPeriod 5 MaxEpochs 20 CheckpointPath tempdir Verbose true 
下载: 导出CSV
表 2 网络训练参数
Table 2. Network training parameters
名称 参数 trainingOptions sgdm Momentum 0.9 MiniBatchSize 1 InitialLearnRate 1e-3 LearnRateSchedule piecewise LearnRateDorpFactor 0.1 LearnRateDorpPeriod 8 MaxEpochs 64 CheckpointPath tempdir Verbose true
下载: 导出CSV
表 3 3种不同初始学习率准确率对比
Table 3. Accuracy comparison of three different initial learning rates
初始学习率 平均准确率/% 时间/s 1e-2 66.1 0.303 1e-3 73.3 0.256 1e-4 83.0 0.233
下载: 导出CSV
表 4 4种不同Max Epochs准确率对比
Table 4. Accuracy comparison of three different Max Epochs
最大训练回合数 平均准确率/% 时间/s 128 87.9 0.222 256 90.9 0.213 320 92.7 0.208 400 93.3 0.204
下载: 导出CSV
表 5 以0.6置信度为标准的断层预测情况分析
Table 5. Fault prediction analysis based on 0.6 confidence
煤层 正确识别 错误识别 准确率/% C3 6 2 75 C7+8 3 2 60 C9 5 2 71.4
下载: 导出CSV
-
[1] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. doi: 10.1145/3065386 [2] Lin M, Chen Q, Yan S. Network in network[Z/OL]. arXiv, 2014(2014-03-04). [3] Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: visualising image classification models and saliency maps[EB/OL]. 2013: arXiv: 1312.6034. https://arxiv.org/abs/1312.6034.pdf .[4] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 7-12, 2015, Boston, MA, USA. IEEE, 2015: 1-9. [5] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 770-778. [6] Zagoruyko S, Komodakis N. Wide residual networks[Z/OL]. arXiv, 2017(2017-06-14). [7] 吴帅, 徐勇, 赵东宁. 基于深度卷积网络的目标检测综述[J]. 模式识别与人工智能, 2018, 31(4): 335-346.Wu Shuai, Xu Yong, Zhao Dongning. Survey of object detection based on deep convolutional network[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(4): 335-346. [8] Xie S N, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 5987-5995. [9] Huang G, Liu S C, van der Maaten L, et al. CondenseNet: an efficient DenseNet using learned group convolutions[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA. IEEE, 2018: 2752-2761. [10] Chen Y, Li J, Xiao H, et al. Dual path networks[C/OL]//Advances in Neural Information Processing Systems. Curran Associates, Inc., 2017. [11] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[EB/OL]. 2017: arXiv, 1709.01507. https://arxiv.org/abs/1709.01507.pdf .[12] Tingdahl K M, de Rooij M. Semi-automatic detection of faults in 3D seismic data[J]. Geophysical Prospecting, 2005, 53(4): 533-542. doi: 10.1111/j.1365-2478.2005.00489.x [13] Zheng Z H, Kavousi P, Di H B. Multi-attributes and neural network-based fault detection in 3D seismic interpretation[J]. Advanced Materials Research, 2013, 838/839/840/841: 1497-1502. [14] Jahan I, Castagna J, Murphy M, et al. Fault detection using principal component analysis of seismic attributes in the Bakken Formation, Williston Basin, North Dakota, USA[J]. Interpretation, 2017, 5(3): 361-372. doi: 10.1190/INT-2016-0209.1 [15] 邹冠贵, 任珂, 吉寅, 等. 基于主成分分析和最近邻算法的断层识别研究[J]. 煤田地质与勘探, 2021, 49(4): 15-23.Zou Guangui, Ren Ke, Ji Yin, et al. Fault recognition based on principal component analysis and k-nearest neighbor algorithm[J]. Coal Geology & Exploration, 2021, 49(4): 15-23. [16] Huang L, Dong X S, Clee T E. A scalable deep learning platform for identifying geologic features from seismic attributes[J]. The Leading Edge, 2017, 36(3): 249-256. doi: 10.1190/tle36030249.1 [17] Wu X M, Liang L M, Shi Y Z, et al. Fault Seg3D: using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation[J]. Geophysics, 2019, 84(3): 35-45. doi: 10.1190/geo2018-0646.1 [18] 路远, 朱仕军, 朱鹏宇, 等. 利用信噪比差异体改进断层自动识别方法[J]. 地球物理学进展, 2014, 29(1): 155-158.Lu Yuan, Zhu Shijun, Zhu Pengyu, et al. Improved fault automatic identification using Signal-to-noise ratio cubes[J]. Progress in Geophysics, 2014, 29(1): 155-158. [19] 李鹏祥, 刘慧博. 基于卷积神经网络的煤矸识别系统的研究[J]. 自动化应用, 2021(9): 8-14, 19. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDHT202109003.htmLi Pengxiang, Liu Huibo. Research on coal gangue identification system based on convolutional neural network[J]. Automation Application, 2021(9): 8-14, 19. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDHT202109003.htm [20] 黄健, 张钢. 深度卷积神经网络的目标检测算法综述[J]. 计算机工程与应用, 2020, 56(17): 12-23.Huang Jian, Zhang Gang. Survey of object detection algorithms for deep convolutional neural networks[J]. Computer Engineering and Applications, 2020, 56(17): 12-23. [21] 朱弥雪, 刘志强, 张旭, 等. 林火视频烟雾检测算法综述[J]. 计算机工程与应用, 2022, 58(14): 16-26.Zhu Mixue, Liu Zhiqiang, Zhang Xu, et al. Review of research on video-based smoke detection algorithms[J]. Computer Engineering and Applications, 2022, 58(14): 16-26. [22] 包成耀, 丁霄霏, 戴立奇, 等. 基于YOLO的轻量化目标检测应用研究[J]. 电脑与电信, 2021(8): 26-29, 48. https://www.cnki.com.cn/Article/CJFDTOTAL-GZDN202108008.htmBao Chengyao, Ding Xiaofei, Dai Liqi, et al. Application research on lightweight target detection based on YOLO[J]. Computer & Telecommunication, 2021(8): 26-29, 48. https://www.cnki.com.cn/Article/CJFDTOTAL-GZDN202108008.htm [23] 刘谱, 张兴会, 张志利, 等. 从RCNN到YOLO的目标检测综述[C/OL]. 中国高科技产业化研究会智能信息处理产业化分会, 2022: 16-23.Liu Pu, Zhang Xinghui, Zhang Zhili, et al. An overview of object detection from RCNN to YOLO[C/OL]. Intelligent Information Processing Industrialization Branch of China High-tech Industrialization Research Society, 2022: 16-23. [24] 李鹏飞, 刘瑶, 李珣, 等. YOLO9000模型的车辆多目标视频检测系统研究[J]. 计算机测量与控制, 2019, 27(8): 21-24, 29. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201908006.htmLi Pengfei, Liu Yao, Li Xun, et al. A detection method of multi-target for vehicles based on YOLO9000 model[J]. Computer Measurement & Control, 2019, 27(8): 21-24, 29. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201908006.htm [25] 郑伟成, 李学伟, 刘宏哲. 基于深度学习的目标检测算法综述[C/OL]. 中国计算机用户协会网络应用分会, 2018: 11-14, 34.Zheng Weicheng, Li Xuewei, Liu Hongzhe. A review of object detection algorithms based ondeep learning[C/OL]. Network Application Branch of China Computer Users Association, 2018: 11-14, 34. [26] Uijlings J R, Sande K E, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171. [27] Girshick R. Fast R-CNN[Z/OL]. arXiv, 2015(2015-09-27). [28] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. [29] Huang J, Rathod V, Sun C, et al. Speed/accuracy trade-offs for modern convolutional object detectors[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017, Honolulu, HI, USA. IEEE, 2017: 3296-3297. [30] 霍进杰, 于浩业, 董雪梅, 等. 地震正演模拟技术在准噶尔盆地西北缘七区地震解释中的应用[J]. 海洋石油, 2013, 33(4): 30-34.Huo Jinjie, Yu Haoye, Dong Xuemei, et al. Application of seismic forward modeling technique to seismic interpretation in seven areas in northwestern margin of Junggar Basin[J]. Offshore Oil, 2013, 33(4): 30-34. -
点击查看大图
图(18) / 表(5)
计量
- 文章访问数: 482
- HTML全文浏览量: 108
- PDF下载量: 260
- 被引次数: 0