基于多深度模型的钻孔结构面智能识别与量化分析

张野; 陈金桥; 李炎隆

doi:10.19509/j.cnki.dzkq.tb20220091

基于多深度模型的钻孔结构面智能识别与量化分析

doi: 10.19509/j.cnki.dzkq.tb20220091

西安理工大学省部共建西北旱区生态水利国家重点实验室, 西安 710048

基金项目:

国家自然科学基金项目 52009109

国家自然科学基金项目 52125904

博士启动基金项目 104-451120005

详细信息

作者简介:
张野(1990—), 男, 讲师, 主要从事水利工程安全评价和智能仿真研究工作。E-mail: zhangye1990@xaut.edu.cn

中图分类号: TP391.41;P641.7
计量
- 文章访问数: 656
- PDF下载量: 74
- 被引次数: 0
出版历程
- 收稿日期: 2022-03-07
- 录用日期: 2022-05-11
- 修回日期: 2022-05-05

Intelligent recognition and quantitative analysis of borehole hydraulic geological images utilizing multiple deep learning models

State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China

摘要

摘要:
钻孔摄影图像是地质工程中分析深部岩体的有效方法和重要手段, 对于了解地表结构面延伸和地下结构面分布具有重要意义, 尤其对水利工程中复杂地质条件的分析有着不可或缺的作用。目前, 岩体中结构面的形态特征分析依赖于人工解译, 其结果具有一定的主观性, 同时不能保证解译的效率。为此提出了一种从数字钻孔摄像图像中智能分割结构面的方法, 并结合图像细化实现岩体结构面几何信息的量化分析。在研究中首先采用Unet、SegNet和DeepLabV3 3种不同的深度网络结构, 进行钻孔摄影图像结构面智能识别分析研究, 并将识别结果与传统的图像处理方法进行了比较。研究结果表明: 深度模型在复杂地质图像分割方面具有优越性。同时将Attention机制加入到编码-解码过程中, 提升了模型的准确性。在精确实现结构面分割的基础上, 采用图像细化方法提取出结构面的骨架, 对骨架图像进行单宽处理并提取有效信息, 最终实现结构面倾向、倾角和厚度的智能解译。对比结构面信息人工提取结果与自动识别结果, 倾向、倾角误差在3°以内, 厚度误差在0.65 mm以内。研究结果验证了本研究提出的结构面信息的智能量化方法的有效性, 在水利工程中具有广泛的应用前景。
- 深度学习 /
- Attention机制 /
- Unet /
- 图像细化 /
- 结构面量化分析 /
- 钻孔 /
- 智能识别
Abstract: Objective
Borehole Televiewer (BHTV) imaging serves as an effective tool for analyzing deep rock formations in geological engineering, offering crucial insights into surface discontinuity extensions and the distribution of buried discontinuities, especially within complex geological environments associated with hydraulic engineering projects. Recognizing the current issues of subjectivity and low efficiency, prevalent in the manual calculation of rock discontinuity morphological characteristics within rock masses, we propose an intelligent approach for segmenting geometric data from digital borehole images. Leveraging the segmentation outcomes, an image thinning technique is employed to facilitate precise quantitative analysis of borehole data.
Methods
In this research, we employ deep learning models to intelligently identify fractures within BHTV images, utilizing various network structures such as Unet, SegNet, and DeepLabV3. The recognition results are compared with traditional image processing methods, demonstrating the advantages of deep models in accurately segmenting complex geological images. Furthermore, we enhance the model's performance by incorporating an attention mechanism into the encoder-decoder process.Once precise segmentation of rock discontinuities is achieved, the fracture skeleton is extracted using the image thinning method, representing fractures as one-pixel-width curves. Ultimately, automated calculations is completed for dip strike, dip angle, and fracture thickness.
Results
This method is applied to segment and calculate borehole televiewer images in hydraulic engineering. Comparing the results of manual extraction and automatic extraction of fracture information, the error of dip strike and dip angle is less than 3°, and the fracture thickness error is less than 0.65 mm.
Conclusion
The results verify that the intelligent calculation method of fracture information proposed in this paper. The proposed method has wide application prospects in hydraulic engineering.
- deep learning /
- Attention mechanism /
- Unet /
- image thinning /
- fracture quantitative analysis /
- borehole /
- intelligent recognition
注释:

(所有作者声明不存在利益冲突)

HTML全文

图 1 Unet模型结构

Figure 1. Unet architecture

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图 2 SegNet模型结构

Figure 2. SegNet architecture

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图 3 不同扩张率下的空洞卷积特征融合

Figure 3. Feature ensemble of dilated convolution with different expansion rates

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图 4 模型测试图像

Figure 4. Test images

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图 5 不同传统方法的图像分割结果

Figure 5. Segmentation of different traditional methods

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图 6 不同模型训练过程中IoU和Loss变化过程

Figure 6. IoU and Loss curves in training process of different models

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图 7 不同模型图像分割结果比较(1, 2, 3指区域编号)

Figure 7. Comparison of image segmentation results using different models

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图 8 测试图像分割结果

Figure 8. Test image segmentation results

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图 9 测试图像结构面骨架

Figure 9. Fracture skeleton of the test image

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图 10 结构面骨架去噪过程

Figure 10. Process of fracture skeleton noise removel

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表 1 不同模型性能评价比较

Table 1. Performance evaluation of different models

训练模型	精确率/%	召回率/%	F1_score/%	IoU/%
AUnet	99.2	99.2	99.2	83.1
Unet	99.5	99.3	99.4	81.7
ASegNet	99.0	98.9	98.9	79.5
SegNet	98.7	98.7	98.6	76.4
DeepLabV3	98.7	98.3	98.5	61.7

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表 2 结构面信息手动提取和自动识别结果对比

Table 2. Comparison of manual and automatic fractures information calculations

编号	倾向/(°)			倾角/(°)			厚度/mm
编号	人工提取	自动识别	差值	人工提取	自动识别	差值	人工提取	自动识别	差值
I_1-1	23.7	26.4	2.7	61.09	60.21	0.88	25.61	25.06	0.55
I_2-1	164.6	162.0	2.6	30.56	29.51	1.05	21.23	21.49	0.26
I_2-2	187.7	190.3	2.6	53.43	51.61	1.82	15.75	15.88	0.13
I_2-3	154.3	157.2	2.9	27.19	26.00	1.19	13.11	12.88	0.23
I_2-4	135.3	132.8	2.5	22.68	19.84	2.84	13.34	13.77	0.43
I_2-5	171.0	171.5	0.5	28.03	27.16	0.87	17.52	16.91	0.61
I_2-6	87.0	84.6	2.4	22.23	20.30	1.93	13.00	12.64	0.36
I_3-1	249.0	246.8	2.2	41.21	38.97	2.24	18.71	18.56	0.15
I_4-1	246.9	248.1	1.2	73.92	72.80	1.12	14.70	14.42	0.28
I_5-1	138.3	138.8	0.5	69.04	67.40	1.64	12.52	12.54	0.02
I_6-1	177.4	176.6	0.8	15.71	14.02	1.69	31.04	30.51	0.53
I_7-1	244.9	242.5	2.4	27.28	24.75	2.53	26.83	26.26	0.57
I_8-1	87.0	88.7	1.7	40.83	38.18	2.65	27.23	28.06	0.83
I_9-1	65.5	62.7	2.8	42.68	39.86	2.82	14.15	14.02	0.13
I_9-2	80.3	78.2	2.1	37.95	35.86	2.09	28.19	28.25	0.06
I_9-3	18.0	15.9	2.1	57.38	56.56	0.82	14.55	14.36	0.19
I_10-1	74.6	75.7	1.1	17.66	16.95	0.71	20.94	21.28	0.34

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