基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法

doi:10.3969/j.issn.0253-4967.2024.03.013

地震地质 ›› 2024, Vol. 46 ›› Issue (3): 739-755.DOI: 10.3969/j.issn.0253-4967.2024.03.013

基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法

宋冬梅¹⁾(), 王浩¹⁾^,^*(), 冯家兴²⁾, 单新建³⁾, 王斌¹⁾

¹⁾ 中国石油大学(华东), 海洋与空间信息学院, 青岛 266580
²⁾ 东方通用航空摄影有限公司, 太原 030032
³⁾ 中国地震局地质研究所, 北京 100029

收稿日期:2023-02-14 修回日期:2023-03-29 出版日期:2024-06-20 发布日期:2024-07-19
通讯作者: *王浩, 男, 1997年生, 硕士, 主要从事三维激光点云地物提取方面的研究, E-mail: z20160115@s.upc.edu.cn。
作者简介:
宋冬梅, 女, 1973年生, 2003年于中国科学院沈阳应用生态研究所获景观生态学专业博士学位, 教授, 现主要研究方向为海洋灾害遥感与遥感影像智能算法研究, E-mail: songdongmei@upc.edu.cn。
基金资助:
国家重点研发计划项目(2019YFC16D9202-4); 国家自然科学基金(U22A20586); 国家自然科学基金(41701513); 国家自然科学基金(41772350); 山东省自然科学基金(ZR2022MD015); 中国地震科学实验场地震构造探查系统项目共同资助。

A FRACTURE ZONE EXTRACTION METHOD FOR LIDAR POINT CLOUD BASED ON MULTI-SCALE NEURAL NETWORK WITH RS-CONV

SONG Dong-mei¹⁾(), WANG Hao¹⁾^,^*(), FENG Jia-xing²⁾, SHAN Xin-jian³⁾, WANG Bin¹⁾

¹⁾ College of Oceanography and Spatial Information, China University of Petroleum, Qingdao 266580, China
²⁾ Oriental General Aerial Photography Co., Taiyuan 030032, China
³⁾ Institute of Geology, China Earthquake Administration, Beijing 100029, China

Received:2023-02-14 Revised:2023-03-29 Online:2024-06-20 Published:2024-07-19

摘要/Abstract

摘要：

断裂带与地震、滑坡等自然灾害的发生有着密切关系, 精准提取断裂带不仅可为地震断层的定量化研究提供指导, 还可为地震灾害风险评估及防震减灾决策的制定提供科学依据。针对现有方法中LiDAR点云断裂带提取不完整、连续性差及错误率高等问题, 文中提出了一种基于 RS-Conv 的多尺度神经网络LiDAR点云断裂带提取方法, 以便更好地解决复杂地形区域的断裂带自动提取问题。该方法首先构建不同空间尺度的邻域点集, 从而更全面地考察点云的局部几何结构特征。考虑到RS-Conv算子能够很好地表征中心点与邻域点的空间关系, 文中以RS-Conv算子作为卷积模块构建了多尺度神经网络模型, 以提取出LiDAR点云不同尺度的深层次特征, 对其进行堆叠并输入到全连接层, 以完成对断裂带点的提取。最后, 在ISPRS点云数据集、川滇点云数据集和鲜水河数据集上对文中所述方法与张量分解方法和Deep Neural Networks(DNN)方法进行了对比实验, 结果表明, 文中方法的分类精度最高, 分类总误差最低仅为0.3%, 较其他方法降低了0.91%~2.79%, 证实了该方法在点云断裂带提取方面的优越性。

关键词: LiDAR点云, 多尺度邻域点集, 深度学习, 断裂带提取

Abstract:

Fracture zones are geological formations resulting from the strong movement of the Earth’s crust, often manifesting as fragile and sensitive areas. These zones are closely linked to natural disasters such as earthquakes and landslides. Accurate extraction of fracture zones is crucial for quantitative studies of earthquake faults, providing a scientific basis for risk assessment and decision-making in earthquake prevention and mitigation. Thus, an in-depth study to determine their distribution patterns and surface geometry is essential for understanding earthquake dynamics and mechanisms.

This paper addresses the shortcomings of existing methods in extracting fracture zones from LiDAR point clouds, which often suffer from incomplete extraction, poor continuity, and high error rates. We propose a method based on a multi-scale neural network with RS-Conv to improve the automatic extraction of fault zones in complex terrain regions. Fracture zones exhibit complex morphologies and scale features; therefore, single-scale neighborhood point sets fail to reveal their intrinsic structural information fully. Our approach begins by constructing neighborhood point sets at different spatial scales to comprehensively examine geometric features at various levels within the point cloud. The RS-Conv operator effectively portrays the spatial relationship between the center point and neighboring points. We then build a multi-scale neural network model using the RS-Conv operator as the convolution module. This model captures the spatial relationships in the point cloud, efficiently extracting deep features at different scales. The extracted multi-scale features are concatenated to form a richer and more comprehensive feature representation, which is inputted into a fully connected layer to classify the centroid and solve the fracture zone extraction problem. We compared our method with the Tensor Decomposition and Deep Neural Networks(DNN)methods using the ISPRS point cloud dataset, the Sichuan-Yunnan point cloud dataset, and the Xianshuihe dataset. Results show that our method achieves the highest classification accuracy across all three datasets. Specifically, our method’s total classification error is only 0.3%, a reduction of 0.91% -2.79%compared to other methods. This significant error reduction demonstrates the accuracy, stability, and reliability of our proposed method in handling complex point cloud data. The main conclusions of this study are as follows:

(1)The construction of neighborhood point sets at different scales reveals that the combination of these scales significantly impacts the model’s classification performance. Selecting appropriate scale combinations is crucial for optimizing the model’s classification accuracy, facilitating better distinction between fracture zone points and non-fracture zone points.

(2)Compared to traditional and machine learning methods, the deep learning network model developed in this study shows significant advantages in extracting fracture zones from point clouds. The model can automatically learn deep features from point cloud data and process large-scale, high-dimensional point cloud datasets, thereby achieving more accurate fracture zone extraction in complex terrain conditions.

(3)Comparative experiments on different datasets further demonstrate the proposed method’s generalization ability. It is effective not only in extracting fracture zones under single terrain conditions but also in maintaining stable performance across multiple terrain conditions. This adaptability enhances the extraction of fracture zones in various terrain scenarios.

In conclusion, the method proposed in this paper offers a novel approach to fracture zone extraction. It achieves higher classification accuracy compared to existing traditional and machine learning methods, effectively addressing the challenge of fracture zone extraction in complex terrain areas.

Key words: LiDAR point cloud, multi-scale neighborhood point sets, deep learning, fracture zone extraction

宋冬梅, 王浩, 冯家兴, 单新建, 王斌. 基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法[J]. 地震地质, 2024, 46(3): 739-755.

SONG Dong-mei, WANG Hao, FENG Jia-xing, SHAN Xin-jian, WANG Bin. A FRACTURE ZONE EXTRACTION METHOD FOR LIDAR POINT CLOUD BASED ON MULTI-SCALE NEURAL NETWORK WITH RS-CONV[J]. SEISMOLOGY AND GEOLOGY, 2024, 46(3): 739-755.

图/表 18

图 1 RS-Conv算子框架示意图

Fig. 1 Schematic diagram of RS-Conv operator framework.

图 2 LiDAR点云断裂带提取方法的流程图

Fig. 2 Flowchart of LiDAR point cloud fracture zone extraction method.

图 3 邻域搜索二维示意图 a 邻域搜索; b kNN算法(k=3); c RPIB算法(k=3)。灰色点为预处理后的LiDAR点云, 橙色点为中心点, 蓝色点为kNN策略选择的邻域点, 绿色点为RPIB策略选择的邻域点; 红色圆圈代表以中心点为中心、以r为邻域半径的邻域范围

Fig. 3 Two-dimensional schematic diagram of the neighborhood search.

图 4 多尺度邻域搜索二维示意图灰色点为预处理后的LiDAR点云, 橙色点为中心点; 红色圆圈对应小尺度半径的邻域范围, 蓝色圆圈对应中尺度半径的邻域范围, 黄色圆圈对应大尺度半径的邻域范围, 红色、蓝色, 黄色点分别为相应的邻域点

Fig. 4 Two-dimensional schematic diagram of multi-scale neighborhood search.

图 5 MS RS-Conv点云断裂带提取框架示意图

Fig. 5 Schematic diagram of MS RS-Conv point cloud fracture zone extraction framework.

图 6 ISPRS点云数据集样本展示绿色点表示真实的非断裂带点, 蓝色点表示真实的断裂带点

Fig. 6 Sample display diagrams of ISPRS point cloud dataset.

图 7 川滇点云数据集样本展示图

Fig. 7 Sample display diagrams of Chuandian point cloud dataset.

图 8 鲜水河点云数据集样本展示图

Fig. 8 Sample display diagrams of Xianshuihe point cloud dataset.

表1 分类结果的混淆矩阵及误差计算方法

Table1 The confusion matrix of classification results and error calculation method

分类结果			预测
		断裂带点	非断裂带点
真值	断裂带点	a	b
	非断裂带点	c	d
Ⅰ类误差		c/(c+d)
Ⅱ类误差		b/(a+b)
总误差		(b+c)/(a+b+c+d)

表 2 基于不同搜索策略与邻域点个数的断裂带点提取结果展示

Table2 Results of fracture zone points extraction based on different search strategies and the number of neighborhood points

模型	邻域点搜索策略	邻域点个数	总误差/%	提取精度/%
MS RS-Conv	kNN	64、128、256	1.27	98.73
		64、128、512	0.52	99.48
		64、256、1024	1.24	98.76
	RPIB	64、128、256	1.46	98.54
		64、128、512	0.99	99.01
		64、256、1024	1.51	98.49

表3 3种断裂带提取方法在ISPRS数据集上的性能比较

Table3 Performance comparison of three fracture zone extraction methods on ISPRS dataset

数据集	方法	Ⅰ类误差/%	Ⅱ类误差/%	总误差/%	提取精度/%
Samp51	张量分解	1.22	9.31	2.82	97.18
	DNN	0.46	4.95	1.35	98.65
	MS RS-Conv	0.41	2.56	0.86	99.14
Samp53	张量分解	2.43	17.37	3.83	96.17
	DNN	1.07	11.39	2.03	97.97
	MS RS-Conv	0.19	10.27	1.04	98.96

图 9 3种断裂带提取方法在Samp51中的实验结果绿色为正确分类的非断裂带点; 蓝色为正确分类的断裂带点; 黄色为错误分类的非断裂带点; 红色为错误分类的断裂带点

Fig. 9 The results of three fracture zone extraction methods on Samp51.

图 10 3种断裂带提取方法在Samp53中的实验结果

Fig. 10 The results of three fracture zone extraction methods on Samp53.

表4 3种断裂带提取方法在川滇数据集上的性能比较

Table4 Performance comparison of three fracture zone extraction methods on Chuandian dataset

数据集	方法	Ⅰ类误差/%	Ⅱ类误差/%	总误差/%	提取精度/%
CD_1	张量分解	1.15	1.47	1.32	98.68
	DNN	0.77	0.97	0.88	99.12
	MS RS-Conv	0.29	0.50	0.41	99.59
CD_2	张量分解	1.39	1.31	1.36	98.64
	DNN	0.63	2.01	1.28	98.72
	MS RS-Conv	0.21	0.52	0.36	99.64

图 11 3种断裂带提取方法在CD_1上的实验结果

Fig. 11 The results of three fracture zone extraction methods on CD_1.

图 12 3种断裂带提取方法在CD_2上的实验结果

Fig. 12 The results of three fracture zone extraction methods on CD_2.

表5 3种断裂带提取方法在鲜水河点云数据集上的性能比较

Table5 Performance comparison of three fracture zone extraction methods on Xianshuihe dataset

数据集	方法	Ⅰ类误差/%	Ⅱ类误差/%	总误差/%	提取精度/%
鲜水河点云断裂带	张量分解	7.74	1.07	2.13	97.87
	DNN	5.11	0.91	1.58	98.42
	MS RS-Conv	1.19	0.13	0.30	99.70

图13 3种断裂带提取方法在鲜水河断裂带上的实验结果

Fig. 13 The results of three fracture zone extraction methods on Xianshuihe fracture zone.

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基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法

A FRACTURE ZONE EXTRACTION METHOD FOR LIDAR POINT CLOUD BASED ON MULTI-SCALE NEURAL NETWORK WITH RS-CONV

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