结构特征约束的三维浅剖声呐层界提取

doi:10.11947/j.AGCS.2025.20240143

测绘学报 ›› 2025, Vol. 54 ›› Issue (2): 262-271.doi: 10.11947/j.AGCS.2025.20240143

• 海洋测量 • 上一篇

结构特征约束的三维浅剖声呐层界提取

李邵波¹^,²^,³(), 赵建虎⁴^,⁵, 王爱学⁴^,⁵(), 丁咚⁶, 吴云龙¹^,³

^1.中国地质大学（武汉）地理与信息工程学院，湖北　武汉　430078
^2.自然资源部海底科学重点实验室，浙江　杭州　310012
^3.中国地质大学（武汉）区域生态过程与环境演变湖北省重点实验室，湖北　武汉　430074
^4.武汉大学测绘学院，湖北　武汉　430079
^5.武汉大学海洋研究院，湖北　武汉　430079
^6.中国海洋大学海洋地球科学学院，山东　青岛　266100

收稿日期:2024-04-11 发布日期:2025-03-11
通讯作者: 王爱学 E-mail:lishaobo@cug.edu.cn;axwang@sgg.whu.edu.cn
作者简介:李邵波（1995—），男，博士，副教授，研究方向为海洋测绘。　E-mail：lishaobo@cug.edu.cn
基金资助:
国家自然科学基金(42304049);中国博士后科学基金(2023M743282);湖北省自然科学基金(2023AFB017);自然资源部海底科学重点实验室开放基金(KLSG2406)

Three-dimensional sub-bottom profiler sonar horizon picking with structural feature constraints

Shaobo LI¹^,²^,³(), Jianhu ZHAO⁴^,⁵, Aixue WANG⁴^,⁵(), Dong DING⁶, Yunlong WU¹^,³

^1.School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
^2.Key Laboratory of Submarine Geosciences, Ministry of Natural Resources, Hangzhou 310012, China
^3.Hubei Key Laboratory of Regional Ecology and Environmental Change, China University of Geosciences, Wuhan 430074, China
^4.School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
^5.Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China
^6.College of Marine Geosciences, Ocean University of China, Qingdao 266100, China

Received:2024-04-11 Published:2025-03-11
Contact: Aixue WANG E-mail:lishaobo@cug.edu.cn;axwang@sgg.whu.edu.cn
About author:LI Shaobo (1995—), male, PhD, associate professor, majors in marine surveying and mapping.　E-mail: lishaobo@cug.edu.cn
Supported by:
The National Natural Science Foundation of China(42304049);China Postdoctoral Science Foundation(2023M743282);Hubei Provincial Natural Science Foundation of China(2023AFB017);The Key Laboratory of Submarine Geosciences, Ministry of Natural Resources(KLSG2406)

摘要/Abstract

摘要：

三维浅剖弥补了二维浅剖在水平成像空间覆盖度上的缺陷，为浅地层三维信息高质量获取提供了可能。但当前浅地层散射回波干扰严重、层界空间变化复杂，传统方法仅能实现二维层界提取且难以准确表达浅地层底质三维空间分布。为此，本文提出了基于层界面状结构和非垂直结构特征约束的浅地层三维层界结构增强RSE3D算法，提升层界提取的效果。首先，给出了顾及采样点空间分布的体数据生成方法，实现三维浅剖体数据生成；然后，将地层结构近似为面状结构并联合非垂直结构特征，提出了RSE3D算法，该方法有效压制了散射等回波干扰，凸显有了有效信息；最后，在此增强处理结果的基础上，借助阈值法实现了层界提取。经实测试验可知，本文方法的层界提取各项指标值均优于80%，体现了该方法在层界提取上的优越性。

关键词: 三维浅地层剖面仪, 层界提取, 结构特征, 增强滤波

Abstract:

Three-dimensional sub-bottom profiler (3D SBP) makes up for the deficiency of two-dimensional sub-bottom profile in the spatial coverage of horizontal imaging, and makes it possible to obtain 3D information of sub-bottom with high quality. However, at present, the interference of scattered echoes in sub-bottom is serious, the spatial changes of horizons are complex, and the traditional methods can only achieve two-dimensional horizon picking and it is difficult to express the three-dimensional spatial distribution of sub-bottom. Therefore, a three-dimensional reflector structure enhancement (RSE3D) algorithm based on plate-like and non-vertical structural features is proposed to improve the effect of horizon picking. Firstly, a volume data generation method considering the spatial distribution of sampling points is presented to realize the 3D sub-bottom profile data generation. Then, the RSE3D algorithm is proposed by approximating the formation structure as a plate structure and taking into account the non-vertical structural features. The method can effectively suppress the echo interference such as scattering and highlight the effective information. On the basis of the enhanced processing results, the horizon picking is finally realized by using the threshold method. Experimental verification and analysis show the index values of horizon picking are better than 80%, which represents the superiority of the proposed algorithm in horizon picking.

Key words: three-dimensional sub-bottom profiler, horizon picking, structural features, enhancement filtering

中图分类号:

P229

李邵波, 赵建虎, 王爱学, 丁咚, 吴云龙. 结构特征约束的三维浅剖声呐层界提取[J]. 测绘学报, 2025, 54(2): 262-271.

Shaobo LI, Jianhu ZHAO, Aixue WANG, Dong DING, Yunlong WU. Three-dimensional sub-bottom profiler sonar horizon picking with structural feature constraints[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(2): 262-271.

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算法名称	F₁值	召回率	精确率	准确率
基于二维Frangi增强滤波	79	84	73	97
基于均匀尺度的三维增强滤波	83	90	78	98

结构特征约束的三维浅剖声呐层界提取

Three-dimensional sub-bottom profiler sonar horizon picking with structural feature constraints

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算法名称	F₁值	召回率	精确率	准确率
二维Frangi增强滤波	73	62	88	99
基于均匀尺度的三维增强滤波	80	73	88	99

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