联合最大相关峭度解卷积与连续性约束的浅地层层界实时提取

孟新宝; 周天; 朱建军; 李铁; 王裴宏; 赵国庆

doi:10.11999/JEIT250727

联合最大相关峭度解卷积与连续性约束的浅地层层界实时提取

doi: 10.11999/JEIT250727 cstr: 32379.14.JEIT250727

孟新宝^{1, 2, 3,},
周天^{1, 2, 3, 4},
朱建军^{1, 2, 3, 4, ,},
李铁^{1, 2, 3, 4},
王裴宏⁴,
赵国庆⁴

1.
哈尔滨工程大学水声技术全国重点实验室哈尔滨 150001
2.
工业和信息化部海洋信息获取与安全工信部重点实验室(哈尔滨工程大学) 哈尔滨 150001
3.
哈尔滨工程大学水声工程学院哈尔滨 150001
4.
哈尔滨工程大学三亚南海创新发展基地三亚 572000

基金项目: 国家自然科学基金“叶企孙”科学基金(U2541204)，国家自然科学基金(42176188)，水声技术全国重点实验室稳定支持计划(JCKYS2024604SSJS004)，海南省自然科学基金(421CXTD442)

详细信息

作者简介:
孟新宝：男，博士生，研究方向为水声信号处理、海底掩埋目标探测

周天：男，教授，研究方向为水声目标探测、水声信号处理

朱建军：男，副教授，研究方向为水下非线性声学探测技术

李铁：男，讲师，研究方向为水声信号处理

王裴宏：男，博士生，研究方向为水声信号处理

赵国庆：男，硕士生，研究方向为水声信号处理、声呐系统设计

通讯作者:
朱建军　zhujianjun@hrbeu.edu.cn

中图分类号: TN911.7;TB566
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- HTML全文浏览量: 104
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- 被引次数: 0
出版历程
- 收稿日期: 2025-08-01
- 修回日期: 2026-03-26
- 录用日期: 2026-03-27
- 网络出版日期: 2026-04-21
- 刊出日期: 2026-06-15

Real-Time Sub-bottom Horizon Picking Based on Maximum Correlated Kurtosis Deconvolution Combined with Continuity Constraint

MENG Xinbao^{1, 2, 3
,},
ZHOU Tian^{1, 2, 3, 4},
ZHU Jianjun^{1, 2, 3, 4
, ,},
LI Tie^{1, 2, 3, 4},
WANG Peihong⁴,
ZHAO Guoqing⁴

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
4.
Sanya Nanhai Innovation and Development Base, Harbin Engineering University, Sanya 572000, China

Funds: The National Natural Science Foundation of China “Ye Qisun” Science Foundation (U2541204), The National Natural Science Foundation of China (42176188), The Stable Support Plan of the National Key Laboratory of Underwater Acoustic Technology (JCKYS2024604SSJS004), The Natural Science Foundation of Hainan Province (421CXTD442)

摘要

摘要: 针对现有浅地层层界提取方法在在线应用中难以兼顾提取质量、虚警抑制与处理延迟的问题，该文提出了联合最大相关峭度解卷积与连续性约束的浅地层层界实时提取方法，并构建预处理、粗提取与精细提取的逐ping处理流程。预处理阶段采用带通滤波级联匹配滤波对回波信号增强，并对匹配滤波输出进行固定时延校正；粗提取阶段在多个切片步长下构造合成周期信号并应用最大相关峭度解卷积方法获得潜在层界序列，随后基于跨步长一致性对潜在层界进行筛选融合以抑制虚警；精细提取阶段引入层界连续性约束，对粗提取结果进行有效层界点的筛选、层界划分与曲线拟合修正，进一步抑制残余虚警并提高层界连续性。仿真结果表明，当回波信噪比高于–10 dB时，层界检测概率超过99.000%，虚警概率低于0.100%，层界定位误差约为1个样本点；实测数据处理结果显示，对不同层界的平均检测概率为91.833%，平均虚警概率为0.004%，平均定位误差约为10个样本点。仿真和实测数据处理均实现了海底表面和沉积层界的有效提取，验证了方法的有效性和实用价值。
- 浅地层剖面 /
- 层界提取 /
- 最大相关峭度解卷积 /
- 跨步长一致性 /
- 连续性约束
Abstract: Objective Sub-bottom profiling is widely used in seabed geological and resource exploration, pipeline route inspection, and port and channel safety assessment. It is also a major topic in underwater acoustic detection. Accurate sub-bottom horizon picking is critical for interpreting sedimentary structures, analyzing seabed substrate characteristics, and identifying buried objects. However, existing horizon-picking methods often cannot balance picking quality, false-alarm suppression, and online real-time performance. To address this problem, this paper proposes a real-time sub-bottom horizon-picking method based on Maximum Correlated Kurtosis Deconvolution (MCKD) combined with continuity constraint. Methods The proposed method includes three stages: preprocessing, coarse horizon extraction, and fine horizon extraction. In preprocessing, raw echoes are enhanced by cascaded band-pass filtering and matched filtering. Fixed-delay correction is then performed to align the picked positions with pulse leading-edge arrivals. In coarse horizon extraction, synthesized periodic signals are constructed under multiple slicing step lengths. MCKD is then applied to enhance impulsive horizon responses and obtain potential horizon sequences. These candidates are screened and fused using a cross-step-length consistency criterion to suppress false alarms. In fine horizon extraction, a continuity constraint is applied within an online sliding window. This process removes isolated points, segments horizons, and performs curve fitting and correction, thereby further reducing residual false alarms and improving horizon continuity. Results and Discussions Simulation and field data experiments were conducted to evaluate detection probability, false-alarm probability, horizon positioning error, processing time, and extracted horizon profiles. Monte Carlo results show that fine horizon extraction further reduces false alarms and positioning errors while maintaining detection performance close to that of coarse horizon extraction (Fig. 5, Fig. 6). When the echo Signal-to-Noise Ratio (SNR) is higher than –15 dB, the detection probability exceeds 70.000%, and the false-alarm probability remains below 0.200%. When the SNR is higher than –10 dB, the detection probability exceeds 99.000%, the false-alarm probability is below 0.100%, and the positioning error is close to one sample point (Fig. 6). In the sub-bottom survey simulation, the proposed method successfully extracts both the seabed surface and the buried sedimentary horizon under different noise conditions. Its results are more refined than those obtained using the Fractional Fourier Transform (FrFT)-based algorithm and are generally comparable to manual interpretation (Fig. 7, Fig. 8). Field data results further confirm the effectiveness of the method. In comparison with signal-based algorithms, the proposed method achieves an average detection probability of 91.833%, an average false-alarm probability of 0.004%, and an average positioning error of 10.15 samples. By contrast, the FrFT-based algorithm produces a much higher false-alarm probability of 3.987% (Table 1). In comparison with image-based algorithms, although their detection probabilities exceed 95%, their false-alarm probabilities and processing times are much higher than those of the proposed method (Table 2). Qualitative results also show that the extracted horizons agree well with manual interpretation trends. The proposed method yields lower background noise, avoids obvious large-scale false horizons, and preserves local fluctuations and possible interruptions in real horizons (Figs. 9–12). Overall, the method provides a better balance for online horizon picking by combining acceptable detection probability and positioning accuracy with an extremely low false-alarm probability and real-time processing capability (Table 1, Table 2). Conclusions This study presents a real-time sub-bottom horizon-picking method based on MCKD combined with continuity constraint. The method is organized into preprocessing, coarse horizon extraction, and fine horizon extraction. It effectively extracts the seabed surface and sedimentary horizons while meeting real-time processing requirements. Simulation results show that, when the SNR exceeds –10 dB, the method achieves a detection probability greater than 99.000%, a false-alarm probability below 0.100%, and a positioning error close to one sample point. Field data processing results show an average detection probability of 91.833%, an average false-alarm probability of 0.004%, and an average positioning error of 10.15 samples. These results confirm the effectiveness and practical value of the proposed method for real-time sub-bottom horizon picking. The method maintains high detection performance, suppresses false alarms, and achieves millisecond-level processing, making it suitable for online sub-bottom horizon-picking tasks in practical applications.
- Sub-bottom profiling /
- Horizon picking /
- Correlated kurtosis /
- Deconvolution /
- Continuity constraint

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图 1 MCKD-CC-RTSHP方法总体框图

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图 2 MCKD潜在层界提取算法实现流程

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图 3 潜在层界跨步长一致性筛选融合框图

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图 4 连续性约束层界精细提取算法框图

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图 5 层界粗提取的ROC曲线

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图 6 无幅度门限时的层界提取性能曲线

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图 7 信噪比10 dB时的层界提取结果剖面图

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图 8 信噪比–10 dB时的层界提取结果剖面图

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图 9 第1组实测数据不同信号类方法的层界提取结果剖面图

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图 10 第2组实测数据不同信号类方法的层界提取结果剖面图

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图 11 第1组实测数据不同图像类方法的层界提取结果剖面图

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图 12 第2组实测数据不同图像类方法的层界提取结果剖面图

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表 1 不同信号类方法的实测层界提取性能参数

方法名称	MCKD-CC-RTSHP方法				FrFT浅剖算法
层界代号	L_1,1	L_1,2	L_2,1	L_2,2	L_1,1	L_1,2	L_2,1	L_2,2
$ {P}_{\mathrm{d}} $ (%)	90.000	92.000	94.000	91.333	100.000	100.000	100.000	100.000
$ {\overline{P}}_{\text{d}} $ (%)	91.833				100.000
$ {P}_{\mathrm{f}} $ (%)	0.006	0.006	0.002	0.002	3.978	3.978	3.995	3.995
$ {\overline{P}}_{\text{f}} $ (%)	0.004				3.987
$ {E}_{\mathrm{RMSE}} $	14.65	6.38	8.57	11.01	9.10	8.08	8.06	9.13
$ {\overline{E}}_{\mathrm{RMSE}} $	10.15				8.59
$ {t}_{\text{ping}} $ (ms)	11.994		11.840		8.798		9.028
$ {\overline{t}}_{\text{ping}} $ (ms)	11.917				8.913

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表 2 不同图像类方法的实测层界提取性能参数

方法名称	图像法A				图像法B
层界代号	$ {\text{L}}_{1,1} $	$ {\text{L}}_{1,2} $	$ {\text{L}}_{2,1} $	$ {\text{L}}_{2,2} $	$ {\text{L}}_{1,1} $	$ {\text{L}}_{1,2} $	$ {\text{L}}_{2,1} $	$ {\text{L}}_{2,2} $
$ {P}_{\mathrm{d}} $ (%)	96.667	100.000	100.000	99.333	96.000	97.333	98.333	98.333
$ {\overline{P}}_{\text{d}} $ (%)	99.000				97.500
$ {P}_{\mathrm{f}} $ (%)	0.479	0.479	0.764	0.764	0.063	0.063	0.095	0.095
$ {\overline{P}}_{\text{f}} $ (%)	0.622				0.079
$ {E}_{\mathrm{RMSE}} $	5.36	3.28	1.61	4.78	9.50	7.41	9.24	10.56
$ {\overline{E}}_{\mathrm{RMSE}} $	3.76				9.18
$ {t}_{\text{img}} $ (ms)	791.488		791.828		1616.751		1668.398
$ {\overline{t}}_{\text{img}} $ (ms)	791.658				1642.575

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