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

MENG Xinbao; ZHOU Tian; ZHU Jianjun; LI Tie; WANG Peihong; ZHAO Guoqing

doi:10.11999/JEIT250727

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MENG Xinbao, ZHOU Tian, ZHU Jianjun, LI Tie, WANG Peihong, ZHAO Guoqing. Real-Time Sub-bottom Horizon Picking Based on Maximum Correlated Kurtosis Deconvolution Combined with Continuity Constraint[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250727

Citation:

MENG Xinbao, ZHOU Tian, ZHU Jianjun, LI Tie, WANG Peihong, ZHAO Guoqing. Real-Time Sub-bottom Horizon Picking Based on Maximum Correlated Kurtosis Deconvolution Combined with Continuity Constraint[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250727

Citation:

MENG Xinbao, ZHOU Tian, ZHU Jianjun, LI Tie, WANG Peihong, ZHAO Guoqing. Real-Time Sub-bottom Horizon Picking Based on Maximum Correlated Kurtosis Deconvolution Combined with Continuity Constraint[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250727

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

doi: 10.11999/JEIT250727 cstr: 32379.14.JEIT250727

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 General Program of National Natural Science Foundation of China (42176188), Project under the Stable Support Plan of the National Key Laboratory of Underwater Acoustic Technology (JCKYS2024604SSJS004), Natural Science Foundation of Hainan Province (421CXTD442)

Received Date: 2025-08-01
Accepted Date: 2026-03-27
Rev Recd Date: 2026-03-26

Available Online: 2026-04-21

Abstract

Abstract

Objective Sub-bottom profiling is widely employed in seabed geological and resource exploration, pipeline route inspection, and port and channel safety assurance, and is regarded as a frontier in underwater acoustic detection research. Accurate extraction of sub-bottom horizons plays a critical role in the interpretation of sedimentary structures, analysis of seabed substrate characteristics, and identification of buried objects. However, existing horizon picking techniques often face difficulty in balancing picking quality, false-alarm control, and online real-time performance. To address this issue, a real-time sub-bottom horizon picking method integrating maximum correlated kurtosis deconvolution and continuity constraint is proposed. Methods The proposed method consists of three stages: preprocessing, coarse horizon extraction, and fine horizon extraction. In preprocessing, the raw echoes are enhanced via cascaded band-pass filtering and matched filtering, followed by a fixed delay correction to align picked positions with the pulse leading-edge arrivals. In coarse extraction, synthesized periodic signals are constructed under multiple slicing step lengths, and maximum correlated kurtosis deconvolution is applied to enhance impulsive horizon responses, yielding potential horizon sequences. These candidates are then screened and fused using a cross-step-length consistency criterion to suppress false alarms. In fine extraction, a continuity constraint is introduced within an online sliding window to filter isolated points, segment horizons, and perform curve fitting and correction, further reducing residual false alarms and improving 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 the fine extraction stage further reduces false alarms and positioning errors while maintaining detection performance close to that of the coarse extraction stage (Fig.5, Fig.6). When the echo signal-to-noise ratio is higher than –15 decibels, the detection probability exceeds 70.000% and the false alarm probability remains below 0.200%; when it is higher than –10 decibels, the detection probability exceeds 99.000%, the false alarm probability falls below 0.100%, and the positioning error approaches one sample interval (Fig.6). In sub-bottom survey simulation, the proposed method successfully extracts both the seabed surface and the buried sedimentary horizon under different noise conditions, with results more refined than those of the comparative algorithm based on fractional Fourier transform and overall comparable to manual interpretation (Fig.7, Fig.8). Field-data results further confirm its effectiveness: for the signal-based comparative 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, while the comparative algorithm based on fractional Fourier transform shows a much higher false alarm probability of 3.987% (Table 1). For the image-based comparative algorithms, although detection probabilities are above 95%, their false alarm probabilities and processing times remain markedly higher than those of the proposed method (Table 2). Qualitative results also show that the extracted horizons agree well with manual interpretation trends, with lower background noise, no obvious large-scale false layers, and good preservation of local fluctuations and interruptions (Fig.9–12). Overall, the proposed method achieves a more favorable balance for online horizon extraction by combining acceptable detection probability and positioning accuracy with 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 maximum correlated kurtosis deconvolution combined with continuity constraint, structured into three stages: preprocessing, coarse extraction, and fine extraction. The method effectively extracts the seabed surface and sedimentary horizons while meeting real-time processing requirements. Simulation results show that when the signal-to-noise ratio exceeds –10 dB, the method achieves a detection probability greater than 99.000%, a false alarm probability below 0.100%, and a positioning error near one sample. Field data processing results indicate an average detection probability of 91.833%, an average false alarm probability of 0.004%, and an average positioning error is 10.15 samples. These findings validate the effectiveness and practical value of the proposed approach for real-time extraction of shallow sub-bottom horizons. The method demonstrates the ability to maintain high detection accuracy while minimizing false alarms and ensuring millisecond-level processing times, making it highly suitable for online sub-bottom horizon extraction tasks in practical applications.
- Sub-bottom,
- Horizon picking,
- Correlated kurtosis,
- Deconvolution,
- Continuity constraint

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References(24)

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