Magnetic detection method for seabed cable in marine engineering surveying

Bo, Yu; Yan-chun, Liu; Zhai, Gangyi; Bian, Guo‐Qing; Fumin, Xiao

doi:10.1007/s11806-007-0085-1

Cited by 9 publications

(2 citation statements)

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“…At present, the recognition of SCMA signals mainly relies on manual methods [1][2][3]. The main challenges of manual methods are the lack of real-time processing options, heavy processing workload and slow processing speed.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Task Learning for Submarine Cable Magnetic Anomaly Recognition

Liu

Yang³

et al. 2023

JMSE

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The recognition of submarine cable magnetic anomaly (SCMA) signals is a challenging task in magnetic signal data processing. In this study, a multi-task convolutional neural network (MTCNN) model is proposed to simultaneously recognize abnormal signals and locate abnormal regions. The residual block is added to the shared feature backbone to improve the ability of the network to extract high-level features and maintain the gradient stability of the model in the training process. The long short-term memory (LSTM) block is added to the classification branch task to learn the internal relationship of the magnetic anomaly time series, so as to improve the network’s ability to recognize magnetic anomalies. Our proposed model can accurately recognize the SCMA signals collected in the East China Sea and the South China Sea. The classification accuracy and the ability to locate the abnormal regions are close to the manual labeling of human analysts. The newly developed model can help analysts reduce the probability of missing and misjudging submarine cable magnetic anomalies, improve the efficiency and accuracy of interpretation, and could even be deployed to an unmanned platform to realize the automatic detection of SCMAs.

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Section: Introductionmentioning

confidence: 99%

A Multi-Task Learning for Submarine Cable Magnetic Anomaly Recognition

Liu

Yang³

et al. 2023

JMSE

View full text Add to dashboard Cite

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“…Sub-bottom profilers are used across shallow and deep oceanic waters with several applications, such as sea-level studies (e.g., Bastos et al, 2010;Koša, 2015;Aquino da Silva et al, 2016;Yoo et al, 2016), sedimentation process (e.g., Miller et al, 2013;Alves and de Mahiques, 2019), gas seeps (e.g., Benites et al, 2015;Michel et al, 2017), geomorphology (e.g., Jobe et al, 2011;Gomes et al, 2016), and others. Marine magnetometer data have also been used for most various studies, as archeological (Boyce et al, 2004), environmental (Boyce et al, 2001), engineering (Yu et al, 2007), and geological, mainly for mineral exploration (Dehler and Potter, 2002) and academic purposes (Müller et al, 1997). Combining both techniques enhances considerably the possibilities of geological and oceanographic interpretation, as multi-data surveys in marine environments allow complementary studies (e.g., Kadima et al, 2011;Demir et al, 2012).…”

Section: Introductionmentioning

confidence: 99%