Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

[1]  R. Bagnold,et al.  Motion of waves in shallow water. Interaction between waves and sand bottoms , 1946, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[2]  Nan Wang,et al.  Modeling the tidal dynamic changes induced by the bridge in Jiaozhou Bay, Qingdao, China , 2014 .

[3]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[4]  D. Twichell.  Bedform distribution and inferred sand transport on Georges Bank, United States Atlantic Continental Shelf , 1983 .

[5]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[6]  Guannan Li,et al.  Chiller sensor fault detection based on empirical mode decomposition threshold denoising and principal component analysis , 2018, Applied Thermal Engineering.

[7]  Manhar Dhanak,et al.  A Deep Learning Approach to Target Recognition in Side-Scan Sonar Imagery , 2018, OCEANS 2018 MTS/IEEE Charleston.

[8]  R. Lindenbergh,et al.  Separating bathymetric data representing multiscale rhythmic bed forms: A geostatistical and spectral method compared , 2008 .

[9]  Xiao Han,et al.  Underwater sonar image classification using adaptive weights convolutional neural network , 2019, Applied Acoustics.

[10]  Yasuyuki Shimizu,et al.  Numerical computation of sand dune migration with free surface flow , 2006 .

[11]  I. Hennings,et al.  On the behaviour of tidal current directions due to the presence of submarine sand waves , 2000 .

[12]  P. Blondeaux,et al.  A model to predict the migration of sand waves in shallow tidal seas , 2016 .

[13]  Xue-wen Chen,et al.  Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.

[14]  A LATTICE BOLTZMANN STUDY OF THE 2D BOUNDARY LAYER CREATED BY AN OSCILLATING PLATE , 2006 .

[15]  S. Aliotta,et al.  Morphology and dynamics of large subtidal dunes in Bahia Blanca estuary, Argentina , 2015 .

[16]  A. Wheeler,et al.  Reversed sediment wave migration in the Irish Sea, NW Europe: A reappraisal of the validity of geometry-based predictive modelling and assumptions , 2012 .

[17]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[18]  R. Durán,et al.  Continental Shelf Landforms , 2018 .

[19]  Narasimhan Sundararajan,et al.  Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  D. Twichell.  Bed form distribution and inferred sand transport on Georges Bank , 1981 .

[21]  Rik Houthuys,et al.  Storm influences on a tidal sandbank's surface (Middelkerke Bank, southern North Sea) , 1994 .

[22]  Yin Ru-guang.  Design and implementation of side-scan sonar data management system , 2011 .

[23]  Maarten G. Kleinhans,et al.  Observed and predicted bed forms and their effect on suspended sand concentrations , 2004 .

[24]  Jørgen Fredsøe,et al.  On the development of dunes in erodible channels , 1974, Journal of Fluid Mechanics.

[25]  J. Perron,et al.  Wavelength selection and symmetry breaking in orbital wave ripples , 2014 .

[26]  L. BartlettP..  The sample complexity of pattern classification with neural networks , 2006 .

[27]  Guoxiang Wu,et al.  Modeling the formation and migration of sand waves: The role of tidal forcing, sediment size and bed slope effects , 2019, Continental Shelf Research.

[28]  Xiaofeng Li,et al.  Ocean sand ridge signatures in the Bohai Sea observed by satellite ocean color and synthetic aperture radar measurements , 2011 .

[29]  G. Besio,et al.  A note on tidally generated sand waves , 2003, Journal of Fluid Mechanics.

[30]  N. Leonardi,et al.  The Impact of Waves and Tides on Residual Sand Transport on a Sediment‐Poor, Energetic, and Macrotidal Continental Shelf , 2019, Journal of Geophysical Research: Oceans.

[31]  Qi Wang,et al.  ECNet: Efficient Convolutional Networks for Side Scan Sonar Image Segmentation , 2019, Sensors.

[32]  David P. Williams,et al.  Adaptive underwater sonar surveys in the presence of strong currents , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[33]  B. Bett,et al.  Hydrodynamic significance of variable ripple morphology across deep-water barchan dunes in the Faroe-Shetland Channel , 2002 .

[34]  Tao Jin,et al.  A Novel Adaptive EEMD Method for Switchgear Partial Discharge Signal Denoising , 2019, IEEE Access.

[35]  P. Roos,et al.  The role of suspended load transport in the occurrence of tidal sand waves , 2014 .

[36]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[37]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[38]  A. Wheeler,et al.  Variations in sediment wave dimensions across the tidally dominated Irish Sea, NW Europe , 2009 .

[39]  O. Hagen,et al.  Terrain referenced navigation using sand dunes and sand ripples in presence of tides , 2018, OCEANS 2018 MTS/IEEE Charleston.

[40]  Amaury Lendasse,et al.  Fast Face Recognition Via Sparse Coding and Extreme Learning Machine , 2013, Cognitive Computation.

[41]  M. Dolan,et al.  Sandbanks, sandwaves and megaripples on Spitsbergenbanken, Barents Sea , 2019, Marine Geology.

[42]  Queralt Guerrero Sánchez.  Bedforms and associated sediment dynamics on the inner shelves at different spatio-temporal scales , 2019 .

[43]  M. Knaapen,et al.  Quantifying bedform migration using multi-beam sonar , 2005 .

[44]  Scott Haag,et al.  Side-Scan Sonar Imaging of Subtidal Benthic Habitats in the Mullica River–Great Bay Estuarine System** , 2004 .

[45]  He Kun,et al.  A novel denoising method for vibration signal of hob spindle based on EEMD and grey theory , 2021 .

[46]  H. Swart,et al.  Sensitivity of tidal sand wavelength to environmental parameters: a combined data analysis and modelling approach , 2011 .

[47]  Ye Yuan,et al.  An OS-ELM based distributed ensemble classification framework in P2P networks , 2011, Neurocomputing.

[48]  M. Knaapen.  Sandwave migration predictor based on shape information , 2005 .

[49]  S. Bennett,et al.  On the transition between 2D and 3D dunes , 2005 .

[50]  Michael Collins,et al.  Water and sediment movement in the vicinity of linear sandbanks: the Norfolk Banks, southern North Sea , 1995 .

[51]  T. Hyakudome,et al.  Micro-bathymetric Evidence for the Effect of Submarine Mass Movement on Tsunami Generation During the 2009 Suruga Bay Earthquake, Japan , 2012 .

[52]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[53]  S. Hulscher,et al.  Modelling sand wave migration in shallow shelf seas , 2002 .

[54]  J. Bourillet,et al.  Eustatic and hydrodynamic controls on the architecture of a deep shelf sand bank (Celtic Sea) , 1999 .

[55]  S. Bennett,et al.  Morphodynamics of small‐scale superimposed sand waves over migrating dune bed forms , 2005 .

[56]  Tao Xiang,et al.  Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[57]  P. Roos,et al.  Modeling tidal sand wave formation in a numerical shallow water model: the role of turbulence formulation , 2013 .

[58]  Jianhu Zhao,et al.  A New Radiometric Correction Method for Side-Scan Sonar Images in Consideration of Seabed Sediment Variation , 2017, Remote. Sens..

[59]  B. Shu.  The Classification Characteristics and Developing Factors of the Sandwaves at the Mouth of Jiaozhou Bay , 2006 .

[60]  C. Delacourt,et al.  Giant dune morphologies and dynamics in a deep continental shelf environment: Example of the banc du four (Western Brittany, France) , 2013 .

[61]  Maarten G. Kleinhans,et al.  Observations of sand waves, megaripples, and hummocks in the Dutch coastal area and their relation to currents and combined flow conditions , 2005 .

[62]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[63]  Maarten G. Kleinhans,et al.  Processes controlling the dynamics of compound sand waves in the North Sea, Netherlands , 2005 .