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Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrades their performances. Toward these problems, we propose a seabed sediment classification method using spatial statistical features extracted from angular response curve (ARC), topography, and geomorphology. First, to reduce interference of noise and residual error of beam pattern correction, we propose a robust method combining the Generic Seafloor Acoustic Backscatter (GSAB) model and Huber loss function to estimate the parameters of ARC which is strongly correlated with seabed sediments. Second, a feature set is constructed by AR features composed of GSAB parameters, BS mosaic and its derivatives, and seabed topography and its derivatives to characterize seabed sediments. After that, feature selection and probability map acquisition are employed based on the random forest algorithm (RF). Finally, a denoising and final sediment map generation method is proposed and applied to probability maps to obtain the sediment map with reasonable sediment distribution and clear boundaries between classes. We implement experiments and achieve the classification accuracy of 93.3%, which verifies the validity of our method. [ABSTRACT FROM AUTHOR]

Manual horizon picking and semiautomatic horizon picking are the traditional methods for data processing of subbottom profiles (SBPs). However, the former is time consuming and laborsome, whereas the latter requires frequent manual intervention and easily suffers from low accuracy and discontinuous picking due to noises. A comprehensive and automatic horizon picking method is proposed by combining the given gray mutation, horizon tracking and filtering, and horizon growth algorithms in this paper. Based on the peaks and valleys corresponding to the strong and weak impedance contrasts in an echo sequence, the gray mutation is used to prepick horizons from a SBP image. According to sediment layer continuity and horizontal resolution, the horizon tracking and filtering are applied to construct discrete horizon segments and remove outliers. In consideration of the correlation of adjacent ping sequences and the horizon orientation, the horizon growth based on the horizon orientation constraints is utilized to connect the discrete horizon segments. Experiments validated the proposed method. The automatic horizon picking results were compared with the bore-hole data and the manual horizon picking results as well, and good consistencies were achieved. The determinations of parameters and the performance of the proposed method are discussed based on the theoretical study and experiments, and some conclusions are drawn. [ABSTRACT FROM AUTHOR]

Airborne LiDAR bathymetry (ALB) has been shown to have the ability to retrieve water turbidity using the waveform parameters (i.e., slopes and amplitudes) of volume backscatter returns. However, directly and accurately extracting the parameters of volume backscatter returns from raw green-pulse waveforms in shallow waters is difficult because of the short waveform. This study proposes a new accurate and efficient method for the remote sensing of suspended sediment concentrations (SSCs) in shallow waters based on the waveform decomposition of ALB. The proposed method approaches raw ALB green-pulse waveforms through a synthetic waveform model that comprises a Gaussian function (for fitting the air-water interface returns), triangle function (for fitting the volume backscatter returns), and Weibull function (for fitting the bottom returns). Moreover, the volume backscatter returns are separated from the raw green-pulse waveforms by the triangle function. The separated volume backscatter returns are used as bases to calculate the waveform parameters (i.e., slopes and amplitudes). These waveform parameters and the measured SSCs are used to build two power SSC models (i.e., SSC (C)-Slope (K) and SSC (C)-Amplitude (A) models) at the measured SSC stations. Thereafter, the combined model is formed by the two established C-K and C-A models to retrieve SSCs. SSCs in the modeling water area are retrieved using the combined model. A complete process for retrieving SSCs using the proposed method is provided. The proposed method was applied to retrieve SSCs from an actual ALB measurement performed using the Optech Coastal Zone Mapping and Imaging LiDAR in a shallow and turbid water area. A mean bias of 0.05 mg/L and standard deviation of 3.8 mg/L were obtained in the experimental area using the combined model. [ABSTRACT FROM AUTHOR]

Sub-bottom sediment classifications have been widely used in marine science and engineering to obtain high-resolution information on types of sediments; however, these are often plagued by inaccuracies. Classification difficulties arise from the inability to effectively filter multiple reflections, extract representative lithology characteristic parameters, identify sub-bottom layer interfaces, extract image samples, control sample quality, optimise characteristic parameters, etc. To generate a highly accurate sub-bottom profile sediment map, a five-step classification method that considers two key lithology characteristic parameters of sub-bottom profile acoustic data was proposed. First, multiple reflections were filtered from the sea surface and sub-bottom layer interfaces of the primary signal. Second, two key characteristic parameters (relative backscattering intensity difference and attenuation compensation residual) were calculated. These reflect the relative differences in backscattering intensity and the attenuation compensation between adjacent interfaces based on the sound intensity attenuation model of a sub-bottom profile. Third, a combined method based on the sediment quality factor and peak trough of the echo signal loss level curve was employed to identify the actual interfaces between layers. An additional technique was proposed to determine the image sample width and preferred characteristic parameters. The resulting high-quality image samples and preferred characteristic parameters not only resulted in a faster convergence rate and increased ability of self-aggregation and identification, but also ensured that the training results met the convergence accuracy requirement. Ultimately, the preferred image samples were trained to classify the overall sub-bottom map of a selected test area of approximately 36 km2 in Bahai Bay, China. Compared with traditional methods, a considerably higher sediment identification accuracy was obtained. The experimental results indicate that the contribution rate of the two key lithology characteristic parameters was 65.49% according to principal component analysis, and the internal and external compatibilities were 97.98% and 84.76% for the training image samples, respectively. The total identification accuracy for the sub-bottom profile map was 98.2%. The two key characteristic parameters accurately captured the acoustic characteristics of sub-bottom sediments, significantly improving sediment classification. These results show that this method could be used to help refine the distributional estimates of submarine mineral resources. • A combined filtering method using predictive deconvolution and iterative feedback was proposed. • Two key parameters were derived from sound intensity attenuation model of sub-bottom profile. • Sample width selection and characteristic parameter preferred-selection method were proposed. • Experimental result indicated that identification accuracy for sub-bottom profile map was 98.2%. [ABSTRACT FROM AUTHOR]

The shift in IF (instantaneous frequency) series and the corresponding relaxation time have the potential to characterize sediment properties. However, these attributes derived from SBP (sub-bottom profiler) data are seldom used for offshore site investigations because of the unsoundness in attribute calculation. To overcome this problem, a new reliable method combining VMD (variational mode decomposition) and WVD (Wigner–Ville distribution), as well as relaxation time, is presented. Since the number of modes in classical VMD should be provided in advance, a modified VMD algorithm, MVMD (modified variational mode decomposition), is proposed here, where the distribution of the frequency domain of modes is taken into account to automatically determine the number of modes. Through the relaxation time model, the IF data of a series of pings calculated through MVMD-WVD are transformed into a relaxation time map. A robust estimation algorithm is applied to the relaxation time map to reduce the effects of interferences and obtain robust relaxation times. The final relaxation time data are used to determine the sediment types. Real data from SBP experiments, as well as borehole sampling and geotechnical analysis results, verified the good performance of the proposed method. [ABSTRACT FROM AUTHOR]