济州岛南部海域海底声呐图像分析与声学底质分类

东海北部外陆架靠近济州岛南部海域,是黄海槽向冲绳海槽延伸的部分,属于黑潮分支黄海暖流的通道入口,分布着脊槽相间的海底底形,对其海底声呐图像的处理分析及声学底质分类的分析研究,有助于了解该通道海底底形表层纹理特征及沉积物分布规律。基于在济州岛南部海域获取的多波束声呐数据,应用图像处理技术和方法,对数据进行了处理,获得了海底声呐影像图,并对其表层纹理特征进行了描述和分析;同时,基于多波束反向散射强度数据,结合19组海底地质取样数据,建立研究区海底反向散射强度与沉积物粒度特征之间的统计关系模型,并以改进的学习向量量化神经网络方法,实现对海底粉砂质砂、黏土质砂以及砂-粉砂-黏土3种底质类型的快速自动分类识别。     

基于SonarWiz的多波束声纳图像智能底质分类技术研究

多波束回波强度信息与海底底质类型具有较强的相关性,通过海底声纳图像能够实现底质类型的划分.为提高海底底质分类质量,依托SonarWiz的智能底质分类优势,在海底声纳图像纹理特征自动分类基础上,引入地形属性信息修正分类结果.以三亚崖州湾附近海域为例,基于实测海底地形数据和海底表面声纳图像,利用数据处理技术和图像分类方法,...     

K-均值聚类算法在多波束底质分类中的应用

针对海底采样点较少时,监督学习训练分类模型困难的问题,研究无监督学习的K-均值聚类分析算法在多波束海底底质分类中的应用。在探讨K-均值聚类分析算法原理的基础上,构建海底底质分类器,针对分类器需预先输入分类结果种类(K值)这一问题,提出了基于底质采样点和分类效果连续性为原则的K值确定方法。实验结果表明:基于K-均值聚类分析算法的海底底质分类器能较好的实现海底底质类型的自动划分,适用于海量多波束底质特征参数的分类。     

用于海底底质分类的多波束声强数据选取研究

高质量的海底声强图是进行多波束海底底质分类、目标识别的基础。要得到"单纯"反映海底底质信息的声强图,就需要对原始声强数据进行地形改正,消除地形因素的影响。在描述了多波束数据中水深数据不能满足声强数据的改正要求问题的基础上,提出了以水深数据覆盖范围为约束的声强数据选取方法。实例计算结果表明:该方法在能有效地选取高质量的声强数据,提高了基于声强图像的海底底质分类精度。     

一种 ReliefF 和随机森林模型组合的多波束 海底底质分类方法

海底底质的快速探测和精细划分对海洋工程建设 、海洋资源开发等具有重要意义。多波束探测是目前声学底质遥测 的有效手段之一, 通常提取多波束反向散射强度图像和地形数据中的多维特征结合分类器进行底质分类。一方面, 若特征空 间维数过高, 分类效率会显著降低; 另一方面, 个别特征容易放大原始数据处理过程中仍存留的异常现象。针对这一问题, 本文提出了一种结合 Re1iefF 算法和随机森林 (Random Forest, RF) 算法的多波束底质分类方法。提取反向散射强度和地形 共 16 维特征, 利用Re1iefF 算法进行特征筛选, 排除低相关性特征, 降低特征空间维数, 结合采样点数据进行模型训练以构 建多波束底质分类模型。试验结合随机森林算法对未经特征筛选 、经主成分分析 (Principa1 Component Ana1ysis, PCA) 特征 优化后的特征进行分类实验作为对比。本文方法 Kappa 系数达到 85%, 分类总精度高于 90%, 精度具有明显优势, 耗时也 比较短。可见, 本文提出的结合 Re1iefF 和随机森林模型的多波束底质分类方法可以在保证分类精度的同时对多维特征进行 优化, 有效地提高了分类效率, 可对海底底质分类研究提供参考。     

介绍几种典型的海底底质分类技术

本文主要介绍了四家海洋仪器公司的最新海底底质分类技术。它们分别利用多波束的反向散射强度数据、单波束的回声波形结构数据和旁侧声纳数据。采用了多参数统计分析、波形结构分析和影像属性分析等方法，实现了快速、高效、大面积地对海底底质进行间接的分类。尽管它们的技术各不相同，但都可以分为监督分类方式和非监督分类方式。     

基于浅地层剖面数据和改进地声模型的底质反演方法

浅地层剖面仪发射的声脉冲能够穿透海底面进入沉积层内部,其回波中携带了丰富的底质信息。地声模型是底质声学与物理性质关系的数学描述,广泛用于海底声学与地声反演研究。本文通过对浅地层剖面数据的处理、解译得到海底反射系数,与考虑底质松密影响的改进Biot-Stoll模型相结合,提出底质反演新方法并开展实例验证。研究结果表明：通过对浅地层剖面原始记录的读取、解译,提取反射波振幅,并结合设备声源级,可有效求取海底反射系数。通过引入相对密度改进孔隙度计算公式,进而在基于Biot-Stoll模型构建海底反射系数和底质平均粒径关系过程中进一步考虑了底质松密的影响。基于山东威海某海域及文献的算例均显示,本文提出的改进地声模型可缩小底质反演与实测结果之间的相对误差、提升基于浅地层剖面数据的海底底质地声反演精度。     

海底声学底质分类技术的研究现状与前景

对海底声学底质分类技术的研究现状进行了分析。介绍了目前典型的底质分类设备、软件以及可采取的分类方法,探讨了我国开展海底声学底质分类技术研究、开发的可行性与具体实现的思路。     

顾及声图纹理特征的海底底质分类方法研究

针对目前海底底质分类要素单一造成的底质分类类别不够细致和底质信息获取效率低等问题,提出一种顾及声图纹理特征的海底底质分类方法,综合利用海底底质来源、地理等属性以及声图纹理特征等分类要素,进一步细化海底底质类别,解决了海底底质分类信息混淆或表达不充分等问题。实例数据分析表明,该分类方法克服了传统方法单一考虑底质粒度信息的缺点,可以真实反映海底底质的时空变化特性以及一定范围内的表面起伏特征,并且所包含的海底底质分类信息量更全面,从而可以为用户提供直接形象的海底底质信息。     

多波束海底底质分类软件Simrad Triton的应用

研究了利用声学信号对海底底质自动分类的技术。介绍了挪威Simrad公司海底底质分类软件Triton的分类原理，它所应用的分类方法及其软件的体系结构。最后，用实例来说明Simrad Triton在海底底质分类中的应用。     

基于粒子群优化算法的PSO-BP海底声学底质分类方法

利用粒子群优化算法（PSO）较强的鲁棒性和全局搜索能力等优点,将PSO算法与BP神经网络相结合,优化了BP神经网络分类时的初始权值和阈值。基于珠江河口三角洲的侧扫声呐图像数据,提取了海底声呐图像中砂、礁石、泥3类典型底质的6种主要特征向量,利用PSO-BP方法对海底底质进行分类识别。实验表明,3类底质分类精度均大于90%,高于BP神经网络70%左右的分类精度,表明PSO-BP方法可有效应用于海底底质的分类识别。     

基于声纳图像纹理特征的海底底质分类方法研究

一般不同底质的海底声纳图的纹理特征不同。重点分析了不同底质声纳图像纹理特征的差异,为底质分类的实现提供了基础。并提取了海底声纳图像的灰度共生矩阵,将灰度共生矩阵的统计特征作为特征向量。最后应用自组织迭代(ISODATA)分类算法依据对特征向量进行分类,从而实现了海底底质的划分。     

Segmentation and classification of shallow subbottom acoustic data,using image processing and neural networks

Subbottom acoustic profiler provides acoustic imaging of the subbottom structure constituting the upper sediment layers of the seabed, which is essential for geological and offshore geo-engineering studies. Delineation of the subbottom structure from a noisy acoustic data and classification of the sediment strata is a challenging task with the conventional signal processing techniques. Image processing techniques utilise the spatial variability of the image characteristics, known for their potential in medical imaging and pattern recognition applications. In the present study, they are found to be good in demarcating the boundaries of the sediment layers associated with weak acoustic reflectivity, masked by noisy background. The study deals with application of image processing techniques, like segmentation in identification of subbottom features and extraction of textural feature vectors using grey level co-occurrence matrix statistics. And also attempted classification using Self Organised Map, an unsupervised neural network model utilising these feature vectors. The methodology was successfully demonstrated in demarcating the different sediment layers from the subbottom images and established the sediments constituting the inferred four subsurface sediment layers differ from each other. The network model was also tested for its consistency, with repeated runs of different configuration of the network. Also the ability of simulated network was tested using a few untrained test images representing the similar environment and the classification results show a good agreement with the anticipated.     

Gas accumulations and their association with particle size distribution patterns in the Ría de Arousa seabed (Galicia, NW Spain): an application of discriminant analysis

Gassy sediments in the Ría de Arousa are preferentially distributed in areas of muddy seabed sediments. The close relationship between seabed sediment parameters and gas distribution is here studied in detail to establish better constraints on the presence of gas. Discriminant analysis was applied to the textural and compositional characteristics of 303 seabed sediment samples to classify gas-related and gas-free areas in the Ría de Arousa. The parameters considered in the classification were: particle size data (percentages of clay, silt, sand and gravel), the total inorganic carbon and the total organic carbon contents of the samples. The samples were initially classified in two groups according to the presence or absence of acoustic turbidity in the seismic profiles, shallower than 150 cm below seabed. Of the total known cases, 85.5% were correctly classified using these variables. Applying the Wilks’ lambda criterion, the most influential textural discriminating variables were the percentage of clay and the percentage of coarse fraction (gravel and sand) in the sediment sample. Discriminant analysis has achieved good differentiation between gas-related and gas-free sediments using near-seabed sediment information. The application of the discriminant method has enabled the estimation of the total area covered by gassy sediments in the Ría de Arousa. The area calculated based on the seismic data (30 km²) is a minimum estimate that is constrained by the limits of the existing seismic data. Based on the sediment information obtained from seabed samples, the statistical method estimates a total area of gassy sediments of 39 km². The new gassy areas recognized are located around the gas field at the inner ría, and the gas field west of Arousa Island, which increase in area by 8.3 and 0.4 km² respectively.     

多波束声呐和侧扫声呐数据融合方法研究综述

多波束声呐系统与侧扫声呐系统均为海底面探测的重要工具,二者均采用声学方法,在工作原理上存在异同。本文简要介绍了二者的研究进展,分别对其数据处理进行了比对分析,认为多波束声呐处理方法侧重于数据的测量精度,而侧扫声呐则主要侧重于图像处理;归纳了当前二者主要的数据匹配融合方法,包括同名特征融合、基于SURF算法的匹配融合以及特征点融合,从数据采集原理上对数据融合方法进行了深入分析,发现在探头定位、单ping数据点分布以及ping之间的数据定位上存在一定的困难,即使经过一定的处理,二者采集的也非简单的平面图像,故二者的数据融合尚存在一定的难度。     

Spatial and temporal pulse design considerations for a marinesediment classification sonar

A linear FM sonar system was developed to support the objective of remote acoustic classification of seafloor sediments. It is a calibrated, wideband, digital, frequency modulated sonar that provides quantitative, high-resolution, low-noise sub-bottom data. Since the linear sonar system can precisely transmit a specified waveform, the calibrated digitally recorded reflection data can be processed to estimate the acoustic impulse response of the seabed and sediment attenuation. An acoustic pulse with special frequency domain weighting characteristics is designed to provide low temporal sidelobe levels and a nearly constant resolution with depth even after passing through a sediment with high losses such as sand. After correlation processing, the wideband acoustic pulse yields an effective beam pattern with high spatial resolution and insignificant sidelobe levels. Data sets generated with the FM profiler indicate that the required temporal and spatial characteristics of the sonar are realized in practice     

南海浅海海底沉积物的声衰减

报道了南海浅海海底沉积物的声衰减性质。给出了测量和计算海底沉积物声衰减的方法。分析讨论了不同频率下的声衰减以及与若干个海底沉积物物理参数的关系,结果表明,同一类型沉积物在高频段时的声衰减要比低频段的声衰减大;同一频段下粗颗粒沉积物的声衰减要比细颗粒的声衰减大;北部湾海底浅层沉积物声衰减在低频100kHz下为80~150dB/m,在高频1MHz下为150~360dB/m;海南岛南部外海海底浅层沉积物声衰减在低频下为66~160dB/m,在高频1MHz下为190~350dB/m;高频段的数据与台湾海峡北部海底表层沉积物声衰减测量分析数据比较接近,而低频段的数据与台湾海峡北部海底表层沉积物声衰减测量分析数据有较大的差别。     

Pockmark-like depressions near the Goliat hydrocarbon field,Barents Sea: Morphology and genesis

Pockmarks are observed worldwide along the continental margins and are inferred to be indicators of fluid expulsion. In the present study, we have analysed multibeam bathymetry and 2D/3D seismic data from the south-western Barents Sea, in relation to gas hydrate stability field and sediment type, to examine pockmark genesis. Seismic attributes of the sediments at and beneath the seafloor have been analysed to study the factors related to pockmark formation. The seabed depths in the study area are just outside the methane hydrate stability field, but the presence of higher order hydrocarbon gases such as ethane and/or propane in the expelled fluids may cause localised gas hydrate formation. The selective occurrence of pockmarks in regions of specific seabed sediment types indicates that their formation is more closely related to the type of seabed sediment than the source path of fluid venting such as faults. The presence of high acoustic backscatter amplitudes at the centre of the pockmarks indicates harder/coarser sediments, likely linked to removal of soft material. The pockmarks show high seismic reflection amplitudes along their fringes indicating deposition of carbonates precipitated from upwelling fluids. High seismic amplitude gas anomalies underlying the region away from the pockmarks indicate active fluid flow from hydrocarbon source rocks beneath, which is blocked by overlying less permeable formations. In areas of consolidated sediments, the upward flow is limited to open fault locations, while soft sediment areas allow diffused flow of fluids and hence formation of pockmarks over a wider region, through removal of fine-grained material.