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1.
Michalopoulou Z.-H. Alexandrou D. de Moustier C. 《Oceanic Engineering, IEEE Journal of》1995,20(3):190-197
In this paper neural and statistical classifiers are applied to the problem of seafloor classification. The feature vectors used consist of acoustic backscatter as a function of angle of incidence. Simulated seafloor backscatter is obtained by employing the Helmholtz-Kirchhoff approximation and the statistical properties of bottom reverberation. These synthetic data are used initially to train multilayer perceptrons and then to test them for their ability to discriminate among signal returns produced by seafloors with different roughness parameters. The same data are also processed with optimum Bayesian classifiers. A comparison of the results indicates a suboptimum performance for the perceptrons. The same procedures are applied to real data collected by the Sea Beam bathymetric system over two Central North Pacific seamounts. In this case, the perceptron performance is similar to that of the statistical classifier, which is no longer optimum, since no prior knowledge of the probability distribution parameters is available. In addition, Self Organizing Maps are applied to both synthetic and real data and are shown to result in a successful separation of the output space into distinct regions corresponding to different seafloor classes 相似文献
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针对海底地形复杂程度分类问题,在考虑传统水深均值的基础上引入坡度和起伏度两个地形因子作为表征海底地形复杂程度的分类指标并进行量化,对水深数据空间分辨率进行统一,建立包含18种典型海底特征的海底地形复杂度分类库,利用BP神经网络对建立的分类库进行训练学习。为验证该方法的有效性和适用性,选取地形复杂度不同的4块实验区分别采用统计学方法和BP神经网络算法进行海底地形复杂度进行分类,对比发现该方法可以实现海区海底平坦、一般、复杂三种地形的自动识别与分类,并保留实验区海底地形复杂度细节信息。 相似文献
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While the average seafloor backscatter strength within a narrow range of grazing angles can be used as a first-order classification tool, this technique often fails to distinguish seafloors of known differing geological character. In order to resolve such ambiguities, it is necessary to examine the variation in backscatter strength as a function of grazing angle. For this purpose, a series of multiply overlapping GLORIA sidescan sonar images (6.5 kHz) have been obtained in water depths ranging from 1000 to 2500 m. To constrain the placement of acoustic backscatter measurements and to measure the true impinging angle of the incident wave, the corresponding seafloor was simultaneously surveyed using the Seabeam multibeam system. As a result of the multiple overlap, the angular response of seafloor backscatter strength may be derived for regions much smaller than the swath width. By using the derived angular response of seafloor backscatter strength in regions for which sediment samples exist, an empirical seafloor classification scheme is proposed based on the shape, variance, and magnitude of the angular response. Because of the observed variability in the shape of the angular response with differing seafloor types, routine normalization of single-pass swath data to an equivalent single grazing angle image cannot be achieved. As a result, for the case of single-pass surveys, confident seafloor classification may only be possible for regions approaching the scale of the swath width 相似文献
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AbstractWe calibrate a technique to use repeated multibeam sidescan surveys in the deep ocean to recover seafloor displacements greater than a few meters. Displacement measurements from seafloor patches (3?km by 20?km) on the port and starboard side of the ship are used to estimate vertical and across-track displacement. We present displacement measurements from a survey of the Ayu Trough southwest of the Marianas Trench using a 12?kHz multibeam. Vertical and across-track displacement errors for the 12?kHz multibeam sonar are typically 0–2?m with RMS uncertainties of 0.25–0.67 m in the across-track and 0.37–0.75 m in the vertical as determined by 3-way closure tests. The uncertainty of the range-averaged sound velocity is a major error source. We estimate that variations in the sound velocity profile, as quantified using expendable bathythermographs (XBTs) during data collection, contribute up to 0.3?m RMS uncertainty in the across-track direction and 1.6?m RMS uncertainty in the vertical direction. 相似文献
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Selection of a set of dominant echo features to classify seafloor sediments using a multilayer perceptron neural network is
investigated at two acoustic frequencies (33 and 210 kHz). Several sets of inputs with different combinations of two, three,
four, five, and six echo features are exploited with three-layer neural networks. The performances of the networks are analyzed
to assess the most discriminating set of echo features for classification of seafloor sediments. The results of the overall
average performances reveal that backscatter strength and time spread are the two most important echo features at 33 kHz,
whereas backscatter strength has higher discriminating characteristics at 210 kHz for seafloor sediment classification. In
addition, a set of four echo features consisting of backscatter strength, time-spread, statistical skewness, and Hausdroff
dimension gives the highest success at both the acoustic frequencies. 相似文献
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A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research. 相似文献
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Processing Multibeam Backscatter Data 总被引：1，自引：0，他引：1
A new highly precise source of data has recently become available using multibeam sonar systems in hydrography. Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. We utilize the seafloor backscatter strength data of each beam from multibeam sonar and the automatic classification technology so that we can get the seafloor type identification maps. In this article, analyzing all kinds of error effects in backscatter strength, data are based on the relationship between backscatter strength and seafloor types. We emphasize particularly analyzing the influences of local bottom slope and near nadir reflection in backscatter strength data. We also give the correction algorithms and results of these two influent factors. After processing the raw backscatter strength data and correcting error effects, we can get processed backscatter strength data which reflect the features of seafloor types only. Applying the processed backscatter strength data and mosaicked seafloor sonar images, we engage in seafloor classification and geomorphy interpretation in future research. 相似文献
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Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems 总被引：5，自引：0，他引：5
This paper examines the potential for remote classification of seafloor terrains using a combination of quantitative acoustic backscatter measurements and high resolution bathymetry derived from two classes of sonar systems currently used by the marine research community: multibeam echo-sounders and bathymetric sidescans sonar systems. The high-resolution bathymetry is important, not only to determine the topography of the area surveyed, but to provide accurate bottom slope corrections needed to convert the arrival angles of the seafloor echoes received by the sonars into true angles of incidence. An angular dependence of seafloor acoustic backscatter can then be derived for each region surveyed, making it possible to construct maps of acoustic backscattering strength in geographic coordinates over the areas of interest. Such maps, when combined with the high-resolution bathymetric maps normally compiled from the data output by the above sonar systems, could be very effective tools to quantify bottom types on a regional basis, and to develop automatic seafloor classification routines. 相似文献
13.
Evangelos Alevizos Mirjam Snellen Dick Simons Kerstin Siemes Jens Greinert 《Marine Geophysical Researches》2018,39(1-2):289-306
This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernförde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r2?>?0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings. 相似文献
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A methodology for acoustic seafloor classification 总被引：3，自引：0，他引：3
A seafloor classification methodology, based on a parameterization of the reverberation probability density function in conjunction with neural network classifiers, is evaluated through computer simulations. Different seafloor provides are represented by a number of scatterer distributions exhibiting various degrees of departure from the nominal Poisson distribution. Using a computer simulation program, these distributions were insonified at different spatial scales by varying the transmitted pulse length. The statistical signature obtained consists of reverberation kurtosis estimates as a function of pulse length. Two neural network classifiers are presented with the task of discriminating among the various scatterer distributions based on obtained acoustic signatures. The results indicate that this approach offers considerable promise for practical, realizable solutions to the problem of remote seafloor classification 相似文献
16.
Shallow-water imaging multibeam sonars: A new tool for investigating seafloor processes in the coastal zone and on the continental shelf 总被引：8，自引：1，他引：8
John E. Hughes Clarke Larry A. Mayer David E. Wells 《Marine Geophysical Researches》1996,18(6):607-629
Hydrographic quality bathymetry and quantitative acoustic backscatter data are now being acquired in shallow water on a routine basis using high frequency multibeam sonars. The data provided by these systems produce hitherto unobtainable information about geomorphology and seafloor geologic processes in the coastal zone and on the continental shelf.Before one can use the multibeam data for hydrography or quantitative acoustic backscatter studies, however, it is essential to be able to correct for systematic errors in the data. For bathymetric data, artifacts common to deep-water systems (roll, refraction, positioning) need to be corrected. In addition, the potentially far greater effects of tides, heave, vessel lift/squat, antenna motion and internal time delays become of increasing importance in shallower water. Such artifacts now cause greater errors in hydrographic data quality than bottom detection. Many of these artifacts are a result of imperfect motion sensing, however, new methods such as differential GPS hold great potential for resolving such limitations. For backscatter data, while the system response is well characterised, significant post processing is required to remove residual effects of imaging geometry, gain adjustments and water column effects. With the removal of these system artifacts and the establishment of a calibrated test site in intertidal regions (where the seabed may be intimately examined by eye) one can build up a sediment classification scheme for routine regional seafloor identification.When properly processed, high frequency multibeam sonar data can provide a view of seafloor geology and geomorphology at resolutions of as little as a few decimetres. Specific applications include quantitative estimation of sediment transport rates in large-scale sediment waves, volume effects of iceberg scouring, extent and style of seafloor mass-wasting and delineation of structural trends in bedrock. In addition, the imagery potentially provides a means of quantitative classification of seafloor lithology, allowing sedimentologists the ability to examine spatial distributions of seabed sediment type without resorting to subjective estimation or prohibitively expensive bottom-sampling programs. Using Simrad EM100 and EM1000 sonars as an example, this paper illustrates the nature and scale of possible artifacts, the necessary post-processing steps and shows specific applications of these sonars. 相似文献
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Sheri N. White William Kirkwood Alana Sherman Mark Brown Richard Henthorn Karen Salamy Peter Walz Edward T. Peltzer Peter G. Brewer 《Deep Sea Research Part I: Oceanographic Research Papers》2005,52(12):2376-2389
The field of ocean geochemistry has recently been expanded to include in situ laser Raman spectroscopic measurements in the deep ocean. While this technique has proved to be successful for transparent targets, such as fluids and gases, difficulty exists in using deep submergence vehicle manipulators to position and control the very small laser spot with respect to opaque samples of interest, such as many rocks, minerals, bacterial mats, and seafloor gas hydrates. We have developed, tested, and successfully deployed by remotely operated vehicle (ROV) a precision underwater positioner (PUP) which provides the stability and precision movement required to perform spectroscopic measurements using the Deep Ocean Raman In situ Spectrometer (DORISS) instrument on opaque targets in the deep ocean for geochemical research. The positioner is also adaptable to other sensors, such as electrodes, which require precise control and positioning on the seafloor. PUP is capable of translating the DORISS optical head with a precision of 0.1 mm in three dimensions over a range of at least 15 cm, at depths up to 4000 m, and under the normal range of oceanic conditions (T, P, current velocity). The positioner is controlled, and spectra are obtained, in real time via Ethernet by scientists aboard the surface vessel. This capability has allowed us to acquire high quality Raman spectra of targets such as rocks, shells, and gas hydrates on the seafloor, including the ability to scan the laser spot across a rock surface in sub-millimeter increments to identify the constituent mineral grains. These developments have greatly enhanced the ability to obtain in situ Raman spectra on the seafloor from an enormous range of specimens. 相似文献
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Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data 总被引：2，自引：2，他引：2
The variation of the backscatter strength with the angle of incidence is an intrinsic property of the seafloor, which can
be used in methods for acoustic seafloor characterization. Although multibeam sonars acquire backscatter over a wide range
of incidence angles, the angular information is normally neglected during standard backscatter processing and mosaicking.
An approach called Angular Range Analysis has been developed to preserve the backscatter angular information, and use it for
remote estimation of seafloor properties. Angular Range Analysis starts with the beam-by-beam time-series of acoustic backscatter
provided by the multibeam sonar and then corrects the backscatter for seafloor slope, beam pattern, time varying and angle
varying gains, and area of insonification. Subsequently a series of parameters are calculated from the stacking of consecutive
time series over a spatial scale that approximates half of the swath width. Based on these calculated parameters and the inversion
of an acoustic backscatter model, we estimate the acoustic impedance and the roughness of the insonified area on the seafloor.
In the process of this inversion, the behavior of the model parameters is constrained by established inter-property relationships.
The approach has been tested using a 300 kHz Simrad EM3000 multibeam sonar in Little Bay, NH. Impedance estimates are compared
to in situ measurements of sound speed. The comparison shows a very good correlation, indicating the potential of this approach for
robust seafloor characterization. 相似文献