Research On Method Of Multibeam Echosounding Data Quality Control

This dissertation mainly focuses on the method of multibeam echosounding data quality control which includes the principles of multibeam echosounding, the errors analysis, correction technology of sound velocity profiles for multibeam survey, detection technology of outliers for multibeam survey,research on constructing trend surface by LS-SVM, the influence of optimized train samples on elimination of sounding outliers in the LS-SVM arithmetic, correction technology of system errors for multibeam survey. The main works and contributions are summarized as follows:1. In the process of sound velocity profile conformation, the traditional empirical orthogonal function analysis method has some shortage, so a new approach based on empirical mode decomposition is presented. The orthogonal time functions and spatial functions of sound velocity profile are given. The new method supplies the essential parameters for the model conformation of survey area. By the calculation analysis of the actual observation data, the new method based on empirical mode decomposition could reconstruct sound velocity profile effectively and accurately.2. The cause of Multibeam echosounding outliers is analysed and a comparion of several common multibeam echosounding outliers'detection methods from the basic principle, the applicable condition, etc is given. Due to the abnormal value detection superiority of the genetic algorithm, the article puts forward the corresponding sounding outliers'detection method. Base on the selected principles of genetic algorithm, the steps for detecting outliers of sounding is given. The actual example shows that the model based on genetic algorithm could detect outliers quickly and effectively.3. Multibeam echosounding is a dynamic process; the depth is not repeating data. Aiming at the character; the Bayes estimation theory and MCMC sampling design method are imported to detecting outliers. Take the depth observation data as the research object, the formula of outliers detection be deduced by the Bayes estimation theory, and the steps of the detection is given; The actual calculation shows that the sounding abnormal value detection methods based on Bayes estimation theory could gain higher accuracy by simple judgment criterion.4. Further research the least squares support vector machine method, recur to the function of the free surface reconstruction, the ls-svm algorithm is imported to constructing the undersea trend surface, and the outliers could be detected by the trend surface.Multibeam echosounding data with the characteristics of mass, so the training sample selection plan is presented; Different kernel functions are given, and the influence of different kernel functions on seafloor trend surface are analyzed; Simulation examples show that in order to avoid the loss of seabed tiny terrain, should combine the actual situation of seabed terrain then choose the correct kernel function.5. On the basis of the influence of different kernel functions on seafloor trend surface structure, the equivalence between trend surface filtering and least squares support vector machine method is proved, and the formulas derivation is given; Combined with the actual situation, different kernel functions are suited for corresponding sea area; A comparion between trend surface filtering and least squares support vector machine method is given.The conclusion is that trend surface filtering is the especial result for least squares support vector machine method when the weight parameter equal to 0.5.6. In the process of sea floor trend surface construction, least squares support vector machines algorithm cannot eliminate the influence of large deviation training samples, aiming at this defect, the article puts forward local sample center distance method.The method could optimize sounding training samples, reduce the influence of abnormal training samples. The actual example shows that sounding training samples could be optimized reasonably by the local sample center distance, thus undersea trend surface could be constructed reasonably, so abnormal values could be eliminated effectively.7. The quantitative analysis is given about the multibeam echosounding error sources, the formulas of the depth uncertainty is obtained; Because of the uncertainty reflects the dispersion of the water depth measurement, the article import it to optimize the sounding training samples. The large contribution rate of the training samples should be chosed by the method. Real calculational results show that the sounding training samples could be optimized reasonably, thus undersea trend surface could be constructed reasonably, so abnormal values could be eliminated effectively.8. Analyzes the cause of system error in the process of multibeam echosounding, although traditional two-step filtering method can solve the problem effectively in overlapping area sounding data don't match, did not attend the other sounding data. Aiming at the shortage, algorithm about eliminating sounding system error based on uncertainty is presented. The actual example shows that the improved two-step filtering method considers the other sounding data to avoid the seabed terrain distortions.