CN108520271B - Submarine geomorphy type sorter design method based on factorial analysis - Google Patents
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Abstract
本发明公开了一种基于因子分析的海底地貌类型分类器设计方法。包括如下步骤：1)根据海底深度测量数据计算深度分布特征，包括偏度、峰度、海底深度标准差、海底深度差异熵、海底粗糙度和海底深度变异系数；2)将深度分布特征作为原始变量，应用因子分析方法提取地貌因子；3)根据地貌因子，应用支持向量机设计地貌类型分类器；4)计算待识别地貌的深度分布特征值和地貌因子，应用设计的分类器识别地貌类型。本发明具有方法简单、计算量小、识别准确率高、节约人力等优点。本发明适用于海底地貌类型识别。
The invention discloses a method for designing a seabed landform type classifier based on factor analysis. The method comprises the following steps: 1) calculating depth distribution characteristics according to seabed depth measurement data, including skewness, kurtosis, seabed depth standard deviation, seafloor depth difference entropy, seafloor roughness and seafloor depth variation coefficient; 2) using depth distribution characteristics as the original 3) According to the geomorphic factors, use the support vector machine to design a geomorphic type classifier; 4) Calculate the depth distribution eigenvalues and geomorphic factors of the geomorphology to be identified, and use the designed classifier to identify the geomorphic type. The invention has the advantages of simple method, small amount of calculation, high recognition accuracy, manpower saving and the like. The invention is applicable to the type identification of seabed topography.
Description
技术领域technical field
本发明涉及海洋测绘、海洋工程、海洋油气资源等技术领域，具体涉及一种基于因子分析的海底地貌类型分类器设计方法。The invention relates to the technical fields of marine surveying and mapping, marine engineering, marine oil and gas resources, etc., in particular to a design method of a seabed landform type classifier based on factor analysis.
背景技术Background technique
地貌是地球表面各种起伏形态的总称。海底地形地貌的类型包括古河道、冲刷槽、海底峡谷、深水水道、海山、碳酸盐台地、陡崖、滑坡等。地貌分类是海底地貌研究和制图的基础，地貌形态可反映地貌成因类型和成因控制形态的内在联系，是进行深层地学知识挖掘的关键。Geomorphology is the general term for various undulating shapes on the earth's surface. The types of submarine topography include paleochannels, scour troughs, submarine canyons, deep water channels, seamounts, carbonate platforms, cliffs, landslides, etc. Geomorphological classification is the basis of seabed geomorphological research and mapping. Geomorphic morphology can reflect the internal relationship between geomorphic genetic types and genetic control forms, and is the key to deep geological knowledge mining.
多波束测深系统能探测宽覆盖、高分辨率的海底地形地貌精细特征。该系统采用条带式测量，能同时测得与航线方向垂直的平面内几十甚至几百个海底被测点的水深值，或者一条一定宽度的全覆盖水深条带，可精确并快速测得沿航线一定宽度内的水下目标的大小、形状以及高低变化。The multi-beam sounding system can detect the fine features of the seabed topography and topography with wide coverage and high resolution. The system adopts strip measurement, which can simultaneously measure the water depth values of dozens or even hundreds of seabed measured points in a plane perpendicular to the route direction, or a full-coverage water depth strip with a certain width, which can be accurately and quickly measured The size, shape and height of underwater targets within a certain width along the route change.
地貌参数是对地貌的数字描述，用来表征地貌的空间分布特征。地貌参数很多，不同学科和领域对其理解和分类也不同。地貌参数可划分为微观参数和宏观参数两大类。微观参数所描述和反映的是具体位置的地貌特征。常用的微观参数主要有：坡度、坡向、坡长、平面曲率、剖面曲率等。宏观参数所描述和反映的是较大区域内的地貌特征。常用的宏观参数主要有：地形深度标准差、地形差异熵、地形粗糙度、高程变异系数等。Geomorphic parameters are digital descriptions of landforms, which are used to characterize the spatial distribution characteristics of landforms. There are many geomorphological parameters, and different disciplines and fields have different understandings and classifications of them. Geomorphic parameters can be divided into two categories: microscopic parameters and macroscopic parameters. Microscopic parameters describe and reflect the geomorphic features of specific locations. Commonly used microscopic parameters mainly include: slope, aspect, slope length, plane curvature, section curvature, etc. Macroscopic parameters describe and reflect the geomorphic features in a larger area. The commonly used macroscopic parameters mainly include: terrain depth standard deviation, terrain difference entropy, terrain roughness, elevation variation coefficient, etc.
海底地形地貌比较复杂，目前，对海底地貌类型的认识有限，大量的地貌类型分析主要依靠人工完成，通过技术人员的观察及经验来区分识别。这种方法可以充分利用技术人员的知识，灵活性好，但需要技术人员具有丰富的地学知识和观察判断经验，具有很大的主观性，存在时效性差、劳动强度大等缺点。特别是由于海底数据具有海量级别，仅仅依靠技术人员的人工能力远远无法承担海量数据的处理任务。The seabed topography is relatively complex. At present, the understanding of the types of seabed landforms is limited, and a large number of geomorphological types are mainly analyzed manually, and the identification is made through the observation and experience of technicians. This method can make full use of the knowledge of technicians and has good flexibility, but it requires technicians to have rich geological knowledge and experience in observation and judgment, which has great subjectivity, poor timeliness, and high labor intensity. In particular, due to the massive level of seabed data, relying solely on the artificial capabilities of technicians is far from being able to undertake the processing tasks of massive data.
发明内容Contents of the invention
本发明应用地形深度分布特征表征地貌的空间结构特点，应用因子分析方法提取反映地貌类型的地貌因子，应用支持向量机建立地貌类型分类器，实现地貌类型识别。具有方法简单、计算量小、识别准确率高、节约人力等优点。适用于海底地貌类型识别。The invention uses the terrain depth distribution feature to represent the spatial structure characteristics of the landform, uses the factor analysis method to extract the landform factors reflecting the landform type, uses the support vector machine to establish the landform type classifier, and realizes the landform type identification. The method has the advantages of simple method, small amount of calculation, high recognition accuracy, saving manpower and the like. It is suitable for identification of seabed landform types.
本发明包括如下步骤：The present invention comprises the steps:
(1)海底深度分布特征计算：(1) Calculation of seabed depth distribution characteristics:
分别根据 计算待识别区域的海底深度标准差m1、偏度m2、峰度m3、差异熵m4、粗糙度m5、变异系数m6，其中， zi为待识别区域内第i个测量点处的海底深度值，为海底深度平均值，i＝1,2,3,……,n，n为待识别区域内测量点数；respectively according to Calculate the seabed depth standard deviation m 1 , skewness m 2 , kurtosis m 3 , difference entropy m 4 , roughness m 5 , and coefficient of variation m 6 of the area to be identified, where, z i is the seabed depth value at the i-th measurement point in the area to be identified, is the average value of seabed depth, i=1,2,3,...,n, n is the number of measurement points in the area to be identified;
(2)地貌因子提取：(2) Geomorphic factor extraction:
原始变量为m1、m2、m3、m4、m5和m6，标准地貌为Al，应用因子分析方法提取原始变量的公共因子fr，各公共因子的方差贡献率为λr，若满足则fs为地貌因子，其中，l＝1,2,3,……,k，r＝1,2,3,……,t，s＝1,2,……,j，k为地貌类型数，根据训练数据确定，t为公共因子数，fs、λr、t根据因子分析确定，θ为阈值，在程序参数中设定，j为地貌因子数；The original variables are m 1 , m 2 , m 3 , m 4 , m 5 and m 6 , the standard landform is A l , the common factor f r of the original variable is extracted by factor analysis method, and the variance contribution rate of each common factor is λ r , if satisfied Then f s is the landform factor, among which, l=1,2,3,...,k, r=1,2,3,...,t, s=1,2,...,j, k is the landform type The number is determined according to the training data, t is the number of public factors, f s , λ r , t are determined according to factor analysis, θ is the threshold value, which is set in the program parameters, and j is the number of topographic factors;
(3)地貌类型分类器设计：(3) Landform type classifier design:
根据地貌因子fs，应用支持向量机确定地貌类型分类器；According to the landform factor f s , the support vector machine is used to determine the landform type classifier;
(4)地貌类型识别：(4) Geomorphic type identification:
计算待识别地貌的深度分布特征值m1、m2、m3、m4、m5和m6，提取地貌因子fs，根据步骤(3)得到的分类器确定待识别地貌的类型。Calculate the depth distribution eigenvalues m 1 , m 2 , m 3 , m 4 , m 5 and m 6 of the landform to be identified, extract the landform factor f s , and determine the type of the landform to be identified according to the classifier obtained in step (3).
附图说明Description of drawings
图1(a)至图1(e)分别为海底台地、冲沟、滑坡、隆起、水道这5种地貌类型的地形图；Figure 1(a) to Figure 1(e) are the topographic maps of five types of landforms, namely, submarine platforms, gullies, landslides, uplifts, and waterways;
图2为5种地貌的深度变异系数与深度差异熵交汇图；Figure 2 is the intersection of the depth variation coefficient and depth difference entropy of the five landforms;
图3为5种地貌的深度变异系数与地形粗糙度交汇图；Fig. 3 is the intersection map of depth variation coefficient and terrain roughness of five landforms;
图4为5种地貌的深度差异熵与深度标准差交汇图；Figure 4 is the intersection of depth difference entropy and depth standard deviation of the five landforms;
图5为5种地貌的地形粗糙度与深度标准差交汇图；Figure 5 is the intersection map of topographic roughness and depth standard deviation for five landforms;
图6为5种地貌的深度变异系数与深度标准差交汇图；Figure 6 is the intersection chart of depth variation coefficient and depth standard deviation of five landforms;
图7为5种地貌的深度标准差与峰度交汇图；Fig. 7 is the intersection map of depth standard deviation and kurtosis of five landforms;
图8为5种地貌类型海底深度分布特征的公共因子的方差贡献率图；Fig. 8 is the variance contribution rate map of the common factors of the seabed depth distribution characteristics of five landform types;
图9为5种地貌类型海底深度分布特征的公共因子的累积方差贡献率图；Fig. 9 is the cumulative variance contribution rate map of the common factors of the distribution characteristics of seabed depth of five landform types;
图10为设计的5种地貌的分类器；Fig. 10 is the classifier of 5 kinds of landforms designed;
图11为本实施例中地貌类型的辨识结果。Fig. 11 is the recognition result of landform type in this embodiment.
具体实施方式Detailed ways
本实施例根据多波束海底测深数据，计算地形深度分布特征，包括海底深度标准差、偏度、峰度、差异熵、粗糙度和变异系数，提取地貌因子，应用支持向量机确定地貌类型分类器，实现海底台地、冲沟、滑坡、隆起、水道这5种地貌类型的识别。In this embodiment, according to the multi-beam seabed bathymetric data, the terrain depth distribution characteristics are calculated, including the seabed depth standard deviation, skewness, kurtosis, difference entropy, roughness and variation coefficient, the geomorphic factors are extracted, and the classification of geomorphic types is determined by using a support vector machine The device realizes the identification of five types of landforms, namely, submarine platforms, gullies, landslides, uplifts, and waterways.
具体识别步骤如下：The specific identification steps are as follows:
(1)海底深度分布特征计算：(1) Calculation of seabed depth distribution characteristics:
分别根据 计算待识别区域的海底深度标准差m1、偏度m2、峰度m3、差异熵m4、粗糙度m5、变异系数m6，其中， zi为待识别区域内第i个测量点处的海底深度值，为海底深度平均值，i＝1,2,3,……,n，n为待识别区域内测量点数。respectively according to Calculate the seabed depth standard deviation m 1 , skewness m 2 , kurtosis m 3 , difference entropy m 4 , roughness m 5 , and coefficient of variation m 6 of the area to be identified, where, z i is the seabed depth value at the i-th measurement point in the area to be identified, is the average seabed depth, i=1,2,3,...,n, n is the number of measurement points in the area to be identified.
在本实施例中，海底深度数据由多波束方法测量得到，图1(a)至图1(e)分别为海底台地、冲沟、滑坡、隆起、水道这5种地貌类型的地形图；图2至图7分别为5种地貌的深度变异系数与深度差异熵交汇图、深度变异系数与地形粗糙度交汇图、深度差异熵与深度标准差交汇图、地形粗糙度与深度标准差交汇图、深度变异系数与深度标准差交汇图、深度标准差与峰度交汇图。In the present embodiment, the depth data of the seabed is measured by a multi-beam method, and Fig. 1(a) to Fig. 1(e) are topographic maps of the five landform types of seabed platform, gully, landslide, uplift, and waterway respectively; Fig. Figures 2 to 7 are the intersection diagrams of depth variation coefficient and depth difference entropy, the intersection diagram of depth variation coefficient and terrain roughness, the intersection diagram of depth difference entropy and depth standard deviation, the intersection diagram of terrain roughness and depth standard deviation of the five landforms, respectively. Depth coefficient of variation and depth standard deviation cross plot, depth standard deviation and kurtosis cross plot.
本实施例中的5种地貌的深度分布特征值的范围差异较大。图2至图7中，冲沟和台地的特征值基本分布在不同的区域内，仅根据深度分布特征值的范围即可较准确识别出来；其他地貌类型的深度分布特征差异较小，在图中存在较多重叠部分，仅根据深度分布特征识别地貌类型难度较大。The ranges of the characteristic values of the depth distribution of the five landforms in this embodiment are quite different. In Figures 2 to 7, the eigenvalues of gullies and terraces are basically distributed in different areas, and can be identified more accurately only based on the range of eigenvalues of the depth distribution; There are a lot of overlapping parts, and it is difficult to identify landform types only based on the depth distribution characteristics.
(2)地貌因子提取：(2) Geomorphic factor extraction:
原始变量为m1、m2、m3、m4、m5和m6，标准地貌为Al，应用因子分析方法提取原始变量的公共因子fr，各公共因子的方差贡献率为λr，若满足则fs为地貌因子，其中，l＝1,2,3,……,k，r＝1,2,3,……,t，s＝1,2,……,j，k为地貌类型数，根据训练数据确定，t为公共因子数，fs、λr、t根据因子分析确定，θ为阈值，在程序参数中设定，j为地貌因子数。The original variables are m 1 , m 2 , m 3 , m 4 , m 5 and m 6 , the standard landform is A l , the common factor f r of the original variable is extracted by factor analysis method, and the variance contribution rate of each common factor is λ r , if satisfied Then f s is the landform factor, among which, l=1,2,3,...,k, r=1,2,3,...,t, s=1,2,...,j, k is the landform type The number is determined according to the training data, t is the number of public factors, f s , λ r , and t are determined according to factor analysis, θ is the threshold value, which is set in the program parameters, and j is the number of topographic factors.
在本实施例中，地貌类型数为k＝5，5种地貌的原始变量的公共因子数为t＝6，图8为5种地貌类型6个海底深度分布特征的6个公共因子的方差贡献率分布，图9为5种地貌类型6个海底深度分布特征的6个公共因子的累积方差贡献率。本实施例中，θ＝80％，得到3个地貌因子。In the present embodiment, the number of landform types is k=5, and the number of public factors of the original variables of 5 landforms is t=6, and Fig. 8 is the variance contribution of 6 common factors of 6 seabed depth distribution characteristics of 5 landform types Figure 9 shows the cumulative variance contribution rate of the six common factors of the six seabed depth distribution characteristics of the five landform types. In this embodiment, θ=80%, and three geomorphic factors are obtained.
(3)地貌类型分类器设计：(3) Landform type classifier design:
根据地貌因子fs，应用支持向量机确定地貌类型分类器。According to the landform factor f s , the support vector machine is used to determine the landform type classifier.
在本实施例中，提取3个地貌因子，图10为得到的地貌类型分类器，其中，图10(a)用于确定滑坡地貌，图10(b)用于确定隆起地貌，图10(c)用于确定台地、水道、冲沟地貌。In the present embodiment, three geomorphic factors are extracted, and Fig. 10 is the obtained geomorphic type classifier, wherein, Fig. 10 (a) is used to determine landslide landform, Fig. 10 (b) is used to determine uplift geomorphology, Fig. 10 (c ) is used to determine the topography of terraces, waterways and gullies.
(4)地貌类型识别：(4) Geomorphic type identification:
计算待识别地貌的深度分布特征值m1、m2、m3、m4、m5和m6，提取地貌因子fs，根据步骤(3)得到的分类器确定待识别地貌的类型。Calculate the depth distribution eigenvalues m 1 , m 2 , m 3 , m 4 , m 5 and m 6 of the landform to be identified, extract the landform factor f s , and determine the type of the landform to be identified according to the classifier obtained in step (3).
图11为本实施例中地貌类型的辨识结果。Fig. 11 is the recognition result of landform type in this embodiment.
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