CN106709439A - Monocline structure landform automatic identification method - Google Patents

Monocline structure landform automatic identification method Download PDF

Info

Publication number
CN106709439A
CN106709439A CN201611164730.0A CN201611164730A CN106709439A CN 106709439 A CN106709439 A CN 106709439A CN 201611164730 A CN201611164730 A CN 201611164730A CN 106709439 A CN106709439 A CN 106709439A
Authority
CN
China
Prior art keywords
vertex
model
elements
scene
attribute
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201611164730.0A
Other languages
Chinese (zh)
Other versions
CN106709439B (en
Inventor
陈楹
李安波
姚蒙蒙
李梦圆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Normal University
Original Assignee
Nanjing Normal University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Normal University filed Critical Nanjing Normal University
Priority to CN201611164730.0A priority Critical patent/CN106709439B/en
Publication of CN106709439A publication Critical patent/CN106709439A/en
Application granted granted Critical
Publication of CN106709439B publication Critical patent/CN106709439B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/40Document-oriented image-based pattern recognition
    • G06V30/42Document-oriented image-based pattern recognition based on the type of document
    • G06V30/422Technical drawings; Geographical maps
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/70Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Databases & Information Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Image Analysis (AREA)

Abstract

本发明公开了一种单斜岩层构造地貌的自动识别方法,包括以下步骤:(1)提取基岩地层对象,依据邻接、倾斜和倾向一致性的规则,对提取得到的邻接对象建立关系,绘制邻接ARG图;(2)基于邻接ARG图,依据邻接关系构建场景模型,并对场景模型进行简化;(3)按照褶皱筛选规则对简化后的场景模型进行筛选,保留非完整褶皱结构的模型,即为单斜岩层构造。本发明实现了单斜岩层构造地貌的自动识别。

The invention discloses an automatic identification method of monoclinic rock formation structure and landform, which comprises the following steps: (1) extracting bedrock stratum objects, establishing a relationship with the extracted adjacent objects according to the rules of adjacency, inclination and inclination consistency, and drawing Adjacency ARG graph; (2) Based on the adjacency ARG graph, the scene model is constructed according to the adjacency relationship, and the scene model is simplified; (3) The simplified scene model is screened according to the fold screening rules, and the model with an incomplete fold structure is retained. It is a monoclinic rock structure. The invention realizes the automatic recognition of the structure and topography of the monoclinic rock layer.

Description

一种单斜岩层构造地貌的自动识别方法A method for automatic identification of monoclinic stratum structure and geomorphology

技术领域technical field

本发明涉及地理信息技术应用领域,尤其涉及一种基于数字地质图,通过空间结构模式匹配,自动化识别单斜岩层构造地貌的方法。The invention relates to the application field of geographic information technology, in particular to a method for automatically identifying monoclinic rock layer structure and geomorphology through spatial structure pattern matching based on digital geological maps.

背景技术Background technique

水平的岩层受地壳变动影响发生倾斜,与水平面形成一定的夹角,岩层向同一方向倾斜,称之为单斜构造。常见的单斜岩层构造地貌包括猪背山、单面山等。单斜可能出现在被破坏的背斜翼部,或出现在已被破坏的穹窿构造的四周、盆地的外围、掀斜的水平岩层或断层的掀斜等处。单斜岩层主要表现为三种形式:一为褶曲构造的一翼;二为断层构造的一盘;三区域内的不均匀抬升或下降运动形成的。The horizontal rock layer is inclined due to the influence of the crustal changes, forming a certain angle with the horizontal plane, and the rock layer is inclined in the same direction, which is called monocline structure. The common monoclinic rock structure landforms include Zhubei Mountain and Shanmian Mountain. The monocline may appear in the damaged anticline wing, or around the destroyed dome structure, the periphery of the basin, tilted horizontal rock formations or tilted faults, etc. The monoclinic strata mainly manifests in three forms: one is a wing of a fold structure;

传统的构造识别主要为人工操作,其工作量大,历时长,且受限于制图人员的技术和认识水平。单斜岩层构造地貌的识别主要依赖于地质资料或野外勘察的结果,在地质图上查找单斜主要是查找在褶皱或断层一侧的系列倾斜地层。陈楹等(陈楹,李安波,姚蒙蒙,等.基于空间结构模式匹配的褶皱地貌类型自动识别[J].地球信息科学学报,2016,18(11))通过空间结构模式匹配的方法,实现了基于数字地质体面数据对背斜、向斜构造地貌进行自动化识别的方法。但该结构模式仅考虑了对称、重复的结构,对于单斜岩层构造地貌则没有很好的进行识别。Traditional structural identification is mainly manual operation, which requires a large workload and takes a long time, and is limited by the technical and understanding level of the cartographers. The identification of monocline rock structure and geomorphology mainly depends on geological data or field survey results. Finding monoclines on geological maps is mainly to find a series of inclined strata on the side of folds or faults. Chen Ying et al. (Chen Ying, Li Anbo, Yao Mengmeng, et al. Automatic recognition of folded landform types based on spatial structure pattern matching[J]. Geo-Information Science Journal, 2016, 18(11)) through the method of spatial structure pattern matching, A method for automatic identification of anticline and syncline structural landforms based on digital geological surface data has been realized. However, this structural model only considers symmetrical and repetitive structures, and does not recognize the monoclinic rock structure and geomorphology well.

单斜岩层构造地貌的形成与周围的地貌变迁和构造演化有密切联系,研究单斜岩层构造地貌可为地质图认知、构造演化推理提供有效支持,是构造解析、参数化三维建模等工作的前提。The formation of monoclinic rock structure and landform is closely related to the surrounding landform changes and structural evolution. The study of monocline rock structure and landform can provide effective support for geological map recognition and structural evolution reasoning. premise.

发明内容Contents of the invention

发明目的:本发明针对现有技术存在的问题,提供一种单斜岩层构造地貌的自动识别方法。该方法采用数字地质图,针对单斜地层倾向一致的规律,以结构模式匹配的方法,基本实现了在基岩山地地区,对单斜岩层构造地貌的自动化识别。相比传统的人工识别方法,实现了基于地质图的自动化识别,也为非专业人员的地质图读图识图提供了帮助。Purpose of the invention: The present invention aims at the problems existing in the prior art, and provides an automatic identification method of monoclinic strata structure and landform. This method adopts digital geological maps, and aims at the law of consistent dip of monocline strata, and basically realizes the automatic recognition of monocline strata structure and geomorphology in bedrock and mountainous areas by means of structural pattern matching. Compared with the traditional manual identification method, automatic identification based on geological maps is realized, and it also provides help for non-professionals to read and recognize geological maps.

技术方案:本发明所述的单斜岩层构造地貌的自动识别方法包括以下步骤:Technical solution: The method for automatic identification of monoclinic rock structure and landform according to the present invention comprises the following steps:

(1)提取基岩地层对象(不包括侵入岩体),依据邻接、倾斜和倾向一致性的规则,对提取得到的邻接对象建立关系,绘制邻接ARG图(属性关系图,attributed relationalgraph);(1) Extract bedrock stratum objects (excluding intrusive rock mass), establish a relationship with the extracted adjacent objects according to the rules of adjacency, inclination and inclination consistency, and draw an adjacency ARG graph (attributed relational graph);

(2)基于邻接ARG图,依据邻接关系构建场景模型,并对场景模型进行简化;(2) Based on the adjacency ARG graph, the scene model is constructed according to the adjacency relationship, and the scene model is simplified;

(3)按照褶皱筛选规则对简化后的场景模型进行筛选,保留非完整褶皱结构的模型,即为单斜岩层构造。(3) Filter the simplified scene model according to the fold screening rules, and keep the model with incomplete fold structure, that is, the monocline rock structure.

其中,步骤(1)具体包括:Wherein, step (1) specifically includes:

(1-1)设置角度阈值α作为判断两个方位角度或走向角度差值是否在可接受范围内的依据,一般取值[22.5,90);设置倾角阈值β作为判断岩层是否达到倾斜标准的依据,一般取值[15,90),具体可根据用户需求自行确定;(1-1) Set the angle threshold α as the basis for judging whether the difference between the two azimuth angles or strike angles is within the acceptable range, and generally take the value [22.5, 90); set the dip angle threshold β as the basis for judging whether the rock formation meets the dip standard The basis, the general value is [15, 90), and the specific value can be determined according to the user's needs;

(1-2)加载矢量格式的地层面要素图层,得到所有面要素集合Stra={si|i=1,2,3,…,n};其中,si表示第i个面要素,面要素包含编号属性Id、地层年代属性Age、产状倾向属性OccT和产状倾角属性OccA,n为面要素的数量;(1-2) Load the stratigraphic element layer in vector format, and get all the surface element sets Stra={s i |i=1,2,3,...,n}; where, s i represents the i-th surface element, The surface elements include the serial number attribute Id, the stratigraphic age attribute Age, the occurrence dip attribute OccT and the occurrence dip attribute OccA, and n is the number of surface elements;

(1-3)分别计算面要素的质心点,得到地层面要素质心点要素集合OriGrav={ogi(xogi,yogi)|i=1,2,3,…,n};其中,ogi表示第i个面要素的质心点要素,(xogi,yogi)为ogi的坐标,质心点要素继承面要素的编号属性Id、地层年代属性Age、产状倾向属性OccT和产状倾角属性OccA;(1-3) Calculate the centroid points of surface elements respectively, and obtain the set of centroid points of stratigraphic elements OriGrav={og i (xog i ,yog i )|i=1,2,3,...,n}; where, og i represents the centroid point element of the i-th surface element, (xog i , yog i ) is the coordinate of og i , and the centroid point element inherits the number attribute Id of the surface element, the stratigraphic age attribute Age, the occurrence tendency attribute OccT and the occurrence Inclination attribute OccA;

(1-4)质心点要素筛选:对集合OriGrav,根据地层年代属性Age,选取非第四纪、非侵入岩体的地层的质心点要素,创建质心点要素集合Grav={gi(xgi,ygi)|i=1,2,3,…,c};其中,gi为保留的第i个质心点要素象,(xgi,ygi)为gi的坐标,c为保留的质心点要素的数量;(1-4) Screening of centroid point elements: For the set OriGrav, according to the stratum age attribute Age, select the centroid point elements of non-Quaternary and non-intrusive rock mass formations, and create a centroid point element set Grav={g i (xg i ,yg i )|i=1,2,3,…,c}; among them, g i is the retained i-th centroid element image, (xg i ,yg i ) is the coordinate of g i , and c is the reserved The number of centroid point features;

(1-5)创建地层对象间关系:对于质心点要素集合Grav,取质心点要素gi和gj,j=1,2,3,…,c,j>i,判断是否满足以下条件:(1-5) Create the relationship between stratum objects: for the centroid point element set Grav, take the centroid point elements g i and g j , j=1,2,3,...,c, j>i, and judge whether the following conditions are met:

a)质心点要素gi和gj的产状倾角属性OccA大于等于倾角阈值β;a) The occurrence dip angle attribute OccA of the centroid point elements g i and g j is greater than or equal to the dip angle threshold β;

b)质心点要素gi和gj的地层年代属性Age不相同;b) The stratigraphic age attributes Age of centroid point elements g i and g j are different;

c)质心点要素gi和gj对应的地层面要素si和sj邻接;c) The stratigraphic layer elements s i and s j corresponding to centroid point elements g i and g j are adjacent;

d)质心点要素gi和gj的产状倾向属性OccT的差值不超过角度阈值α;d) The difference between the occurrence tendency attribute OccT of centroid point elements g i and g j does not exceed the angle threshold α;

e)质心点要素gi和gj所在直线的走向与质心点要素gi或gj的产状倾向属性OccT的差值不超过角度阈值α;e) The difference between the direction of the line where the centroid elements g i and g j are located and the occurrence tendency attribute OccT of the centroid element g i or g j does not exceed the angle threshold α;

对于满足以上所有条件的质心点要素gi和gj创建关系rk<gi,gj>,并存储关系rk中左Create relation r k <g i , g j > for centroid point elements g i and g j satisfying all the above conditions, and store relation r k in the left

对象gi和右对象gj的编号属性Id;The number attribute Id of the object g i and the right object g j ;

(1-6)完成所有质心点要素间的关系创建,得到关系集合Rel={rk<gi,gj>|k=1,2,3,…,m},m为创建的关系的数量;(1-6) Complete the creation of the relationship between all centroid elements, and get the relationship set Rel={r k <g i , g j >|k=1,2,3,...,m}, m is the created relationship quantity;

(1-7)根据质心点要素绘制顶点集合,根据要素间的关系绘制边,则基于质心点要素集合Grav与关系集合Rel完成邻接ARG图。(1-7) Draw the vertex set according to the centroid point element, and draw the edge according to the relationship between the elements, then complete the adjacency ARG graph based on the centroid point element set Grav and the relationship set Rel.

其中,步骤(2)具体包括:Wherein, step (2) specifically includes:

(2-1)基于邻接ARG图,以邻接关系为条件对所有连接边创建场景模型,得到场景模型集合OriM={OMi|i=1,2,3,…,p},OMi表示第i个场景模型,p为场景模型的数量,场景模型OMi包括顶点集合和边集合。例如对有邻接关系的边ru<gi,gj>与rv<gi,gk>(u≠v)构建场景模型OM,场景模型OM包括顶点集合OMGrav={gi,g,gk},以及边集合OMRel={ru,rv},并计算且记录过顶点gi,g,gk的边的数量分别为2,1,1;(2-1) Based on the adjacency ARG graph, create a scene model for all connected edges based on the adjacency relationship, and obtain the scene model set OriM={OM i |i=1,2,3,...,p}, OM i represents the first There are i scene models, p is the number of scene models, and the scene model OM i includes a set of vertices and a set of edges. For example, construct a scene model OM for edges r u <g i , g j > and r v <g i , g k >(u≠v) with adjacent relationships, and the scene model OM includes a set of vertices OMGra v = {g i , g ,g k }, and the edge set OMRel={r u ,r v }, and calculate and record the number of edges g i , g, g k are 2, 1, 1 respectively;

(2-2)查找端点:在场景模型的顶点集合中,存在简化端点与普通端点两种类型的端点,按以下步骤依次查找简化端点和普通端点:(2-2) Find endpoints: In the vertex set of the scene model, there are two types of endpoints, simplified endpoints and ordinary endpoints. Follow the steps below to search for simplified endpoints and ordinary endpoints in turn:

1)查找满足以下所有条件的顶点作为简化端点:1) Find vertices that satisfy all of the following conditions as simplified endpoints:

a)过该顶点的边的数量大于等于2;a) The number of edges passing through the vertex is greater than or equal to 2;

b)与该顶点连接的端点集合中存在地层年代属性相同的情况;b) There is a situation with the same stratum age attribute in the set of endpoints connected to the vertex;

c)与该顶点连接的边集合中存在至少一对夹角小于90°的边;c) There is at least one pair of edges with an included angle less than 90° in the set of edges connected to the vertex;

2)查找满足连接的边的数量等于1的条件的顶点作为普通端点;2) Find the vertex that satisfies the condition that the number of connected edges is equal to 1 as a common endpoint;

(2-3)递归重建模型:依次将所有简化端点和未标记的普通端点作为起始端点按照以下步骤进行递归,不存在简化端点的场景模型直接将普通端点作为起始端点按照以下步骤进行递归;(2-3) Recursively rebuild the model: use all simplified endpoints and unmarked common endpoints as starting endpoints in turn to recurse according to the following steps, and for scene models without simplified endpoints, directly use ordinary endpoints as starting endpoints to recurse according to the following steps ;

1)将起始端点gi加入顶点链集合Link,将与其连接的其他顶点gij视为gi的孩子,j=1,2,3,…,ti,ti为与端点gi有连接的顶点数量:1) Add the starting endpoint g i to the vertex chain set Link, and regard the other vertices g ij connected to it as the children of g i , j=1,2,3,...,t i , t i is the same as the end point g i Number of connected vertices:

2)若Link长度<2,则将顶点gij加入Link,对其进行步骤4)的判断;若Link长度>=2,则对其进行步骤3)判断;2) If the Link length<2, then add the vertex g ij to the Link, and perform step 4) judgment on it; if the Link length>=2, then perform step 3) judgment on it;

3)对顶点gij进行规则判断,j=1,2,3,…,ti:若顶点gij同时满足条件①为简化端点或未被标记;②在Link中不存在;③Link中最后两个顶点组成的边的方位角度与Link中最后一个顶点指向gij的方位角度差值小于预设角度阈值α,则将顶点gij加入Link,标记顶点gij,继续步骤4)判断;反之,则继续对下一个孩子顶点gi(j+1)进行本步判断,直到gi的所有孩子判断完毕,跳到步骤5);3) Judging the vertices g ij by rules, j=1,2,3,...,t i : if the vertices g ij satisfy the conditions at the same time ① is a simplified endpoint or is not marked; ② does not exist in the Link; ③ the last two points in the Link The difference between the azimuth angle of the edge composed of two vertices and the azimuth angle of the last vertex in the Link pointing to g ij is less than the preset angle threshold α, then add the vertex g ij to the Link, mark the vertex g ij , and continue to step 4) to judge; otherwise, Then continue to judge the next child vertex g i(j+1) in this step until all children of g i are judged and skip to step 5);

4)对顶点gij进行端点判断:若顶点gij为简化端点或普通端点,则该Link结束,根据当前Link集合重建模型M,将当前顶点gij从Link中移除;反之,则将顶点gij视为gi,对其孩子gij重复步骤2)判断;4) Judging the endpoint of vertex g ij : if the vertex g ij is a simplified endpoint or an ordinary endpoint, the Link ends, and the model M is reconstructed according to the current Link set, and the current vertex g ij is removed from the Link; otherwise, the vertex g ij is removed from the Link. g ij is regarded as g i , repeat step 2) judgment for its child g ij ;

5)当前父级顶点gi的所有孩子遍历完毕,将顶点gi从Link中移除,继续对下一个顶点g(i+1)或下一个起始端点重复步骤2)判断,直到最后一个端点判断完毕,递归结束;5) After traversing all the children of the current parent vertex g i , remove the vertex g i from the Link, and continue to repeat step 2) for the next vertex g (i+1 ) or the next starting point until the last After the endpoint is judged, the recursion ends;

(2-4)按以下规则对递归得到的场景模型进行筛选:(2-4) Screen the recursive scene model according to the following rules:

1)剔除模型内仅包含两个顶点和一个边的场景模型;1) Eliminate scene models that only contain two vertices and one edge in the model;

2)对于同一简化端点为起点或终点重建的多个场景模型,按以下原则进行剔除:2) For multiple scene models reconstructed with the same simplified endpoint as the starting point or ending point, the following principles are used to eliminate them:

a)若模型的顶点集合元素数量相同,集合内顶点对应的地层年代属性Age分别相同,则保留对应的地层面面积大的场景模型;a) If the number of elements in the vertex set of the model is the same, and the stratum age attributes corresponding to the vertices in the set are the same respectively, then the corresponding scene model with a large stratum layer area is retained;

b)若模型的顶点集合不完全相同,则保留集合元素较多的场景模型;b) If the vertex sets of the models are not exactly the same, then keep the scene model with more set elements;

(2-5)对筛选后的场景模型进行重新编号,得到模型集合AllM={M1,M2,M3,…,Mq},q为场景模型数量。(2-5) Renumber the screened scene models to obtain a model set AllM={M 1 ,M 2 ,M 3 ,...,M q }, where q is the number of scene models.

其中,步骤(3)具体包括:Wherein, step (3) specifically includes:

(3-1)模型Vonoroi单元剖分:根据步骤(2)得到的场景模型进行Voronoi单元剖分(王新生,刘纪远,庄大方,等.基于GIS的任意发生元Voronoi图逼近方法[J].地理科学进展,2004,23(4)),剖分后的Voronoi多边形单元继承模型编号属性;(3-1) Voronoi unit division of the model: Carry out Voronoi unit division based on the scene model obtained in step (2) (Wang Xinsheng, Liu Jiyuan, Zhuang Dafang, etc. Approximation method of Voronoi diagram for arbitrary occurrence elements based on GIS[J]. Geography Science Advances, 2004, 23(4)), the divided Voronoi polygon unit inherits the model number attribute;

(3-2)对模型进行褶皱筛选:对于相互邻接的Voronoi多边形单元中的场景模型Mi与Mj进行比较,若同时满足以下条件则判定两个场景模型组成完整褶皱,剔除这两个模型:(3-2) Perform fold screening on the model: compare the scene models M i and M j in adjacent Voronoi polygonal units, and if the following conditions are met at the same time, it is determined that the two scene models form complete folds, and these two models are eliminated :

a)场景模型Mi与Mj的顶点集合一致;a) The vertex sets of the scene model M i and M j are consistent;

b)按照一致的顶点顺序,两个场景模型的走向相反;b) According to the consistent vertex order, the direction of the two scene models is opposite;

(3-3)完成褶皱筛选后保留的场景模型视为单斜模型,全图筛选后得到单斜模型集合FinM={FMh|h=1,2,3,…,w},w为单斜模型数量,FMh表示第h个单斜模型,FMh包括顶点集合FMGravh和边集合FMRelh(3-3) The scene model retained after the fold screening is regarded as a monoclinic model, and the monoclinic model set FinM={FM h |h=1,2,3,...,w} is obtained after the full image screening, where w is a monoclinic model The number of oblique models, FM h represents the hth monoclinic model, FM h includes the vertex set FMGrav h and the edge set FMRel h ;

(3-4)对筛选后的模型集合FinM中每一个场景模型,进行地层面要素合并,得到对应的单斜面要素,形成单斜面要素集合Poly={pr|r=1,2,3,…,w},pr为场景模型FMr的单斜面要素。(3-4) For each scene model in the screened model set FinM, merge the stratum level elements to obtain the corresponding single-slope elements to form a single-slope element set Poly={p r | r =1,2,3, …,w}, p r is the single slope element of the scene model FM r .

有益效果:本发明与现有技术相比,其显著优点是:本发明采用数字地质图,针对单斜地层倾向一致的规律,以结构模式匹配的方法,实现了在基岩山地地区,对单斜岩层构造地貌的自动化识别。相比传统的人工识别方法,实现了基于地质图的自动化识别,也为非专业人员的地质图读图识图提供了帮助。单斜岩层构造地貌的形成与周围的地貌变迁和构造演化有密切联系,研究单斜岩层构造地貌可为地质图认知、构造演化推理提供有效支持,是构造解析、参数化三维建模等工作的前提。Beneficial effects: Compared with the prior art, the present invention has the remarkable advantages that: the present invention adopts digital geological maps, aims at the law of consistent dip of monocline formations, and realizes monocline single-cline strata in the bedrock mountainous area by means of structural pattern matching. Automatic identification of oblique strata structural landforms. Compared with the traditional manual identification method, automatic identification based on geological maps is realized, and it also provides help for non-professionals to read and recognize geological maps. The formation of monoclinic rock structure and landform is closely related to the surrounding landform changes and structural evolution. The study of monocline rock structure and landform can provide effective support for geological map recognition and structural evolution reasoning. premise.

附图说明Description of drawings

图1为本发明方法的流程图;Fig. 1 is the flowchart of the inventive method;

图2为实施例地质体面图层数据示意图;Fig. 2 is the schematic diagram of embodiment geological surface layer data;

图3为实施例质心点要素图层数据示意图;Fig. 3 is a schematic diagram of embodiment centroid point feature layer data;

图4为实施例筛选质心点要素构建关系示意图;Fig. 4 is a schematic diagram of the construction relationship of centroid point elements in the embodiment;

图5为示例(a)场景模型图层(b)顶点集合属性(c)边集合属性示意图;Fig. 5 is a schematic diagram of example (a) scene model layer (b) vertex set attribute (c) edge set attribute;

图6为复杂情况下的场景模型示例图Figure 6 is an example diagram of a scene model in a complex situation

图7为简化筛选后模型数据示意图;Fig. 7 is a schematic diagram of model data after simplified screening;

图8为模型数据Voronoi剖分示意图;Fig. 8 is a schematic diagram of Voronoi subdivision of model data;

图9为完整褶皱模型示意图;Figure 9 is a schematic diagram of a complete fold model;

图10为识别的单斜面图层数据示意图。Figure 10 is a schematic diagram of the identified single-slope layer data.

具体实施方式detailed description

下面结合附图并通过描述一个自动识别单斜岩层构造地貌的实例,来进一步说明本发明的效果。本发明的方法流程图如图1所示,包括以下步骤:The effect of the present invention will be further illustrated below in conjunction with the accompanying drawings and by describing an example of automatically identifying monoclinic rock formation structure and topography. Method flowchart of the present invention as shown in Figure 1, comprises the following steps:

(一)邻接ARG图生成(1) Adjacency ARG graph generation

步骤11:设置角度阈值α为45°,设置倾角阈值β为15°;Step 11: Set the angle threshold α to 45°, and the inclination threshold β to 15°;

步骤12:本实施例选择南京紫金山的地质体面图层的shp格式数据为示例,如图2所示,加载矢量格式的该图地层面要素图层,得到所有面要素集合Stra={si|i=1,2,3,…,78}。存储面要素的编号属性Id、地层年代属性Age、产状倾向属性OccT和产状倾角属性OccA。数据采用十六方位模型表示产状倾向,将其转换成数值,在此,以方位区间的中心方位角度来替代;Step 12: In this embodiment, the shp format data of the geological surface layer of Zijin Mountain in Nanjing is selected as an example, as shown in Figure 2, the layer element layer of this map in vector format is loaded, and all surface element sets Stra={s i are obtained |i=1,2,3,...,78}. The serial number attribute Id, the stratigraphic age attribute Age, the occurrence dip attribute OccT and the occurrence dip attribute OccA of the storage area elements are stored. The data adopts the sixteen azimuth model to represent the tendency of occurrence, and converts it into a numerical value. Here, it is replaced by the central azimuth angle of the azimuth interval;

步骤13:分别计算面要素的质心点,得到地层面要素质心点要素集合OriGrav={ogi(xogi,yogi)|i=1,2,3,…,78}。质心点要素继承面要素的编号属性Id、地层年代属性Age、产状倾向属性OccT和产状倾角属性OccA;Step 13: Calculate the centroid points of the surface elements respectively, and obtain the set of centroid points of the stratum level elements OriGrav={og i (xog i , yog i )|i=1,2,3,...,78}. The centroid point element inherits the serial number attribute Id of the surface element, the stratum age attribute Age, the occurrence tendency attribute OccT, and the occurrence dip angle attribute OccA;

步骤14:筛选质心点,根据地层年代属性Age,选取非第四纪、非侵入岩体的地层的质心点要素,得到质心点对象集合Grav={gi(xgi,ygi)|i=1,2,3,…,62},筛选得到的质心点对象图层如图3所示;Step 14: Screen the centroid points, and select the centroid point elements of non-Quaternary and non-intrusive rock mass strata according to the stratum age attribute Age, and obtain the centroid point object set Grav={g i (xg i ,yg i )|i= 1,2,3,...,62}, the filtered centroid point object layer is shown in Figure 3;

步骤15:创建地层对象间关系。对于质心点对象集合Grav,取质心点对象gi和gj(j=1,2,3,…,62,j>i)判断是否满足以下条件:Step 15: Create relationships between stratigraphic objects. For the set of centroid point objects Grav, take the centroid point objects g i and g j (j=1,2,3,...,62, j>i) to judge whether the following conditions are met:

a)质心点要素gi和gj的产状倾角属性OccA大于等于倾角阈值β;a) The occurrence dip angle attribute OccA of the centroid point elements g i and g j is greater than or equal to the dip angle threshold β;

b)质心点要素gi和gj的地层年代属性Age不相同;b) The stratigraphic age attributes Age of centroid point elements g i and g j are different;

c)质心点要素gi和gj对应的地层面要素si和sj邻接;c) The stratigraphic layer elements s i and s j corresponding to centroid point elements g i and g j are adjacent;

d)质心点要素gi和gj的产状倾向属性OccT的差值不超过角度阈值α;d) The difference between the occurrence tendency attribute OccT of centroid point elements g i and g j does not exceed the angle threshold α;

e)质心点要素gi和gj所在直线的走向与质心点要素gi或gj的产状倾向属性OccT的差值不超过角度阈值α;e) The difference between the direction of the line where the centroid elements g i and g j are located and the occurrence tendency attribute OccT of the centroid element g i or g j does not exceed the angle threshold α;

对于满足以上所有条件的质心点要素gi和gj创建关系rk<gi,gj>,并存储关系rk中左Create relation r k <g i , g j > for centroid point elements g i and g j satisfying all the above conditions, and store relation r k in the left

对象gi和右对象gj的编号属性Id,得到集合Rel={rk<gi,gj>|k=1,2,3,…,21},根据质The numbering attribute Id of the object g i and the right object g j , get the set Rel={r k <g i , g j >|k=1,2,3,...,21}, according to the quality

心点对象和关系集合绘制顶点和边,完成ARG图,如图4所示。The heart point object and relationship collection draw vertices and edges to complete the ARG graph, as shown in Figure 4.

(二)场景模型构建与简化(2) Scene model construction and simplification

步骤21:基于ARG图,以相邻关系为条件创建场景模型。以模型OM5为例(如图5(a)所示),其顶点集合OMGrav5包括8个对象OMGrav5={g23,g24,g27,g28,g30,g31,g32,g33},属性如图5(b)所示,其边集合OMRel5包括7个边OMRel5={r9<g23,g28>,r10<g23,g30>,r11<g23,g31>,r12<g23,g33>,r13<g27,g28>,r14<g31,g32>,r15<g24,g27>},属性如图5(c)所示。在模型中顶点连接的边的数量分别为4、1、2、2、1、2、1、1;Step 21: Based on the ARG graph, create a scene model based on the adjacent relationship. Taking the model OM 5 as an example (as shown in Figure 5(a)), its vertex set OMGrav 5 includes 8 objects OMGrav 5 = {g 23 , g 24 , g 27 , g 28 , g 30 , g 31 , g 32 , g 33 }, as shown in Figure 5(b), its edge set OMRel 5 includes 7 edges OMRel 5 = {r 9 <g 23 , g 28 >, r 10 <g 23 , g 30 >, r 11 <g 23 , g 31 >, r 12 <g 23 , g 33 >, r 13 <g 27 , g 28 >, r 14 <g 31 , g 32 >, r 15 <g 24 , g 27 >}, attribute As shown in Figure 5(c). The number of edges connected by vertices in the model is 4, 1, 2, 2, 1, 2, 1, 1;

步骤22:全图进行场景模型构建,得到场景模型集合OriM={OMi|i=1,2,3,…,8};Step 22: Construct the scene model of the whole image, and obtain the scene model set OriM={OM i |i=1,2,3,...,8};

步骤23:对场景模型进行简化。可能存在受后期断层影响而造成场景模型结构复杂的情况(如图6所示),需要对场景模型进行简化,以场景模型OM5为例,场景模型的简化过程阐述如步骤24~26所示。Step 23: Simplify the scene model. There may be cases where the structure of the scene model is complicated due to the influence of later faults (as shown in Figure 6), and the scene model needs to be simplified. Taking the scene model OM 5 as an example, the process of simplifying the scene model is described in steps 24-26. .

步骤24:查找端点。Step 24: Find Endpoints.

1)在场景模型OM5的顶点集合OMGrav5中,顶点g23满足以下条件,则顶点g23为简化端点:1) In the vertex set OMGrav 5 of the scene model OM 5 , the vertex g 23 satisfies the following conditions, then the vertex g 23 is a simplified endpoint:

a)过顶点g23的边的数量为4,大于2;a) The number of edges passing through the vertex g 23 is 4, greater than 2;

b)与顶点g23连接的顶点集合中,g31g33的地层年代属性一致b) In the set of vertices connected to vertex g 23 , the stratigraphic age attributes of g 31 and g33 are consistent

c)包含顶点g23的所有边集合中存在夹角小于90°的边;c) There are edges with angles less than 90° in all edge sets including vertex g 23 ;

2)其次,根据经过的边的数量等于1的条件寻找普通端点,则顶点g24,g30,g32,g33为普通端点;2) Secondly, according to the condition that the number of edges passed through is equal to 1, common endpoints are searched for, then vertices g 24 , g 30 , g 32 , and g 33 are common endpoints;

步骤25:递归重建模型。创建一个顶点链集合Link,对于场景模型的简化端点g23进行递归。首先将简化端点g23加入Link,其通过边连接的其他顶点g23j(j=1,2,3,…,4)分别为g28、g30、g31、g33,则进行递归如下:Step 25: Recursively rebuild the model. Create a vertex chain set Link, and perform recursion on the simplified endpoint g 23 of the scene model. First, the simplified endpoint g 23 is added to Link, and the other vertices g 23j (j=1,2,3,…,4) connected by edges are g 28 , g 30 , g 31 , g 33 respectively, and the recursion is as follows:

1)此时Link中仅包含g23,长度小于2,则将顶点g28加入Link,标记g281) At this time, only g 23 is included in the Link, and the length is less than 2, then the vertex g 28 is added to the Link, and g 28 is marked;

2)对顶点g28进行端点判断,g28既不是简化端点也不是普通端点,则找到其通过边连接的其他顶点g28j(j=1,2)分别为g23,g272) judge the endpoint of vertex g 28 , g 28 is neither a simplified endpoint nor a common endpoint, then find its other vertices g 28j (j=1,2) connected by edges are g 23 , g 27 respectively;

3)此时Link中包含g23、g28,长度等于2,则对顶点g23进行规则判断。若顶点gij同时满足条件①为简化端点或未被标记;②在Link中不存在;③Link中最后两个顶点组成的边的方位角度与Link中最后一个顶点指向gij的方位角度差值小于预设角度阈值α,则将顶点gij加入Link,标记顶点gij。对于顶点g23,其为简化端点,已存在于Link,因此不满足条件②,则跳过该点,进而对g27进行规则判断;3) At this time, the Link contains g 23 and g 28 , and the length is equal to 2, then the rule judgment is performed on the vertex g 23 . If the vertex g ij satisfies the conditions at the same time ① is a simplified endpoint or is not marked; ② does not exist in the Link; ③ the difference between the azimuth angle of the edge formed by the last two vertices in the Link and the azimuth angle of the last vertex in the Link pointing to g ij is less than If the angle threshold α is preset, the vertex g ij is added to Link, and the vertex g ij is marked. For the vertex g 23 , it is a simplified endpoint and already exists in the Link, so the condition ② is not satisfied, skip this point, and then make a rule judgment on g 27 ;

4)对于顶点g27,其未标记,且在Link中不存在,计算g23指向g28的角度为341.3°,g28指向g27的角度为90.3°,两个角度相差大于45°,不满足条件③,则跳过该点,此时g28的孩子判断完毕;4) For the vertex g 27 , which is not marked and does not exist in the Link, the calculated angle from g 23 to g 28 is 341.3°, and the angle from g 28 to g 27 is 90.3°. The difference between the two angles is greater than 45°. If the condition ③ is met, then skip this point, and the child of g 28 has finished judging at this time;

5)将顶点g28从Link中移除,此时Link中仅包含g23,长度小于2,则将顶点g30加入Link,标记g305) Vertex g 28 is removed from the Link. At this time, the Link only contains g 23 and the length is less than 2, then the vertex g 30 is added to the Link, marking g 30 ;

6)对顶点g30进行端点判断,g30为普通端点,则该Link结束,新建模型M,MGrav={g23,g30},MRel={r10<g23,g30>}。将g30从Link中移除,此时Link中仅包含g23,长度小于2,则将顶点g31加入Link,标记g316) Judge the endpoint of vertex g 30 , if g 30 is a common endpoint, then the Link ends, and a new model M is created, MGrav={g 23 , g 30 }, MRel={r 10 <g 23 , g 30 >}. Remove g 30 from the Link. At this time, the Link only contains g 23 and the length is less than 2, then add the vertex g 31 to the Link and mark g 31 ;

7)对顶点g31进行端点判断,g31既不是简化端点,也不是普通端点,则找到其通过边连接的其他顶点g31j(j=1,2)分别为g23,g327) To judge the endpoint of vertex g 31 , g 31 is neither a simplified endpoint nor a common endpoint, then find its other vertices g 31j (j=1, 2) connected by edges as g 23 and g 32 respectively;

8)此时Link中包含g23、g31,长度等于2,则对顶点g23进行规则判断。对于顶点g23,其为简化端点,在Link中存在不满足条件②,则跳过该点,进而对g32进行规则判断;8) At this time, the Link contains g 23 and g 31 , and the length is equal to 2, then the rule judgment is performed on the vertex g 23 . For the vertex g 23 , which is a simplified endpoint, if there is an unsatisfied condition ② in the Link, skip this point, and then make a rule judgment on g 32 ;

9)对于顶点g32,其未标记,且在Link中不存在,计算g23指向g31的角度为251.4°,g31指向g32的角度为256.0°,两个角度相差小于阈值45°,则将顶点g32加入Link,标记g329) For the vertex g 32 , which is unmarked and does not exist in the Link, the calculated angle from g 23 to g 31 is 251.4°, and the angle from g 31 to g 32 is 256.0°, and the difference between the two angles is less than the threshold value of 45°. Then add vertex g 32 to Link, and mark g 32 ;

10)对顶点g32进行端点判断,g32是普通端点,则该Link结束,新建模型M,MGrav={g23,g31,g32},MRel={r11<g23,g31>,r14<g31,g32>}。将g32从Link中移除,此时,g31的孩子判断结束;10) Judging the endpoint of vertex g 32 , g 32 is a common endpoint, then the Link ends, and a new model M is created, MGrav={g 23 , g 31 , g 32 }, MRel={r 11 <g 23 , g 31 > , r 14 <g 31 , g 32 >}. Remove g 32 from the Link, at this point, the judgment of the child of g 31 ends;

11)将顶点g31从Link中移除,此时Link中仅包含g23,长度小于2,则将顶点g33加入Link,标记g3311) Vertex g 31 is removed from the Link. At this time, only g 23 is included in the Link, and the length is less than 2, then the vertex g 33 is added to the Link, and g 33 is marked;

12)对顶点g33进行端点判断,g33是普通端点,则该Link结束,新建模型M,MGrav={g23,g33},MRel={r12<g23,g33>}。将g33从Link中移除,此时,g23的孩子判断结束;12) Judge the endpoint of vertex g 33 , if g 33 is a common endpoint, then the Link ends, and a new model M is created, MGrav={g 23 , g 33 }, MRel={r 12 <g 23 , g 33 >}. Remove g 33 from Link, at this time, the judgment of the child of g 23 ends;

13)对未标记的普通端点进行遍历。依据判断规则与流程,对未被标记的普通端点为g24的孩子进行判断,得到模型M,其中包括顶点集合MGrav={g24,g27、g28},边集合MRel={r13<g27,g28>,r15<g24,g27>};13) Traverse unmarked common endpoints. According to the judging rules and procedures, judge the unmarked child whose common end point is g 24 , and obtain the model M, which includes the vertex set MGrav={g 24 , g 27 , g 28 }, and the edge set MRel={r 13 < g 27 , g 28 >, r 15 <g 24 , g 27 >};

14)普通端点g30、g32、g33都被标记,不再遍历,当前场景模型递归结束。示例场景模型OriM5简化完成得到4个模型如下:14) The common endpoints g 30 , g 32 , and g 33 are all marked and no longer traversed, and the recursion of the current scene model ends. The sample scene model OriM 5 is simplified to get 4 models as follows:

M1={MGrav1,MRel1},MGrav1={g23,g30},MRel1={r10<g23,g30>}M 1 ={MGrav 1 , MRel 1 }, MGrav 1 ={g 23 , g 30 }, MRel 1 ={r 10 <g 23 , g 30 >}

M2={MGrav2,MRel2},MGrav2={g23,g31,g32},MRel2={r11<g23,g31>,r14<g31,g32>}M 2 ={MGrav 2 , MRel 2 }, MGrav 2 ={g 23 , g 31 , g 32 }, MRel 2 ={r 11 <g 23 , g 31 >, r 14 <g 31 , g 32 >}

M3={MGrav3,MRel3},MGrav3={g23,g33},MRel3={r12<g23,g33>}M 3 ={MGrav 3 , MRel 3 }, MGrav 3 ={g 23 , g 33 }, MRel 3 ={r 12 <g 23 , g 33 >}

M4={MGrav4,MRel4},MGrav4={g24,g27,g28},MRel4={r13<g27,g28>,r15<g24,g27>}M 4 ={MGrav 4 , MRel 4 }, MGrav 4 ={g 24 , g 27 , g 28 }, MRel 4 ={r 13 <g 27 , g 28 >, r 15 <g 24 , g 27 >}

步骤26:按规则对场景模型OM5简化的4个模型进行筛选,其中,模型M1与M3只包含两个顶点与一条边,则剔除模型M1与M3,最终保留M2与M4两个模型;Step 26: Screen the 4 simplified models of the scene model OM 5 according to the rules. Among them, the models M 1 and M 3 only contain two vertices and one edge, then remove the models M 1 and M 3 , and finally keep M 2 and M 4 two models;

步骤27:全图简化筛选后得到5个模型,重新编号后标注如图7所示。Step 27: After simplifying and filtering the whole image, 5 models are obtained, and the renumbered models are marked as shown in Figure 7.

(三)单斜模式匹配与筛选(3) Monoclinic pattern matching and screening

步骤31:根据梳理后的模型进行Voronoi单元剖分,剖分后的多边形继承模型编号属性,生成的Voronoi多边形要素如图8所示;Step 31: Carry out Voronoi unit subdivision according to the combed model, and the subdivided polygon inherits the model number attribute, and the generated Voronoi polygon element is shown in Figure 8;

步骤32:对Voronoi图中相互邻接的模型进行筛选。对于相互邻接的Voronoi多边形单元中的场景模型Mi与Mj进行比较,若同时满足以下条件则判定两个场景模型组成完整褶皱,剔除这两个模型:Step 32: Screen the adjacent models in the Voronoi diagram. Compare the scene models M i and M j in the adjacent Voronoi polygonal units, and if the following conditions are met at the same time, it is determined that the two scene models form a complete fold, and these two models are eliminated:

a)场景模型Mi与Mj的顶点集合一致;a) The vertex sets of the scene model M i and M j are consistent;

b)按照一致的顶点顺序,两个场景模型的走向相反(如图9所示);b) According to the consistent vertex order, the direction of the two scene models is opposite (as shown in Figure 9);

示例中不存在完整褶皱,无需要剔除的单斜对象,因此,得到最终的模型集合FinM={FMh|h=1,2,3,…,5},与模型M的数量一致;There are no complete folds in the example, and there are no monoclinic objects that need to be eliminated. Therefore, the final model set FinM={FM h |h=1,2,3,...,5} is obtained, which is consistent with the number of model M;

步骤33:对满足单斜结构的模型FMh,将顶点集合对应的地层面要素对象合并,新建一个单斜面要素ph。对筛选后的模型集合FinM进行地层面要素合并,得到单斜面要素集合Poly={ph|h=1,2,3,…,5},如图10所示。Step 33: For the model FM h that satisfies the monocline structure, merge the stratum layer element objects corresponding to the vertex set to create a new monocline element p h . Merge the elements of the stratum layer on the filtered model set FinM to obtain the single-slope element set Poly={p h |h=1,2,3,...,5}, as shown in Figure 10.

以上所揭露的仅为本发明一种较佳实施例而已,不能以此来限定本发明之权利范围,因此依本发明权利要求所作的等同变化,仍属本发明所涵盖的范围。What is disclosed above is only a preferred embodiment of the present invention, which cannot limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope of the present invention.

Claims (4)

1.一种单斜岩层构造地貌的自动识别方法,其特征在于该方法包括以下步骤:1. an automatic identification method of monoclinic strata structure landform, it is characterized in that the method comprises the following steps: (1)提取基岩地层对象,依据邻接、倾斜和倾向一致性的规则,对提取得到的邻接对象建立关系,绘制邻接ARG图;(1) Extract bedrock stratum objects, establish relationships with the extracted adjacent objects according to the rules of adjacency, inclination and dip consistency, and draw an adjacency ARG diagram; (2)基于邻接ARG图,依据邻接关系构建场景模型,并对场景模型进行简化;(2) Based on the adjacency ARG graph, the scene model is constructed according to the adjacency relationship, and the scene model is simplified; (3)按照褶皱筛选规则对简化后的场景模型进行筛选,保留非完整褶皱结构的模型,即为单斜岩层构造。(3) Filter the simplified scene model according to the fold screening rules, and keep the model with incomplete fold structure, that is, the monocline rock structure. 2.根据权利要求1所述的单斜岩层构造地貌的自动识别方法,其特征在于:步骤(1)具体包括:2. the automatic recognition method of monoclinic strata structure topography according to claim 1, is characterized in that: step (1) specifically comprises: (1-1)设置角度阈值α作为判断两个方位角度或走向角度差值是否在可接受范围内的依据,设置倾角阈值β作为判断岩层是否达到倾斜标准的依据;(1-1) The angle threshold α is set as the basis for judging whether the difference between two azimuth angles or strike angles is within an acceptable range, and the dip angle threshold β is set as the basis for judging whether the rock formation reaches the inclination standard; (1-2)加载矢量格式的地层面要素图层,得到所有面要素集合Stra={si|i=1,2,3,…,n};其中,si表示第i个面要素,面要素包含编号属性Id、地层年代属性Age、产状倾向属性OccT和产状倾角属性OccA,n为面要素的数量;(1-2) Load the stratigraphic element layer in vector format, and get all the surface element sets Stra={s i |i=1,2,3,...,n}; where, s i represents the i-th surface element, The surface elements include the serial number attribute Id, the stratigraphic age attribute Age, the occurrence dip attribute OccT and the occurrence dip attribute OccA, and n is the number of surface elements; (1-3)分别计算面要素的质心点,得到地层面要素质心点要素集合OriGrav={ogi(xogi,yogi)|i=1,2,3,…,n};其中,ogi表示第i个面要素的质心点要素,(xogi,yogi)为ogi的坐标,质心点要素继承面要素的编号属性Id、地层年代属性Age、产状倾向属性OccT和产状倾角属性OccA;(1-3) Calculate the centroid points of surface elements respectively, and obtain the set of centroid points of stratigraphic elements OriGrav={og i (xog i ,yog i )|i=1,2,3,...,n}; where, og i represents the centroid point element of the i-th surface element, (xog i , yog i ) is the coordinate of og i , and the centroid point element inherits the number attribute Id of the surface element, the stratigraphic age attribute Age, the occurrence tendency attribute OccT and the occurrence Inclination attribute OccA; (1-4)质心点要素筛选:对集合OriGrav,根据地层年代属性Age,选取非第四纪、非侵入岩体的地层的质心点要素,创建质心点要素集合Grav={gi(xgi,ygi)|i=1,2,3,…,c};其中,gi为保留的第i个质心点要素象,(xgi,ygi)为gi的坐标,c为保留的质心点要素的数量;(1-4) Screening of centroid point elements: For the set OriGrav, according to the stratum age attribute Age, select the centroid point elements of non-Quaternary and non-intrusive rock mass formations, and create a centroid point element set Grav={g i (xg i ,yg i )|i=1,2,3,…,c}; among them, g i is the retained i-th centroid element image, (xg i ,yg i ) is the coordinate of g i , and c is the reserved The number of centroid point features; (1-5)创建地层对象间关系:对于质心点要素集合Grav,取质心点要素gi和gj,j=1,2,3,…,c,j>i,判断是否满足以下条件:(1-5) Create the relationship between stratum objects: for the centroid point element set Grav, take the centroid point elements g i and g j , j=1,2,3,...,c, j>i, and judge whether the following conditions are met: a)质心点要素gi和gj的产状倾角属性OccA大于等于倾角阈值β;a) The occurrence dip angle attribute OccA of the centroid point elements g i and g j is greater than or equal to the dip angle threshold β; b)质心点要素gi和gj的地层年代属性Age不相同;b) The stratigraphic age attributes Age of centroid point elements g i and g j are different; c)质心点要素gi和gj对应的地层面要素si和sj邻接;c) The stratigraphic layer elements s i and s j corresponding to centroid point elements g i and g j are adjacent; d)质心点要素gi和gj的产状倾向属性OccT的差值不超过角度阈值α;d) The difference between the occurrence tendency attribute OccT of centroid point elements g i and g j does not exceed the angle threshold α; e)质心点要素gi和gj所在直线的走向与质心点要素gi或gj的产状倾向属性OccT的差值不超过角度阈值α;e) The difference between the direction of the line where the centroid elements g i and g j are located and the occurrence tendency attribute OccT of the centroid element g i or g j does not exceed the angle threshold α; 对于满足以上所有条件的质心点要素gi和gj创建关系rk<gi,gj>,并存储关系rk中左对象gi和右对象gj的编号属性Id;Create a relationship r k <g i , g j > for the centroid point elements g i and g j that meet all the above conditions, and store the number attribute Id of the left object g i and the right object g j in the relationship r k ; (1-6)完成所有质心点要素间的关系创建,得到关系集合Rel={rk<gi,gj>|k=1,2,3,…,m},m为创建的关系的数量;(1-6) Complete the creation of the relationship between all centroid elements, and get the relationship set Rel={r k <g i , g j >|k=1,2,3,...,m}, m is the created relationship quantity; (1-7)根据质心点要素绘制顶点集合,根据要素间的关系绘制边,则基于质心点要素集合Grav与关系集合Rel完成邻接ARG图。(1-7) Draw the vertex set according to the centroid point element, and draw the edge according to the relationship between the elements, then complete the adjacency ARG graph based on the centroid point element set Grav and the relationship set Rel. 3.根据权利要求1所述的单斜岩层构造地貌的自动识别方法,其特征在于:步骤(2)具体包括:3. the automatic identification method of monoclinic strata structure topography according to claim 1, is characterized in that: step (2) specifically comprises: (2-1)基于邻接ARG图,以邻接关系为条件对所有连接边创建场景模型,得到场景模型集合OriM={OMi|i=1,2,3,…,p},OMi表示第i个场景模型,p为场景模型的数量,场景模型OMi包括顶点集合和边集合;(2-1) Based on the adjacency ARG graph, create a scene model for all connected edges based on the adjacency relationship, and obtain the scene model set OriM={OM i |i=1,2,3,...,p}, OM i represents the first i scene models, p is the number of scene models, and the scene model OM i includes a set of vertices and a set of edges; (2-2)查找端点:在场景模型的顶点集合中,存在简化端点与普通端点两种类型的端点,按以下步骤依次查找简化端点和普通端点:(2-2) Find endpoints: In the vertex set of the scene model, there are two types of endpoints, simplified endpoints and ordinary endpoints. Follow the steps below to search for simplified endpoints and ordinary endpoints in turn: 1)查找满足以下所有条件的顶点作为简化端点:1) Find vertices that satisfy all of the following conditions as simplified endpoints: a)过该顶点的边的数量大于等于2;a) The number of edges passing through the vertex is greater than or equal to 2; b)与该顶点连接的端点集合中存在地层年代属性相同的情况;b) There is a situation with the same stratum age attribute in the set of endpoints connected to the vertex; c)与该顶点连接的边集合中存在至少一对夹角小于90°的边;c) There is at least one pair of edges with an included angle less than 90° in the set of edges connected to the vertex; 2)查找满足连接的边的数量等于1的条件的顶点作为普通端点;2) Find the vertex that satisfies the condition that the number of connected edges is equal to 1 as a common endpoint; (2-3)递归重建模型:依次将所有简化端点和未标记的普通端点作为起始端点按照以下步骤进行递归,不存在简化端点的场景模型直接将普通端点作为起始端点按照以下步骤进行递归;(2-3) Recursively rebuild the model: use all simplified endpoints and unmarked common endpoints as starting endpoints in turn to recurse according to the following steps, and for scene models without simplified endpoints, directly use ordinary endpoints as starting endpoints to recurse according to the following steps ; 1)将起始端点gi加入顶点链集合Link,将与其连接的其他顶点gij视为gi的孩子,j=1,2,3,…,ti,ti为与端点gi有连接的顶点数量:1) Add the starting endpoint g i to the vertex chain set Link, and regard the other vertices g ij connected to it as the children of g i , j=1,2,3,...,t i , t i is the same as the end point g i Number of connected vertices: 2)若Link长度<2,则将顶点gij加入Link,对其进行步骤4)的判断;若Link长度>=2,则对其进行步骤3)判断;2) If the Link length<2, then add the vertex g ij to the Link, and perform step 4) judgment on it; if the Link length>=2, then perform step 3) judgment on it; 3)对顶点gij进行规则判断,j=1,2,3,…,ti:若顶点gij同时满足条件①为简化端点或未被标记;②在Link中不存在;③Link中最后两个顶点组成的边的方位角度与Link中最后一个顶点指向gij的方位角度差值小于预设角度阈值α,则将顶点gij加入Link,标记顶点gij,继续步骤4)判断;反之,则继续对下一个孩子顶点gi(j+1)进行本步判断,直到gi的所有孩子判断完毕,跳到步骤5);3) Judging the vertices g ij by rules, j=1,2,3,...,t i : if the vertices g ij satisfy the conditions at the same time ① is a simplified endpoint or is not marked; ② does not exist in the Link; ③ the last two points in the Link The difference between the azimuth angle of the edge composed of two vertices and the azimuth angle of the last vertex in the Link pointing to g ij is less than the preset angle threshold α, then add the vertex g ij to the Link, mark the vertex g ij , and continue to step 4) to judge; otherwise, Then continue to judge the next child vertex g i(j+1) in this step until all children of g i are judged and skip to step 5); 4)对顶点gij进行端点判断:若顶点gij为简化端点或普通端点,则该Link结束,根据当前Link集合重建模型M,将当前顶点gij从Link中移除;反之,则将顶点gij视为gi,对其孩子gij重复步骤2)判断;4) Judging the endpoint of vertex g ij : if the vertex g ij is a simplified endpoint or an ordinary endpoint, the Link ends, and the model M is reconstructed according to the current Link set, and the current vertex g ij is removed from the Link; otherwise, the vertex g ij is removed from the Link. g ij is regarded as g i , repeat step 2) judgment for its child g ij ; 5)当前父级顶点gi的所有孩子遍历完毕,将顶点gi从Link中移除,继续对下一个顶点g(i+1)或下一个起始端点重复步骤2)判断,直到最后一个端点判断完毕,递归结束;5) After traversing all the children of the current parent vertex g i , remove the vertex g i from the Link, and continue to repeat step 2) for the next vertex g (i+1 ) or the next starting point until the last After the endpoint is judged, the recursion ends; (2-4)按以下规则对递归得到的场景模型进行筛选:(2-4) Screen the recursive scene model according to the following rules: 1)剔除模型内仅包含两个顶点和一个边的场景模型;1) Eliminate scene models that only contain two vertices and one edge in the model; 2)对于同一简化端点为起点或终点重建的多个场景模型,按以下原则进行剔除:2) For multiple scene models reconstructed with the same simplified endpoint as the starting point or ending point, the following principles are used to eliminate them: a)若模型的顶点集合元素数量相同,集合内顶点对应的地层年代属性Age分别相同,则保留对应的地层面面积大的场景模型;a) If the number of elements in the vertex set of the model is the same, and the stratum age attributes corresponding to the vertices in the set are the same respectively, then the corresponding scene model with a large stratum layer area is retained; b)若模型的顶点集合不完全相同,则保留集合元素较多的场景模型;b) If the vertex sets of the models are not exactly the same, then keep the scene model with more set elements; (2-5)对筛选后的场景模型进行重新编号,得到模型集合AllM={M1,M2,M3,…,Mq},q为场景模型数量。(2-5) Renumber the screened scene models to obtain a model set AllM={M 1 ,M 2 ,M 3 ,...,M q }, where q is the number of scene models. 4.根据权利要求1所述的单斜岩层构造地貌的自动识别方法,其特征在于:步骤(3)具体包括:4. the automatic recognition method of monoclinic strata structure topography according to claim 1, is characterized in that: step (3) specifically comprises: (3-1)模型Vonoroi单元剖分:根据步骤(2)得到的场景模型进行Voronoi单元剖分,剖分后的Voronoi多边形单元继承模型编号属性;(3-1) Model Vonoroi unit division: carry out Voronoi unit division according to the scene model that step (2) obtains, the Voronoi polygonal unit after the division inherits the model serial number attribute; (3-2)对模型进行褶皱筛选:对于相互邻接的Voronoi多边形单元中的场景模型Mi与Mj进行比较,若同时满足以下条件则判定两个场景模型组成完整褶皱,剔除这两个模型:(3-2) Perform fold screening on the model: compare the scene models M i and M j in adjacent Voronoi polygonal units, and if the following conditions are met at the same time, it is determined that the two scene models form complete folds, and these two models are eliminated : a)场景模型Mi与Mj的顶点集合一致;a) The vertex sets of the scene model M i and M j are consistent; b)按照一致的顶点顺序,两个场景模型的走向相反;b) According to the consistent vertex order, the direction of the two scene models is opposite; (3-3)完成褶皱筛选后保留的场景模型视为单斜模型,全图筛选后得到单斜模型集合FinM={FMh|h=1,2,3,…,w},w为单斜模型数量,FMh表示第h个单斜模型,FMh包括顶点集合FMGravh和边集合FMRelh(3-3) The scene model retained after the fold screening is regarded as a monoclinic model, and the monoclinic model set FinM={FM h |h=1,2,3,...,w} is obtained after the full image screening, where w is a monoclinic model The number of oblique models, FM h represents the hth monoclinic model, FM h includes the vertex set FMGrav h and the edge set FMRel h ; (3-4)对筛选后的模型集合FinM中每一个场景模型,进行地层面要素合并,得到对应的单斜面要素,形成单斜面要素集合Poly={ph|h=1,2,3,…,w},ph为场景模型FMh的单斜面要素。(3-4) For each scene model in the screened model set FinM, merge the stratum level elements to obtain the corresponding single-slope elements to form a single-slope element set Poly={p h|h =1,2,3, …,w}, ph is the single-slope element of the scene model FM h .
CN201611164730.0A 2016-12-16 2016-12-16 An automatic identification method for tectonic landforms of monoclinic rock formations Active CN106709439B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201611164730.0A CN106709439B (en) 2016-12-16 2016-12-16 An automatic identification method for tectonic landforms of monoclinic rock formations

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201611164730.0A CN106709439B (en) 2016-12-16 2016-12-16 An automatic identification method for tectonic landforms of monoclinic rock formations

Publications (2)

Publication Number Publication Date
CN106709439A true CN106709439A (en) 2017-05-24
CN106709439B CN106709439B (en) 2020-04-03

Family

ID=58938876

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201611164730.0A Active CN106709439B (en) 2016-12-16 2016-12-16 An automatic identification method for tectonic landforms of monoclinic rock formations

Country Status (1)

Country Link
CN (1) CN106709439B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111583776A (en) * 2020-04-28 2020-08-25 南京师范大学 A method for obtaining the development time sequence of intrusive rock mass

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101770026A (en) * 2009-01-07 2010-07-07 中国科学院电子学研究所 Method for estimating terrain by polarization interference of data of synthetic aperture radar and software thereof
CN102288944A (en) * 2011-05-12 2011-12-21 西安电子科技大学 Super-resolution height measuring method based on topographic matching for digital array meter wave radar
CN105956066A (en) * 2016-04-28 2016-09-21 南京师范大学 Automated identification method for fold landform type
CN106023197A (en) * 2016-05-18 2016-10-12 南京师范大学 Automated identification and extraction method of vertical stratum

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101770026A (en) * 2009-01-07 2010-07-07 中国科学院电子学研究所 Method for estimating terrain by polarization interference of data of synthetic aperture radar and software thereof
CN102288944A (en) * 2011-05-12 2011-12-21 西安电子科技大学 Super-resolution height measuring method based on topographic matching for digital array meter wave radar
CN105956066A (en) * 2016-04-28 2016-09-21 南京师范大学 Automated identification method for fold landform type
CN106023197A (en) * 2016-05-18 2016-10-12 南京师范大学 Automated identification and extraction method of vertical stratum

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
杨树文 等: ""基于SPOT 5图像的岩溶地貌单元自动提取方法"", 《国土资源遥感》 *
陈楹 等: "基于空间结构模式匹配的褶皱地貌类型自动识别", 《地球信息科学学报》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111583776A (en) * 2020-04-28 2020-08-25 南京师范大学 A method for obtaining the development time sequence of intrusive rock mass

Also Published As

Publication number Publication date
CN106709439B (en) 2020-04-03

Similar Documents

Publication Publication Date Title
Workman et al. Wide-area image geolocalization with aerial reference imagery
CN105574259B (en) A kind of Urban cognition ground drawing generating method based on internet word frequency
CN111858810B (en) A modeling elevation point screening method for road DEM construction
CN105258704A (en) Multi-scale space-time hot point path detection method based on rapid road network modeling
CN106204446B (en) Building merging method for topographic map
CN108197583A (en) The building change detecting method of optimization and image structure feature is cut based on figure
CN109993064B (en) Method for extracting connection path between road network nodes in picture
CN107679498A (en) A kind of airborne laser point cloud downtown roads recognition methods
CN102930561A (en) Delaunay-triangulation-based grid map vectorizing method
CN105956066B (en) A kind of automatic identification method of fold geomorphic type
CN105701848A (en) Automatic generation method of stratum boundary map layer
CN111062958A (en) Urban road element extraction method
CN106023197A (en) Automated identification and extraction method of vertical stratum
CN115829990B (en) A natural fracture identification method based on imaging logging image processing
Cao et al. Urban land use classification based on aerial and ground images
CN116152259B (en) A Calculation Method of Reservoir Permeability Based on Graph Neural Network
Zhang Practice Teaching of Landscape Survey Course Based on eCognition Remote Sensing Image Interpretation* Technology.
CN106339985A (en) A Method of Selecting Mosaic Lines from Vector Building Data to Aerial Image Mosaic
CN115937708A (en) A method and device for automatic identification of roof information based on high-definition satellite images
CN110363848A (en) A kind of method for visualizing and device of the pore network model based on digital cores
CN106709439A (en) Monocline structure landform automatic identification method
CN106777117A (en) A kind of automatic identifying method of horizontal stratum tectonic landform
CN111046884A (en) A method for extracting slope geological hazards based on multi-feature-assisted watershed algorithm
CN106504219A (en) Constrained path morphology high-resolution remote sensing image road Enhancement Method
US12211126B2 (en) Run-length stripping method for generating skeleton lines of complex plain river networks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant