Remote Quantitative Analysis Method and Device for Modern Sedimentary Source-Sink System

Abstract

The present invention discloses a method and device for remote sensing quantitative analysis of a modern sediment source-sink system. The method includes: obtaining an optical remote sensing image and a digital elevation model image; identifying the boundaries of source-sink units based on the preprocessed optical remote sensing image and the digital elevation model image; and characterizing the sedimentary characteristics of each unit of the source-sink system by using the preprocessed optical remote sensing image and the digital elevation model image. The present invention makes full use of remote sensing digital elevation information and spectral information, introduces remote sensing technology into the analysis of modern sediment source-sink systems, compensates for the defect that other source-sink analysis methods cannot quantitatively extract sedimentary units, accurately obtains the outer boundaries of sediment source areas and the sediment transport channels of gully waterways, becomes a scientific and reliable new technical means for the analysis of modern sediment source-sink systems, helps to establish sedimentary models useful for oil and gas exploration and development production, and further improves the productivity and efficiency of oilfield exploration and development.

Description

Technical Field

[0001] The present invention relates to the technical field of modern sedimentology analysis, and in particular, to a method and device for remote sensing quantitative analysis of a modern sediment source-sink system. Background Art

[0002] The research on the "source-sink system" (Source to Sink, S2S) is the research forefront in the international sedimentology field and also a research hotspot in the current earth science field. It aims to understand the geological history from the perspective of geomorphic evolution and decipher the information of geomorphic evolution and geological history changes in the sedimentary record. Its thinking method is to analyze the sediment transport process and evolution history according to three processes: erosion - transportation - deposition.

[0003] In 1999, the US National Science Foundation (NFS) and the Joint Oceanographic Institutions (JOI) organized experts to formulate the "source to sink" scientific plan for the sedimentology and stratigraphy project group of the Continental Margins Program (MARGINS). The overall research goal of this plan is to identify the relationships between sediment production, transportation, deposition, and preservation processes at multiple temporal and spatial scales, from turbidity currents to deposition, from sediment clasts to sequence stratigraphy and basin analysis, etc., on the continental margin (Li Tiegang, 2003). Based on the S2S research plan, foreign scholars have achieved rich results in the research on the sedimentation process of the continental margin "source-sink" system. Foreign scholars focus on the influencing factors of sediment output, transformation, and accumulation from the source to the sink, and deeply explore the driving mechanism of the sediment process from the source to the sink. Semi-quantitative to quantitative research has been attempted on the source-sink system of oceanic and continental margin basins. In recent years, the source-sink system of sedimentary basins has also received extensive attention in China. In 2000, China launched the national major basic research program "Key Issues in the Formation and Evolution of China's Marginal Seas and Important Resources". Tentative research has been carried out on the continental margin "source-sink" system. In the Pearl River Estuary Basin in the northern South China Sea, a general model of the erosion - deposition process of the Miocene passive continental margin land - ocean margin - sink system has been established. Xu Changgui (2013) took the Bohai Sea area as an example and initially applied the research idea from "source" to "sink" to the study of the sedimentary system of faulted lake basins, and believed that the entire process of sediment being eroded, transported, and deposited in the provenance area could be regarded as a complete source-sink system to discuss the development mechanism of sandstone. Lin Changsong et al. (2015) identified three types of source-sink with specific provenance backgrounds, transportation channels, and corresponding sedimentary systems in the Cenozoic Pearl River Estuary Basin.

[0004] Through literature research, it is found that domestic and foreign research mainly focuses on the unit coupling of the source-sink system, the prediction of sedimentation pattern scale, and the analysis of its control factors, and mostly conducts qualitative and semi-quantitative analysis, while there is less quantitative research and fine dissection on the source-sink system.

[0005] In recent years, the space technology for earth observation has been continuously developed globally. The number and types of satellites at home and abroad have been increasing day by day. In addition to optical images with high revisit periods and high spatial resolutions, the data sources of topographic remote sensing images have also become increasingly rich, the expression of topographic information has become more and more refined, and remote sensing technology has the ability to conduct large-scale, fine and three-dimensional observations, and can accurately describe the spatial and geological characteristics of a complete modern sedimentary system from source to sink. However, there is no existing technology solution for applying remote sensing technology to the analysis of source-sink systems. Summary of the Invention

[0006] In order to solve at least one of the technical problems in the above background technology, the present invention proposes a method and device for remote sensing quantitative analysis of modern sedimentary source-sink systems.

[0007] To achieve the above object, according to one aspect of the present invention, a method for remote sensing quantitative analysis of modern sedimentary source-sink systems is provided. The method includes:

[0008] Obtain optical remote sensing images and digital elevation model images, and preprocess the optical remote sensing images and the digital elevation model images;

[0009] Identify the source-sink unit boundaries based on the preprocessed optical remote sensing images and digital elevation model images, wherein the source-sink unit boundaries include: provenance area boundaries, gully waterways, and sediment body boundaries;

[0010] Characterize the sedimentary characteristics of each unit of the source-sink system by using the preprocessed optical remote sensing images and digital elevation model images, wherein the sedimentary characteristics include: lithological and geomorphic characteristics of the provenance area, channel morphological characteristics of the transportation area, and sediment distribution characteristics of the deposition area.

[0011] Optionally, the identifying the source-sink unit boundaries based on the preprocessed optical remote sensing images and digital elevation model images includes:

[0012] Identify the provenance area boundaries based on basin analysis;

[0013] Identify the gully waterways based on flow direction and flow rate analysis;

[0014] Identify the sediment body boundaries based on digital elevation model image-assisted spectral indices.

[0015] To achieve the above object, according to another aspect of the present invention, a device for remote sensing quantitative analysis of modern sedimentary source-sink systems is provided. The device includes:

[0016] A remote sensing image preprocessing unit, configured to obtain optical remote sensing images and digital elevation model images, and preprocess the optical remote sensing images and the digital elevation model images;

[0017] A source-sink unit boundary recognition unit, configured to recognize the source-sink unit boundary based on the preprocessed optical remote sensing image and the digital elevation model image, wherein the source-sink unit boundary includes: a provenance area boundary, a gully watercourse, and a sediment body boundary;

[0018] A feature characterization unit, configured to characterize the sedimentation features of each unit of the source-sink system by using the preprocessed optical remote sensing image and the digital elevation model image, wherein the sedimentation features include: the lithological and geomorphic features of the provenance area, the channel morphology features of the transportation area, and the sediment distribution features of the deposition area.

[0019] Optionally, the source-sink unit boundary recognition unit includes:

[0020] A provenance area boundary recognition module, configured to recognize the provenance area boundary based on basin analysis;

[0021] A gully watercourse recognition module, configured to recognize the gully watercourse based on flow direction and flow rate analysis;

[0022] A sediment body boundary recognition module, configured to recognize the sediment body boundary based on the digital elevation model image assisted by spectral indices.

[0023] To achieve the above object, according to another aspect of the present invention, there is also provided a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned remote sensing quantitative analysis method for modern sediment source-sink systems are implemented.

[0024] To achieve the above object, according to another aspect of the present invention, there is also provided a computer-readable storage medium storing a computer program, and when the computer program is executed in a computer processor, the steps in the above-mentioned remote sensing quantitative analysis method for modern sediment source-sink systems are implemented.

[0025] The beneficial effects of the present invention are as follows: The present invention makes full use of remote sensing digital elevation information and spectral information, introduces remote sensing technology into the analysis of modern sediment source-sink systems, compensates for the defect that other source-sink analysis methods cannot quantitatively extract sedimentation units, accurately obtains the external boundary of the sediment source area and the sediment transportation channels of gully watercourses, and becomes a scientific and credible new technical means for the analysis of modern sediment source-sink systems, which helps to establish sedimentation models useful for oil and gas exploration and development production, and further improves the productivity and efficiency of oilfield exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0027] Figure 1 This is a schematic flow chart of the remote sensing quantitative analysis method for a modern sediment source-sink system of the present invention;

[0028] Figure 2 This is a schematic diagram of provenance boundary identification based on basin analysis;

[0029] Figure 3 This is a schematic diagram of valley waterway identification based on flow direction and flow analysis;

[0030] Figure 4 It is the pre-processed optical and DEM remote sensing image map;

[0031] Figure 5 It is the provenance area boundary map;

[0032] Figure 6 It is the vectorized map of river network extraction results;

[0033] Figure 7 This is the AFI extraction result of the alluvial fan spectral index;

[0034] Figure 8 It is the boundary map of the alluvial fan sedimentary body in the study area;

[0035] Figure 9 is the supervised classification result map of the study area;

[0036] Figure 10 This is the result map of lithologic information extraction in the study area;

[0037] Figure 11 It is the provenance area slope extraction map;

[0038] Figure 12 It is the provenance area slope extraction map;

[0039] Figure 13 This is the river classification result map;

[0040] Figure 14 It is the distribution map of the points where the slope of the main river is taken;

[0041] Figure 15 It is a lateral cross-section of the main river channel;

[0042] Figure 16 It is the section distribution map of the main river channel;

[0043] Figure 17 It is a remote sensing image map of an alluvial fan deposition area;

[0044] Figure 18 It is a directional filtering waterway linear enhancement map;

[0045] Figure 19 It is a remote sensing interpretation map of the distribution of alluvial fan deposition waterways;

[0046] Figure 20 It is a schematic diagram of the computer device in the embodiment of the present invention. Detailed implementation manners

[0047] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] It should be noted that the terms "including" and "having" in the description and claims of the present invention and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0050] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0051] The object of the present invention is to provide a remote sensing quantitative analysis method for modern sediment source-sink systems based on DEM (Digital Elevation Model), fully exploiting remote sensing digital elevation information and spectral information, introducing remote sensing terrain analysis technology into the field of modern source-sink system analysis, proposing a brand-new quantitative analysis method for modern source-sink systems, accurately obtaining the external boundary of the sediment source area and the sediment transport channels of gully waterways, quantitatively describing the sediment characteristics of source-sink system units, and providing a basis for analyzing the sedimentation process of source-sink systems. This helps geologists carry out scientific and systematic theoretical research on modern sedimentation, establish sedimentation models useful for oilfield exploration and development production, and thereby improve the productivity and efficiency of oilfield exploration and development.

[0052] Figure 1 is a flowchart of the remote sensing quantitative analysis method for the modern sediment source-sink system in an embodiment of the present invention. As Figure 1 shown, the remote sensing quantitative analysis method for the modern sediment source-sink system in this embodiment includes steps S1 to S3.

[0053] Step S1: Obtain optical remote sensing images and digital elevation model images, and preprocess the optical remote sensing images and the digital elevation model images.

[0054] The bands of the optical remote sensing images include visible light and near-infrared bands. The preprocessing of the optical images includes image mosaicking and image enhancement. Image mosaicking is to perform seamless splicing and color homogenization processing on multiple scenes of synchronous images, and image enhancement is to highlight the target features in the optical images through stretching processing.

[0055] The preprocessing of the digital elevation model (DEM) images includes peak shaving and depression filling. In the DEM matrix, depression cells refer to cells where the elevations of the adjacent 8 cells are not lower than the elevation of this cell, and peak cells refer to cells where the elevations of the adjacent 8 cells are not higher than the elevation of this cell. Depression filling and peak shaving are to replace the depression and peak cells with the values of the nearest cells to avoid breaks in the extracted water system and incorrect water flow directions caused by the depressions and peaks in the original DEM data.

[0056] In a specific embodiment of the present invention, the research area of the present invention is located in the northwestern margin area of the Junggar Basin, with longitude and latitude of 83°39′ - 84°49′ east longitude and 45°9′ - 45°52′ north latitude. The main water system is the Liushugou River, which is a perennial runoff river. The flow rate varies with seasons, and the period from May to July every year is the high-water period. According to the research requirements of the source-sink system, medium-resolution optical remote sensing images and digital elevation model (DEM) images of the research area are collected and preprocessed. The optical image selected is the Landsat 8 OLI image data with a spatial resolution of 30 meters. The image undergoes geometric correction, image mosaicking, and image enhancement preprocessing to obtain the processed result image with high-precision positioning and high-resolution recognition (as shown in Figure 4 ); the DEM image selected is the ASTER GDEM V2 DEM data with a resolution of 30 meters. After data peak shaving and depression filling processing, the values of the nearest cells are used to replace the depression and peak cells, effectively avoiding the breaks in the extracted water system and incorrect water flow directions caused by the peak depressions in the original DEM data, and forming the result image (as shown in Figure 4 ). Subsequent image analysis and feature extraction are all based on the result image data after preprocessing.

[0057] Step S2: Identify the source-sink unit boundary based on the preprocessed optical remote sensing image and the digital elevation model image. Among them, the source-sink unit boundary includes: the provenance area boundary, the gully watercourse, and the sediment body boundary.

[0058] In the embodiment of the present invention, identifying the source-sink unit boundary includes: identifying the provenance area boundary based on basin analysis; identifying the gully watercourse based on flow direction and flow rate analysis; and identifying the sediment body boundary based on the digital elevation model image-assisted spectral index.

[0059] In an embodiment of the present invention, identifying the provenance area boundary based on basin analysis includes: determining the water flow direction data and the accumulated water volume data according to the digital elevation model image, and performing basin analysis based on the water flow direction data and the accumulated water volume data to divide the river basin, and the obtained river basin range is the provenance area boundary.

[0060] Figure 2It is a schematic diagram for identifying the boundary of the provenance area based on basin analysis. In the embodiments of the present invention, the provenance area refers to the erosion area that can stably provide provenance for the basin during a specific historical period. The provenance area of the source-sink system should specifically be the area where the clastic parent rock exists in the upper and middle reaches of the river, that is, the basin coverage area of the upper and middle reach river networks. The identification of the provenance area boundary based on basin analysis first extracts the water flow direction data from the DEM data based on the D8 algorithm, and then calculates the runoff accumulation (flow) data. Finally, basin analysis is performed based on these data to divide the basin, and the obtained basin range is the provenance area boundary. The runoff accumulation is calculated from the confluence accumulation matrix based on the water flow direction data determined by the steepest slope descent method.

[0061] In the embodiments of the present invention, the identification of the provenance area boundary based on basin analysis first calculates the water flow direction of the grid cells in the DEM data. Surface runoff always flows from high to low in the basin space and finally discharges out of the basin through the basin outlet. In order to accurately delineate the basin boundary, the D8 algorithm is used to calculate the outflow direction of the water flow in each grid cell. The extraction of the source area boundary in the study area is based on basin analysis based on the flow direction data, and the watershed extraction result is used as the source area boundary. The result can be as Figure 5 shown.

[0062] In an embodiment of the present invention, the identification of the gully watercourse based on flow direction and flow analysis includes: determining the water flow direction data and runoff accumulation data according to the digital elevation model image, and extracting the river network based on the water flow direction data and runoff accumulation data according to the catchment area threshold, and identifying the gully watercourse.

[0063] Figure 3 It is a schematic diagram for identifying the gully watercourse based on flow direction and flow analysis. In the embodiments of the present invention, the identification of the gully watercourse based on flow direction and flow analysis is based on the water flow direction data and runoff accumulation (flow) data, and the river network is extracted according to the determined catchment area threshold. The catchment area threshold refers to the critical catchment area that can form and maintain the river channel, which can be determined by the river network density method or an empirical threshold.

[0064] In the embodiments of the present invention, for the identification of the gully watercourse based on flow direction and flow analysis, the water flow of the study area can be calculated. Assuming that each grid in the catchment area has a unit of water volume, it moves downward according to the grid flow direction, and the grid it passes through will increase its cumulative flow value by 1 unit. In this way, the upstream flow value accumulated by each grid can be calculated. The flow accumulation value represents the number of upstream catchment grid cells of each grid, and multiplying it by the grid area can obtain the upstream catchment area of each grid. The identification of the gully watercourse based on flow direction and flow analysis is to set an empirical catchment threshold for the flow accumulation grid, obtain the river network grid data through value attribute extraction, and finally perform river network vectorization extraction (such as Figure 6 ).

[0065] In one embodiment of the present invention, identifying the sediment body boundary based on the digital elevation model image assisted spectral index includes: calculating the alluvial fan spectral index through the red, green, blue, and near-infrared bands of the optical remote sensing image, and using the alluvial fan spectral index to extract the distribution range of the alluvial fan from the digital elevation model image; extracting the contour lines from the digital elevation model image, and overlaying the contour lines on the alluvial fan distribution range to determine the apex and lateral edge boundaries of the alluvial fan, so as to obtain the sediment body boundary.

[0066] In an embodiment of the present invention, for the sediment body boundary identification based on the DEM assisted spectral index, the distribution range of the alluvial fan is extracted by using the alluvial fan spectral index, and the contour lines generated by the DEM are overlaid on the alluvial fan distribution range to determine the accurate apex and lateral edge boundaries of the alluvial fan. The highest elevation point in the alluvial fan distribution range is the apex, and the contour line convex towards the point with higher elevation is the lateral edge of the fan. The formula for the alluvial fan spectral index AFI is as follows:

[0067] AFI = R / NIR - B / G

[0068] Where, R is the red band, G is the green band, B is the blue band, and NIR is the near-infrared band.

[0069] In an embodiment of the present invention, for the sediment body boundary identification based on the DEM assisted spectral index, by calculating the alluvial fan index AFI (AFI = R / NIR - B / G), as Figure 7 , the contour lines at an interval of 30 m in the study area are extracted from the digital elevation DEM data, the generated alluvial fan distribution range is overlaid on the contour lines, and the accurate apex and lateral edge boundaries of the alluvial fan are determined through the contour line trend. The highest elevation point in the alluvial fan distribution range is the apex, and the contour line convex towards the point with higher elevation is the low-lying trench. Connecting these points can accurately determine the lateral edge of the fan body, and obtain the alluvial fan sediment body boundary map of the study area (as Figure 8 ).

[0070] Step S3, using the preprocessed optical remote sensing image and the digital elevation model image to characterize the sedimentary characteristics of each unit of the source-sink system, where the sedimentary characteristics include: the lithological and geomorphic characteristics of the source area, the channel morphology characteristics of the transportation area, and the sedimentary distribution characteristics of the deposition area.

[0071] In the embodiments of the present invention, the main steps for characterizing the lithological features of the provenance area are vegetation index extraction, SAM (Spectral Angle) supervised classification, and PCA transformation analysis. To better distinguish the lithological areas in the study area, before extracting the lithology of the source area, it is necessary to extract the vegetation index of the study area. The vegetation index is calculated using the Normalized Difference Vegetation Index (NDVI). After the vegetation index extraction is completed, a spectral angle classifier can be used to perform supervised classification on the vegetation, water areas, and various lithologies in the study area. Different lithologies are identified and classified mainly by refining the empirical spectral characteristics of lithologies to distinguish the differences in different bands. Finally, the lithological information of the classified image is transformed through PCA transformation operations, and then the lithological representation color is adjusted through RGB transformation. The characterization of the geomorphic features of the provenance area is based on DEM data to calculate the slope and aspect within the source-sink area. The slope is the rate of change of the surface in the horizontal (dz / dx) and vertical (dz / dy) directions starting from the central pixel, describing the degree of inclination of the ground surface at that point. The aspect refers to the direction with the largest rate of change of values from the pixel to its adjacent pixels, describing the direction of the largest change in the elevation value at that point.

[0072] In a specific embodiment of the present invention, for the characterization of the lithological features of the provenance area, based on the processed Landsat 8 OLI remote sensing data, the NDVI vegetation index is extracted, and spectral angle supervised classification is performed on the study area. There are 8 lithologies in the study area, and the number of samples selected for each lithology is more than 20, as shown in Table 1 below. The results of the spectral angle supervised classification are as Figure 9 shown. After the supervised classification is completed, PCA transformation analysis is started to perform transformation extraction on the lithology, and a PCA transformation result map is obtained. Finally, color adjustment is performed through RGB color transformation to enable better differentiation between lithologies (as Figure 10 shown). For the characterization of the geomorphic features of the provenance area, the slope (as Figure 11 shown) and aspect (as Figure 12 shown) within the source-sink area are calculated based on DEM data.

[0073]

[0074]

[0075] Table 1 Table of lithology classification and sample selection in the study area

[0076] In the embodiments of the present invention, the characterization of the river channel morphological features in the handling area is based on the river network data extracted from the DEM to characterize feature parameters such as river channel grading, main river channel slope, river channel width-depth, and river channel curvature; river channel grading is a method of assigning level numbers to the connecting lines in the river network based on the DEM river grading, that is, the Strahler river grading method. The river channel slope is the elevation change along the downstream direction of the river. The main river channel slope map is a river channel slope surface map formed with the river section length as the abscissa and the corresponding point elevation as the ordinate; the river channel width-depth is described by the lateral profile of the river channel, that is, the cross-section perpendicular to the river channel is used to describe the geometric shape of the river channel width-depth; the river channel curvature is the ratio of the actual length of a river section to the straight-line length between the two endpoints of the river section, also known as the river bend coefficient of the river section.

[0077] In a specific embodiment of the present invention, the characterization of the river channel morphological features in the handling area is based on the river network data extracted from the DEM to characterize feature parameters such as river channel grading, main river channel slope, river channel width-depth, and river channel curvature River network grading is a method of assigning level numbers to the connecting lines in the river network. This level is a method of identifying and classifying river types according to the number of tributaries. River channel grading principle: In order to distinguish main and tributary rivers, the Strahler river grading method is often used for grading. This method can be described as follows: 1. Small rivers directly originating from the river source are first-level rivers; 2. The river formed by the confluence of two rivers of the same level has a higher level than the original; 3. The level of the river formed by the confluence of two rivers of different levels is the higher of the two rivers. And so on to the main stream, which is the river of the highest level in the river system. When performing river channel grading, the processing order of "from upstream to downstream" is adopted, and finally the river diversity result map of the study area is obtained (as Figure 13 shown).

[0078] The river channel slope is the elevation change along the downstream direction of the river. To calculate the main river channel slope, a river channel slope surface map is formed with the river section length as the abscissa and the corresponding point elevation as the ordinate (as Figure 14 , as shown in Table 2 below).

[0079]

[0080] Table 2 Main river channel slope table

[0081] The river channel width-depth is described by the lateral profile of the river channel, that is, the cross-section perpendicular to the river channel is used to describe the geometric shape of the river channel width-depth. The lateral slope of the river based on the DEM can show the geometric shape of the gully river channel width-depth. Randomly extract the lateral profiles of 6 sections of the main river channel for comparison as Figure 15 ; the curvature is the ratio of the actual length of a certain river section to the straight-line length of the river section, called the river bend coefficient of the river section. The extraction result of the main river channel curvature is as Figure 16 and Table 3 below.

[0082] River section number River section length Length of river section endpoints Curvature 1 1710.48851092000 1563.46998828000 1.09403348 2 1028.84498253000 965.70734216500 1.06537968 3 1162.49369024000 1101.60087509000 1.05527666 4 1375.12709117000 1151.51551759000 1.19418894 5 1377.48031960000 1013.92991117000 1.35855576 ... ... ... ... 30 2069.46639129000 1922.94299587000 1.07619747 31 2578.08773852000 2040.18535378000 1.26365368 32 4454.26931322000 3820.09588442000 1.16600982 33 5862.78520212000 5023.85440195000 1.16698947 34 9900.57790491000 8650.55081394000 1.1445026 35 19961.65649700000 17648.21624950000 1.13108635

[0083] Table 3 River channel curvature

[0084] In the embodiments of the present invention, characterizing the sedimentary distribution characteristics of a sedimentary area involves remote sensing interpretation and identification of sedimentary facies distribution. The main steps are establishing sedimentary microfacies identification interpretation markers, enhancing remote sensing images of sedimentary characteristics, and remote sensing interpretation of sedimentary microfacies. Based on existing sedimentary facies patterns and analysis of remote sensing images of the study area, a sedimentary microfacies system and microfacies identification interpretation markers are established. Remote sensing image enhancement is used to highlight the sedimentary characteristics of the microfacies, and remote sensing identification of sedimentary microfacies distribution is achieved based on the established interpretation markers.

[0085] In a specific embodiment of the present invention, the sediment distribution characteristics of the sedimentary area are characterized by remote sensing interpretation and identification of the distribution of sedimentary facies belts. The sedimentary microfacies of the alluvial fan sediment body water channel is the sedimentary skeleton of the fan body. Figure 17 The interpretation mark of the waterway is established, and the linear body along the main waterway direction is enhanced by the directional filtering method (as shown in Figure 18 ), highlighting the linear sedimentary characteristics of the channel microfacies, and realizing remote sensing identification of the distribution of alluvial fan channels based on enhanced images and established interpretation symbols (as shown in Figure 19 shown).

[0086] As can be seen from the above embodiments, the present invention provides a modern sediment source-sink system remote sensing quantitative analysis method based on DEM (digital elevation model), which makes full use of remote sensing digital elevation information and spectral information. The digital elevation information can display the spatial distribution and coupling relationship of each unit of the source-sink system through remote sensing terrain analysis technology, and the spectral information can assist in expressing the sedimentary characteristics of each unit of the source-sink system from multiple angles. Compared with other source-sink system analysis methods, the present invention has two significant features: 1) It innovatively introduces remote sensing technology for the first time, and based on the large-scale global and precise quantitative characteristics of remote sensing images, it transforms it into a fast and accurate technical means for modern sediment source-sink system analysis that is easy to implement technically; 2) The invention innovatively proposes a provenance identification technology for watershed analysis based on DEM, which makes up for the defect of other source-sink analysis methods that cannot quantitatively extract sedimentary units, accurately obtains the external boundaries of the sediment source area and the sediment transportation channels of the valley waterway, and quantitatively describes the sedimentary characteristics of the source-sink system units, making it a scientific and credible new technical means for modern sediment source-sink system analysis.

[0087] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0088] Based on the same inventive concept, an embodiment of the present invention further provides a remote sensing quantitative analysis device for a modern sediment source-sink system, which can be used to implement the remote sensing quantitative analysis method for a modern sediment source-sink system described in the above embodiments, as described in the following embodiments. Since the principle of the remote sensing quantitative analysis device for a modern sediment source-sink system to solve problems is similar to that of the remote sensing quantitative analysis method for a modern sediment source-sink system, the embodiments of the remote sensing quantitative analysis device for a modern sediment source-sink system can refer to the embodiments of the remote sensing quantitative analysis method for a modern sediment source-sink system, and the repeated parts will not be described again. As used hereinafter, the term "unit" or "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0089] In an embodiment of the present invention, the remote sensing quantitative analysis device for a modern sediment source-sink system of the present invention includes:

[0090] A remote sensing image preprocessing unit, configured to obtain an optical remote sensing image and a digital elevation model image, and preprocess the optical remote sensing image and the digital elevation model image;

[0091] A source-sink unit boundary recognition unit, configured to recognize the source-sink unit boundary based on the preprocessed optical remote sensing image and the digital elevation model image, wherein the source-sink unit boundary includes: a provenance area boundary, a gully watercourse, and a sediment body boundary;

[0092] A feature characterization unit, configured to characterize the sediment characteristics of each unit of the source-sink system by using the preprocessed optical remote sensing image and the digital elevation model image, wherein the sediment characteristics include: the lithological and geomorphic characteristics of the provenance area, the channel morphology characteristics of the transportation area, and the sediment distribution characteristics of the deposition area.

[0093] In an embodiment of the present invention, the source-sink unit boundary recognition unit includes:

[0094] A provenance area boundary recognition module, configured to recognize the provenance area boundary based on basin analysis;

[0095] A gully watercourse recognition module, configured to recognize the gully watercourse based on flow direction and flow rate analysis;

[0096] A sediment body boundary recognition module, configured to recognize the sediment body boundary based on the digital elevation model image-assisted spectral index.

[0097] In an embodiment of the present invention, the remote sensing image preprocessing unit includes:

[0098] A first processing module, configured to perform image mosaicking and image enhancement processing on the optical remote sensing image;

[0099] A second processing module for performing peak shaving and depression filling on the digital elevation model image.

[0100] In an embodiment of the present invention, the provenance area boundary recognition module is specifically configured to:

[0101] Determine water flow direction data and water accumulation data based on the digital elevation model image, and perform basin analysis based on the water flow direction data and water accumulation data to divide the basin, and the obtained basin range is the provenance area boundary.

[0102] In an embodiment of the present invention, the gully watercourse recognition module is specifically configured to:

[0103] Determine water flow direction data and water accumulation data based on the digital elevation model image, and extract the river network according to the catchment area threshold based on the water flow direction data and water accumulation data, and identify the gully watercourse.

[0104] In an embodiment of the present invention, the sediment body boundary recognition module is specifically configured to:

[0105] Calculate the alluvial fan spectral index through the red, green, blue, and near-infrared four bands of the optical remote sensing image, and use the alluvial fan spectral index to extract the alluvial fan distribution range from the digital elevation model image;

[0106] Extract the contour lines from the digital elevation model image, and overlay the contour lines on the alluvial fan distribution range to determine the fan apex and side edge boundaries of the alluvial fan, and obtain the sediment body boundary.

[0107] To achieve the above object, according to another aspect of the present application, a computer device is further provided. As Figure 20 shown, the computer device includes a memory, a processor, a communication interface, and a communication bus. A computer program that can run on the processor is stored on the memory. When the processor executes the computer program, the steps in the method of the above embodiment are implemented.

[0108] The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above types of chips.

[0109] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the corresponding program units in the method embodiments of the present invention described above. By running the non-transitory software programs, instructions, and modules stored in the memory, the processor executes various functional applications of the processor and processes the work data, that is, implements the method in the method embodiments described above.

[0110] The memory may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely provided relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0111] The one or more units are stored in the memory and, when executed by the processor, implement the method in the above embodiments.

[0112] Specific details of the above computer device can be understood by referring to the corresponding relevant descriptions and effects in the above embodiments, and will not be elaborated here.

[0113] To achieve the above object, according to another aspect of the present application, there is also provided a computer-readable storage medium storing a computer program, and when the computer program is executed in a computer processor, it implements the steps in the above modern deposition source-sink system remote sensing quantitative analysis method. Those skilled in the art can understand that to implement all or part of the processes in the above method embodiments, it can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the method embodiments as described above. Among them, the storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (abbreviation: HDD), or solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memories.

[0114] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0115] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A modern sediment source-sink system remote sensing quantitative analysis method, characterized by: include: Acquiring optical remote sensing images and digital elevation model images, and preprocessing the optical remote sensing images and the digital elevation model images; Identifying source-sink unit boundaries based on the pre-processed optical remote sensing image and the digital elevation model image, wherein the source-sink unit boundaries include: provenance area boundaries, valley waterways, and sedimentary body boundaries; The optical remote sensing image and the digital elevation model image after preprocessing are used to characterize the sedimentary characteristics of each unit of the source-sink system, wherein the sedimentary characteristics include: the lithologic geomorphological characteristics of the provenance area, the river channel morphological characteristics of the transportation area, and the sedimentary distribution characteristics of the sedimentary area; the steps of characterizing the lithologic characteristics of the provenance area include: extracting the vegetation index of the study area based on remote sensing data, and calculating the vegetation index using the normalized vegetation index; after the vegetation index is extracted, the spectral angle classifier is used to supervise the classification of the vegetation, water area and various lithologies in the study area, and the differences in different bands of different lithologies are extracted through empirical lithologic spectral characteristics to identify and classify them; finally, the lithologic information of the classified image is transformed by PCA transformation operation, and then the R GB transformation adjusts the color of lithologic representation; the geomorphological characteristics of the provenance area are characterized by calculating the slope and direction in the source-sink area based on DEM data; the river morphological characteristics of the transportation area are characterized by river classification, main channel slope, channel width and depth, and channel curvature based on river network data extracted from DEM; the sedimentary distribution characteristics of the sedimentary area are characterized by remote sensing interpretation and identification of sedimentary facies belt distribution, and the steps include: establishment of sedimentary microfacies identification and interpretation marks, sedimentary feature remote sensing image enhancement and sedimentary microfacies remote sensing interpretation. Based on the existing sedimentary facies belt model and remote sensing image analysis of the study area, a sedimentary microfacies system and microfacies identification and interpretation marks are established. The sedimentary characteristics of the microfacies are highlighted through remote sensing image enhancement processing, and remote sensing identification of sedimentary microfacies distribution is achieved based on the established interpretation marks.

2. The remote sensing quantitative analysis method of modern sediment source-sink system according to claim 1, characterized in that: The identifying of source-sink unit boundaries based on the pre-processed optical remote sensing image and the digital elevation model image includes: Identifying the provenance boundary based on basin analysis; identifying the valley waterway based on flow direction and flow analysis; The boundary of the sediment body is identified based on digital elevation model image-assisted spectral index.

3. The remote sensing quantitative analysis method of modern sediment source-sink system according to claim 1, characterized in that: The preprocessing of the optical remote sensing image and the digital elevation model image includes: performing image mosaicking and image enhancement processing on the optical remote sensing image; The digital elevation model image is subjected to peak clipping and depression filling processing.

4. The remote sensing quantitative analysis method of modern sediment source-sink system according to claim 2, characterized in that: The identifying the provenance boundary based on basin analysis includes: The water flow direction data and the water accumulation data are determined based on the digital elevation model image, and basin analysis is performed based on the water flow direction data and the water accumulation data to divide the watershed. The obtained watershed range is the boundary of the provenance area.

5. The remote sensing quantitative analysis method of modern sediment source-sink system according to claim 2, characterized in that: The identifying of the valley waterway based on flow direction and flow analysis includes: Water flow direction data and water accumulation data are determined according to the digital elevation model image, and a river network is extracted based on the water flow direction data and water accumulation data according to a catchment area threshold to identify the valley waterway.

6. The remote sensing quantitative analysis method of modern sediment source-sink system according to claim 2, characterized in that: The method of identifying the sedimentary body boundary based on the digital elevation model image-assisted spectral index includes: Calculating the alluvial fan spectral index using the red, green, blue and near-infrared bands of the optical remote sensing image, and extracting the alluvial fan distribution range from the digital elevation model image using the alluvial fan spectral index; Contour lines are extracted from the digital elevation model image, and the contour lines are superimposed on the alluvial fan distribution range to determine the fan top and side edge boundaries of the alluvial fan, thereby obtaining the sedimentary body boundary.

7. A modern sediment source-sink system remote sensing quantitative analysis device, characterized in that: include: A remote sensing image preprocessing unit, configured to obtain optical remote sensing images and digital elevation model images, and preprocess the optical remote sensing images and the digital elevation model images; a source-sink unit boundary identification unit, configured to identify source-sink unit boundaries based on the pre-processed optical remote sensing image and the digital elevation model image, wherein the source-sink unit boundaries include: provenance area boundaries, valley waterways, and sedimentary body boundaries; The feature characterization unit is used to characterize the sedimentary characteristics of each unit of the source-sink system using the pre-processed optical remote sensing image and the digital elevation model image, wherein the sedimentary characteristics include: the lithologic and geomorphologic characteristics of the provenance area, the river channel morphology characteristics of the transportation area, and the sedimentary distribution characteristics of the sedimentary area; the step of characterizing the lithologic characteristics of the provenance area includes: extracting the vegetation index of the study area based on remote sensing data, and calculating the vegetation index using the normalized vegetation index; after the vegetation index is extracted, using the spectral angle classifier to supervise the classification of the vegetation, water area and various lithologies in the study area, and extracting the differences between different lithologies in different bands through empirical lithologic spectral characteristics to identify and classify them; finally, performing lithologic information transformation on the classified image through the PCA transformation operation. , and then adjust the color of the lithologic representation through RGB transformation; the geomorphological characteristics of the provenance area are characterized by calculating the slope and direction in the source-sink area based on DEM data; the river morphological characteristics of the transportation area are characterized by river network data extracted from DEM, and the river classification, main river channel slope, river channel width and depth, and river channel curvature are characterized; the sedimentary distribution characteristics of the sedimentary area are characterized by remote sensing interpretation and identification of sedimentary facies belt distribution, and the steps include: establishment of sedimentary microfacies identification and interpretation marks, sedimentary feature remote sensing image enhancement and sedimentary microfacies remote sensing interpretation. According to the existing sedimentary facies belt model and remote sensing image analysis of the study area, the sedimentary microfacies system and microfacies identification and interpretation marks are established, the sedimentary characteristics of the microfacies are highlighted through remote sensing image enhancement processing, and the remote sensing identification of the sedimentary microfacies distribution is realized according to the established interpretation marks.

8. The modern sediment source-sink system remote sensing quantitative analysis device according to claim 7, characterized in that: The source-sink unit boundary identification unit includes: A provenance area boundary identification module, configured to identify the provenance area boundary based on basin analysis; A gully waterway identification module, configured to identify the gully waterway based on flow direction and flow analysis; The sediment boundary recognition module is used to identify the sediment boundary based on the digital elevation model image-assisted spectral index.

9. The modern sediment source-sink system remote sensing quantitative analysis device according to claim 7, characterized in that: The remote sensing image preprocessing unit includes: A first processing module is used to perform image mosaicking and image enhancement processing on the optical remote sensing image; The second processing module is used to perform peak clipping and valley filling processing on the digital elevation model image.

10. The modern sediment source-sink system remote sensing quantitative analysis device according to claim 8, characterized in that: The provenance area boundary identification module is specifically used to: The water flow direction data and the water accumulation data are determined based on the digital elevation model image, and basin analysis is performed based on the water flow direction data and the water accumulation data to divide the watershed. The obtained watershed range is the boundary of the provenance area.

11. The modern sediment source-sink system remote sensing quantitative analysis device according to claim 8, characterized in that: The gully and waterway identification module is specifically used to: Water flow direction data and water accumulation data are determined according to the digital elevation model image, and a river network is extracted based on the water flow direction data and water accumulation data according to a catchment area threshold to identify the valley waterway.

12. The modern sediment source-sink system remote sensing quantitative analysis device according to claim 8, characterized in that: The sediment boundary recognition module is specifically used to: Calculating the alluvial fan spectral index using the red, green, blue and near-infrared bands of the optical remote sensing image, and extracting the alluvial fan distribution range from the digital elevation model image using the alluvial fan spectral index; Contour lines are extracted from the digital elevation model image, and the contour lines are superimposed on the alluvial fan distribution range to determine the fan top and side edge boundaries of the alluvial fan, thereby obtaining the sedimentary body boundary.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed in a computer processor, the computer program implements the method according to any one of claims 1 to 6.

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