NL2028908B1 - Method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements - Google Patents
Method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements Download PDFInfo
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Abstract
The invention discloses a method for predicting seafloor hydrothermal metallogenic prospective area based on the features of multi-indeX elements; which comprises the following steps of: Feature information extraction; Target domain division; Data meshing; Statistical units selection; Statistical units assignment; According to the target domain of known mining points; the extraction results of above-mentioned features are screened for variable factors; then construct matriX for characteristic analysis; and calculate the weight of each factor; Substitute the weights into the overall target domain statistical unit matrix; calculate the metallogenic strength of each unit; and perform further test and analyze; delineared the metallogenic prospective area of sulfide. The invention has the advantages that the prediction method of the seafloor hydrothermal mineralization prospect area has higher credibility.
Description
Method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements
The invention belongs to the geological technical field of seafloor hydrothermal mineralization, and relates to a method for predicting seafloor hydrothermal metallogenic prospective area based on the features of multi-index elements.
At present, the evaluation of deep-sea sulfide resources is mainly performed based on data of topography, gravity anomaly, magnetic anomaly, earthquakes, tectonic and other data that obtained by actual investigation and information collection. The index data or weights used in the calculation process are basically obtained according to the preliminary statistical features, during which the pre-possesed knowledge dominants with too much intervention and resulting insufficient information extraction. Such problems can be seen from examples such as the three-steps tonnage model of seafloor massive sulfide (SMS) deposits (Hannington
M, Jamieson J, Monecke T, et al, 2011), weights-of-evidence method (Wen
Zhugqing et al, 2010; Chen Jianping et al, 2017; Fang Jie et al, 2015; Shao Ke, et al, 2015) and so on. As for the the vast submarine area, in the initial stage of submarine hydrothermal prospecting, the number of discovered occurrences is relatively small, and the way that the weights-of-evidence method using preliminary statistical features of occurrences and background environment to judge the metallogenic contribution of geological elements in the whole area will lead to many misjudgments. In addition to the tendency, features such as topography, gravity and magnetism are also uneven in local areas. And it is easy to overlook small micro-abnormalities which might be the signs and results of the existence of deposits or mining points by using basic statistical features for analysis, and by using contour lines for embodiment, etc. Furthermore, there is limited response of single geological environment factor to the features of submarine hydrothermal deposits, which cannot be used to indicate the features and distribution of metallogenic areas. Geological mineralization is often the result of the comprehensive action of many factors. Therefore, it is necessary to fully explore the micro-anomaly information implied by each factor, so as to comprehend the reflected ore-forming features of multiple angles, and it is also required to choose appropriate information evaluation methods for comprehensively utilizing multi-factor indicators to carry out important work in the prediction and evaluation of sulfide resources spatial distribution.
The purpose of the present invention is to provide a method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements, which adopts the technical scheme as carried out according to the following steps:
Step 1: Feature information extraction. Use unsupervised classification, slope method, accumulated gradient method, overlay analysis method and raster calculation method and so on to extract feature information of topography, free-air gravity anomaly, Bouguer gravity anomaly, magnetic anomaly and other information. The extracted topography features include surface roughness, landform classification, slope variability, surface undulation, topography valleys and ridges; The extracted free-air gravity anomaly features include gravity anomaly classification, linear low anomaly and linear high anomaly information of free-air gravity anomaly; The extracted Bouguer gravity anomaly features include
Bouguer gravity anomaly classification and micro linear Bouguer gravity anomaly linear micro; And the extracted magnetic anomaly features include magnetic anomaly classification and micro linear magnetic anomaly, etc.
Step 2: Target domain division. Obtain a comprehensive understanding of the environmental background of the target area and the relevant knowledge of the research target, divide the target area and boundary according to the research needs and selected feature analysis methods, and the characteristics contained in the geological background information of the study area, and conduct method tests respectively.
Step 3: Data meshing. Processing and meshing the data according to the research objectives, the accuracy of the data itself, the data distribution, etc;
Step 4: Statistical units selection. Divide the statistical units according to the research objectives, research scheme and data accuracy, the statistical units are adjacent to each other and cover the whole study area;
Step 5: Statistical unit assignment. Using spatial superposition method and grid calculation method to assign values to statistical units;
Step 6: For the known target domain of the mine site, the above feature extraction results are filtered into variable factors, then a feature analysis matrix is constructed, and the weights of each factor are obtained.
Step 7: According to the result of step 6, substitute the weights into the statistical unit matrix of the whole target domain, calculate the metallogenic strength of each unit, and perform further test and analyze, to delineate the metallogenic prospective area of sulfide.
The invention has the beneficial effects that the method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements has higher credibility.
The present invention will be described in detail with reference to specific embodiments.
The method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements comprises the following steps:
Step 1: ArcGIS platform was used and the functions of ISO Cluster Unsupervised
Classification, slope, raster calculator, direction, accumulation, etc were integrated to formulate tool modules. Unsupervised classification, slope calculation, gradient accumulation calculation, overlay analysis and raster mathematical calculation were carried out on topography, free-air gravity anomaly, Bouguer gravity anomaly, magnetic anomaly and other data, and feature information was extracted for information such as surface roughness, landform classification, slope variability, surface undulation, topography valleys and ridges. Gravity anomaly classification, linear low anomaly and linear high anomaly information of free-air gravity anomaly, Bouguer gravity anomaly classification and linear micro anomaly information of Bouguer gravity anomaly, magnetic anomaly classification and linear micro anomaly information of magnetic anomaly were extracted. In total, more than 20 feature information which can be used as factor variables are extracted;
Step 2: Combined with the research objectives, the target areas and boundaries were delineated by using the spatial data processing function of ArcGIS;
Step 3: Based on the transformation and processing functions of vector data and raster data of ArcGIS, the data were processed and meshed in different degrees according to the research objectives, the accuracy of the data itself, the data 5 distribution status, etc;
Step 4: Statistical units were divided to cover the whole study area, and ArcGIS spatial analysis function was used to assign values to the units;
Step 6: According to the result of step 5, the weights were substituted into the statistical units matrix of the whole target domain to calculate the metallogenic strength of each unit, further test and analyze were performed and the sulfide metallogenic prospective area was delineated.
Took the Atlantic ridge area as the research target to extract the features of topography, gravity anomaly, magnetic anomaly, rock and other data respectively, and conducted a series of processing and analysis for the spatial relationship of a single feature element to hydrothermal mineralization, and screened out variable factors. Comprehensive calculation of 17 selected variable factors was performed by using the feature analysis method, and 17 factor weights were obtained, then the distribution of mineralization favorability in the Atlantic ridge area was obtained after calculation.
The results of feature analysis showed that the mineralization advantage of the
Atlantic ridge area has a clear boundary, the area of middle and high latitudes in the North Atlantic are more favorable, and the area of low latitudes and high latitudes in the South Atlantic are more favorable. According to the analysis of rock and Bouguer gravity anomalies, the oceanic ridge of about 20-28° in the South
Atlantic Ocean is a region with relatively no loss of mantle. The rock type is mainly basic rock, the oceanic crust is thin, hydrothermal activity is easy to find, but the block size is relatively small. While in the low latitude area of the South
Atlantic Ridge, the mantle is depleted from 10° to 20° with relatively high topography, rich magma, deep hydrothermal eruption and the mineralization source, and the block size might be relatively large.
The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Any simple alterations, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention belong to the scope of the technical scheme of the present invention.
THE EMBODIMENTS DEFINING THE INVENTION ARE AS FOLLOWS: 1. A method for predicting seafloor hydrothermal metallogenic prospective area based on the features of multi-index elements, which is characterized by comprising the following steps:
Step 1: Feature information extraction;
Step 2: Target domain division;
Step 3: Data meshing;
Step 4: Statistical units selection;
Step 5: Statistical units assignment;
Step 6: For the known target domain of the mine site, the above feature extraction results are filtered into variable factors, then a feature analysis matrix is constructed, and the weights of each factor are obtained;
-
Step 7: Substitute the weights into the statistical unit matrix of the whole target domain, calculate the metallogenic strength of each unit, and perform further test and analyze, to delineate the metallogenic prospective area of sulfide. 2. A method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements according to embodiment 1, which is characterized in that step 1 is to extract feature information of information such as topography, free-air gravity anomaly, Bouguer gravity anomaly and magnetic anomaly by using unsupervised classification, slope method, accumulated gradient method, overlay analysis method and raster calculation method; The extracted topography features include surface roughness, landform classification, slope variability, surface undulation, topography valleys and ridges; The extracted free- air gravity anomaly features include gravity anomaly classification, linear low anomaly and linear high anomaly information of free-air gravity anomaly; The extracted Bouguer gravity anomaly features include Bouguer gravity anomaly classification and micro linear Bouguer gravity anomaly linear micro; And the extracted magnetic anomaly features include magnetic anomaly classification and micro linear magnetic anomaly. 3. A method for predicting seafloot hydrothermal metallogenic prospective area based on the features of multi-index elements according to embodiment 1 which is characterized in that step 2 is to delineate target domain and boundaries according to research needs and selected feature analysis methods, as well as the features contained in the geological background information of the study area, and carry out method tests respectively. 4. A method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements according to embodiment 1, which is characterized in that step 4 is to divided statistical units according to research objectives, research schemes and data accuracy, and the statistical units are adjacent to each other and covering the whole study area.
5. A method for predicting submarine hydrothermal metallogenic prospective area based on the features of multi-index elements according to embodiment 1, which is characterized in that step 5 is to assign values to statistical units by using spatial overlay method and raster calculation method.
Claims (5)
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