CN114612779A - Geological mineral exploration method based on space-time big data analysis - Google Patents
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Abstract
The invention discloses a geological mineral exploration method based on space-time big data analysis, which comprises the following steps: acquiring an original satellite remote sensing image, acquiring basic unit distribution of the remote sensing image, performing hierarchical classification on the land cover type of the remote sensing image, acquiring a mineral resource distribution map, acquiring a mineral resource survey map and performing geological mineral survey; according to the invention, the original satellite remote sensing image is obtained by collecting the satellite remote sensing data of the area to be surveyed, the original satellite remote sensing image based on the space-time big data is obtained, the image quality is improved by carrying out image preprocessing on the original satellite remote sensing image, effective help is brought to geological mineral surveying work, the distribution map of mineral resources is richer and more comprehensive by carrying out hierarchical classification from coarse to fine on the land coverage type of the remote sensing image and supplementing, confirming and correcting the classification result, so that the geological mineral surveying and prospecting technology is effectively improved to a certain extent, and the accuracy and the efficiency of prospecting can be greatly improved.
Description
Technical Field
The invention relates to the technical field of geological exploration, in particular to a geological mineral exploration method based on space-time big data analysis.
Background
Geology generally refers to the properties and characteristics of the earth, mainly refers to the material composition, structure, development history and the like of the earth, and comprises the stratigraphic differences, physical properties, chemical properties, rock properties, mineral compositions, output states and contact relations of rock stratums and rock masses, the structure development history, the biological evolution history and the climate change history of the earth, and occurrence conditions and distribution rules of mineral resources, etc., mineral generally refers to all natural minerals or rock resources buried underground and available for human use, can be divided into metal, nonmetal, combustible organic and the like, is a non-renewable resource, and geological mineral exploration is based on advanced geological science theory, on the basis of occupying a large amount of field geological observation and collecting and arranging related geological data, the reliable geological mineral information data is obtained by adopting comprehensive geological means and methods such as geological measurement, physical exploration, pit drilling exploration engineering and the like.
With the continuous increase of the social demand for energy resources, energy shortage is caused, the development of the geological mineral exploration industry can bring more energy for national economic development and daily life of people, the geological mineral exploration method also arouses wide attention of people, but most of the existing geological mineral exploration methods are not fine enough, the topographic features of a geological mineral exploration area cannot be accurately and effectively obtained, and the mineral resource distribution map and the mineral resource exploration map of the geological mineral exploration area cannot be accurately obtained, so that the difficulty is brought to the geological mineral exploration and the mineral exploration is not facilitated, and the natural resources are not developed and utilized.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a geological mineral exploration method based on space-time big data analysis, which solves the problem that the mineral exploration and mineral finding are difficult in the geological mineral exploration because the mineral resource distribution map and the mineral resource exploration map of the precise geological mineral exploration area cannot be obtained in the existing geological mineral exploration method.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme: a geological mineral exploration method based on space-time big data analysis comprises the following steps:
the method comprises the following steps: collecting multispectral satellite remote sensing image data of a region to be surveyed by using an earth observation satellite, and performing space-time big data analysis and distant field delineation of a regional mineralization geological background by using multispectral data with the resolution of 5-10m to obtain an original satellite remote sensing image based on the space-time big data;
step two: firstly, carrying out image preprocessing on an original satellite remote sensing image by using an image enhancement algorithm, then carrying out basic processing and analysis on the remote sensing image by using a mathematical calculation and intelligent statistical algorithm, and realizing basic classification and information identification according to the difference of mathematical statistics of unknown ground objects on the remote sensing image in a characteristic space to obtain the basic unit distribution of the remote sensing image;
step three: under the support of geoscience knowledge, learning a sample data set of known mineral products in a region to be surveyed to obtain a nonlinear mapping network structure of a basic unit of the remote sensing image, and classifying the land coverage type of the remote sensing image from coarse to fine by using a lung-stabilizing data classification algorithm based on machine learning;
step four: under the support of a structured geoscience knowledge base, performing supplementary confirmation and correction on classification results in the aspect of real and obedience geoscience rules through a logistic regression reasoning algorithm based on the symbology, and then combining visual interpretation to obtain a mineral resource distribution map;
step five: the method comprises the steps of firstly extracting mineralization alteration abnormal information according to a mineral resource distribution map, predicting an ore finding target area, then carrying out basic geological investigation to obtain a mineral resource investigation map, and finally carrying out geological mineral investigation according to the obtained mineral resource investigation map.
The further improvement lies in that: in the second step, the image preprocessing comprises the following specific steps: the method comprises the steps of firstly carrying out image enhancement on an original satellite image, and then carrying out image cutting work including radiometric calibration, atmospheric correction, orthorectification and image fusion on the enhanced original satellite image by using a remote sensing image processing platform.
The further improvement lies in that: the method for enhancing the image of the satellite image is a frequency domain method, and specifically comprises the following steps: the method comprises the steps of firstly taking an original satellite image as a two-dimensional signal, carrying out signal enhancement based on two-dimensional Fourier transform on the two-dimensional signal, then removing noise in the image by adopting a low-pass filtering method, and then enhancing the edge of the image by adopting a high-pass filtering method, namely enhancing a high-frequency signal.
The further improvement lies in that: the method for enhancing the image of the satellite image is a spatial domain method, and specifically comprises the following steps: and calculating the image gray level of the original satellite image by adopting a median filtering method, and weakening the image noise.
The further improvement lies in that: the image cutting work comprises the following specific steps: the method comprises the steps of firstly converting DN output values stored by a satellite sensor of an earth observation satellite into radiation brightness values in an actual view field, then converting the brightness values after radiation calibration into actual earth surface reflectivity, then correcting deformed pixels on an image caused by topographic relief or sensor errors based on a digital elevation model, and then performing fusion processing on the image after the incidence correction by using an adaptive fusion algorithm.
The further improvement lies in that: in the fourth step, the structured geoscience knowledge base comprises a spectrum knowledge base, a terrain knowledge base and a land utilization knowledge base.
The further improvement lies in that: and fifthly, before geological mineral exploration, effectively predicting the accident situation with high probability in the geological mineral exploration process in advance, and making a corresponding emergency early warning scheme.
The further improvement lies in that: and fifthly, when geological mineral exploration is carried out, the mechanical equipment for exploration is kept in a flat and stable position, safety protection measures are adopted to prevent the foundation from collapsing and sliding, meanwhile, sufficient lighting and ventilation are guaranteed for the exploration operation environment, and the construction and application of drainage facilities are enhanced, so that water in a water-bearing layer is prevented from permeating into a construction site.
The invention has the beneficial effects that: the invention acquires the original satellite remote sensing image by acquiring the satellite remote sensing data of the area to be surveyed, obtains the fine and accurate topographic characteristics of the surveying area based on the analysis of space-time big data, ensures the precision of a mineral resource distribution diagram and a mineral resource surveying diagram, avoids the phenomena of geometric position deviation and radiation distortion of the acquired image data by carrying out image preprocessing on the original satellite remote sensing image, eliminates the deviation between the remote sensing data and the actual information of the earth surface, improves the image quality, brings effective help to the geological mineral surveying work, classifies the land coverage type of the remote sensing image from coarse to fine, and supplements, confirms and corrects the classification result, so that the mineral resource distribution diagram is richer and more comprehensive, thereby effectively promoting the geological mineral surveying and prospecting technology to a certain extent, and greatly improving the accuracy and the efficiency of prospecting, the actual demands of people on mineral resources in daily life after economic development are met, and the utilization rate of natural resources is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, the present embodiment provides a geological mineral exploration method based on spatiotemporal big data analysis, including the following steps:
the method comprises the following steps: collecting multispectral satellite remote sensing image data of a region to be surveyed by using an earth observation satellite, and performing space-time big data analysis and distant field delineation of a regional mineralization geological background by using multispectral data with 5m resolution to obtain an original satellite remote sensing image based on the space-time big data;
step two: firstly, carrying out image preprocessing on an original satellite remote sensing image by using an image enhancement algorithm, then carrying out basic processing and analysis on the remote sensing image by using a mathematical calculation and intelligent statistical algorithm, and realizing basic classification and information identification according to the difference of mathematical statistics of unknown ground objects on the remote sensing image in a characteristic space to obtain the basic unit distribution of the remote sensing image;
the image preprocessing comprises the following specific steps: firstly, carrying out image enhancement on an original satellite image, and then carrying out image cutting work including radiometric calibration, atmospheric correction, orthorectification and image fusion on the enhanced original satellite image by using a remote sensing image processing platform, wherein the method for carrying out image enhancement on the satellite image is a frequency domain method, and specifically comprises the following steps: the method comprises the following steps of firstly taking an original satellite image as a two-dimensional signal, performing signal enhancement based on two-dimensional Fourier transform on the two-dimensional signal, then removing noise in the image by adopting a low-pass filtering method, and then enhancing the edge of the image by adopting a high-pass filtering method, namely enhancing a high-frequency signal, wherein the image cutting work comprises the following specific steps: firstly, a DN output value stored by a satellite sensor of an earth observation satellite is converted into a radiation brightness value in an actual view field, then the brightness value after radiation calibration is converted into an actual surface reflectivity, then based on a digital elevation model, a deformed pixel on an image caused by topographic relief or sensor error is corrected, and then an adaptive fusion algorithm is utilized to perform fusion processing on the image after the radiation correction;
step three: under the support of geoscience knowledge, learning a sample data set of known mineral products in a region to be surveyed to obtain a nonlinear mapping network structure of a basic unit of the remote sensing image, and classifying the land coverage type of the remote sensing image from coarse to fine by using a lung-stabilizing data classification algorithm based on machine learning;
step four: under the support of a structural geoscience knowledge base, supplementing, confirming and correcting a classification result from the perspective of real and obeying geoscience rules through a logistic regression reasoning algorithm based on symbology, and then combining visual interpretation to obtain a mineral resource distribution map, wherein the structural geoscience knowledge base comprises a spectrum knowledge base, a terrain knowledge base and a land utilization knowledge base;
step five: the method comprises the steps of firstly extracting mineralization alteration abnormal information according to a mineral resource distribution map, predicting an ore-finding target area, then carrying out basic geological investigation to obtain a mineral resource exploration map, finally carrying out geological mineral exploration work according to the obtained mineral resource exploration map, effectively predicting accident conditions with high probability in the geological mineral exploration process before the geological mineral exploration work, and making a corresponding emergency early warning scheme.
Example two
Referring to fig. 1, the present embodiment provides a geological mineral exploration method based on spatiotemporal big data analysis, including the following steps:
the method comprises the following steps: collecting multispectral satellite remote sensing image data of a region to be surveyed by using an earth observation satellite, and performing space-time big data analysis and distant field delineation of a regional mineralization geological background by using multispectral data with 10m resolution to obtain an original satellite remote sensing image based on the space-time big data;
step two: firstly, carrying out image preprocessing on an original satellite remote sensing image by using an image enhancement algorithm, then carrying out basic processing and analysis on the remote sensing image by using a mathematical calculation and intelligent statistical algorithm, and realizing basic classification and information identification according to the difference of mathematical statistics of unknown ground objects on the remote sensing image in a characteristic space to obtain the basic unit distribution of the remote sensing image;
the image preprocessing comprises the following specific steps: firstly, carrying out image enhancement on an original satellite image, and then carrying out image cutting work including radiometric calibration, atmospheric correction, orthorectification and image fusion on the enhanced original satellite image by using a remote sensing image processing platform, wherein the method for carrying out image enhancement on the satellite image is a spatial domain method, and specifically comprises the following steps: calculating the image gray level of an original satellite image by adopting a median filtering method, and weakening the image noise, wherein the specific formula is g (x, y) ═ f (x, y) × h (x, y), wherein f (x, y) is the original image, h (x, y) is a space conversion function, g (x, y) represents the processed image, and the image cutting work comprises the specific steps of: firstly, a DN output value stored by a satellite sensor of an earth observation satellite is converted into a radiation brightness value in an actual view field, then the brightness value after radiation calibration is converted into an actual surface reflectivity, then based on a digital elevation model, a deformed pixel on an image caused by topographic relief or sensor error is corrected, and then an adaptive fusion algorithm is utilized to perform fusion processing on the image after the radiation correction;
step three: under the support of geoscience knowledge, learning a sample data set of known mineral products in a region to be surveyed to obtain a nonlinear mapping network structure of a basic unit of the remote sensing image, and classifying the land coverage type of the remote sensing image from coarse to fine by using a lung-stabilizing data classification algorithm based on machine learning;
step four: under the support of a structured geoscience knowledge base, performing supplementary confirmation and correction on classification results in the aspect of reality and geoscience law obeying through a logistic regression reasoning algorithm based on the symbology, and then combining visual interpretation to obtain a mineral resource distribution map, wherein the structured geoscience knowledge base comprises a spectrum knowledge base, a terrain knowledge base and a land utilization knowledge base;
step five: the method comprises the steps of firstly extracting mineralization alteration abnormal information according to a mineral resource distribution map, predicting an ore-finding target area, then carrying out basic geological investigation to obtain a mineral resource exploration map, finally carrying out geological mineral exploration work according to the obtained mineral resource exploration map, effectively predicting accident conditions with high probability in the geological mineral exploration process before the geological mineral exploration work, and making a corresponding emergency early warning scheme.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A geological mineral exploration method based on space-time big data analysis is characterized by comprising the following steps:
the method comprises the following steps: collecting multispectral satellite remote sensing image data of a region to be surveyed by using an earth observation satellite, and performing space-time big data analysis and distant field delineation of a regional mineralization geological background by using multispectral data with the resolution of 5-10m to obtain an original satellite remote sensing image based on the space-time big data;
step two: firstly, carrying out image preprocessing on an original satellite remote sensing image by using an image enhancement algorithm, then carrying out basic processing and analysis on the remote sensing image by using a mathematical calculation and intelligent statistical algorithm, and realizing basic classification and information identification according to the difference of mathematical statistics of unknown ground objects on the remote sensing image in a characteristic space to obtain the basic unit distribution of the remote sensing image;
step three: under the support of geoscience knowledge, a nonlinear mapping network structure of a base unit of the remote sensing image is obtained by learning a sample data set of known mineral products of a region to be surveyed, and then the land coverage type of the remote sensing image is classified from coarse to fine by using a lung-stabilizing data classification algorithm based on machine learning;
step four: under the support of a structured geoscience knowledge base, performing supplementary confirmation and correction on classification results in the aspect of real and obedience geoscience rules through a logistic regression reasoning algorithm based on the symbology, and then combining visual interpretation to obtain a mineral resource distribution map;
step five: the method comprises the steps of firstly extracting mineralization alteration abnormal information according to a mineral resource distribution map, predicting an ore finding target area, then carrying out basic geological investigation to obtain a mineral resource investigation map, and finally carrying out geological mineral investigation according to the obtained mineral resource investigation map.
2. The geological mineral exploration method based on space-time big data analysis, as claimed in claim 1, wherein: in the second step, the image preprocessing comprises the following specific steps: the method comprises the steps of firstly carrying out image enhancement on an original satellite image, and then carrying out image cutting work including radiometric calibration, atmospheric correction, orthorectification and image fusion on the enhanced original satellite image by using a remote sensing image processing platform.
3. The geological mineral exploration method based on space-time big data analysis as claimed in claim 2, characterized in that: the method for image enhancement of the satellite image is a frequency domain method, and specifically comprises the following steps: the method comprises the steps of firstly taking an original satellite image as a two-dimensional signal, carrying out signal enhancement based on two-dimensional Fourier transform on the two-dimensional signal, then removing noise in the image by adopting a low-pass filtering method, and then enhancing the edge of the image by adopting a high-pass filtering method, namely enhancing a high-frequency signal.
4. The geological mineral exploration method based on space-time big data analysis as claimed in claim 2, characterized in that: the method for enhancing the image of the satellite image is a spatial domain method, and specifically comprises the following steps: and calculating the image gray level of the original satellite image by adopting a median filtering method, and weakening the image noise.
5. The geological mineral exploration method based on space-time big data analysis as claimed in claim 2, characterized in that: the image cutting work comprises the following specific steps: the method comprises the steps of firstly converting DN output values stored by a satellite sensor of an earth observation satellite into radiation brightness values in an actual view field, then converting the brightness values after radiation calibration into actual earth surface reflectivity, then correcting deformed pixels on an image caused by topographic relief or sensor errors based on a digital elevation model, and then performing fusion processing on the image after the incidence correction by using an adaptive fusion algorithm.
6. The geological mineral exploration method based on space-time big data analysis, as claimed in claim 1, wherein: in the fourth step, the structured geoscience knowledge base comprises a spectrum knowledge base, a terrain knowledge base and a land utilization knowledge base.
7. The geological mineral exploration method based on space-time big data analysis, as claimed in claim 1, wherein: and fifthly, before geological mineral exploration, effectively predicting the accident situation with high probability in the geological mineral exploration process in advance, and making a corresponding emergency early warning scheme.
8. The geological mineral exploration method based on space-time big data analysis, as claimed in claim 1, wherein: and fifthly, when geological mineral exploration is carried out, the mechanical equipment for exploration is kept in a flat and stable position, safety protection measures are adopted to prevent the foundation from collapsing and sliding, meanwhile, sufficient lighting and ventilation are guaranteed for the exploration operation environment, and the construction and application of drainage facilities are enhanced, so that water in a water-bearing layer is prevented from permeating into a construction site.
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