(Institute of Remote Sensing and GIS, Chengdu University of Technology, Chengdu 6 10059)
The geological structural traces revealed by remote sensing images are the comprehensive results of crustal movement since geological history, including all previous structural changes and feature identification information related to mineralization. Therefore, layered analysis of remote sensing information field is an important means to effectively separate metallogenic information in model-free deposit prediction. The theoretical basis of model-free deposit prediction method is that the formation of deposit is the comprehensive result of various geological factors. Under the special geological environment, the multi-stage tectonic movement composite superposition area has the conditions to form large and super-large deposits. The existence of huge ore source bodies inevitably leads to significant differences between them and the surrounding environment (background) in material composition, structure and so on, including ore-forming rock assemblage, structural style, mineralization alteration, geophysical and geochemical field and even the earth biosphere above them caused by deep ore source anomalies. Therefore, this method does not need to have a known ore-bearing model or a known ore-bearing model as a basis for prediction in advance, but decomposes and extracts the above-mentioned metallogenic anomaly information and establishes a natural source model of ore-controlling geological bodies as an important way for scientific prospecting. Because this method does not need a known ore-bearing model or a known ore-bearing model as the basis for prediction, it has important theoretical significance and practical value not only for predicting deposits in new areas with low research level, but also for finding new types and large and super-large deposits in old areas with high research level.
Remote sensing geological stratification analysis, mathematical geology, remote sensing image stratification analysis, model-free deposit prediction method
1 Introduction
At present, in the research of metallogenic prognosis at home and abroad, the traditional prediction method of mineral deposit model based on similarity and analogy theory has been improved day by day after decades of practice, and remarkable results have been achieved in geological prospecting. However, the application premise of this method is that there must be a certain number of known ore-bearing models and non-ore-bearing models as the basis of analogy. Therefore, due to the limitation of data level, known deposit model and researchers, it is mainly suitable for areas with high geological research degree and easy to establish deposit model. Moreover, due to the limitation of similarity analogy theory, only a fairly large-scale deposit similar to the known deposit can be found, but it is difficult to find a new deposit type or a point-like large or super-large deposit. Therefore, with the increasing difficulty of ore prospecting, how to combine multiple disciplines, apply new technologies, new methods and new thinking to open up new fields and find hidden deposits and new types of deposits, especially large and super-large deposits, has become an urgent theoretical and methodological problem to be solved in geological prospecting at present and in the future. Therefore, through long-term prospecting practice, under the guidance of the theory of seeking differences and seeking common ground, we put forward a new idea of model-free deposit prediction method, and then further developed it to put forward the method theory of layered analysis of remote sensing information field and model-free deposit prediction. This method has been tried in different structural composite areas and achieved remarkable results [5 ~ 7, 10 ~ 13].
For example, in the study of structural composite deformation areas in Sichuan, Guizhou and other places, the author applied the "layered analysis method of remote sensing images", established multi-stage structural deformation fields in the study area through remote sensing image processing and layered analysis of folds and faults, especially horizontal and large joint systems, and combined with field research, inversed the structural stress fields in each stage [10 ~ 12], and gained new distinctive understanding.
In the structural analysis and metallogenic prediction of Langdai area in western Guizhou [12], the author selectively interpreted the main folds and their related transverse joint systems in this area by using aerial photos and satellite photos, and combined with field investigation, made a deep analysis of the structural deformation characteristics in this area. According to the macroscopic structural characteristics and the spatial distribution law of transverse large joints in this area, the deformation field of Langdai triangular structural pattern is established, and on this basis, the nodal stress field controlled by the triangular boundary condition (large fault) in Yanshan period in this area is obtained. It is characterized in that the stress and deformation intensity decrease in an arc from the edge and apex of the triangle to the center of the triangle, and the rock deformation occurs in strict accordance with the obtained stress network, forming folds of different orders at different parts with corresponding joints; The edges and vertices of the triangle are deformed strongly, and the folds tend to be flat when they extend to the middle of the triangle. Nine folds in different directions are embedded with each other, connected as a whole, partially compounded and coordinated, which controls the formation and distribution of different types of deposits in this area. For example, a series of iron ore deposits, lead-zinc deposits and mineralization points (related to hydrothermal activity in genesis) are concentrated near the two corners of the northwest and southwest triangular structures. The coal mines are relatively concentrated in the triangular structure. This is because the triangle vertex has the largest stress intensity and developed faults, which provides favorable space conditions for hydrothermal activity and mineralization enrichment, thus forming a series of metal deposits, but destroying the enrichment of coal and natural gas and failing to form industrial value deposits. However, in the triangular structure, the stress is weakened, the folds are gentle, the faults are undeveloped, and there is no ore-guiding and ore-storing structure, which is not conducive to metal mineralization, but the fold deformation is beneficial to coal mine enrichment. According to the analysis of regional ore-bearing beds, the source beds of iron ore and lead-zinc ore (Devonian and Carboniferous) are widely distributed in this area. According to the characteristics of deformation field and stress field of triangular structure in this area and the law of mineralization enrichment in northwest and southwest corners, it can be predicted that the same type of industrial deposits are expected to be found at the apex of triangular structure in the east. Secondly, regionally, the Paleozoic in Qianxi has the geological conditions for oil and gas generation. Coal-formed gas has been found in Panxian triangle structure in the southwest of the exploration area. The geological and structural conditions in this area are similar, and it is expected to find gas fields with industrial value in the middle of the triangle structure or in the dome structure.
3.3 Quantitative analysis and metallogenic prediction of linear information field and related information combination of different levels of remote sensing.
The "layered" quantification of linear volume field is based on the above-mentioned layered linear structure analysis results, using grid method to quantify and statistically analyze linear structures at all levels (combined with annular structures in metallogenic analysis), find out the statistical laws of linear and annular structures at all levels and stages and their internal relations with mineralization, and extract useful information quantitatively and semi-quantitatively. Different quantitative methods are used for different research purposes. In the remote sensing analysis and oil-gas prospect prediction of favorable oil-gas-bearing structures in Xichang area [13], based on the interpretation of linear structures and annular structural systems in the whole area, the author first quantifies the regional fault system, establishes the two-dimensional and three-dimensional color quantitative analytical models of the regional linear body field (fault system), and determines the relatively stable structural block (the second level of Xiaoxiangling-Mishi fault block) in the tectonic active area in combination with other data. Then, the selected area is subject to thematic image processing to extract relevant information. On this basis, the joint quantitative processing method of annular structure and its closely related annular and radial large joints (weighted by the reciprocal measurement of the distance from the ring center to the outer edge of the ring) is adopted, and the relevant parameters are selected in combination with geophysical seismic data, aeromagnetic gravity data and field investigation data, and the two-dimensional and three-dimensional color quantitative analytical natural models of local structures of important structural blocks are established through image graphic processing and GIS. Through the characteristic analysis and discrimination of information field, the boundary between local tectonic uplift (anomaly) and background is determined, and the relevant information of hidden structures in abdomen is quantitatively extracted as the basis for evaluating local structures and predicting oil and gas prospects. Finally, through comprehensive analysis, the oil and gas prospect is predicted.
3.4 Mathematical analysis of background and anomaly of remote sensing linear volume field
Remote sensing linear volume field contains many non-geological information and other irrelevant information. The purpose of mathematical analysis is to determine the background of linear volume field through mathematical geology, extract and enhance weak useful information, suppress noise interference, and find out (abnormal) signs with geological prospecting indication significance.
The exception is the background. The so-called background refers to the spatial trend of spatial variables in the research area and represents the overall distribution characteristics of variables in the research area. For linear volume field, with the regional variation trend of linear structure as the background, the annular and linear bodies reflecting the existence of local structures are anomalies, such as annular and radial large joint dense areas. There are many methods to determine the background, such as moving average method, trend analysis method and kriging method. Trend analysis decomposes the observed value into two parts: trend and residual. The former is the background, and the latter represents the abnormal characteristics of local changes. We can also study the background field through the autocorrelation analysis of mineralization marker variables and several other variables, and then decompose the anomalies in different background fields.
Background conditions can be divided into single background and multi-background according to the number of geological populations. The former refers to those areas that have only experienced geological processes once, and the values of various factors come from a population; The latter refers to the area that has experienced many geological processes, and the values of various factors come from multiple populations. The method of determining multi-background is complicated, but for the linear volume field caused by multi-period tectonic movement, if the multi-period linear volume is decomposed by layers first, then the background of each level and its differences can be treated as a single background, and then the background of each level (period) can be comprehensively analyzed, which can greatly simplify the problem and effectively improve the analysis accuracy.
Spatial and frequency domain filtering of remote sensing information field is an effective method to decompose and extract linear structure. Generally speaking, the low-frequency information obtained by low-pass filtering can reflect the information characteristics of deep (hidden geological structures) in remote sensing information field, while the high-frequency information obtained by Qualcomm filtering can reflect the characteristic information of surface and shallow geological structures. The linear volume field in different directions can be decomposed by different direction filtering templates [8]. Statistical analysis or special expressions after sharpening and binarization, such as spatial frequency map and density map, central symmetry map, dominance map, coefficient of variation map, spatial distance measurement map and histograms of various parameter types, can reduce human interference factors and effectively reflect and extract indicative characteristic parameters of geological structures and metallogenic anomalies.
Information entropy is a mathematical measure of complexity and inhomogeneity, which can be used to identify or quantitatively characterize the geological variation and unity of some ore-controlling factors in the field of remote sensing information. The characteristics of linear volume field are studied by relative entropy. If the geological variable takes the length or frequency (or length/frequency) of the linear body, the obtained spatial density entropy can represent the difference of the spatial distribution of the linear body density field. If the variable is the azimuth of a linear body, the obtained azimuth entropy can reveal the uneven change degree of the linear body field in azimuth distribution. The entropy anomaly of linear bodies can reflect the characteristics of multi-stage tectonic activity and spatial superposition.
Secondly, fractal theory and other methods are also useful tools to study the field of remote sensing information. Using fractal theory to study the spatial characteristics of linear and annular structures is helpful to find out the relationship between tectonic activity and mineralization and quantitatively describe the spatial variation law of related natural geological indicators.
3.5 Multi-source information synthesis and synthetic image processing supported by "3S" to quickly evaluate and optimize the prospecting target area.
In the study of large-scale metallogenic prognosis, for a large number of geological, physical, chemical and remote sensing data, multi-source information synthesis and composite image processing technology can quickly extract useful information and get intuitive image expression of the results, which has achieved good results in geological prospecting. However, the problems of this method are that the commonly used remote sensing images are the product of rough correction, and there is some distortion (image point displacement) in space; Moreover, geophysical and geochemical information itself has a certain spatial drift. If this distorted image is superimposed with the shifted information field, it will produce false comprehensive anomalies and sometimes lead people astray. The solution is to adopt multi-source information synthesis and composite image processing technology supported by three "S". Firstly, under the control of GIS and GPS control points, the remote sensing image is accurately corrected (especially the projection difference correction), and the orthographic projection remote sensing image map is made. Then, through the structural analysis of regionalized variables, the structural correlation of various geoscience information at different scales and levels is found out, and a unified random field conceptual model is established. Through GIS, all kinds of information are accurately matched in space, and computer visualization technology is fully used to establish the natural information source model of ore source body, and comprehensive prospecting information is quantitatively extracted, so as to achieve the purpose of rapid evaluation and optimization of prospecting target area. Using this method, the author made a metallogenic (gold) prediction in Nanjiang area [14], and achieved remarkable results.
Nanjiang area is in a special geological and structural environment, with good metallogenic geological conditions, many kinds of minerals and scattered occurrences. However, relatively speaking, the research on gold deposits is very low, and only two placer gold mineralization points have been found in Heping River, Wanjinshan in the west. In this study, the developed remote sensing orthophoto is used to make new discoveries and delineate the prospecting area through information extraction, data processing of geochemical data such as gold, silver and copper 14, multi-source information synthesis of geology, material, chemistry and remote sensing, and synthetic image processing. Through field investigation, it is found that placer gold mineralization is common in river valleys along the structural belt identified in image processing, and it is in a high anomaly area on the composite information map of gold geochemical anomaly and remote sensing image.
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