CN111445541B - Mineral raster image information extraction and evaluation method - Google Patents
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Abstract
The application discloses a mineral raster image information extraction and evaluation method, which comprises the steps of processing an original image, establishing a numerical fitting equation by utilizing RGB colors and element content values Y, obtaining content values of different elements represented by different RGB colors, extracting various elements and content matrixes in minerals, realizing separation of mineral phases, simultaneously extracting element distribution characteristics of different mineral phases, having the advantages of various data display modes, section cutting display, element ratio surface distribution analysis and the like, more intuitively displaying distribution patterns of different elements in mineral distribution characteristics and multiple elements in different mineral positions, facilitating later data analysis and interpretation, better developing mineral geochemistry research, and realizing editing and information enhancement processing of a raster image.
Description
Technical Field
The application relates to the technical field of mineral images, in particular to a mineral raster image information extraction and evaluation method.
Background
Analysis of solid samples is generally carried out by total analysis, e.g. the principal element analysis is measured by the XRF melt-spun method or the cake-pressing method, and the trace elements are measured by ICP-MS. With the progress of in-situ analysis technology, in-situ element content analysis can be performed on geological samples, particularly minerals, for example, main element analysis is performed through an Electron Probe (EPMA), main trace element analysis is performed through Secondary Ion Mass Spectrometry (SIMS) or laser ablation plasma mass spectrometry (LA-ICP-MS), and the like, so that an original raster image of a certain element is obtained, and the image expresses the content of the element in different minerals and is a semi-quantitative expression mode. Because the components of different minerals in the thin slice are greatly different, elements in different positions of the same mineral are also different, and the expression of a certain element in the mineral in the same picture can cover important information, so that the original raster image is difficult to completely express rich geochemical information. In addition, the original raster image is the expression condition of a certain element in all minerals in the original slice, and because the content of the element before the minerals is greatly changed, the separation of different mineral phases is difficult to realize by the practical mode, and simultaneously, the zone information of the minerals is covered, so that the more accurate content of the main trace elements in the minerals cannot be obtained.
The spatial distribution of the mineral element components is the most critical element for understanding the mineral growth process. However, most of the conventional analysis methods in common use cannot meet the requirement of element surface scanning, or the spatial resolution is not high, and the element change characteristics on the 2D scale of the mineral, especially the spatial distribution characteristics of trace elements, cannot be precisely described. The research on the spatial distribution characteristics of the trace elements has wide application potential in a plurality of earth science fields, such as the research on ancient climatology (the research on odontolith, stalagmite and coral), the petrology, the mineral deposit science, the archaeology, the ancient biology, the anthropology (bones, teeth and the like) and the like. For example, the distribution profile of specific trace elements in the teeth of an ancient human may reveal early dietary changes characteristic of the ancient human; the ancient climate change characteristics and the like are inverted through the trace element change characteristics recorded by the growth textures of the stalagmite shoots or the corals; the change of trace elements in mineral rocks can be used for tracing magma, fluid evolution characteristics and the like, more and more students begin to use the techniques such as EPMA, SIMS or LA-ICP-MS to carry out surface scanning work on odontolith, stalagmite, rock, bone and the like, and the distribution conditions of main elements (Si, ti, al, mg, ca, mn, na, K, P) and trace elements (Sc, V, co, ni, cu, ga, rb, sr, Y, zr, nb, cs, ba, hf, ta, pb, th, U, la, ce, pr, nd, sm, eu, gd, tb, dy, ho, er, tm, yb, lu and the like) on the surface of the odontolith, stalagmite, rock, bone and the like can be obtained through the work.
In conclusion, the techniques such as EPMA, SIMS or LA-ICP-MS can obtain the raster image of the main trace elements on the surface of the mineral, the raster image data is different from the vector graphic data, the advantages of the raster image data are that the data structure is simple, the edibility is poor, however, the raster image data is not processed and analyzed well in the prior art, and richer geological information (such as mineral classification, full expression of element zones of the mineral, multi-element combined expression and the like) is highlighted.
Disclosure of Invention
Aiming at the following defects of the mineral element grid image obtained by EPMA, SIMS or LA-ICP-MS and other technologies: (1) separation of different mineral phases is not possible; (2) the zone information of a single mineral cannot be extracted; (3) The section change, the element ratio surface distribution, the multi-element graphic display and the like of a certain element cannot be clearly expressed. The invention provides a mineral raster image information extraction and evaluation method, which realizes the identification and separation of mineral phases by performing the processes of data analysis, extraction, enhancement and the like on raster images, can well identify the hidden information such as growth ring zones, trace element distribution rules and the like in the same minerals, better assists in developing the geochemistry research of the minerals and has stronger research and practical values.
According to an aspect of an embodiment of the present invention, there is provided a method of mineral raster image information extraction and evaluation, comprising the steps of:
s101, processing mineral crystal forms and color levels in mineral raster images through image processing software to obtain mineral main and trace element raster images and color level images thereof;
s102, decomposing the color gradation image data into R component data, G component data and B component data, and automatically assigning Y data from large to small;
s103, according to the fitting relation established between the R, G and B component data and the Y data, solving fitting parameters and a fitting equation;
s104, performing information extraction, assignment and calculation on R, G and B component data in the mineral main and trace element raster images by using the established fitting equation to obtain mineral raster image information data;
s105, importing data into XMapTools software to combine fitting coefficient R 2 Carrying out data interpretation, comparing original images and extracting the effectiveness evaluation of information;
s106, if the mineral grid image belongs to the main quantity element, performing data correction on the obtained mineral grid image by using an electronic probe analysis result to obtain more accurate mineral oxide content; if the trace elements belong to the trace elements, acquiring element annulus distribution characteristics according to the operational relationship among the trace elements;
and S107, classifying different minerals in the main element according to the content difference of the mineral oxides by using a K mean statistical method.
Optionally, in S103, the fitting relationship includes polynomial fitting, least square fitting, and machine learning method fitting.
Optionally, a binary cubic polynomial fit of the form R, G and Y is performed
Y 1 ＝a+bR 1 +cG 1 +dR 1 G 1 +eR 1 2 +fG 1 2 +gR 1 2 G 1 +hR 1 G 1 2 +iR 1 3 +jG 1 3
Y 2 ＝a+bR 2 +cG 2 +dR 2 G 2 +eR 2 2 +fG 2 2 +gR 2 2 G 2 +hR 2 G 2 2 +iR 2 3 +jG 2 3
In the formula, a, b, c, d, e, f, g, h, i and j are fitting parameters; r 1 、G 1 、Y 1 Respectively representing element content values corresponding to R component data, G component data and Y in the color gradation image; r 2 、G 2 、Y 2 Respectively representing the estimated values of the element contents corresponding to the R component data, the G component data and the Y in the mineral raster image.
Optionally, in S105, the fitting reliability is determined according to the fitting coefficient R 2 Evaluation of the formula
In the formula, SS res Representing the sum of the squares of the differences of the true and predicted values; SS tot Representing the sum of the squares of the differences of the true values and the mean values.
Optionally, fitting coefficients R are used 2 More than or equal to 0.99 as a discrimination standard.
Optionally, in S106, the element annulus distribution characteristics include a rare earth distribution pattern map, a total rare earth element content Σ REE, a light rare earth element/heavy rare earth element LREE/HREE, and a degree of differentiation of light and heavy rare earth elements.
Has the advantages that: the invention provides a mineral raster image information extraction and evaluation method, which is characterized in that based on the relation between RGB color of each pixel point in a raster image and content value Y of different elements, the element content (Y value) of each raster unit in the raster image is obtained through simulation, and the method can be used for assigning values to each pixel point in the raster image according to the existing raster color scale to obtain an element matrix of the raster image and storing the element matrix in a txt document; the processing and enhancement of the raster image can be finally realized by processing the txt files of different elements in XMapTools software. The method has the following advantages: (1) separation of different mineral phases is realized; (2) Quantitative analysis of target minerals is realized, the minerals which are difficult to distinguish in scanning analysis are quantified, and the names of the minerals are determined according to the content; (3) The girdle of different elements in the same mineral can be enhanced; (4) The distribution characteristics of different elements in the minerals and rare earth distribution pattern diagrams of multiple elements in different mineral positions can be visually displayed.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method of extracting and evaluating mineral raster image information;
FIG. 2 is an original raster image of Si, ti, mg, al, ca, fe, mn, na, K, etc.
FIG. 3 is a graph showing the distribution of different mineral phases in muscovite quartz schists containing acetylene gas.
FIG. 4 shows FeO, caO and TiO of tourmaline in muscovite quartz schist containing tourmaline gas 2 、Na 2 O、MgO、Al 2 O 3 And FeO/(FeO + MgO), mgO/(FeO + MgO), na 2 O/(Na 2 O + CaO) profile.
FIG. 5 is a grid image of the distribution of trace element content in the original garnet.
FIG. 6 is a graph showing the distribution of the total content of trace elements and rare earth elements in garnet after grid data processing.
FIG. 7 is a graph of normalized rare earth partitioning patterns of spherulite at different positions in garnet.
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. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. 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.
Referring to fig. 1, an embodiment of the present invention provides a method for extracting and evaluating mineral raster image information, which establishes a corresponding fitting equation for a mineral raster image by using a relationship between RGB component data of a tone scale image and a content value Y, and extracts an element content value by applying the fitting equation to the mineral raster image, thereby finally realizing classification of different minerals and evaluation of element zone distribution information of different minerals. The raster image means that each pixel thereof represents RGB colors using 3 uint8 type integers, and the numerical range thereof is an integer 0 to an integer 255. The mineral raster image is composed of mineral images and corresponding tone scale images. The color level image is represented by different RGB colors to represent the content value Y of different elements in the mineral, and the color level image corresponds to the content value Y of different elements in the mineral one by one.
The method mainly comprises two closely related links of image information extraction and image information evaluation, and comprises the following steps:
(1) Image information extraction
In order to ensure the accuracy of data extraction, before extraction, the color gradation image and the mineral image in the mineral raster image need to be separated by using image processing software and stored separately in an image format.
Firstly, processing the color gradation image, decomposing R component data, G component data and B component data in the color gradation image, and automatically assigning Y to the color of the color gradation image according to the content of decomposition elements. Selecting a proper numerical fitting method (such as polynomial fitting, least square fitting, machine learning method fitting and the like), establishing a fitting relation between RGB and Y, solving fitting parameters and a fitting equation, and determining whether the application in the mineral image is feasible or not according to fitting reliability.
Taking polynomial fitting as an example, for simple expression, R, G and Y are used for carrying out binary cubic polynomial fitting in the form of
Y 1 ＝a+bR 1 +cG 1 +dR 1 G 1 +eR 1 2 +fG 1 2 +gR 1 2 G 1 +hR 1 G 1 2 +iR 1 3 +jG 1 3
Y 2 ＝a+bR 2 +cG 2 +dR 2 G 2 +eR 2 2 +fG 2 2 +gR 2 2 G 2 +hR 2 G 2 2 +iR 2 3 +jG 2 3
In the formula, a, b, c, d, e, f, g, h, i and j are fitting parameters (polynomial coefficients). R is 1 、G 1 、Y 1 Respectively representing element content values corresponding to R component data, G component data and Y in the color gradation image; r 2 、G 2 、Y 2 Respectively representing the estimated values of the element contents corresponding to the R component data, the G component data and the Y in the mineral image.
Fitting reliability according to fitting coefficient R 2 Evaluation of the formula
SS res The sum of the squares of the differences between the true and predicted values is represented, i.e. the error between the predicted and true values. SS tot The sum of the squares of the differences between the true values and the mean value, i.e. the squared difference of the true values, is represented. It should be noted that R 2 The value size represents the trustworthiness, i.e. how large or small the value is. According to the embodiment of the invention, the fitting coefficient R is used 2 More than or equal to 0.99 is used as a discrimination standard to decide whether the application in the mineral image is feasible or not.
And then processing the mineral image, separating R, G and B data in the mineral image in the same process as the processing process of the color gradation image, substituting the R, G and B data into the fitting parameters and the fitting equation to obtain content matrix information data of the mineral raster image corresponding to the color, and storing the content matrix information data in a txt format. And importing the txt format data into XMaptools software to combine the fitting coefficient R 2 And (5) data interpretation is carried out, and the extracted information is compared with the original image to carry out effectiveness evaluation.
(2) Image information evaluation
The main elements (Si, ti, mg, al, ca, fe, mn, na, K, etc.) of the mineral images were evaluated. Using the analysis result of the electronic probe to correct the obtained mineral grid image to obtain more accurate content of mineral oxide, such as SiO 2 、TiO 2 、MgO、Al 2 O 3 、CaO、FeO、MnO、Na 2 O、K 2 O, and the like. Then, different minerals (such as feldspar, quartz, cord stone, calcite, mica, tourmaline and the like) are classified according to the content difference of the mineral oxides by using a K mean statistical method (automatic classification), and the spatial distribution relation among the different minerals can be seen in the image.
The mineral images were evaluated for trace elements (rare earth element REEs, etc.). According to the operational relationship among the trace elements, the distribution characteristics of the element zones are obtained, such as rare earth partitioning pattern diagram, sigma REE (total content of rare earth elements), LREE/HREE (light rare earth element/heavy rare earth element, REE differentiation degree), (La/Sm) N (N stands for meteorite standardization, degree of light and heavy rare earth element differentiation), and further can be used for the chemistry of mineral crystalsAnd analyzing with crystal growth kinetics.
Example 1
And (4) extracting and evaluating a main element mineral raster image.
(1) EPMA obtains the main elements Si, ti, mg, al, ca, fe, mn, na, K and the like distribution grid image (figure 2) of the muscovite quartz schist containing tourmaline, which is the most original grid image, the image has the defects that the boundary lines of different mineral phases are fuzzy, the mineral rings are not clear, the displayed element content is a semi-quantitative result, the relative content is high and low, and different elements (such as SiO, for example) cannot be obtained 2 、TiO 2 、MgO、Al 2 O 3 、CaO、FeO、MnO、Na 2 O、K 2 O) quantitative results, or ratios (e.g., feO/(FeO + MgO), caO/(CaO + K) 2 O)) profile.
(2) Extracting information in an original image, establishing a numerical fitting equation by utilizing RGB colors and element content values Y, and obtaining matrixes of different elements Si, ti, mg, al, ca, fe, mn, na, K and the like represented by different RGB colors, wherein the matrixes of the elements are stored in a txt format;
(3) Data processing and interpretation, introducing the txt format Si, ti, mg, al, ca, fe, mn, na and K matrix into XMapTools software, correcting elements such as Si, ti, mg, al, ca, fe, mn, na and K according to mineral phases (such as feldspar, quartz, cord stone, calcite, mica, tourmaline and the like, and figure 3) with the existing standard point data by using a K mean value statistical method (automatic classification), and obtaining more accurate SiO 2 、TiO 2 、MgO、Al 2 O 3 、CaO、FeO、MnO、Na 2 O、K 2 The O content (figures 4 a-f), corresponding ratio data (such as FeO/(FeO + MgO), caO/(CaO + K2O) equimolar ratio, figures 4 g-i) can be obtained by matrix operation for the elements, different mineral phases are extracted from the extracted and enhanced images relative to the original images, the annulus characteristics are obviously enhanced, and meanwhile, the distribution diagram of different elements in mineral distribution characteristics and the distribution diagram of multiple elements in different mineral positions can be visually shown according to requirements, so that the later data analysis and interpretation are facilitated, and the geochemistry of minerals is better developedAnd (5) researching.
Example 2
And extracting and evaluating the raster image of the trace element minerals.
(1) The laser ablation plasma mass spectrometry (LA-ICP-MS) obtains a grid image (FIG. 5) of Sc, Y, la, ce, pr, nd, sm, eu, gd, tb, dy, ho, er, tm, yb, lu and other elements in garnet of a mineral area in Tibet, which is the most primitive grid image, and the mineral girdle of the image is clearer, but the rare earth partition pattern diagram, sigma REE (total content of rare earth elements), LREE/HREE (light rare earth element/heavy rare earth element, REE partition degree), (La/Sm) cannot be carried out N (N represents spherulite meteorite standardization, light and heavy rare earth element differentiation degree), thereby influencing the evaluation of mineral crystal chemistry and crystal growth kinetics.
(2) Extracting information in an original image, establishing a numerical fitting equation by utilizing RGB colors and element content values Y, and obtaining matrixes of different elements such as Sc, Y, la, ce, pr, nd, sm, eu, gd, tb, dy, ho, er, tm, yb, lu and the like represented by different RGB colors, wherein the matrixes of the elements are stored in a txt format;
(3) Data processing and interpretation, introducing the above-mentioned Sc, Y, la, ce, pr, nd, sm, eu, gd, tb, dy, ho, er, tm, yb, lu in txt format into XMapTools software, correcting the Sc, Y, la, ce, pr, nd, sm, eu, gd, tb, dy, ho, er, tm, yb, lu and other elements using the existing standard dot data to obtain relatively accurate Sc, Y, la, ce, pr, nd, sm, eu, gd, tb, dy, ho, er, tm, yb, lu contents, and obtaining the corresponding rare earth element total content (Σ REE) and rare earth distribution pattern diagram for these elements through matrix operation, and the corrected Y, ce, sm, and Σ REE contents are shown in fig. 6. A plot of the rare earth partitioning pattern at different positions in the garnet is shown in fig. 7. Compared with the original image, the extracted and enhanced image has obviously enhanced girdle characteristics, and simultaneously can visually display the distribution characteristics of different elements in the mineral and the rare earth distribution pattern diagram of multiple elements in different mineral positions according to the requirements, thereby facilitating the later data analysis and interpretation and better developing the geochemistry research of the mineral.
The invention provides a mineral raster image information extraction and evaluation method, which comprises the steps of processing an original image, establishing a numerical fitting equation by utilizing RGB colors and element content values Y, obtaining content values of different elements represented by different RGB colors, extracting a matrix of various elements and contents thereof in minerals, realizing separation of mineral phases, simultaneously extracting element distribution characteristics of different mineral phases, having various data display modes and section cutting display, and having the advantages of element ratio surface distribution analysis (CaO, feO, mnO, na2O, K2O) and the like, and more intuitively displaying mineral distribution characteristics of different elements and distribution diagrams (such as rare earth distribution mode diagrams) of multiple elements at different mineral positions, so that the method is convenient for later data analysis and interpretation, better develops mineral geochemical research, realizes editing and information enhancement processing of the raster image, has very strong research and practical values in the field of mineralogy, and is a novel or indispensable mineralogical auxiliary means and method.
In addition, the above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the merits of the embodiments. In the embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A mineral raster image information extraction and evaluation method is characterized by comprising the following steps:
s101, processing mineral crystal forms and color levels in mineral raster images through image processing software to obtain mineral main and trace element raster images and color level images thereof;
s102, decomposing the color gradation image data into R component data, G component data and B component data, and automatically assigning Y data from large to small;
s103, according to the fitting relation established between the R, G and B component data and the Y data, solving fitting parameters and a fitting equation;
s104, performing information extraction, assignment and calculation on R, G and B component data in the mineral main and trace element raster images by using the established fitting equation to obtain mineral raster image information data;
s105, importing the data into XMapTools software and combining the fitting coefficient R 2 Carrying out data interpretation, comparing original images and extracting the effectiveness evaluation of information;
s106, if the mineral grid image belongs to the main quantity element, performing data correction on the obtained mineral grid image by using an electronic probe analysis result to obtain more accurate mineral oxide content; if the trace elements belong to the trace elements, acquiring element annulus distribution characteristics according to the operational relationship among the trace elements;
and S107, classifying different minerals in the main element according to the content difference of the mineral oxides by using a K mean statistical method.
2. The method for extracting and evaluating mineral raster image information as claimed in claim 1, wherein in S103, the fitting relationship includes polynomial fitting, least squares fitting, machine learning method fitting.
3. The method of claim 2, wherein R, G and Y are used to perform a binary cubic polynomial fit in the form of a polynomial
Y 1 ＝a+bR 1 +cG 1 +dR 1 G 1 +eR 1 2 +fG 1 2 +gR 1 2 G 1 +hR 1 G 1 2 +iR 1 3 +jG 1 3
Y 2 ＝a+bR 2 +cG 2 +dR 2 G 2 +eR 2 2 +fG 2 2 +gR 2 2 G 2 +hR 2 G 2 2 +iR 2 3 +jG 2 3
In the formula, a, b, c, d, e, f, g, h, i and j are fitting parameters; r 1 、G 1 、Y 1 Respectively representing the element content values corresponding to the R component data, the G component data and the Y in the tone scale image; r 2 、G 2 、Y 2 Respectively representing the estimated values of the element contents corresponding to the R component data, the G component data and the Y in the mineral raster image.
4. The method for extracting and evaluating mineral raster image information as claimed in claim 1, wherein in S105, the fitting reliability is based on a fitting coefficient R 2 Evaluation of the formula
In the formula, SS res Representing the sum of the squares of the differences of the true and predicted values; SS tot Representing the sum of the squares of the differences of the true values and the mean values.
5. The method of claim 4, wherein the fitting coefficient R2 ≥ 0.99 is used as a criterion.
6. The method as claimed in claim 1, wherein in S106, the distribution characteristics of the elemental girdle comprise a rare earth partition pattern diagram, a total rare earth element content Σ REE, a light rare earth element/heavy rare earth element LREE/HREE, and a degree of differentiation between light and heavy rare earth elements.
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| CN106682377A (en) * | 2015-11-05 | 2017-05-17 | 核工业北京地质研究院 | Method for extracting and quantitatively estimating hyperspectra of drilling rock core altered mineral |
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| CN108573206A (en) * | 2017-10-11 | 2018-09-25 | 成都理工大学 | Metallogenic prognosis method based on analytic hierarchy process (AHP) |
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| WO2009113670A1 (en) * | 2008-03-13 | 2009-09-17 | 独立行政法人科学技術振興機構 | Method and device for visualizing distribution of local electric field |
| CN106682377A (en) * | 2015-11-05 | 2017-05-17 | 核工业北京地质研究院 | Method for extracting and quantitatively estimating hyperspectra of drilling rock core altered mineral |
| CN108573206A (en) * | 2017-10-11 | 2018-09-25 | 成都理工大学 | Metallogenic prognosis method based on analytic hierarchy process (AHP) |
| CN107796841A (en) * | 2017-10-27 | 2018-03-13 | 中国科学院地质与地球物理研究所 | A kind of method of high accuracy analysis olivine trace element |
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