CN113554071A - Method and system for identifying associated minerals in rock sample - Google Patents
Method and system for identifying associated minerals in rock sample Download PDFInfo
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- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 147
- 239000011707 mineral Substances 0.000 title claims abstract description 147
- 239000011435 rock Substances 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000005065 mining Methods 0.000 claims abstract description 22
- 238000012216 screening Methods 0.000 claims abstract description 13
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 230000003595 spectral effect Effects 0.000 claims description 56
- 238000004422 calculation algorithm Methods 0.000 claims description 15
- 238000001514 detection method Methods 0.000 claims description 15
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 10
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 6
- 229910052949 galena Inorganic materials 0.000 claims description 6
- XCAUINMIESBTBL-UHFFFAOYSA-N lead(ii) sulfide Chemical compound [Pb]=S XCAUINMIESBTBL-UHFFFAOYSA-N 0.000 claims description 6
- 238000005192 partition Methods 0.000 claims description 6
- 229910052950 sphalerite Inorganic materials 0.000 claims description 6
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 5
- 229910019142 PO4 Inorganic materials 0.000 claims description 5
- 229910052770 Uranium Inorganic materials 0.000 claims description 5
- 229910052802 copper Inorganic materials 0.000 claims description 5
- 239000010949 copper Substances 0.000 claims description 5
- 239000013078 crystal Substances 0.000 claims description 5
- ZXOKVTWPEIAYAB-UHFFFAOYSA-N dioxido(oxo)tungsten Chemical compound [O-][W]([O-])=O ZXOKVTWPEIAYAB-UHFFFAOYSA-N 0.000 claims description 5
- 229910052742 iron Inorganic materials 0.000 claims description 5
- 229910052759 nickel Inorganic materials 0.000 claims description 5
- 239000010452 phosphate Substances 0.000 claims description 5
- NBIIXXVUZAFLBC-UHFFFAOYSA-K phosphate Chemical compound [O-]P([O-])([O-])=O NBIIXXVUZAFLBC-UHFFFAOYSA-K 0.000 claims description 5
- 229910052761 rare earth metal Inorganic materials 0.000 claims description 5
- 150000002910 rare earth metals Chemical class 0.000 claims description 5
- JFALSRSLKYAFGM-UHFFFAOYSA-N uranium(0) Chemical compound [U] JFALSRSLKYAFGM-UHFFFAOYSA-N 0.000 claims description 5
- 229910052845 zircon Inorganic materials 0.000 claims description 5
- GFQYVLUOOAAOGM-UHFFFAOYSA-N zirconium(iv) silicate Chemical compound [Zr+4].[O-][Si]([O-])([O-])[O-] GFQYVLUOOAAOGM-UHFFFAOYSA-N 0.000 claims description 5
- ZSLUVFAKFWKJRC-IGMARMGPSA-N 232Th Chemical compound [232Th] ZSLUVFAKFWKJRC-IGMARMGPSA-N 0.000 claims description 3
- 229910052776 Thorium Inorganic materials 0.000 claims description 3
- 230000001154 acute effect Effects 0.000 claims description 3
- 238000003708 edge detection Methods 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 235000010755 mineral Nutrition 0.000 description 112
- 238000001228 spectrum Methods 0.000 description 16
- 238000005070 sampling Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- RHDUVDHGVHBHCL-UHFFFAOYSA-N niobium tantalum Chemical compound [Nb].[Ta] RHDUVDHGVHBHCL-UHFFFAOYSA-N 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 241000132092 Aster Species 0.000 description 2
- 238000001675 atomic spectrum Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 229910052847 thorite Inorganic materials 0.000 description 2
- XSSPKPCFRBQLBU-UHFFFAOYSA-N thorium(iv) orthosilicate Chemical compound [Th+4].[O-][Si]([O-])([O-])[O-] XSSPKPCFRBQLBU-UHFFFAOYSA-N 0.000 description 2
- GYHNNYVSQQEPJS-UHFFFAOYSA-N Gallium Chemical compound [Ga] GYHNNYVSQQEPJS-UHFFFAOYSA-N 0.000 description 1
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052948 bornite Inorganic materials 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 229910052947 chalcocite Inorganic materials 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 229910052733 gallium Inorganic materials 0.000 description 1
- 229910052732 germanium Inorganic materials 0.000 description 1
- GNPVGFCGXDBREM-UHFFFAOYSA-N germanium atom Chemical compound [Ge] GNPVGFCGXDBREM-UHFFFAOYSA-N 0.000 description 1
- 229910052738 indium Inorganic materials 0.000 description 1
- APFVFJFRJDLVQX-UHFFFAOYSA-N indium atom Chemical compound [In] APFVFJFRJDLVQX-UHFFFAOYSA-N 0.000 description 1
- 235000011963 major mineral Nutrition 0.000 description 1
- 239000011738 major mineral Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 229910052718 tin Inorganic materials 0.000 description 1
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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Abstract
The invention discloses a method and a system for identifying associated minerals in a rock sample, which comprises the steps of collecting a cross-sectional image of the cross section of the rock sample; preprocessing, screening and segmenting the sectional images to obtain sub-mine images, wherein the sub-mine images form a first image set; classifying and identifying each sub-mine image in the first image set; calculating the number of associated ore images marked as associated ores in the first image set; when the number of the associated ore images exceeds a threshold value, the rock samples are judged to contain the associated ores, otherwise, the rock samples do not contain the associated ores, whether the rock samples contain the associated ores or not can be intelligently, quickly and accurately identified, so that the ores containing the associated ores can be quickly separated and screened, the mining value can be improved, and the method and the device are applied to the field of ore screening and classification.
Description
Technical Field
The disclosure belongs to the field of machine vision technology and ore identification, and particularly relates to a method and a system for identifying associated minerals in a rock sample.
Background
Associated minerals are present in certain deposits containing other minerals, and many minerals contain associated minerals. Associated ores are in the same ore deposit (ore body) and have no independent mining value, but can be mined and utilized together with main associated ores. Associated minerals are relatively major minerals that are often associated within the same deposit (ore body) due to their similar geochemical properties and common source of material.
Associated minerals are generally not found independently in independent minerals, such as chalcocite in bornite; or mixed minerals (main minerals) contained in main minerals, such as galena, sphalerite, tin, indium, gallium, germanium and the like, are only mined and separated under the condition of high value if the content of associated minerals is not high generally, and the associated minerals are difficult to mine because only a small part of a large amount of minerals are attached with the associated minerals, and most of the minerals do not contain the associated minerals, so that the separation and screening of the minerals with the associated minerals from the existing minerals are difficult, and the existing technical means are difficult to rapidly identify whether the mined minerals have the associated minerals.
Disclosure of Invention
The present invention is directed to a method and system for identifying associated minerals in a rock sample, which solves one or more of the problems of the prior art and provides at least one of the advantages of the present invention.
To achieve the above object, according to an aspect of the present disclosure, there is provided a method for identifying associated minerals in a rock sample, the method comprising the steps of:
s100, collecting a cross-section image of a rock sample cross section;
s200, preprocessing, screening and segmenting the sectional image to obtain each sub-mine area image, wherein each sub-mine area image forms a first image set;
s300, classifying and identifying each sub-mine image in the first image set;
s400, calculating the number of the associated ore images marked as associated ores in the first image set;
s500, judging that the rock sample contains the associated ores when the number of the associated ore images exceeds a threshold value, otherwise, judging that the rock sample does not contain the associated ores.
Further, in S100, the method of acquiring a cross-sectional image of the rock sample includes: and acquiring an image of the cross section of the rock sample by any one of a hyperspectral camera, a hyperspectral imager, a linear array CCD industrial camera and a near infrared light image sensor to obtain a cross section image.
Further, in S100, the rock sample includes any one ore of porphyry copper ore, wolframite, galena, sphalerite, iron ore, crystal ore, nickel ore, rare earth ore, tantalum niobium ore, zircon ore, phosphate ore, uranium ore, and thorite ore.
Further, in S200, the method for preprocessing, screening and segmenting the cross-sectional image to obtain each sub-mine area image, where each sub-mine area image constitutes the first image set, includes: the cross section image is subjected to Gaussian filtering and graying to obtain a gray level image, the gray level image is calculated by a watershed algorithm to obtain boundary points, the boundary points are connected to obtain edge lines, a water collecting basin region formed by the edge lines is used as a sub-mining area image, and a first image set is formed by the sub-mining area images.
Further, in S200, the method for preprocessing, screening and segmenting the cross-sectional image to obtain each sub-mine area image, where each sub-mine area image constitutes the first image set, includes: the cross-section image is subjected to Gaussian filtering and graying to obtain a gray level image, the gray level image is detected by a Sobel edge detection operator to obtain edge lines, a closed image area formed by the edge lines is used as a sub-mine area image, and a first image set is formed by the sub-mine area images.
Further, in S300, the method for classifying and identifying each sub-mine image in the first image set includes:
s301, let the first image set be G1 ═ G1iLet K be the number of images in the mine area in the first image set G1, and set variables i, j, i ∈ [1, K ∈]，G1iThe ith sub-mine image in the first image set is obtained; let i have a value of 1;
s302, using a corner detection algorithm to pair G1iThe corner detection is carried out to obtain G1iFrom each corner point to G1iThe geometric center point distances of (a) are ordered from small to large to obtain an ordered corner point set H1 ═ Hj}，HjIs G1iThe jth corner point in the set of corner points H1; let S be G1iThe number of corner points of (a) is made to be 1, i belongs to [1, S ∈](ii) a The corner detection algorithm is a Harris corner detection algorithm or a Shi-Tomasi corner detection algorithm;
s303, (because the sub-mine area images distributed on the ore represent different mineral areas, and the density of the mineral areas is different, resulting in different stresses, so that the shape of the sub-mine area image on the surface of the ore is in a cracked and stretched state, the directly obtained sub-mine area image is difficult to be accurately identified for the identification of associated ores, and the error identification rate is very high, therefore, if the sub-mine area image is to be accurately extracted, the following processing needs to be performed on the sub-mine area image to highlight the characteristics of the associated ores and the main minerals),
connection HjAnd Hj+1Obtain line segment L1, connection HjAnd Hj+2Obtain line segment L2, connection Hj+1And Hj+2The line segment L3 is obtained as HjAn included angle which is a peak and takes L1 and L2 as sides is & lt A & gt, and Hj+1An included angle of a peak and L1 and L3 as sides is ^ B and Hj+2An included angle which is a peak and takes L2 and L3 as sides is < C; hjSegment of the perpendicular line on side L3 or HjThe line segment of the line to the midpoint of L3 is C1;
s304, if any angle of the angle A, the angle B and the angle C is an obtuse angle, (if the sampled mineral is identified, namely associated ore and main ore in the image shape of the sub-ore areaThe objects are entangled together due to too small, if the sampled sub-mine area image is too small, the non-mineral area is easy to collect, so that the H needs to be correctedjPosition of) if point HjIf there is a projection point on the edge L3, the point H isjThe projection point onto the edge L3 is HPjIf point HjThe midpoint of L3 is HP if there is no projected point on the edge L3jI.e. at point HjThe point of intersection of the perpendicular to the side L3 and the side L3 or the midpoint of L3 is HPj；
Connection point HPjTo point HjForm a straight line C1 at point HjTo point HPjIs a first direction, with a point HPjTo point HjIs a second direction;
if < A is not an obtuse angle, HjIs moved along the straight line C1 in the first direction by a distance Δ L to update HjWhere Δ L ═ Max (D2) -Min (D1) |, Max (D2) being the largest element in the calculation set D2, Min (D1) being the smallest element in the calculation set D1;
sets D1 and D2 are respectively a maximum distance threshold value set and a minimum distance threshold value set between adjacent sampling points obtained from the step S3041 to the step S3043;
s3041: setting the initial value of the variable k to be 1, and setting the empty sets D1 and D2;
s3042: calculating a corner point H in an ordered set of corner points H1kTo corner point Hk+1Euclidean distance d1, corner point HkTo corner point Hk+2Euclidean distance d2, corner point Hk+1To corner point Hk+2The euclidean distance d 3; hkIs the Kth corner point in H1; adding the maximum value of D1, D2 and D3 into the set D1, and adding the minimum value of D1, D2 and D3 into the set D2;
s3043: if k +2 < S, increasing the value of k by 1 and transferring to the step S3042, otherwise, outputting the obtained maximum distance threshold value set D1 and minimum distance threshold value set D2;
if < A is an obtuse angle, HjIs moved in a second direction along the line C1 by a distance Δ L to update HjThe position coordinates of (a);
s305, if the angle A, the angle B and the angle C are acute angles, (the sampled minerals are identified, the sampled region is too small, the sampling of the main minerals and the associated minerals can be mixed to cause distortion, and the sampled region needs to be amplified), and a point H is madej+1The projection point on the side L2 is HPj+1Let point Hj+2The projection point on the side L1 is HPj+2Connection point HPj+1To point Hj+1Form a line C2 at a point HPj+1To point Hj+1Is a third direction; connection point HPj+2To point Hj+2Form a line C3 at a point HPj+2To point Hj+2Is a fourth direction; h is to bej+1Is moved by a distance Δ L in the third direction along the straight line C2, thereby updating Hj+1The position coordinates of (a); h is to bej+2Is moved a distance al in a fourth direction along the straight line C3, thereby updating Hj+2The position coordinates of (a);
s306, updating the vertex Hj、Hj+1And Hj+2The formed triangular area is marked as a mining area to be identified;
s307, three vertexes H of the mining area to be identified are obtainedj、Hj+1And Hj+2The spectral waveform data of the positions respectively extract the spectral waveform characteristics of the spectral waveform data of the three vertex positions, and the mineral type identification is carried out through matching of a spectral database according to the wavelength of the spectral waveform characteristic positions;
s308, if three vertexes Hj、Hj+1And Hj+2If the mineral type identified by the positions of any two vertexes is a first mineral, marking the mining area to be identified as a first mineral subarea; if three vertices Hj、Hj+1And Hj+2If the mineral type identified by the positions of any two vertexes is a second mineral, marking the mining area to be identified as a second mineral subarea; if j +2 < S, increasing the value of j by 1 and going to step S303, otherwise going to step S309;
s309, if i is less than K, increasing the value of i by 1 and transferring to the step S302, otherwise, finishing the classification and identification of each sub-mine image in the first image set.
Further, in S307, the method of acquiring the spectral waveform data of the mine area to be identified is: the spectral waveform data is obtained through any one of a ground feature spectrometer, a hyperspectral camera, a hyperspectral imager and a near-infrared spectrometer.
Further, in S307, the method for identifying the mineral type through matching of the spectral database according to the wavelength of the spectral waveform feature position includes:
extracting spectral waveform characteristics of spectral waveform data at the positions of the three vertexes, wherein the spectral waveform characteristics comprise first-order differential, second-order differential, wave crest and wave trough of a spectral waveform;
and carrying out wavelength matching on the spectral waveform characteristics of the minerals in the spectral database and the spectral waveform characteristics of the minerals in the spectral database to obtain matched minerals consistent with the spectral waveform characteristics of the spectral waveform data of the three vertexes.
Further, in S307, the spectrum database specifically includes a USGS spectrum database, an ASD atomic spectrum database, a JPL standard spectrum database, an ASTER spectrum database, a HIPAS spectrum database, and an JHU spectrum database, and further includes documents: zhangyingtong, Xiaoqing, build light by smelling, and the like, the development of a ground object spectrum database and the current application situation [ J ] the remote sensing academic newspaper, 2017,21(001):12-26, and any one of the spectrum databases related in the document.
Further, in S400, the method for calculating the number of associated ore images marked as associated ores in the first image set includes: respectively calculating the number Ta1 marked as a first mineral partition in the first image set and the number Ta2 marked as a second mineral partition in the first image set, and when Ta1 is more than Ta2, taking the first mineral as a main mineral and the second mineral as an associated mineral; when Ta1 is not more than Ta2, the first mineral is taken as associated mineral, and the second mineral is taken as main mineral; and calculating a first mineral subarea or a second mineral subarea of the associated mine in the first image set as the number of the associated mine images.
Further, in S308, the main mineral includes any one of copper ore, wolframite, galena, sphalerite, iron ore, crystal ore and nickel ore; the associated ore comprises any one of rare earth ore, tantalum-niobium ore, zircon ore, phosphate ore, uranium ore and thorium ore.
Further, in S500, the value range of the threshold is set to [0.2,0.5] times of the number of images of the main mineral in the first image set or the threshold is set to [5,20 ].
The invention also provides a system for identifying associated minerals in a rock sample, the system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the image acquisition unit is used for acquiring a cross-section image of the cross section of the rock sample;
the image segmentation unit is used for preprocessing, screening and segmenting the cross-section image to obtain sub-mine images, and the sub-mine images form a first image set;
the classification and identification unit is used for classifying and identifying each sub-mine image in the first image set;
the associated ore identification unit is used for calculating the number of the associated ore images marked as the associated ores in the first image set;
and the associated ore judging unit is used for judging that the rock sample contains the associated ore when the number of the associated ore images exceeds a threshold value, otherwise, the rock sample does not contain the associated ore.
The beneficial effect of this disclosure does: the invention provides a method and a system for identifying associated minerals in rock samples, which can intelligently, quickly and accurately identify whether each rock sample contains associated minerals, so that ores containing associated minerals can be quickly separated and screened out, and the mining value can be improved.
Drawings
The foregoing and other features of the present disclosure will become more apparent from the detailed description of the embodiments shown in conjunction with the drawings in which like reference characters designate the same or similar elements throughout the several views, and it is apparent that the drawings in the following description are merely some examples of the present disclosure and that other drawings may be derived therefrom by those skilled in the art without the benefit of any inventive faculty, and in which:
FIG. 1 is a flow chart of a method for identifying associated minerals in a rock sample;
fig. 2 is a diagram illustrating a system for identifying associated minerals in a rock sample.
Detailed Description
The conception, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, aspects and effects of the present disclosure. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, a flow chart of a method for identifying associated minerals in a rock sample is shown, and a method for identifying associated minerals in a rock sample according to an embodiment of the present invention is described below with reference to fig. 1, the method including the following steps:
s100, collecting a cross-section image of a rock sample cross section;
s200, preprocessing, screening and segmenting the sectional image to obtain each sub-mine area image, wherein each sub-mine area image forms a first image set;
s300, classifying and identifying each sub-mine image in the first image set;
s400, calculating the number of the associated ore images marked as associated ores in the first image set;
s500, judging that the rock sample contains the associated ores when the number of the associated ore images exceeds a threshold value, otherwise, judging that the rock sample does not contain the associated ores.
Further, in S100, the method of acquiring a cross-sectional image of the rock sample includes: and acquiring an image of the cross section of the rock sample by any one of a hyperspectral camera, a hyperspectral imager, a linear array CCD industrial camera and a near infrared light image sensor to obtain a cross section image.
Further, in S100, the rock sample includes any one ore of porphyry copper ore, wolframite, galena, sphalerite, iron ore, crystal ore, nickel ore, rare earth ore, tantalum niobium ore, zircon ore, phosphate ore, uranium ore, and thorite ore.
Further, in S200, the method for preprocessing, screening and segmenting the cross-sectional image to obtain each sub-mine area image, where each sub-mine area image constitutes the first image set, includes: the cross section image is subjected to Gaussian filtering and graying to obtain a gray level image, the gray level image is calculated by a watershed algorithm to obtain boundary points, the boundary points are connected to obtain edge lines, a water collecting basin region formed by the edge lines is used as a sub-mining area image, and a first image set is formed by the sub-mining area images.
Further, in S200, the method for preprocessing, screening and segmenting the cross-sectional image to obtain each sub-mine area image, where each sub-mine area image constitutes the first image set, includes: the cross-section image is subjected to Gaussian filtering and graying to obtain a gray level image, the gray level image is detected by a Sobel edge detection operator to obtain edge lines, a closed image area formed by the edge lines is used as a sub-mine area image, and a first image set is formed by the sub-mine area images.
Further, in S300, the method for classifying and identifying each sub-mine image in the first image set includes:
s301, let the first image set be G1 ═ G1iLet K be the number of images in the mine area in the first image set G1, and set variables i, j, i ∈ [1, K ∈]，G1iThe ith sub-mine image in the first image set is obtained; let i have a value of 1;
s302, using a corner detection algorithm to pair G1iThe corner detection is carried out to obtain G1iFrom each corner point to G1iThe geometric center point distances of (a) are ordered from small to large to obtain an ordered corner point set H1 ═ Hj}，HjIs G1iThe jth corner point in the set of corner points H1; let S be G1iThe number of corner points of (a) is made to be 1, i belongs to [1, S ∈](ii) a The corner detection algorithm is a Harris corner detection algorithm or a Shi-Tomasi corner detection algorithm;
s303, (because the sub-mine area images distributed on the ore represent different mineral areas, and the density of the mineral areas is different, resulting in different stresses, so that the shape of the sub-mine area image on the surface of the ore is in a cracked and stretched state, the directly obtained sub-mine area image is difficult to be accurately identified for the identification of associated ores, and the error identification rate is very high, therefore, if the sub-mine area image is to be accurately extracted, the following processing needs to be performed on the sub-mine area image to highlight the characteristics of the associated ores and the main minerals),
connection HjAnd Hj+1Obtain line segment L1, connection HjAnd Hj+2Obtain line segment L2, connection Hj+1And Hj+2The line segment L3 is obtained as HjAn included angle which is a peak and takes L1 and L2 as sides is & lt A & gt, and Hj+1An included angle of a peak and L1 and L3 as sides is ^ B and Hj+2An included angle which is a peak and takes L2 and L3 as sides is < C; hjSegment of the perpendicular line on side L3 or HjThe line segment of the line to the midpoint of L3 is C1;
s304, if any angle of &,. B and &isobtuse angle, (if sampling mineral identification is carried out at the moment, namely associated minerals and main minerals in the shape of the sub-mine area image are entangled together due to too small size, if the sampled sub-mine area image is too small, a non-mineral area is easily collected, and therefore H needs to be correctedjPosition of) if point HjIf there is a projection point on the edge L3, the point H isjThe projection point onto the edge L3 is HPjIf point HjThe midpoint of L3 is HP if there is no projected point on the edge L3jI.e. at point HjThe point of intersection of the perpendicular to the side L3 and the side L3 or the midpoint of L3 is HPj；
Connection point HPjTo point HjForm a straight line C1 at point HjTo point HPjIs a first direction, with a point HPjTo point HjIs a second direction;
if < A is not an obtuse angle, HjIs moved along the straight line C1 in the first direction by a distance Δ L to update HjWhere Δ L ═ Max (D2) -Min (D1) |, Max (D2) being the largest element in the calculation set D2, Min (D1) being the smallest element in the calculation set D1;
sets D1 and D2 are respectively a maximum distance threshold value set and a minimum distance threshold value set between adjacent sampling points obtained from the step S3041 to the step S3043;
s3041: setting the initial value of the variable k to be 1, and setting the empty sets D1 and D2;
s3042: calculating a corner point H in an ordered set of corner points H1kTo corner point Hk+1Euclidean distance d1, corner point HkTo corner point Hk+2Euclidean distance d2, corner point Hk+1To corner point Hk+2The euclidean distance d 3; hkIs the Kth corner point in H1; adding the maximum value of D1, D2 and D3 into the set D1, and adding the minimum value of D1, D2 and D3 into the set D2;
s3043: if k +2 < S, increasing the value of k by 1 and transferring to the step S3042, otherwise, outputting the obtained maximum distance threshold value set D1 and minimum distance threshold value set D2;
if < A is an obtuse angle, HjIs moved in a second direction along the line C1 by a distance Δ L to update HjThe position coordinates of (a);
s305, if the angle A, the angle B and the angle C are acute angles, (the sampled minerals are identified, the sampled region is too small, the sampling of the main minerals and the associated minerals can be mixed to cause distortion, and the sampled region needs to be amplified), and a point H is madej+1The projection point on the side L2 is HPj+1Let point Hj+2The projection point on the side L1 is HPj+2Connection point HPj+1To point Hj+1Form a line C2 at a point HPj+1To point Hj+1Is a third direction; connection point HPj+2To point Hj+2Form a line C3 at a point HPj+2To point Hj+2Is a fourth direction; h is to bej+1Is moved by a distance Δ L in the third direction along the straight line C2, thereby updating Hj+1The position coordinates of (a); h is to bej+2Is moved a distance al in a fourth direction along the straight line C3, thereby updating Hj+2The position coordinates of (a);
s306, updating the vertex Hj、Hj+1And Hj+2The formed triangular area is marked as a mining area to be identified;
s307, three vertexes H of the mining area to be identified are obtainedj、Hj+1And Hj+2Position ofRespectively extracting spectral waveform characteristics of the spectral waveform data at the three vertex positions, and performing mineral type identification through matching of a spectral database according to the wavelength of the spectral waveform characteristic position;
s308, if three vertexes Hj、Hj+1And Hj+2If the mineral type identified by the positions of any two vertexes is a first mineral, marking the mining area to be identified as a first mineral subarea; if three vertices Hj、Hj+1And Hj+2If the mineral type identified by the positions of any two vertexes is a second mineral, marking the mining area to be identified as a second mineral subarea; if j +2 < S, increasing the value of j by 1 and going to step S303, otherwise going to step S309;
s309, if i is less than K, increasing the value of i by 1 and transferring to the step S302, otherwise, finishing the classification and identification of each sub-mine image in the first image set.
Further, in S307, the method of acquiring the spectral waveform data of the mine area to be identified is: the spectral waveform data is obtained through any one of a ground feature spectrometer, a hyperspectral camera, a hyperspectral imager and a near-infrared spectrometer.
Further, in S307, the method for identifying the mineral type through matching of the spectral database according to the wavelength of the spectral waveform feature position includes:
extracting spectral waveform characteristics of spectral waveform data at the positions of the three vertexes, wherein the spectral waveform characteristics comprise first-order differential, second-order differential, wave crest and wave trough of a spectral waveform;
and carrying out wavelength matching on the spectral waveform characteristics of the minerals in the spectral database and the spectral waveform characteristics of the minerals in the spectral database to obtain matched minerals consistent with the spectral waveform characteristics of the spectral waveform data of the three vertexes.
Further, in S307, the spectrum database specifically includes a USGS spectrum database, an ASD atomic spectrum database, a JPL standard spectrum database, an ASTER spectrum database, a HIPAS spectrum database, and an JHU spectrum database, and further includes documents: zhangyingtong, Xiaoqing, build light by smelling, and the like, the development of a ground object spectrum database and the current application situation [ J ] the remote sensing academic newspaper, 2017,21(001):12-26, and any one of the spectrum databases related in the document.
Further, in S400, the method for calculating the number of associated ore images marked as associated ores in the first image set includes: respectively calculating the number Ta1 marked as a first mineral partition in the first image set and the number Ta2 marked as a second mineral partition in the first image set, and when Ta1 is more than Ta2, taking the first mineral as a main mineral and the second mineral as an associated mineral; when Ta1 is not more than Ta2, the first mineral is taken as associated mineral, and the second mineral is taken as main mineral; and calculating a first mineral subarea or a second mineral subarea of the associated mine in the first image set as the number of the associated mine images.
Further, in S308, the main mineral includes any one of copper ore, wolframite, galena, sphalerite, iron ore, crystal ore and nickel ore; the associated ore comprises any one of rare earth ore, tantalum-niobium ore, zircon ore, phosphate ore, uranium ore and thorium ore.
Further, in S500, the value range of the threshold is set to [0.2,0.5] times of the number of images of the main mineral in the first image set or the threshold is set to [5,20 ].
An identification system for associated minerals in a rock sample according to an embodiment of the present disclosure is shown in fig. 2, which is a structural diagram of an identification system for associated minerals in a rock sample according to the present disclosure, and the identification system for associated minerals in a rock sample according to the embodiment includes: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor when executing the computer program implementing the steps in one of the above-described embodiments of the system for identifying associated minerals in a rock sample.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the image acquisition unit is used for acquiring a cross-section image of the cross section of the rock sample;
the image segmentation unit is used for preprocessing, screening and segmenting the cross-section image to obtain sub-mine images, and the sub-mine images form a first image set;
the classification and identification unit is used for classifying and identifying each sub-mine image in the first image set;
the associated ore identification unit is used for calculating the number of the associated ore images marked as the associated ores in the first image set;
and the associated ore judging unit is used for judging that the rock sample contains the associated ore when the number of the associated ore images exceeds a threshold value, otherwise, the rock sample does not contain the associated ore.
The system for identifying the associated minerals in the rock samples can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The system for identifying associated minerals in the rock sample can be operated by a system comprising, but not limited to, a processor and a memory. It will be understood by those skilled in the art that the example is merely illustrative of the system for identifying an associated mineral in a rock sample and is not intended to be limiting, and may include more or less than a proportion of the components, or some components in combination, or different components, for example, the system for identifying an associated mineral in a rock sample may further include an input output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control centre for the system for operating the system for identifying associated minerals in the one rock sample, various interfaces and lines being used to connect the various parts of the system for operating the system for identifying associated minerals in the whole one rock sample.
The memory may be used to store the computer programs and/or modules, and the processor may be configured to implement the various functions of the associated mineral identification system in the one rock sample by executing or executing the computer programs and/or modules stored in the memory, and by invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Although the description of the present disclosure has been rather exhaustive and particularly described with respect to several illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, so as to effectively encompass the intended scope of the present disclosure. Furthermore, the foregoing describes the disclosure in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the disclosure, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (10)
1. A method of identifying associated minerals in a rock sample, the method comprising the steps of:
s100, collecting a cross-section image of a rock sample cross section;
s200, preprocessing, screening and segmenting the sectional image to obtain each sub-mine area image, wherein each sub-mine area image forms a first image set;
s300, classifying and identifying each sub-mine image in the first image set;
s400, calculating the number of the associated ore images marked as associated ores in the first image set;
s500, judging that the rock sample contains the associated ores when the number of the associated ore images exceeds a threshold value, otherwise, judging that the rock sample does not contain the associated ores.
2. The method for identifying associated minerals in a rock sample according to claim 1, wherein in S100, the method for acquiring the cross-sectional image of the rock sample comprises: and acquiring an image of the cross section of the rock sample by any one of a hyperspectral camera, a hyperspectral imager, a linear array CCD industrial camera and a near infrared light image sensor to obtain a cross section image.
3. The method of claim 1, wherein in S100 the rock sample comprises any one ore selected from porphyry copper ore, wolframite, galena, sphalerite, iron ore, crystal ore, nickel ore, rare earth ore, tantalite, zircon, phosphate ore, uranium ore and thorium ore.
4. The method for identifying associated minerals in a rock sample according to claim 1, wherein in S200, the cross-sectional images are preprocessed, screened and segmented to obtain sub-mine images, and the sub-mine images form a first image set by: the cross section image is subjected to Gaussian filtering and graying to obtain a gray level image, the gray level image is calculated by a watershed algorithm to obtain boundary points, the boundary points are connected to obtain edge lines, a water collecting basin region formed by the edge lines is used as a sub-mining area image, and a first image set is formed by the sub-mining area images.
5. The method for identifying associated minerals in a rock sample according to claim 1, wherein in S200, the cross-sectional images are preprocessed, screened and segmented to obtain sub-mine images, and the sub-mine images form a first image set by: the cross-section image is subjected to Gaussian filtering and graying to obtain a gray level image, the gray level image is detected by a Sobel edge detection operator to obtain edge lines, a closed image area formed by the edge lines is used as a sub-mine area image, and a first image set is formed by the sub-mine area images.
6. The method for identifying associated minerals in rock samples according to claim 1, wherein in S300, the method for classifying and identifying each sub-mine image in the first image set comprises:
s301, let the first image set be G1 ═ G1iLet K be the number of images in the mine area in the first image set G1, and set variables i, j, i ∈ [1, K ∈]，G1iThe ith sub-mine image in the first image set is obtained; let i have a value of 1;
s302, using a corner detection algorithm to pair G1iThe corner detection is carried out to obtain G1iFrom each corner point to G1iThe geometric center point distances of (a) are ordered from small to large to obtain an ordered corner point set H1 ═ Hj}，HjIs G1iThe jth corner point in the set of corner points H1; let S be G1iThe number of corner points of (a) is made to be 1, i belongs to [1, S ∈](ii) a The corner detection algorithm is a Harris corner detection algorithm or a Shi-Tomasi corner detection algorithm;
s303, connection HjAnd Hj+1Obtain line segment L1, connection HjAnd Hj+2Obtain line segment L2, connection Hj+1And Hj+2The line segment L3 is obtained as HjAn included angle which is a peak and takes L1 and L2 as sides is & lt A & gt, and Hj+1An included angle of a peak and L1 and L3 as sides is ^ B and Hj+2An included angle which is a peak and takes L2 and L3 as sides is < C; hjSegment of the perpendicular line on side L3 or HjThe line segment of the line to the midpoint of L3 is C1;
s304, if any angle among the angle A, the angle B and the angle C is an obtuse angle, if the point H isjIf there is a projection point on the edge L3, the point H isjThe projection point onto the edge L3 is HPjIf point HjThe midpoint of L3 is HP if there is no projected point on the edge L3jI.e. at point HjThe point of intersection of the perpendicular to the side L3 and the side L3 or the midpoint of L3 is HPj；
Connection point HPjTo point HjForm a straight line C1 at point HjTo point HPjIs a first direction, with a point HPjTo point HjIs a second direction;
if < A is not an obtuse angle, HjIs moved along the straight line C1 in the first direction by a distance Δ L to update HjWhere Δ L ═ Max (D2) -Min (D1) |, Max (D2) being the largest element in the calculation set D2, Min (D1) being the smallest element in the calculation set D1;
sets D1 and D2 are the maximum distance threshold value set and the minimum distance threshold value set obtained from steps S3041 to S3043, respectively;
s3041: setting the initial value of the variable k to be 1, and setting the empty sets D1 and D2;
s3042: calculating a corner point H in an ordered set of corner points H1kTo corner point Hk+1Euclidean distance d1, corner point HkTo corner point Hk+2Euclidean distance d2, corner point Hk+1To corner point Hk+2The euclidean distance d 3; hkIs the Kth corner point in H1; adding the maximum value of D1, D2 and D3 into the set D1, and adding the minimum value of D1, D2 and D3 into the set D2;
s3043: if k +2 < S, increasing the value of k by 1 and transferring to the step S3042, otherwise, outputting the obtained maximum distance threshold value set D1 and minimum distance threshold value set D2;
if < A is an obtuse angle, HjIs moved in a second direction along the line C1 by a distance Δ L to update HjThe position coordinates of (a);
s305, if the angle A, the angle B and the angle C are acute angles, making a point Hj+1The projection point on the side L2 is HPj+1Let point Hj+2The projection point on the side L1 is HPj+2Connection point HPj+1To point Hj+1Form a line C2 at a point HPj+1To point Hj+1Is a third direction; connection point HPj+2To point Hj+2Form a line C3 at a point HPj+2To point Hj+2Is a fourth direction; h is to bej+1Is located along the line C2 to the firstMoving distance DeltaL in three directions, thereby updating Hj+1The position coordinates of (a); h is to bej+2Is moved a distance al in a fourth direction along the straight line C3, thereby updating Hj+2The position coordinates of (a);
s306, updating the vertex Hj、Hj+1And Hj+2The formed triangular area is marked as a mining area to be identified;
s307, three vertexes H of the mining area to be identified are obtainedj、Hj+1And Hj+2The spectral waveform data of the positions respectively extract the spectral waveform characteristics of the spectral waveform data of the three vertex positions, and the mineral type identification is carried out through matching of a spectral database according to the wavelength of the spectral waveform characteristic positions;
s308, if three vertexes Hj、Hj+1And Hj+2If the mineral type identified by the positions of any two vertexes is a first mineral, marking the mining area to be identified as a first mineral subarea; if three vertices Hj、Hj+1And Hj+2If the mineral type identified by the positions of any two vertexes is a second mineral, marking the mining area to be identified as a second mineral subarea; if j +2 < S, increasing the value of j by 1 and going to step S303, otherwise going to step S309;
s309, if i is less than K, increasing the value of i by 1 and transferring to the step S302, otherwise, finishing the classification and identification of each sub-mine image in the first image set.
7. The method for identifying associated minerals in a rock sample according to claim 6, wherein in S307, the method for acquiring spectral waveform data of the mining area to be identified comprises the following steps: the spectral waveform data is obtained through any one of a ground feature spectrometer, a hyperspectral camera, a hyperspectral imager and a near-infrared spectrometer.
8. The method for identifying the associated minerals in the rock sample according to claim 6, wherein in S307, the method for identifying the type of the minerals through matching of the spectral database according to the wavelengths of the spectral waveform feature positions comprises the following steps:
extracting spectral waveform characteristics of spectral waveform data at the positions of the three vertexes, wherein the spectral waveform characteristics comprise first-order differential, second-order differential, wave crest and wave trough of a spectral waveform;
and carrying out wavelength matching on the spectral waveform characteristics of the minerals in the spectral database and the spectral waveform characteristics of the minerals in the spectral database to obtain matched minerals consistent with the spectral waveform characteristics of the spectral waveform data of the three vertexes.
9. The method for identifying associated minerals in rock samples according to claim 1, wherein in S400, the method for calculating the number of associated mine images marked as associated minerals in the first image set comprises: respectively calculating the number Ta1 marked as a first mineral partition in the first image set and the number Ta2 marked as a second mineral partition in the first image set, and when Ta1 is more than Ta2, taking the first mineral as a main mineral and the second mineral as an associated mineral; when Ta1 is not more than Ta2, the first mineral is taken as associated mineral, and the second mineral is taken as main mineral; and calculating a first mineral subarea or a second mineral subarea of the associated mine in the first image set as the number of the associated mine images.
10. A system for identifying associated minerals in a rock sample, the system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the image acquisition unit is used for acquiring a cross-section image of the cross section of the rock sample;
the image segmentation unit is used for preprocessing, screening and segmenting the cross-section image to obtain sub-mine images, and the sub-mine images form a first image set;
the classification and identification unit is used for classifying and identifying each sub-mine image in the first image set;
the associated ore identification unit is used for calculating the number of the associated ore images marked as the associated ores in the first image set;
and the associated ore judging unit is used for judging that the rock sample contains the associated ore when the number of the associated ore images exceeds a threshold value, otherwise, the rock sample does not contain the associated ore.
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