- Email: [email protected]
Fast mineral identification using elemental LIBS technique
IFAC Workshop on Mining, Minerals and Metal Processing IFAC IFAC Workshop Workshop on on Mining, Mining, Minerals Minerals and and Metal Metal Processing Processing IFAC Workshop on Mining, Minerals and Metal Processing August 25-28, 2015. Oulu, Finland IFAC Workshop on Mining, Minerals and Metal August 25-28, 25-28, 2015. 2015. Oulu, Oulu, Finland Finland Available Processing online at www.sciencedirect.com August August August 25-28, 25-28, 2015. 2015. Oulu, Oulu, Finland Finland
ScienceDirect IFAC-PapersOnLine 48-17 (2015) 119–124
Fast mineral identification using using elemental Fast Fast mineral mineral identification identification using elemental elemental LIBS technique LIBS technique LIBS technique ∗∗ ∗∗ Navid Khajehzadeh ∗∗∗∗ Tommi K. Kauppinen ∗∗ Navid Navid Khajehzadeh Khajehzadeh ∗ Tommi Tommi K. K. Kauppinen Kauppinen ∗∗ ∗∗ Navid Khajehzadeh Tommi K. Kauppinen ∗ ∗ Department of Electrical Engineering and Automation, Aalto ∗ Department of Electrical Electrical Engineering Engineering and Aalto ∗ Department of and Automation, Automation, Aalto ∗ Department Automation, University School of of Electrical Electrical Engineering engineering,and Helsinki, FinlandAalto (e-mail: University School of Electrical engineering, Helsinki, Finland University School of Electrical engineering, Helsinki, Finland (e-mail: University School of [email protected]). Electrical engineering, Helsinki, Finland (e-mail: (e-mail: [email protected]). [email protected]). ∗∗ [email protected]). ∗∗ Department of Electrical Engineering and Automation, Aalto ∗∗ Department of Electrical Engineering and Automation, Aalto ∗∗ of Electrical Engineering and Automation, Aalto ∗∗ Department Department of Electrical Engineering and Automation, Aalto University School of Electrical engineering, Helsinki, Finland (e-mail: University School of Electrical engineering, Helsinki, Finland (e-mail: University School of Electrical engineering, Helsinki, Finland (e-mail: University School of Electrical engineering, Helsinki, Finland (e-mail: [email protected]). [email protected]). [email protected]). [email protected]).
Abstract: Rapid Rapid and and on-line scanning scanning of of rock and and drillcore drillcore samples gives gives fast results results that can can Abstract: Abstract: Rapid and on-line on-line of rock and drillcore samples gives fast fast results that that can Abstract: Rapid on-line scanning scanning of rock rock and exploration drillcore samples samples fast can be used used to to ease ease theand decision-making process during and to togives guide theresults futurethat drilling be the decision-making process during exploration and guide the future drilling be used used to ease ease the the decision-making decision-making process process during during exploration exploration and and to to guide guide the the future future drilling drilling be activitiestowithout without delays. Recently, Recently, faster faster and and more more efficient efficient ore ore characterization characterization by combining combining activities delays. by activities without delays. Recently, faster and more efficient ore characterization by combining activities without delays. Recently, faster and more efficient ore characterization by combining various laser-based laser-based and contactless contactless measurement measurement techniques techniques has has drawn drawn tremendous tremendous attention in in various various laser-based and and contactless measurement techniques has drawn tremendous attention attention in various contactless measurement techniques has and drawn attentionthe in research.laser-based However, and complexity of different different measurement setups thetremendous difference between between research. However, complexity of measurement setups and the difference the research. However, complexity of measurement setups and the between the research. complexity of different different measurement and Considering the difference difference the sources ofHowever, light make it non-economic and complicated forsetups industry. thebetween wide range sources sources of of light light make make it it non-economic non-economic and and complicated complicated for for industry. industry. Considering Considering the the wide wide range range sources of light make it non-economic and complicated for industry. Considering the wide range of the elements which can be detected by Laser-Induced Breakdown Spectroscopy and bearing of the which can by Breakdown Spectroscopy and bearing of the elements which can be be detected detected by Laser-Induced Breakdown Spectroscopy and bearing of the elements elements which detected by Laser-Induced Laser-Induced Breakdown Spectroscopy andLIBS bearing in mind mind that LIBS LIBS is aa can verybe simple spectroscopic technique, the importance importance of applying applying for in that is very simple spectroscopic technique, the of LIBS for in mind that LIBS is a very simple spectroscopic technique, the importance of applying LIBS for in mind that LIBS is a very simple spectroscopic technique, the importance of applying LIBS for fast scanning purposes is certified. This study proposes a simple statistical analysis technique fast scanning purposes is certified. This study proposes a simple statistical analysis technique fast scanning scanning purposes purposes is is certified. certified. This This study proposes proposes aa simple simple statistical statistical analysis analysis technique fast leading to mineral mineral identification from thestudy elemental results of LIBS. It It is shown shown that technique LIBS can leading leading to to mineral mineral identification identification from from the the elemental elemental results results of of LIBS. LIBS. It It is is shown shown that that LIBS LIBS can can leading to identification from the elemental results of LIBS. is that LIBS can be used for calibrating and giving complementary information to other fast scanning techniques be used used for for calibrating calibrating and and giving giving complementary complementary information information to to other other fast fast scanning scanning techniques techniques be be used for calibrating and giving complementary information to other fast scanning techniques like Laser-Induced Laser-Induced Fluorescence Fluorescence imaging. The The application application of the the point-wise LIBS LIBS measurement like like Laser-Induced Fluorescence imaging. imaging. application of of point-wise LIBS measurement measurement like Laser-Induced imaging.ofThe The of the the point-wise point-wise measurement technique for online onlineFluorescence and fast fast estimation estimation the application minerals abundance abundance from the theLIBS surface of the rock rock technique for and of the minerals from technique for for online online and and fast fast estimation estimation of of the the minerals minerals abundance abundance from from the the surface surface of of the the rock rock technique surface of the and drillcore samples is discussed. and and drillcore samples is discussed. and drillcore drillcore samples samples is is discussed. discussed. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Laser-induced breakdown breakdown spectroscopy; drillcore drillcore sample; mineral mineral detection; Keywords: Keywords: Laser-induced Laser-induced breakdown breakdown spectroscopy; spectroscopy; drillcore drillcore sample; sample; mineral mineral detection; detection; Keywords: Laser-induced spectroscopy; sample; detection; principal component analysis; singular value decomposition; calibration; mineral abundance principal principal component analysis; singular value decomposition; calibration; mineral abundance principal component component analysis; analysis; singular singular value value decomposition; decomposition; calibration; calibration; mineral mineral abundance abundance 1. INTRODUCTION 1. 1. INTRODUCTION INTRODUCTION 1. INTRODUCTION This study addresses addresses the issue issue of rapidly rapidly finding and and loThis This study study addresses addresses the the issue issue of of rapidly rapidly finding finding and and loloThis the locatingstudy minerals from from the the surface of of rock rock and andfinding drillcore samsamcating minerals surface of drillcore cating minerals minerals from from the the surface surface of of rock rock and and drillcore drillcore samsamcating ples using Laser-Induced Laser-Induced Breakdown Spectroscopy Spectroscopy (LIBS). ples ples using using Laser-Induced Laser-Induced Breakdown Breakdown Spectroscopy Spectroscopy (LIBS). (LIBS). ples using Breakdown (LIBS). LIBS is a well-known and widely applied elemental meaLIBS is a well-known and widely applied elemental meaLIBS is a well-known and widely applied elemental meaLIBS is a technique. well-knownDeveloping and widely and applied elemental surement applying suchmeafast surement technique. Developing and applying such surement technique. technique. Developing Developing and and applying applying such such fast fast surement fast scanning methods methods make make the the costly costly and and time-consuming time-consuming scanning scanning methods methods make the the costly and and time-consuming scanning laboratory analysismake secondarycostly in decisiontime-consuming making of the the laboratory laboratory analysis analysis secondary secondary in in decision decision making making of of the the laboratory analysis secondary in decision making of exploration phase. This in turn eases both the environmenexploration exploration phase. phase. This This in in turn turn eases eases both both the the environmenenvironmenexploration phase. This in turn eases both the environmental and and economic economic pressure in exploration. exploration. Prior knowledge tal pressure in Prior knowledge tal and economic pressure in exploration. Prior knowledge tal and mineralogical economic pressure in exploration. Prior about contents of the the rock rock andknowledge drillcore about mineralogical contents of and about mineralogical mineralogical contents contents of of the the rock rock and and drillcore drillcore about drillcore samples is essential for geologists and makes the process samples samples is is essential essential for for geologists geologists and and makes makes the the process process samples is essential for geologists and makes the process of logging more effective, thus saving time and manpower. of of logging logging more more effective, effective, thus thus saving saving time time and and manpower. manpower. of more effective, thus saving time and manpower. It logging also alleviates alleviates the negative negative economic and social effects It also the economic and social It also alleviates the negative economic and social effects It also alleviates the negative economic and social effects effects of the exploration phase of mining. of of the the exploration exploration phase phase of of mining. mining. of the exploration phase of mining. X-ray Fluorescence (XRF) (Arkadiev et al., al., 2006) and and X-ray Fluorescence (XRF) X-ray Fluorescence Fluorescence (XRF) (XRF) (Arkadiev (Arkadiev et et al., al., 2006) 2006) and and X-ray (Arkadiev et 2006) reflectance spectroscopy in the visible and infrared ranges reflectance reflectance spectroscopy spectroscopy in in the the visible visible and and infrared infrared ranges ranges reflectance spectroscopy the visible ranges (see Huntington Huntington et al., al., in 2006; Tappertand et infrared al., 2011) are (see et 2006; Tappert et al., (see Huntington et al., 2006; Tappert et al., 2011) 2011) are are (see Huntington et al., 2006; Tappert et al., 2011) the prevailing prevailing online online surface surface scanning scanning techniques techniques in in are inthe the prevailing prevailing online online surface surface scanning scanning techniques techniques in in ininthe industry. However, However, XRF XRF is is an an elemental elemental technique technique and and not not dustry. dustry. However, However, XRF is is an an elemental elemental technique technique and and not not dustry. appropriate whenXRF the target is to detect detect light elements elements appropriate appropriate when when the the target target is is to to detect detect light light elements elements appropriate when the target is to light (usually with atomic atomic number less less than 20). 20). Furthermore, (usually (usually with with atomic atomic number number less less than than 20). 20). Furthermore, Furthermore, (usually with Furthermore, various elements elements maynumber appear with withthan similar characteristic characteristic various may appear similar various elements may appear with similar characteristic various maymakes appearthe with similar characteristic emissionelements lines which decision-making process emission emission lines lines which which makes makes the the decision-making decision-making process process emission lines which makes the decision-making process uncertain. Reflectance spectroscopy is also a widely apuncertain. uncertain. Reflectance Reflectance spectroscopy spectroscopy is is also also a widely apapuncertain. Reflectance spectroscopy is also aa widely widely applied online surface scanning technique, however there are plied plied online online surface surface scanning scanning technique, technique, however however there there are are plied online surface scanning technique, however there are still considerable considerable number of mineral mineral groups left unknown unknown still number of groups left still considerable considerable number number of of mineral mineral groups groups left left unknown unknown still
for reflectance spectroscopy. spectroscopy. The wide wide range of of the eleelefor for reflectance reflectance spectroscopy. spectroscopy. The The wide wide range range of of the the eleelefor reflectance The range the ments which can be detected by LIBS and the simplicity ments ments which which can can be be detected detected by by LIBS LIBS and and the the simplicity simplicity ments which can be detected by LIBS and the simplicity of the the measurement measurement setup provides enough motivation to of setup provides enough motivation to of the measurement setup provides enough motivation to of the LIBS measurement setup provides enough motivation to apply for fast fast scanning scanning purposes. The main main challenge apply LIBS for purposes. The challenge apply LIBS LIBS for for fast fast scanning scanning purposes. The The main challenge challenge apply is to interpret interpret the existing purposes. minerals from main the elemental is is to to interpret interpret the the existing existing minerals minerals from from the the elemental elemental is to the existing minerals from the elemental contents provided by by LIBS. Thereby, Thereby, one major major phase contents contents provided provided by by LIBS. LIBS. Thereby, Thereby, one one major major phase phase contents provided LIBS. one phase of this research is about mineral interpretation from the of this research is about mineral interpretation from of this research is about mineral interpretation from the of this research is about mineral interpretation from the the elemental contents using statistical statistical methods. elemental contents using methods. elemental contents contents using using statistical statistical methods. methods. elemental Combining different contactless measurement techniques Combining Combining different different contactless contactless measurement measurement techniques techniques Combining contactless techniques can lead lead to todifferent faster and and better measurement characterization of drill can faster better characterization can lead lead to to faster and and better better characterization characterization of of drill drill can of drill core samples samples faster and it it has has been been investigated investigated and and proved proved in core and core samples samples and and it it has has been been investigated investigated and and proved proved in in core in various research works. For example, it has been shown various various research research works. works. For For example, example, it it has has been been shown shown various research works. For example, it has been shown how LIBS LIBS technique provides complementary information how technique provides complementary information how LIBS technique provides complementary information how LIBS technique provides complementary information for hyperspectral images (Haavisto et al., 2013). 2013). Also, a for for hyperspectral hyperspectral images images (Haavisto (Haavisto et et al., al., 2013). 2013). Also, Also, a for hyperspectral images (Haavisto et al., Also, aa combined LIF-imaging and Raman spectroscopy technique combined LIF-imaging and Raman spectroscopy technique combined LIF-imaging LIF-imaging and and Raman Raman spectroscopy spectroscopy technique technique combined has been introduced introduced as aa reliable reliable technique generating generating has has been been introduced introduced as as aa reliable reliable technique technique generating generating has been as technique a mineral map from the surface of the drillcore samples aaa mineral mineral map map from from the the surface surface of of the the drillcore drillcore samples samples mineral map from the surface of the drillcore samples (Kauppinen et al., al., 2014). Although the merger of different different (Kauppinen et 2014). Although the merger of (Kauppinen et et al., al., 2014). 2014). Although Although the the merger of of different (Kauppinen measurement techniques enhances the merger capabilitydifferent of fast measurement measurement techniques techniques enhances enhances the the capability capability of of fast fast measurement techniques enhances the capability of fast scanning techniques, it should be taken into account that scanning scanning techniques, techniques, it it should should be be taken taken into into account account that that scanning techniques, it should be taken into account that the complexity of of the laser laser setups and and the difference difference the the complexity complexity of of the the laser laser setups setups and and the the difference difference the complexity setups the between the sources sourcesthe of light light makes makes it non-economic non-economic and between the of it between the the sources sources of of light light makes makes it it non-economic non-economic and and between and complicated for industry. Since LIBS and LIF techniques complicated complicated for for industry. industry. Since Since LIBS LIBS and and LIF LIF techniques techniques complicated for industry. Since LIBS and LIF techniques can apply apply the the same source of excitation excitation light at similar can same source of light at can apply the same source of excitation light at similar similar can apply the(UV same source of considering excitation light similar wavelengths range) and the at simplicity wavelengths (UV range) and considering the simplicity wavelengths (UV range) and considering the simplicity wavelengths (UV range) andthe considering theofsimplicity of LIBS measurement measurement setup, combination LIF and of of LIBS LIBS measurement measurement setup, setup, the the combination combination of of LIF LIF and and of LIBS setup, the combination of LIF and LIBS can be a favorable approach. LIBS LIBS can can be be aaa favorable favorable approach. approach. LIBS can be favorable approach.
2405-8963 © IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright © 2015, IFAC 2015 119 Copyright IFAC 2015 119 Peer review© of International Federation of Automatic Control. Copyright ©under IFAC responsibility 2015 119 Copyright 119 Copyright © © IFAC IFAC 2015 2015 119 10.1016/j.ifacol.2015.10.089
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
This study concentrates on the topic of mineral detection using elemental results of the LIBS measurement technique. A method of data processing is demonstrated leading to application of LIBS technique as a capable method in order to calibrate and give complementary information to other surface scanning techniques. Furthermore, abundance estimation from the surface of the rock and drillcore samples using LIBS is discussed.
4
x 10
515
1.5
495 491
For LIBS, a large number of analytical methods for data processing is available (Tucker et al., 2010). Principal Component Analysis (PCA) is very commonly used for LIBS data processing. However, applying PCA for LIBS is usually performed off-line so that a large set of measured spectra is stored into a database. PCA takes the space between data points and rotates it such that the maximum variability is visible. Then data clustering is performed on the rotated data (Basilevsky, 2009). Finally the spectra of the selected clusters are reproduced in the original space. Those clusters represent the spectra with the most influential and correlated peaks. However, the clustering step of the analysis is an ambiguous process and usually cannot be performed automatically (see the report of Rosen-Gooding et al. (2014)). The peaks are translated using a reference database of atomic emission lines.The most regularly used database is the Atomic Line Database by The National Institute of Standards and Technology (NIST) (Ralchenko, 2005). The interpretation of the LIBS lines using reference database is usually performed indecisively and based on personal impressions since multiple elements may emit at similar lines. Moreover, the intensity of the emission lines can be affected by many factors such as the intensity of the laser light, the distance of the sample from the laser, the shape of the sample and other factors like the moisture or external lights. Therefore, such conventional analysis methods restrict LIBS technique for online and fast mineral or elemental identification purposes. Nonetheless, the ratios of the line intensities of the similar samples are usually constant(Gaft et al., 2007) and this fact is the key point of this research enabling fast mineral detection using LIBS results. Conventional methods of LIBS data processing can be considered as unsupervised learning algorithms. This work proposes a supervised learning algorithm resulting in online application of LIBS to mineral detection. Since the term supervised stems from utilization of a known dataset (called the training dataset) to make predictions, in this work target minerals are studied in advance so that purred (or high-grade) minerals of interest are measured by LIBS technique. For each target mineral, average of multiple measured spectra is evaluated and the most representative, significant and repetitive peaks are selected as the characteristic of the target mineral. The lines are not selected based on the intensity values of the associated elements and it makes this method more reliable since humanbased and perceptual interpretations of the LIBS lines are eliminated. When a database of minerals reference spectra is manually produced, each new spectrum measured by LIBS is inserted into the algorithm alongside the reference spectra. Using a suitable statistical technique (e.g. PCA or Singular Value Decomposition (SVD)) and a suitable dis120
510
1
513
488
516 519
486
0.5
2. PROBLEM STATEMENT AND OBJECTIVES
521
Hematite Chalcopyrite
2
Intensity [a.u]
IFAC MMM 2015 120 August 25-28, 2015. Oulu, Finland
0 460
470
480
490
nm
500
510
520
530
Fig. 1. Important atomic transition lines selected as indicators for Hematite and Chalcopyrite tance metric function, the reference spectrum most similar to the new measurement is identified. Accomplishment of this work demonstrates the feasibility of applying LIBS technique for rapid mineral detection. 2.1 Developing reference spectra As explained before, interpretation of the minerals from the elemental information produced by LIBS technique necessitates having a database containing minerals reference spectra. The spectra can be produced either manually by referring to a reference database of atomic emission lines or by direct measuring of high grade samples of interest. This section explains the procedure of building the reference spectra by direct measuring. The LIBS spectra are broad bands from 350 nm to 800 nm. Figure 1 represents atomic transition lines selected as indicators for Hematite (Fe2 O3 ) and Chalcopyrite (CuFeS2 ) minerals. Both minerals have a number of similar transition lines (e.g. 486 nm,488nm ,491 nm , 495 and etc.) due to having iron in common. However, the difference reveals at wavelengths such as 510 nm, 515 nm, 521 nm for Chalcopyrite and 513 nm, 516 nm and 519 nm for Hematite. Baseline correction has been performed using an asymmetric least squares smoothing algorithm (Eilers and Boelens 2005) to highlight the peaks. A small wavelength range around each peak is selected and the rest are set to zero. Reference spectra of other desired minerals are produced similarly. Figure 2 demonstrates the generated reference spectra of Dolomite, Chalcopyrite, Hematite, Magnesite and Quartz minerals. Each spectrum is normalized to unit variance. It is important to note that the reference spectra are achieved by measuring the high grade or purred minerals of interest. Otherwise impurities of the samples add to the number of irrelevant peaks significantly and the intensities of the peaks may be affected which makes the spectrum inappropriate for mineral detection. Expertise is required to select the proper peaks of the minerals. Selection of irrelevant peaks adds to the complexity and influences the accuracy of the results. For instance, it is essential to select emission lines of Copper (Cu) for Chalcopyrite (CuFeS2 ) mineral. Otherwise, many Iron-bearing minerals such as Hematite (Fe2 O3 ) will show characteristic spectrum similar to Chalcopyrite. The transition lines selected for reference spectra of five minerals are listed in Table 1. The intensity of the emission lines are
IFAC MMM 2015 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
equations is less than number of variables. It provides a good approximation by substituting a modified version of A into the problem where small singular values have been eliminated.
Table 1. Atomic transition lines used in this study as characteristics of the reference minerals Mineral Dolomite Chalcopyrite Magnesite Quartz Hematite
Line (nm) 374, 383, 393, 397, 422, 430, 518 487, 489, 492, 495.5, 510.5, 515, 521.5 383, 385, 518, 568.5, 589 385,390, 500, 634.5, 636.5 487, 489, 492, 495.5, 500, 513.5, 516.5, 519, 522.5, 527
in arbitrary unit. Intensity values of the reference spectra are considered as reference values for identification. 2.2 Analytical Approach Having a database containing reference spectra of the minerals of interest, the next step is to evaluate the spectrum of each new measured pulse and determine whether it contains any of those minerals in the database. The easiest way of data processing is line identification, which is based on straight-forward identification of the peaks in the wavelength spectra. However, straight line identification is time-consuming, inaccurate and based on human perception which may be influenced by many peripheral factors. Suppose X denotes an n × m matrix of reference spectra where n=k+1, k corresponding to the number of reference spectra and m the number of wavelengths (variables). The last row of the matrix holds the new measured spectrum to be evaluated. When the problem is about fast or real-time identification of the minerals from LIBS spectra, it is very plausible that the number of samples is much less than the number of wavelengths, (n
Dolomite Chalcopyrite Hematite Magnesite Quartz
12
Intensity [a.u]
The matrix of the score values (U ×T ) represents the spectra in n dimensions (note: when n
l
(vi,l − cl )2
, i = 1, ..., k
and
l = 1, 2, 3 (2)
c is the point associated with the new measured spectrum and vi is representative for the ith reference spectrum of the original space. Nearest neighbors of the point c satisfying the condition d < t are selected as the existing minerals in the measured spectrum. It is important to note that one spectrum may contain more than one mineral. Therefore, there will be two or more neighbors with approximately similar distances from the target point. 2.3 LIBS Measurement Setup The laser-induced plasma was created using an ULTRA 100 Q-switched Nd:YAG laser operating at 266 nm with 25 mJ/pulse and up to 20 pulses/s repetition rate. The pulse duration was 8 ns. An optical fiber used to collect the radiation from the plasma and the spectrum was obtained by a USB4000 Fiber Optic Spectrometer covering from 350 nm to 1000 nm. In order to avoid continuum radiation, usually the detector is triggered with a delay. Another way to avoid continuum radiation is to detect the emission at a few millimeters above the sample as the plasma plume expands and cools down. In this study, the second method was sufficient. 3. RESULTS
14
10
8
3.1 LIBS for calibrating other techniques A combined Laser-Induced Fluorescence and Raman spectroscopy method for mineral identification has been introduced by Kauppinen et al. (2014) and the viability of the approach has been deeply discussed. However, there are still a number of important minerals non-responsive with both LIF-imaging and Raman spectroscopy techniques. For example, important sulfides like Chalcopyrite and Pentlandite or iron-bearing minerals like Hematite are neither fluorescent nor easily traceable by Raman spectroscopy. Using the proposed analytical approach for LIBS
6
4
2
0
121
400
450
500
nm
550
600
Fig. 2. Generated reference spectra 121
IFAC MMM 2015 122 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
mineral identification, this work demonstrates rapid identification of Chalcopyrite, Hematite and Quartz minerals from the surface of the rock and drillcore samples.
Fig. 4. Scatter plot of the first three scores. Chalcopyrite is the nearest neighbor. contains peaks at other wavelengths associated with other elements (e.g. 500 nm, 527 nm, 568 nm, 570 nm, 589 nm and etc) , the algorithm represented Chalcopyrite as the most compatible mineral because the variation of the data is chiefly following Chalcopyrite reference spectrum. Fig. 3. (a) Ordinary close-up photograph of a drillcore surface from Kevitsa mine,depth: 18 meters, (b) laserinduced fluorescence (LIF) image of the drillcore surface Figure 3-a is showing an ordinary close-up photograph of one piece of the drillcore sample from the depth of 18 m collected from Kevitsa mine, Finland. Figure 3-b represents laser-induced fluorescence (LIF) image of the specified region in Figure 3-a (white circle) on the surface of the drillcore . LIF imaging was performed using the same Nd:YAG laser utilized for LIBS technique. The red fluorescent colour is Albite and the bluish-white colour is Magnesite. However, in figure 3-a the golden grain (highlighted by arrow) did not fluoresce and they were not identified by Raman spectroscopy. The position of the golden color was measured by 10 pulses of LIBS. The analysis was performed to mimic the online or real time analysis of each new pulse. After baseline correction and scaling to unit variance, the average of the measured spectra was appended to the matrix of the reference spectra. Using SVD and the first three principal components, data were analyzed in a new 3-D space.
The proposed analytical method implies the feasibility of applying LIBS technique for rapid calibration of other surface scanning methods specially those functioning based on image processing of the drillcore surface. 3.2 Multiple mineral detection when mineral grains are mixed When the laser beam hits more than one mineral grain at the same time, the spectrum holds a mixture of multiple lines of minerals. Numerous analytical methods applied to online mineral detection processes are incapable of identifying such spectra. Here we discuss the capability of the proposed method for identification of more than one mineral in a spectrum. Furthermore, online application of LIBS technique for sorting of the drillcore samples is discussed. Figure 6 represents one piece of high-grade (>60%) Hematite drillcore sample from the depth of 168 m below the ground level. The dark-gray, brownish and white color veins represent high-level Hematite, low-level Hematite and Quartz respectively. The white arrow points
Chalopyrite Reference
Figure 4 shows the scatter plot of the first three scores where each data point stands for a full spectrum in the original space. The red point corresponds to the spectrum measured from the golden spot on the surface of the drillcore. The euclidean distance of the red point and the rest of the points was calculated. A threshold of t=10 was defined to recognize possible candidate minerals. The distance between the average of the measured spectra and Chalcopyrite was less than ten and the distance with other reference minerals was larger than the threshold. Therefore, it can be clearly deduced that the golden spot represents Chalcopyrite mineral. Figure 5 shows the spectra corresponding with the red point and Chalcopyrite in the original space. Although the measured spectrum 122
Intensity [a.u]
6
Average of measured spectra
5
4
3
2
1
380
400
420
440
460
480
nm
500
520
540
560
580
Fig. 5. Chalcopyrite reference and spectrum of the new measured point
IFAC MMM 2015 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
123
Fig. 6. A high-grade (>60%) Hematite drillcore sample from the depth of 168 m to the position where Hematite and Quartz grains are mixed. Figure 7 illustrates the spectrum measured from the position specified by white arrow in Figure 6 along with Hematite and Quartz reference spectra. Double-arrows indicate the wavelength range where measured spectrum is following Hematite and the single arrows represent the wavelengths where peaks of Quartz occur. Although the measured spectrum contains peaks compatible with other reference minerals, the variations of the data and the ratio of the peaks are mostly compatible with Hematite and Quartz. This sentiment is authenticated in figure 8 where both Hematite and Quartz minerals situate in the close neighborhood of the measured spectrum (red point) in the new 3-D space. In three-dimensional Euclidean space, the distance between the measured spectrum and Dolomite, Chalcopyrite, Hematite, Magnesite and Quartz were 18.4, 16.03, 6.8, 39.29 and 8.47 respectively. 3.3 LIBS for online drillcore sorting. Point-wise measurement techniques such as LIBS can be used for sorting the drillcore samples containing composite minerals of interest so that small fraction of the sample is representative of the large volume. The red line in Figure 6 shows the scanning line of LIBS. Mineral detection from LIBS elemental data was performed on a 10 cm piece of drillcore and the scanning resolution was 5 mm . Each
Fig. 8. Scatter plot of the first three scores. Quartz and Hematite are the nearest neighbors. position was scanned by ten pulses and the average of the spectra was taken as representative of each scanned position. In total 18 averaged spectra were produced and inserted into the algorithm when generated. 11 out of 18 spectra were associated to Hematite and 5 spectra to Quartz. Two spectra consisted of both Quartz and Hematite. As in this case Hematite is the main criterion for ore grade, it can be roughly estimated that the sample is from a high-grade (>60%) ore zone. Figure 9 shows another 10 cm piece of low-level (∼ 30%) Hematite drillcore sample from the depth of 205 m below the ground level. The sample was scanned through the red line. 10 out 18 spectra were detected as Quartz, 6 out of 18 as Hematite and 2 spectra contained both Hematite and Quartz. Therefore, by online scanning of the drillcore sample the grade of ore can be roughly approximated to 39%. Increasing the scanning resolution makes the abundance approximation more precise but it reduces the speed of measurement.
4 3.5
Hematite Reference Quartz Reference Measured spectrum
Intensity [a.u]
3
Fig. 9. A low-grade (<30%) Hematite drillcore sample from the depth of 205 m
2.5 2 1.5
1 0.5
400
450
500
nm
550
600
Fig. 7. New measured spectrum versus Hematite and Quartz reference spectra. 123
It is important to emphasize that for online abundance estimation of the minerals using LIBS technique, selecting the samples containing mixed materials so that the surface is indicator of the whole volume is of paramount importance. Future research will focus on reporting the fast abundance estimation from larger volumes of the samples. The proposed analytical approach can be extended to measure the concentration of the minerals and it provides the backbone of the future work.
IFAC MMM 2015 124 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
4. CONCLUSION This paper presented an analytical method leading to rapid mineral identification from the elemental contents of the LIBS technique. Using a statistical method (SVD), the minerals content of each spectrum measured by LIBS technique was evaluated. This evaluation was performed to imitate online and rapid mineral identification. Analysis was performed in a reduced dimension of the rotated space of the data points. A similarity search procedure was performed between the averaged spectrum of each measured position and the reference spectra produced manually. The results show that the proposed method can give complementary information to other surface scanning techniques like LIF-imaging. The feasibility of detecting multiple minerals in one spectrum was presented. Ultimately, application of online LIBS technique for sorting purposes was discussed shortly. ACKNOWLEDGEMENTS We would like to acknowledge Aalto University for supporting us and providing us with the working environment to write this paper. We would also like to extend our thanks to the geologists of the Kevitsa and Kittila mines, IMA Engineering Ltd Oy and Mine On-Line Service Oy for their great support. REFERENCES Arkadiev, V., Kn¨ upfer, W., and Langhoff, N. (2006). Xray sources. In B. Beckhoff, N. Langhoff, B. Kanngieer, R. Wedell, and H. Wolff (eds.), Handbook of practical X-ray fluorescence analysis., 36–53. Springer. Basilevsky, A.T. (2009). Statistical factor analysis and related methods: theory and applications, volume 418. John Wiley & Sons. Gaft, M., Sapir-Sofer, I., Modiano, H., and Stana, R. (2007). Laser induced breakdown spectroscopy for bulk minerals online analyses. Spectrochimica Acta Part B: Atomic Spectroscopy, 62(12), 1496–1503. Haavisto, O., Kauppinen, T., and H¨ akk¨ anen, H. (2013). Laser-induced breakdown spectroscopy for rapid elemental analysis of drillcore. In IFAC MMM2013 Automatic Control for Sustainable Development - PROCEEDINGS, 87–91. Huntington, J., Whitbourn, L., Mason, P., Berman, M., and Schodlok, M.C. (2006). Hylogging - voluminous industrial-scale reflectance spectroscopy of the earths subsurface. In Proceedings of ASD and IEEE GRS; Art, Science and Applications of Reflectance Spectroscopy Symposium, volume 2. Kauppinen, T., Khajehzadeh, N., and Haavisto, O. (2014). Laser-induced fluorescence images and raman spectroscopy studies on rapid scanning of rock drillcore samples. International Journal of Mineral Processing, 132, 26–33. Madsen, R.E., Hansen, L.K., and Winther, O. (2004). Singular value decomposition and principal component analysis. Neural Networks, 1, 1–5. Petersen, K.B., Pedersen, M.S., et al. (2008). The matrix cookbook. Technical University of Denmark, 450, 7–15. Ralchenko, Y. (2005). Nist atomic spectra database. Memorie della Societa Astronomica Italiana Supplementi, 8, 96. 124
Rosen-Gooding, A., Ollila, A., Gordon, S., Newsom, H., Williams, A., Martinez, R., Wiens, R., and Clegg, S. (2014). Laser-induced breakdown spectroscopy as a tool to differentiate compositions of iron-bearing minerals. LPI Contributions, 1791, 1174. Tappert, M., Rivard, B., Giles, D., Tappert, R., and Mauger, A. (2011). Automated drill core logging using visible and near-infrared reflectance spectroscopy: a case study from the olympic dam iocg deposit, south australia. Economic Geology, 106, 289–296. Tucker, J., Dyar, M., Schaefer, M., Clegg, S., and Wiens, R. (2010). Optimization of laser-induced breakdown spectroscopy for rapid geochemical analysis. Chemical Geology, 277(1), 137–148.
ScienceDirect IFAC-PapersOnLine 48-17 (2015) 119–124
Fast mineral identification using using elemental Fast Fast mineral mineral identification identification using elemental elemental LIBS technique LIBS technique LIBS technique ∗∗ ∗∗ Navid Khajehzadeh ∗∗∗∗ Tommi K. Kauppinen ∗∗ Navid Navid Khajehzadeh Khajehzadeh ∗ Tommi Tommi K. K. Kauppinen Kauppinen ∗∗ ∗∗ Navid Khajehzadeh Tommi K. Kauppinen ∗ ∗ Department of Electrical Engineering and Automation, Aalto ∗ Department of Electrical Electrical Engineering Engineering and Aalto ∗ Department of and Automation, Automation, Aalto ∗ Department Automation, University School of of Electrical Electrical Engineering engineering,and Helsinki, FinlandAalto (e-mail: University School of Electrical engineering, Helsinki, Finland University School of Electrical engineering, Helsinki, Finland (e-mail: University School of [email protected]). Electrical engineering, Helsinki, Finland (e-mail: (e-mail: [email protected]). [email protected]). ∗∗ [email protected]). ∗∗ Department of Electrical Engineering and Automation, Aalto ∗∗ Department of Electrical Engineering and Automation, Aalto ∗∗ of Electrical Engineering and Automation, Aalto ∗∗ Department Department of Electrical Engineering and Automation, Aalto University School of Electrical engineering, Helsinki, Finland (e-mail: University School of Electrical engineering, Helsinki, Finland (e-mail: University School of Electrical engineering, Helsinki, Finland (e-mail: University School of Electrical engineering, Helsinki, Finland (e-mail: [email protected]). [email protected]). [email protected]). [email protected]).
Abstract: Rapid Rapid and and on-line scanning scanning of of rock and and drillcore drillcore samples gives gives fast results results that can can Abstract: Abstract: Rapid and on-line on-line of rock and drillcore samples gives fast fast results that that can Abstract: Rapid on-line scanning scanning of rock rock and exploration drillcore samples samples fast can be used used to to ease ease theand decision-making process during and to togives guide theresults futurethat drilling be the decision-making process during exploration and guide the future drilling be used used to ease ease the the decision-making decision-making process process during during exploration exploration and and to to guide guide the the future future drilling drilling be activitiestowithout without delays. Recently, Recently, faster faster and and more more efficient efficient ore ore characterization characterization by combining combining activities delays. by activities without delays. Recently, faster and more efficient ore characterization by combining activities without delays. Recently, faster and more efficient ore characterization by combining various laser-based laser-based and contactless contactless measurement measurement techniques techniques has has drawn drawn tremendous tremendous attention in in various various laser-based and and contactless measurement techniques has drawn tremendous attention attention in various contactless measurement techniques has and drawn attentionthe in research.laser-based However, and complexity of different different measurement setups thetremendous difference between between research. However, complexity of measurement setups and the difference the research. However, complexity of measurement setups and the between the research. complexity of different different measurement and Considering the difference difference the sources ofHowever, light make it non-economic and complicated forsetups industry. thebetween wide range sources sources of of light light make make it it non-economic non-economic and and complicated complicated for for industry. industry. Considering Considering the the wide wide range range sources of light make it non-economic and complicated for industry. Considering the wide range of the elements which can be detected by Laser-Induced Breakdown Spectroscopy and bearing of the which can by Breakdown Spectroscopy and bearing of the elements which can be be detected detected by Laser-Induced Breakdown Spectroscopy and bearing of the elements elements which detected by Laser-Induced Laser-Induced Breakdown Spectroscopy andLIBS bearing in mind mind that LIBS LIBS is aa can verybe simple spectroscopic technique, the importance importance of applying applying for in that is very simple spectroscopic technique, the of LIBS for in mind that LIBS is a very simple spectroscopic technique, the importance of applying LIBS for in mind that LIBS is a very simple spectroscopic technique, the importance of applying LIBS for fast scanning purposes is certified. This study proposes a simple statistical analysis technique fast scanning purposes is certified. This study proposes a simple statistical analysis technique fast scanning scanning purposes purposes is is certified. certified. This This study proposes proposes aa simple simple statistical statistical analysis analysis technique fast leading to mineral mineral identification from thestudy elemental results of LIBS. It It is shown shown that technique LIBS can leading leading to to mineral mineral identification identification from from the the elemental elemental results results of of LIBS. LIBS. It It is is shown shown that that LIBS LIBS can can leading to identification from the elemental results of LIBS. is that LIBS can be used for calibrating and giving complementary information to other fast scanning techniques be used used for for calibrating calibrating and and giving giving complementary complementary information information to to other other fast fast scanning scanning techniques techniques be be used for calibrating and giving complementary information to other fast scanning techniques like Laser-Induced Laser-Induced Fluorescence Fluorescence imaging. The The application application of the the point-wise LIBS LIBS measurement like like Laser-Induced Fluorescence imaging. imaging. application of of point-wise LIBS measurement measurement like Laser-Induced imaging.ofThe The of the the point-wise point-wise measurement technique for online onlineFluorescence and fast fast estimation estimation the application minerals abundance abundance from the theLIBS surface of the rock rock technique for and of the minerals from technique for for online online and and fast fast estimation estimation of of the the minerals minerals abundance abundance from from the the surface surface of of the the rock rock technique surface of the and drillcore samples is discussed. and and drillcore samples is discussed. and drillcore drillcore samples samples is is discussed. discussed. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Laser-induced breakdown breakdown spectroscopy; drillcore drillcore sample; mineral mineral detection; Keywords: Keywords: Laser-induced Laser-induced breakdown breakdown spectroscopy; spectroscopy; drillcore drillcore sample; sample; mineral mineral detection; detection; Keywords: Laser-induced spectroscopy; sample; detection; principal component analysis; singular value decomposition; calibration; mineral abundance principal principal component analysis; singular value decomposition; calibration; mineral abundance principal component component analysis; analysis; singular singular value value decomposition; decomposition; calibration; calibration; mineral mineral abundance abundance 1. INTRODUCTION 1. 1. INTRODUCTION INTRODUCTION 1. INTRODUCTION This study addresses addresses the issue issue of rapidly rapidly finding and and loThis This study study addresses addresses the the issue issue of of rapidly rapidly finding finding and and loloThis the locatingstudy minerals from from the the surface of of rock rock and andfinding drillcore samsamcating minerals surface of drillcore cating minerals minerals from from the the surface surface of of rock rock and and drillcore drillcore samsamcating ples using Laser-Induced Laser-Induced Breakdown Spectroscopy Spectroscopy (LIBS). ples ples using using Laser-Induced Laser-Induced Breakdown Breakdown Spectroscopy Spectroscopy (LIBS). (LIBS). ples using Breakdown (LIBS). LIBS is a well-known and widely applied elemental meaLIBS is a well-known and widely applied elemental meaLIBS is a well-known and widely applied elemental meaLIBS is a technique. well-knownDeveloping and widely and applied elemental surement applying suchmeafast surement technique. Developing and applying such surement technique. technique. Developing Developing and and applying applying such such fast fast surement fast scanning methods methods make make the the costly costly and and time-consuming time-consuming scanning scanning methods methods make the the costly and and time-consuming scanning laboratory analysismake secondarycostly in decisiontime-consuming making of the the laboratory laboratory analysis analysis secondary secondary in in decision decision making making of of the the laboratory analysis secondary in decision making of exploration phase. This in turn eases both the environmenexploration exploration phase. phase. This This in in turn turn eases eases both both the the environmenenvironmenexploration phase. This in turn eases both the environmental and and economic economic pressure in exploration. exploration. Prior knowledge tal pressure in Prior knowledge tal and economic pressure in exploration. Prior knowledge tal and mineralogical economic pressure in exploration. Prior about contents of the the rock rock andknowledge drillcore about mineralogical contents of and about mineralogical mineralogical contents contents of of the the rock rock and and drillcore drillcore about drillcore samples is essential for geologists and makes the process samples samples is is essential essential for for geologists geologists and and makes makes the the process process samples is essential for geologists and makes the process of logging more effective, thus saving time and manpower. of of logging logging more more effective, effective, thus thus saving saving time time and and manpower. manpower. of more effective, thus saving time and manpower. It logging also alleviates alleviates the negative negative economic and social effects It also the economic and social It also alleviates the negative economic and social effects It also alleviates the negative economic and social effects effects of the exploration phase of mining. of of the the exploration exploration phase phase of of mining. mining. of the exploration phase of mining. X-ray Fluorescence (XRF) (Arkadiev et al., al., 2006) and and X-ray Fluorescence (XRF) X-ray Fluorescence Fluorescence (XRF) (XRF) (Arkadiev (Arkadiev et et al., al., 2006) 2006) and and X-ray (Arkadiev et 2006) reflectance spectroscopy in the visible and infrared ranges reflectance reflectance spectroscopy spectroscopy in in the the visible visible and and infrared infrared ranges ranges reflectance spectroscopy the visible ranges (see Huntington Huntington et al., al., in 2006; Tappertand et infrared al., 2011) are (see et 2006; Tappert et al., (see Huntington et al., 2006; Tappert et al., 2011) 2011) are are (see Huntington et al., 2006; Tappert et al., 2011) the prevailing prevailing online online surface surface scanning scanning techniques techniques in in are inthe the prevailing prevailing online online surface surface scanning scanning techniques techniques in in ininthe industry. However, However, XRF XRF is is an an elemental elemental technique technique and and not not dustry. dustry. However, However, XRF is is an an elemental elemental technique technique and and not not dustry. appropriate whenXRF the target is to detect detect light elements elements appropriate appropriate when when the the target target is is to to detect detect light light elements elements appropriate when the target is to light (usually with atomic atomic number less less than 20). 20). Furthermore, (usually (usually with with atomic atomic number number less less than than 20). 20). Furthermore, Furthermore, (usually with Furthermore, various elements elements maynumber appear with withthan similar characteristic characteristic various may appear similar various elements may appear with similar characteristic various maymakes appearthe with similar characteristic emissionelements lines which decision-making process emission emission lines lines which which makes makes the the decision-making decision-making process process emission lines which makes the decision-making process uncertain. Reflectance spectroscopy is also a widely apuncertain. uncertain. Reflectance Reflectance spectroscopy spectroscopy is is also also a widely apapuncertain. Reflectance spectroscopy is also aa widely widely applied online surface scanning technique, however there are plied plied online online surface surface scanning scanning technique, technique, however however there there are are plied online surface scanning technique, however there are still considerable considerable number of mineral mineral groups left unknown unknown still number of groups left still considerable considerable number number of of mineral mineral groups groups left left unknown unknown still
for reflectance spectroscopy. spectroscopy. The wide wide range of of the eleelefor for reflectance reflectance spectroscopy. spectroscopy. The The wide wide range range of of the the eleelefor reflectance The range the ments which can be detected by LIBS and the simplicity ments ments which which can can be be detected detected by by LIBS LIBS and and the the simplicity simplicity ments which can be detected by LIBS and the simplicity of the the measurement measurement setup provides enough motivation to of setup provides enough motivation to of the measurement setup provides enough motivation to of the LIBS measurement setup provides enough motivation to apply for fast fast scanning scanning purposes. The main main challenge apply LIBS for purposes. The challenge apply LIBS LIBS for for fast fast scanning scanning purposes. The The main challenge challenge apply is to interpret interpret the existing purposes. minerals from main the elemental is is to to interpret interpret the the existing existing minerals minerals from from the the elemental elemental is to the existing minerals from the elemental contents provided by by LIBS. Thereby, Thereby, one major major phase contents contents provided provided by by LIBS. LIBS. Thereby, Thereby, one one major major phase phase contents provided LIBS. one phase of this research is about mineral interpretation from the of this research is about mineral interpretation from of this research is about mineral interpretation from the of this research is about mineral interpretation from the the elemental contents using statistical statistical methods. elemental contents using methods. elemental contents contents using using statistical statistical methods. methods. elemental Combining different contactless measurement techniques Combining Combining different different contactless contactless measurement measurement techniques techniques Combining contactless techniques can lead lead to todifferent faster and and better measurement characterization of drill can faster better characterization can lead lead to to faster and and better better characterization characterization of of drill drill can of drill core samples samples faster and it it has has been been investigated investigated and and proved proved in core and core samples samples and and it it has has been been investigated investigated and and proved proved in in core in various research works. For example, it has been shown various various research research works. works. For For example, example, it it has has been been shown shown various research works. For example, it has been shown how LIBS LIBS technique provides complementary information how technique provides complementary information how LIBS technique provides complementary information how LIBS technique provides complementary information for hyperspectral images (Haavisto et al., 2013). 2013). Also, a for for hyperspectral hyperspectral images images (Haavisto (Haavisto et et al., al., 2013). 2013). Also, Also, a for hyperspectral images (Haavisto et al., Also, aa combined LIF-imaging and Raman spectroscopy technique combined LIF-imaging and Raman spectroscopy technique combined LIF-imaging LIF-imaging and and Raman Raman spectroscopy spectroscopy technique technique combined has been introduced introduced as aa reliable reliable technique generating generating has has been been introduced introduced as as aa reliable reliable technique technique generating generating has been as technique a mineral map from the surface of the drillcore samples aaa mineral mineral map map from from the the surface surface of of the the drillcore drillcore samples samples mineral map from the surface of the drillcore samples (Kauppinen et al., al., 2014). Although the merger of different different (Kauppinen et 2014). Although the merger of (Kauppinen et et al., al., 2014). 2014). Although Although the the merger of of different (Kauppinen measurement techniques enhances the merger capabilitydifferent of fast measurement measurement techniques techniques enhances enhances the the capability capability of of fast fast measurement techniques enhances the capability of fast scanning techniques, it should be taken into account that scanning scanning techniques, techniques, it it should should be be taken taken into into account account that that scanning techniques, it should be taken into account that the complexity of of the laser laser setups and and the difference difference the the complexity complexity of of the the laser laser setups setups and and the the difference difference the complexity setups the between the sources sourcesthe of light light makes makes it non-economic non-economic and between the of it between the the sources sources of of light light makes makes it it non-economic non-economic and and between and complicated for industry. Since LIBS and LIF techniques complicated complicated for for industry. industry. Since Since LIBS LIBS and and LIF LIF techniques techniques complicated for industry. Since LIBS and LIF techniques can apply apply the the same source of excitation excitation light at similar can same source of light at can apply the same source of excitation light at similar similar can apply the(UV same source of considering excitation light similar wavelengths range) and the at simplicity wavelengths (UV range) and considering the simplicity wavelengths (UV range) and considering the simplicity wavelengths (UV range) andthe considering theofsimplicity of LIBS measurement measurement setup, combination LIF and of of LIBS LIBS measurement measurement setup, setup, the the combination combination of of LIF LIF and and of LIBS setup, the combination of LIF and LIBS can be a favorable approach. LIBS LIBS can can be be aaa favorable favorable approach. approach. LIBS can be favorable approach.
2405-8963 © IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Copyright © 2015, IFAC 2015 119 Copyright IFAC 2015 119 Peer review© of International Federation of Automatic Control. Copyright ©under IFAC responsibility 2015 119 Copyright 119 Copyright © © IFAC IFAC 2015 2015 119 10.1016/j.ifacol.2015.10.089
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
This study concentrates on the topic of mineral detection using elemental results of the LIBS measurement technique. A method of data processing is demonstrated leading to application of LIBS technique as a capable method in order to calibrate and give complementary information to other surface scanning techniques. Furthermore, abundance estimation from the surface of the rock and drillcore samples using LIBS is discussed.
4
x 10
515
1.5
495 491
For LIBS, a large number of analytical methods for data processing is available (Tucker et al., 2010). Principal Component Analysis (PCA) is very commonly used for LIBS data processing. However, applying PCA for LIBS is usually performed off-line so that a large set of measured spectra is stored into a database. PCA takes the space between data points and rotates it such that the maximum variability is visible. Then data clustering is performed on the rotated data (Basilevsky, 2009). Finally the spectra of the selected clusters are reproduced in the original space. Those clusters represent the spectra with the most influential and correlated peaks. However, the clustering step of the analysis is an ambiguous process and usually cannot be performed automatically (see the report of Rosen-Gooding et al. (2014)). The peaks are translated using a reference database of atomic emission lines.The most regularly used database is the Atomic Line Database by The National Institute of Standards and Technology (NIST) (Ralchenko, 2005). The interpretation of the LIBS lines using reference database is usually performed indecisively and based on personal impressions since multiple elements may emit at similar lines. Moreover, the intensity of the emission lines can be affected by many factors such as the intensity of the laser light, the distance of the sample from the laser, the shape of the sample and other factors like the moisture or external lights. Therefore, such conventional analysis methods restrict LIBS technique for online and fast mineral or elemental identification purposes. Nonetheless, the ratios of the line intensities of the similar samples are usually constant(Gaft et al., 2007) and this fact is the key point of this research enabling fast mineral detection using LIBS results. Conventional methods of LIBS data processing can be considered as unsupervised learning algorithms. This work proposes a supervised learning algorithm resulting in online application of LIBS to mineral detection. Since the term supervised stems from utilization of a known dataset (called the training dataset) to make predictions, in this work target minerals are studied in advance so that purred (or high-grade) minerals of interest are measured by LIBS technique. For each target mineral, average of multiple measured spectra is evaluated and the most representative, significant and repetitive peaks are selected as the characteristic of the target mineral. The lines are not selected based on the intensity values of the associated elements and it makes this method more reliable since humanbased and perceptual interpretations of the LIBS lines are eliminated. When a database of minerals reference spectra is manually produced, each new spectrum measured by LIBS is inserted into the algorithm alongside the reference spectra. Using a suitable statistical technique (e.g. PCA or Singular Value Decomposition (SVD)) and a suitable dis120
510
1
513
488
516 519
486
0.5
2. PROBLEM STATEMENT AND OBJECTIVES
521
Hematite Chalcopyrite
2
Intensity [a.u]
IFAC MMM 2015 120 August 25-28, 2015. Oulu, Finland
0 460
470
480
490
nm
500
510
520
530
Fig. 1. Important atomic transition lines selected as indicators for Hematite and Chalcopyrite tance metric function, the reference spectrum most similar to the new measurement is identified. Accomplishment of this work demonstrates the feasibility of applying LIBS technique for rapid mineral detection. 2.1 Developing reference spectra As explained before, interpretation of the minerals from the elemental information produced by LIBS technique necessitates having a database containing minerals reference spectra. The spectra can be produced either manually by referring to a reference database of atomic emission lines or by direct measuring of high grade samples of interest. This section explains the procedure of building the reference spectra by direct measuring. The LIBS spectra are broad bands from 350 nm to 800 nm. Figure 1 represents atomic transition lines selected as indicators for Hematite (Fe2 O3 ) and Chalcopyrite (CuFeS2 ) minerals. Both minerals have a number of similar transition lines (e.g. 486 nm,488nm ,491 nm , 495 and etc.) due to having iron in common. However, the difference reveals at wavelengths such as 510 nm, 515 nm, 521 nm for Chalcopyrite and 513 nm, 516 nm and 519 nm for Hematite. Baseline correction has been performed using an asymmetric least squares smoothing algorithm (Eilers and Boelens 2005) to highlight the peaks. A small wavelength range around each peak is selected and the rest are set to zero. Reference spectra of other desired minerals are produced similarly. Figure 2 demonstrates the generated reference spectra of Dolomite, Chalcopyrite, Hematite, Magnesite and Quartz minerals. Each spectrum is normalized to unit variance. It is important to note that the reference spectra are achieved by measuring the high grade or purred minerals of interest. Otherwise impurities of the samples add to the number of irrelevant peaks significantly and the intensities of the peaks may be affected which makes the spectrum inappropriate for mineral detection. Expertise is required to select the proper peaks of the minerals. Selection of irrelevant peaks adds to the complexity and influences the accuracy of the results. For instance, it is essential to select emission lines of Copper (Cu) for Chalcopyrite (CuFeS2 ) mineral. Otherwise, many Iron-bearing minerals such as Hematite (Fe2 O3 ) will show characteristic spectrum similar to Chalcopyrite. The transition lines selected for reference spectra of five minerals are listed in Table 1. The intensity of the emission lines are
IFAC MMM 2015 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
equations is less than number of variables. It provides a good approximation by substituting a modified version of A into the problem where small singular values have been eliminated.
Table 1. Atomic transition lines used in this study as characteristics of the reference minerals Mineral Dolomite Chalcopyrite Magnesite Quartz Hematite
Line (nm) 374, 383, 393, 397, 422, 430, 518 487, 489, 492, 495.5, 510.5, 515, 521.5 383, 385, 518, 568.5, 589 385,390, 500, 634.5, 636.5 487, 489, 492, 495.5, 500, 513.5, 516.5, 519, 522.5, 527
in arbitrary unit. Intensity values of the reference spectra are considered as reference values for identification. 2.2 Analytical Approach Having a database containing reference spectra of the minerals of interest, the next step is to evaluate the spectrum of each new measured pulse and determine whether it contains any of those minerals in the database. The easiest way of data processing is line identification, which is based on straight-forward identification of the peaks in the wavelength spectra. However, straight line identification is time-consuming, inaccurate and based on human perception which may be influenced by many peripheral factors. Suppose X denotes an n × m matrix of reference spectra where n=k+1, k corresponding to the number of reference spectra and m the number of wavelengths (variables). The last row of the matrix holds the new measured spectrum to be evaluated. When the problem is about fast or real-time identification of the minerals from LIBS spectra, it is very plausible that the number of samples is much less than the number of wavelengths, (n
Dolomite Chalcopyrite Hematite Magnesite Quartz
12
Intensity [a.u]
The matrix of the score values (U ×T ) represents the spectra in n dimensions (note: when n
l
(vi,l − cl )2
, i = 1, ..., k
and
l = 1, 2, 3 (2)
c is the point associated with the new measured spectrum and vi is representative for the ith reference spectrum of the original space. Nearest neighbors of the point c satisfying the condition d < t are selected as the existing minerals in the measured spectrum. It is important to note that one spectrum may contain more than one mineral. Therefore, there will be two or more neighbors with approximately similar distances from the target point. 2.3 LIBS Measurement Setup The laser-induced plasma was created using an ULTRA 100 Q-switched Nd:YAG laser operating at 266 nm with 25 mJ/pulse and up to 20 pulses/s repetition rate. The pulse duration was 8 ns. An optical fiber used to collect the radiation from the plasma and the spectrum was obtained by a USB4000 Fiber Optic Spectrometer covering from 350 nm to 1000 nm. In order to avoid continuum radiation, usually the detector is triggered with a delay. Another way to avoid continuum radiation is to detect the emission at a few millimeters above the sample as the plasma plume expands and cools down. In this study, the second method was sufficient. 3. RESULTS
14
10
8
3.1 LIBS for calibrating other techniques A combined Laser-Induced Fluorescence and Raman spectroscopy method for mineral identification has been introduced by Kauppinen et al. (2014) and the viability of the approach has been deeply discussed. However, there are still a number of important minerals non-responsive with both LIF-imaging and Raman spectroscopy techniques. For example, important sulfides like Chalcopyrite and Pentlandite or iron-bearing minerals like Hematite are neither fluorescent nor easily traceable by Raman spectroscopy. Using the proposed analytical approach for LIBS
6
4
2
0
121
400
450
500
nm
550
600
Fig. 2. Generated reference spectra 121
IFAC MMM 2015 122 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
mineral identification, this work demonstrates rapid identification of Chalcopyrite, Hematite and Quartz minerals from the surface of the rock and drillcore samples.
Fig. 4. Scatter plot of the first three scores. Chalcopyrite is the nearest neighbor. contains peaks at other wavelengths associated with other elements (e.g. 500 nm, 527 nm, 568 nm, 570 nm, 589 nm and etc) , the algorithm represented Chalcopyrite as the most compatible mineral because the variation of the data is chiefly following Chalcopyrite reference spectrum. Fig. 3. (a) Ordinary close-up photograph of a drillcore surface from Kevitsa mine,depth: 18 meters, (b) laserinduced fluorescence (LIF) image of the drillcore surface Figure 3-a is showing an ordinary close-up photograph of one piece of the drillcore sample from the depth of 18 m collected from Kevitsa mine, Finland. Figure 3-b represents laser-induced fluorescence (LIF) image of the specified region in Figure 3-a (white circle) on the surface of the drillcore . LIF imaging was performed using the same Nd:YAG laser utilized for LIBS technique. The red fluorescent colour is Albite and the bluish-white colour is Magnesite. However, in figure 3-a the golden grain (highlighted by arrow) did not fluoresce and they were not identified by Raman spectroscopy. The position of the golden color was measured by 10 pulses of LIBS. The analysis was performed to mimic the online or real time analysis of each new pulse. After baseline correction and scaling to unit variance, the average of the measured spectra was appended to the matrix of the reference spectra. Using SVD and the first three principal components, data were analyzed in a new 3-D space.
The proposed analytical method implies the feasibility of applying LIBS technique for rapid calibration of other surface scanning methods specially those functioning based on image processing of the drillcore surface. 3.2 Multiple mineral detection when mineral grains are mixed When the laser beam hits more than one mineral grain at the same time, the spectrum holds a mixture of multiple lines of minerals. Numerous analytical methods applied to online mineral detection processes are incapable of identifying such spectra. Here we discuss the capability of the proposed method for identification of more than one mineral in a spectrum. Furthermore, online application of LIBS technique for sorting of the drillcore samples is discussed. Figure 6 represents one piece of high-grade (>60%) Hematite drillcore sample from the depth of 168 m below the ground level. The dark-gray, brownish and white color veins represent high-level Hematite, low-level Hematite and Quartz respectively. The white arrow points
Chalopyrite Reference
Figure 4 shows the scatter plot of the first three scores where each data point stands for a full spectrum in the original space. The red point corresponds to the spectrum measured from the golden spot on the surface of the drillcore. The euclidean distance of the red point and the rest of the points was calculated. A threshold of t=10 was defined to recognize possible candidate minerals. The distance between the average of the measured spectra and Chalcopyrite was less than ten and the distance with other reference minerals was larger than the threshold. Therefore, it can be clearly deduced that the golden spot represents Chalcopyrite mineral. Figure 5 shows the spectra corresponding with the red point and Chalcopyrite in the original space. Although the measured spectrum 122
Intensity [a.u]
6
Average of measured spectra
5
4
3
2
1
380
400
420
440
460
480
nm
500
520
540
560
580
Fig. 5. Chalcopyrite reference and spectrum of the new measured point
IFAC MMM 2015 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
123
Fig. 6. A high-grade (>60%) Hematite drillcore sample from the depth of 168 m to the position where Hematite and Quartz grains are mixed. Figure 7 illustrates the spectrum measured from the position specified by white arrow in Figure 6 along with Hematite and Quartz reference spectra. Double-arrows indicate the wavelength range where measured spectrum is following Hematite and the single arrows represent the wavelengths where peaks of Quartz occur. Although the measured spectrum contains peaks compatible with other reference minerals, the variations of the data and the ratio of the peaks are mostly compatible with Hematite and Quartz. This sentiment is authenticated in figure 8 where both Hematite and Quartz minerals situate in the close neighborhood of the measured spectrum (red point) in the new 3-D space. In three-dimensional Euclidean space, the distance between the measured spectrum and Dolomite, Chalcopyrite, Hematite, Magnesite and Quartz were 18.4, 16.03, 6.8, 39.29 and 8.47 respectively. 3.3 LIBS for online drillcore sorting. Point-wise measurement techniques such as LIBS can be used for sorting the drillcore samples containing composite minerals of interest so that small fraction of the sample is representative of the large volume. The red line in Figure 6 shows the scanning line of LIBS. Mineral detection from LIBS elemental data was performed on a 10 cm piece of drillcore and the scanning resolution was 5 mm . Each
Fig. 8. Scatter plot of the first three scores. Quartz and Hematite are the nearest neighbors. position was scanned by ten pulses and the average of the spectra was taken as representative of each scanned position. In total 18 averaged spectra were produced and inserted into the algorithm when generated. 11 out of 18 spectra were associated to Hematite and 5 spectra to Quartz. Two spectra consisted of both Quartz and Hematite. As in this case Hematite is the main criterion for ore grade, it can be roughly estimated that the sample is from a high-grade (>60%) ore zone. Figure 9 shows another 10 cm piece of low-level (∼ 30%) Hematite drillcore sample from the depth of 205 m below the ground level. The sample was scanned through the red line. 10 out 18 spectra were detected as Quartz, 6 out of 18 as Hematite and 2 spectra contained both Hematite and Quartz. Therefore, by online scanning of the drillcore sample the grade of ore can be roughly approximated to 39%. Increasing the scanning resolution makes the abundance approximation more precise but it reduces the speed of measurement.
4 3.5
Hematite Reference Quartz Reference Measured spectrum
Intensity [a.u]
3
Fig. 9. A low-grade (<30%) Hematite drillcore sample from the depth of 205 m
2.5 2 1.5
1 0.5
400
450
500
nm
550
600
Fig. 7. New measured spectrum versus Hematite and Quartz reference spectra. 123
It is important to emphasize that for online abundance estimation of the minerals using LIBS technique, selecting the samples containing mixed materials so that the surface is indicator of the whole volume is of paramount importance. Future research will focus on reporting the fast abundance estimation from larger volumes of the samples. The proposed analytical approach can be extended to measure the concentration of the minerals and it provides the backbone of the future work.
IFAC MMM 2015 124 August 25-28, 2015. Oulu, Finland
Navid Khajehzadeh et al. / IFAC-PapersOnLine 48-17 (2015) 119–124
4. CONCLUSION This paper presented an analytical method leading to rapid mineral identification from the elemental contents of the LIBS technique. Using a statistical method (SVD), the minerals content of each spectrum measured by LIBS technique was evaluated. This evaluation was performed to imitate online and rapid mineral identification. Analysis was performed in a reduced dimension of the rotated space of the data points. A similarity search procedure was performed between the averaged spectrum of each measured position and the reference spectra produced manually. The results show that the proposed method can give complementary information to other surface scanning techniques like LIF-imaging. The feasibility of detecting multiple minerals in one spectrum was presented. Ultimately, application of online LIBS technique for sorting purposes was discussed shortly. ACKNOWLEDGEMENTS We would like to acknowledge Aalto University for supporting us and providing us with the working environment to write this paper. We would also like to extend our thanks to the geologists of the Kevitsa and Kittila mines, IMA Engineering Ltd Oy and Mine On-Line Service Oy for their great support. REFERENCES Arkadiev, V., Kn¨ upfer, W., and Langhoff, N. (2006). Xray sources. In B. Beckhoff, N. Langhoff, B. Kanngieer, R. Wedell, and H. Wolff (eds.), Handbook of practical X-ray fluorescence analysis., 36–53. Springer. Basilevsky, A.T. (2009). Statistical factor analysis and related methods: theory and applications, volume 418. John Wiley & Sons. Gaft, M., Sapir-Sofer, I., Modiano, H., and Stana, R. (2007). Laser induced breakdown spectroscopy for bulk minerals online analyses. Spectrochimica Acta Part B: Atomic Spectroscopy, 62(12), 1496–1503. Haavisto, O., Kauppinen, T., and H¨ akk¨ anen, H. (2013). Laser-induced breakdown spectroscopy for rapid elemental analysis of drillcore. In IFAC MMM2013 Automatic Control for Sustainable Development - PROCEEDINGS, 87–91. Huntington, J., Whitbourn, L., Mason, P., Berman, M., and Schodlok, M.C. (2006). Hylogging - voluminous industrial-scale reflectance spectroscopy of the earths subsurface. In Proceedings of ASD and IEEE GRS; Art, Science and Applications of Reflectance Spectroscopy Symposium, volume 2. Kauppinen, T., Khajehzadeh, N., and Haavisto, O. (2014). Laser-induced fluorescence images and raman spectroscopy studies on rapid scanning of rock drillcore samples. International Journal of Mineral Processing, 132, 26–33. Madsen, R.E., Hansen, L.K., and Winther, O. (2004). Singular value decomposition and principal component analysis. Neural Networks, 1, 1–5. Petersen, K.B., Pedersen, M.S., et al. (2008). The matrix cookbook. Technical University of Denmark, 450, 7–15. Ralchenko, Y. (2005). Nist atomic spectra database. Memorie della Societa Astronomica Italiana Supplementi, 8, 96. 124
Rosen-Gooding, A., Ollila, A., Gordon, S., Newsom, H., Williams, A., Martinez, R., Wiens, R., and Clegg, S. (2014). Laser-induced breakdown spectroscopy as a tool to differentiate compositions of iron-bearing minerals. LPI Contributions, 1791, 1174. Tappert, M., Rivard, B., Giles, D., Tappert, R., and Mauger, A. (2011). Automated drill core logging using visible and near-infrared reflectance spectroscopy: a case study from the olympic dam iocg deposit, south australia. Economic Geology, 106, 289–296. Tucker, J., Dyar, M., Schaefer, M., Clegg, S., and Wiens, R. (2010). Optimization of laser-induced breakdown spectroscopy for rapid geochemical analysis. Chemical Geology, 277(1), 137–148.