CN104964950A - Multi-element wave peak-based laser-induced breakdown spectroscopy rock fragment type identification method - Google Patents
Multi-element wave peak-based laser-induced breakdown spectroscopy rock fragment type identification method Download PDFInfo
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Abstract
The invention provides a rock fragment type identification method. The method comprises the following steps: 1, measuring the spectra of N standard rock fragments in a range of 200-950nm by using a laser-induced breakdown spectrograph; 2, carrying out spectral line identification on the spectra of the N standard rock fragments, and finding out the wave peak spectral lines of a preset number of elements with the top contents; 3, combining the wave peak spectral lines of the selected elements in the standard rock fragments to form a characteristic spectrum as the input value of a BP nerve network, and training the BP nerve network to obtain a BP nerve network structure; 4, measuring the spectrum of a rock fragment to be identified in the range of 200-950nm by using the laser-induced breakdown spectrograph; 5, carrying out spectral line identification on the spectrum of the rock fragment to be identified to obtain spectral lines of elements same to the elements in step 2; and 6, combining the spectral line wave peaks of the selected elements in the rock fragment to be identified as the input value of the BP nerve network, and identifying the rock fragment to be identified by using the obtained BP nerve network to obtain an identification result.
Description
Technical field
The present invention relates to landwaste type identification technical field, particularly a kind of Laser-induced Breakdown Spectroscopy landwaste kind identification method based on multielement crest.
Background technology
In recent years, along with the develop rapidly of petroleum drilling new technology, the landwaste returned out by shaft bottom is very in small, broken bits, even powdered, makes traditional cutting description work become very difficult.
At present, Laser-induced Breakdown Spectroscopy is applied to rock type knowledge method for distinguishing and mainly contains: first method utilizes Laser-induced Breakdown Spectroscopy in conjunction with partial least squares discriminant analysis, realize the automatic identification to rock sample, it is full spectrum model that its empirical model taked mainly contains two kinds: one, this kind of method recognition result is relatively high, but need data to be processed many, ground unrest impact is simultaneously larger.Second method is peak strength and ratio model (characteristic model), and this method data processing is fairly simple, but recognition correct rate declines to some extent.The third method chooses the essential elements such as Si, Al, Ca, Fe, according to these element spectral line of emission strength build characteristic variables, then carries out landwaste type identification in conjunction with neural network.4th kind is first carry out principal component analysis (PCA) to full spectrum, and recycling neural network carries out landwaste type identification.In these methods, although some recognition effect is relatively good, data processing compares and expends time in, and then recognition effect is poor for other method.
Summary of the invention
In view of this, the invention provides a kind of Laser-induced Breakdown Spectroscopy landwaste kind identification method based on multielement crest can taking into account data-handling efficiency and recognition result accuracy.
A kind of landwaste kind identification method, it comprises the following steps:
S1, laser induced breakdown spectrograph is utilized to measure the spectrum of N kind standard landwaste within the scope of 200-950nm;
S2, linewidth parameters is carried out to N kind standard landwaste spectrum, find out the crest spectral line of the element of the forward predetermined number of wherein content;
In S3, selection standard landwaste, the crest spectral line group composite character spectrum of selected element is as the input value of BP neural network, trains, obtain BP neural network structure to BP neural network;
S4, laser induced breakdown spectrograph is utilized to measure the spectrum of landwaste to be identified within the scope of 200-950nm;
S5, linewidth parameters is carried out to landwaste spectrum to be identified, draw the spectral line with identical element in step S2;
S6, the spectral line crest choosing in landwaste to be measured selected element are combined into the input value of characteristic spectrum as BP neural network, utilize the BP neural network obtained to identify landwaste to be measured, obtain recognition result.
Advantageous Effects: the present invention passes through the input value of the crest spectral line group composite character spectrum of element higher for content in standard landwaste as BP neural network, input value as BP neural network is not the full spectrum that sample measures spectrum, neither the singlet line intensity of element.Like this using the characteristic spectral line of crest spectral line composition as the input value of BP neural network, few far away than full spectral analysis method of its input data, but also good than full analysis of spectrum of its recognition effect; Although the characteristic variable method that this method input data form than peak strength is many, but its recognition effect will be better than far away peak strength recognition methods, although input data add simultaneously, but increase also few, therefore its identify required for time do not have large increase, take into account data-handling efficiency and recognition result accuracy well.
Accompanying drawing explanation
Fig. 1 is the Laser-induced Breakdown Spectroscopy landwaste kind identification method process flow diagram based on multielement crest that embodiment of the present invention provides.
Embodiment
As shown in Figure 1, a kind of landwaste kind identification method, it comprises the following steps:
S1, laser induced breakdown spectrograph is utilized to measure the spectrum of N kind standard landwaste within the scope of 200-950nm.Alternatively, following 9 kinds of rock samples can be selected, be pressed into standard landwaste by sheeter.The content of the type of rock sample, the place of production and wherein essential element is as following table.
S2, linewidth parameters is carried out to N kind standard landwaste spectrum, find out the crest spectral line of the element of the forward predetermined number of wherein content.Alternatively, in standard landwaste, selected element comprises Mn, Mg, Si, Fe, Al, Ti, Ca, Na, K.
In S3, selection standard landwaste, the crest spectral line group composite character spectrum of selected element is as the input value of BP neural network, trains, obtain BP neural network structure to BP neural network.
Alternatively, described step S3 comprises:
The centre wavelength of the crest spectral line of Mn, Mg, Si, Fe, Al, Ti, Ca, Na, K element is got respectively: 279.482,285.213,288.158,301.898,309.271,365.350,393.367,588.995,766.490nm.
Wavelength coverage is got respectively: 279.121-279.824,284.895-285.583,287.831-288.511,301.595-302.232,308.923-309.594,365.056-365.668,393.067-393.647,588.382-589.755,765.930-767.103nm, and above-mentioned crest spectral line data is combined into characteristic spectrum.(1) number of element is the amount doesn't matter, and 5-15 kind element can; (2) centre wavelength of often kind of element can get different values, and wavelength coverage is desirable different value also.The present invention is not restricted this.
Often kind of rock sample gets (X+Y) stack features spectroscopic data, and wherein X, Y are positive integer, altogether N × (X+Y) stack features spectroscopic data; Get X group data before often kind of sample, N kind standard landwaste is N × X group altogether, as the data training group of BP neural network; Y group data after often kind of sample, N kind standard landwaste is N × Y group altogether, as the data detection group of BP neural network, trains BP neural network, checks, obtain BP neural network structure.Alternatively, X, Y can for the integers being greater than 10, and X, Y value are larger, and the time spent is longer, but better effects if.
BP neural network can adopt Matlab software simulating, with spectral wavelength (or major component factor) for input variable.During data analysis, spectroscopic data collection is divided into two groups, one group is carried out BP neural metwork training, and another group carries out BP neural network recognization.By testing the different BP neural network parameter value of many groups, finally determine the BP neural network structure containing at least one hidden layer, input layer number is the variables number of input, node in hidden layer can for being greater than the integer of input variable number, and can be such as 20, node in hidden layer be more, institute's spended time is longer, but better effects if, output layer interstitial content in two kinds of situation: the first situation is 5 nodes, and 9 kinds of samples are divided into 5 classes; The second situation is 9 nodes, realizes the mutual differentiation of 9 kinds of samples.
S4, laser induced breakdown spectrograph is utilized to measure the spectrum of landwaste to be identified within the scope of 200-950nm.
S5, linewidth parameters is carried out to landwaste spectrum to be identified, draw the spectral line with identical element in step S2.
S6, the spectral line crest choosing in landwaste to be measured selected element are combined into the input value of characteristic spectrum as BP neural network, utilize the BP neural network obtained to identify landwaste to be measured, obtain recognition result.In step S6, selected element is identical with step S2.
Alternatively, carry out identification to landwaste to be measured in step S6 to comprise and carry out classification to landwaste to be measured and mutually distinguish.
Alternatively, in standard landwaste, the crest spectral line combination of selected element is made up of the crest of each element, determines the span of each peak wavelength according to the shape of the crest of each element and size.
The invention process has the following advantages:
1, adopt factorial analysis or carry out feature extraction according to the crest of part essential element and can greatly reduce the input variable of BP neural network and shorten program runtime.
2, according to the crest of part essential element, feature extraction is carried out to full spectrum, a large amount of interfere informations in full spectrum can be reduced, improve the discrimination of BP neural network.Sample to be classified and discrimination that sample is distinguished mutually is not less than 98%.
3, BP neural network not only can well be classified to testing sample, well can also distinguish testing sample.
To sum up, the present invention can retain the most of useful information in spectrum, and reduces a large amount of interfere information, efficiently realizes the classification of testing sample fast and mutually distinguishes.
Professional can also recognize further, in conjunction with unit and the algorithm steps of each example of embodiment disclosed herein description, can realize with electronic hardware, computer software or the combination of the two, in order to the interchangeability of hardware and software is clearly described, in the above description according to the functional composition and the step that generally describe each example.These functions perform with hardware or software mode actually, depend on application-specific and the design constraint of technical scheme.Professional and technical personnel can use distinct methods to realize described function to each specifically should being used for, but this realization should not exceed scope of the present invention.
The software module that the method described in conjunction with embodiment disclosed herein or the step of algorithm can directly use hardware, processor to perform, or the combination of the two is implemented.Software module can be placed in any other forms of storage medium known in random access memory, internal memory, ROM (read-only memory), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field.
Be understandable that, for the person of ordinary skill of the art, other various corresponding change and distortion can be made by technical conceive according to the present invention, and all these change the protection domain that all should belong to the claims in the present invention with distortion.
Claims (4)
1. a landwaste kind identification method, is characterized in that, it comprises the following steps:
S1, laser induced breakdown spectrograph is utilized to measure the spectrum of N kind standard landwaste within the scope of 200-950nm;
S2, linewidth parameters is carried out to N kind standard landwaste spectrum, find out the crest spectral line of the element of the forward predetermined number of wherein content;
In S3, selection standard landwaste, the crest spectral line group composite character spectrum of selected element is as the input value of BP neural network, trains, obtain BP neural network structure to BP neural network;
S4, laser induced breakdown spectrograph is utilized to measure the spectrum of landwaste to be identified within the scope of 200-950nm;
S5, linewidth parameters is carried out to landwaste spectrum to be identified, draw the spectral line with identical element in step S2;
S6, the spectral line crest choosing in landwaste to be measured selected element are combined into the input value of characteristic spectrum as BP neural network, utilize the BP neural network obtained to identify landwaste to be measured, obtain recognition result.
2. landwaste kind identification method as claimed in claim 1, is characterized in that,
In standard landwaste, selected element comprises Mn, Mg, Si, Fe, Al, Ti, Ca, Na, K.
3. landwaste kind identification method as claimed in claim 2, it is characterized in that, described step S3 comprises:
The centre wavelength of the crest spectral line of Mn, Mg, Si, Fe, Al, Ti, Ca, Na, K element is got respectively: 279.482,285.213,288.158,301.898,309.271,365.350,393.367,588.995,766.490nm;
Wavelength coverage is got respectively: 279.121-279.824,284.895-285.583,287.831-288.511,301.595-302.232,308.923-309.594,365.056-365.668,393.067-393.647,588.382-589.755,765.930-767.103nm, and above-mentioned crest spectral line data is combined into characteristic spectrum;
Often kind of rock sample gets X+Y stack features spectroscopic data, and wherein X, Y are positive integer, altogether N × (X+Y) stack features spectroscopic data; Get X group data before often kind of sample, N kind standard landwaste is N × X group altogether, as the data training group of BP neural network; Y group data after often kind of sample, N kind standard landwaste is N × Y group altogether, as the data detection group of BP neural network, trains BP neural network, checks, obtain BP neural network structure.Landwaste kind identification method as claimed in claim 1, is characterized in that, carries out identification comprise and carry out classification to landwaste to be measured and mutually distinguish in step S6 to landwaste to be measured.
4. landwaste kind identification method as claimed in claim 3, it is characterized in that, in standard landwaste, crest spectral line combination crest of element selected by each of the element of the predetermined number that content is forward forms, and selected by each, the shape of the crest of element and size determine the span of each peak wavelength.
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Cited By (18)
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| CN105717074A (en) * | 2016-01-22 | 2016-06-29 | 浙江大学 | Method for identifying matcha powder and green tea powder by aid of laser-induced breakdown spectra on basis of characteristic wavelengths |
| CN105938098A (en) * | 2016-07-07 | 2016-09-14 | 四川大学 | Rock soil ignition loss prediction method and system based on laser-induced breakdown spectroscopy |
| CN105938099A (en) * | 2016-07-07 | 2016-09-14 | 四川大学 | Rock character judging method and system based on laser-induced breakdown spectroscopy |
| CN106404748A (en) * | 2016-09-05 | 2017-02-15 | 华中科技大学 | Multispectral combined laser induced breakdown spectroscopy cereal crop producing area identification method |
| CN106525818A (en) * | 2016-10-13 | 2017-03-22 | 中国科学院上海技术物理研究所 | LIBS Mars substance analysis method based on multi-database support and multi-link correction |
| CN106707355A (en) * | 2015-11-12 | 2017-05-24 | 中石化石油工程技术服务有限公司 | Automatic lithology identification method of sedimentary rock |
| CN106841170A (en) * | 2016-12-05 | 2017-06-13 | 西北大学 | A kind of coal ash category identification method based on wavelet neural network algorithm combination LIBS technologies |
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| CN108596085A (en) * | 2018-04-23 | 2018-09-28 | 浙江科技学院 | The method for building up of soil heavy metal content detection model based on convolutional neural networks |
| CN108596021A (en) * | 2018-03-13 | 2018-09-28 | 长江大学 | A kind of landwaste kind identification method and system based on laser induced breakdown spectroscopy differential signal |
| CN108844898A (en) * | 2018-03-13 | 2018-11-20 | 长江大学 | A kind of landwaste kind identification method and system |
| CN109696425A (en) * | 2019-01-25 | 2019-04-30 | 长江大学 | A kind of landwaste kind identification method and system based on laser induced breakdown spectroscopy |
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| CN111239103A (en) * | 2020-01-21 | 2020-06-05 | 上海海关工业品与原材料检测技术中心 | Method for identifying iron ore production country and brand |
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