CN109709045A - Landwaste kind identification method and system based on optoacoustic spectroscopy integrated signal - Google Patents
Landwaste kind identification method and system based on optoacoustic spectroscopy integrated signal Download PDFInfo
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Abstract
The invention discloses a kind of landwaste kind identification method and system based on optoacoustic spectroscopy integrated signal, the landwaste drilled for identification.The method include that utilizing the optoacoustic spectroscopy of photo-acoustic spectrometer measurement standard landwaste；After optoacoustic spectroscopy Integral Processing, the integrated signal of standard landwaste optoacoustic spectroscopy is obtained；BP neural network is trained with the integrated signal of standard landwaste optoacoustic spectroscopy, obtains BP neural network structure；Landwaste to be identified is identified by the BP neural network.Operation difficulty can be simplified using optoacoustic spectroscopy in the present invention, the time of identification can be saved by BP neural network identification, the accuracy of landwaste identification can be improved by carrying out type identification to landwaste by using optoacoustic spectroscopy integrated signal.
Description
Technical field
The invention belongs to petroleum, natural gas drilling field more particularly to a kind of landwaste based on optoacoustic spectroscopy integrated signal
Kind identification method and system.
Background technique
Landwaste is in drilling process, and the rock fragment for then taking ground to is bored on stratum by drill bit.Practical field drilling well
Journey can understand rock property, formation variation and oil, gas-bearing formation situation according to landwaste type.Due to the progress of modern technologies, probing
Obtained landwaste has become very fine crushing, and the method for tional identification landwaste has become more backward.
Currently, be directed to landwaste identification, frequently with technology be the wave crest spectral line that the higher element of content is obtained using spectrum
Identified, first method is the full spectrum model of spectrum: this model data to be treated are relatively more, while ambient noise shadow
Sound is larger, and recognition result is relatively high.Second method is peak strength and ratio model (characteristic model), this side operator
It is fairly simple according to handling, but recognition correct rate is declined.The third method is to choose the essential elements such as Si, Al, Ca, Fe, root
According to these element spectral line of emission strength build characteristic variables, landwaste type identification is carried out in conjunction with neural network.4th kind is pair
Full spectrum first carries out principal component analysis, and neural network is recycled to carry out landwaste type identification.Above method data processing expends the time
It is longer, and complicated operation, is inconvenient.
Summary of the invention
The embodiment of the invention provides a kind of landwaste kind identification method and system based on optoacoustic spectroscopy integrated signal, energy
The tiny landwaste that enough identification probings obtain.
In a first aspect, a kind of landwaste kind identification method based on optoacoustic spectroscopy integrated signal is provided, this method comprises:
S1, optoacoustic spectroscopy of the N kind standard landwaste within the scope of preset wavelength is measured using photo-acoustic spectrometer；
S2, the N kind standard landwaste optoacoustic spectroscopy is subjected to Integral Processing to wavelength, obtains N kind standard landwaste optoacoustic light
The integrated signal of spectrum；
S3, input value of the integrated signal of N kind standard landwaste optoacoustic spectroscopy as standard BP neural network is chosen, to BP mind
It is trained through network, the BP neural network after being trained；
S4, optoacoustic spectroscopy of the landwaste to be identified within the scope of preset wavelength is measured using photo-acoustic spectrometer, and by its light
Sound spectrum versus wavelength carries out Integral Processing, obtains the integrated signal of landwaste optoacoustic spectroscopy to be identified；
S5, landwaste to be identified is identified by the BP neural network after the training.
Second aspect, provides a kind of landwaste identification system based on optoacoustic spectroscopy integrated signal, which includes:
Acquisition module: for measuring N kind standard landwaste and landwaste to be identified in preset wavelength model using photo-acoustic spectrometer
Enclose interior optoacoustic spectroscopy；
Integration module: for the N kind standard landwaste and landwaste optoacoustic spectroscopy to be identified to be carried out Integral Processing to wavelength,
Obtain the integrated signal of N kind standard landwaste and landwaste optoacoustic spectroscopy to be identified；
Training module: for choosing the integrated signal of N kind standard landwaste optoacoustic spectroscopy as the defeated of standard BP neural network
Enter value, BP neural network is trained, the BP neural network after being trained；
Identification module: for identifying landwaste to be identified by the BP neural network after the training.
The invention has the following advantages:
The present invention after Integral Processing, inputs BP neural network by the optoacoustic spectroscopy of photo-acoustic spectrometer measurement standard landwaste,
After being trained to neural network, by the neural network recognization landwaste, operation difficulty can be simplified using optoacoustic spectroscopy, passed through
BP neural network identification can save the time expended, and use optoacoustic spectroscopy integrated signal pair especially by the embodiment of the present invention
Landwaste, which carries out type identification, can improve the accuracy of landwaste identification.
Detailed description of the invention
It, below will be to needed in the technology of the present invention description in order to illustrate more clearly of technical solution of the present invention
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for ability
For the those of ordinary skill of domain, without any creative labor, it can also be obtained according to these attached drawings others
Attached drawing.
Fig. 1 is landwaste kind identification method flow diagram provided in an embodiment of the present invention；
Fig. 2 is landwaste identification system structural schematic diagram provided in an embodiment of the present invention；
Fig. 3 is Photoa-counstic spectra before integral provided in an embodiment of the present invention；
Fig. 4 is Photoa-counstic spectra after integral provided in an embodiment of the present invention；
Fig. 5 is Photoa-counstic spectra after differential provided in an embodiment of the present invention.
Specific embodiment
The embodiment of the invention provides a kind of landwaste kind identification methods and device based on optoacoustic spectroscopy integrated signal, use
The fine rock chips obtained in identification probing, it is convenient that formation variation, rock property etc. are analyzed.
In order to make the invention's purpose, features and advantages of the invention more obvious and easy to understand, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that disclosed below
Embodiment be only a part of the embodiment of the present invention, and not all embodiment.Based on the embodiments of the present invention, this field
Those of ordinary skill's all other embodiment obtained without making creative work, belongs to protection of the present invention
Range.
Referring to Fig. 1, the landwaste recognition methods based on optoacoustic spectroscopy integrated signal includes:
S101, optoacoustic spectroscopy of the N kind standard landwaste within the scope of preset wavelength is measured using photo-acoustic spectrometer.
The optoacoustic spectroscopy is irradiated on sample with pulsed light, after the light absorbing energy of sample because expand with heat and contract with cold and
Acoustical signal is generated, which is exactly optoacoustic spectroscopy with the relation curve of the wavelength change of light.Landwaste is different, and physics is special
Property is not also identical, and thus, obtained optoacoustic spectroscopy is not also identical.It is obtained in embodiments of the present invention using optoacoustic spectroscopy
Time of measuring is short, as a result more stable, and measurement method is simple.
Wherein N is the positive integer not less than 5, and measurement standard landwaste type is more, more accurate to the identification of landwaste to be identified.
Different optoacoustic spectroscopy wave-length coverages, example can be selected according to the performance parameter and needs of production of the optoacoustic spectroscopy instrument
Such as the wave-length coverage of optional 700-1500nm or 700-2000nm.
S102, the N kind standard landwaste optoacoustic spectroscopy is subjected to Integral Processing to wavelength, obtains N kind standard landwaste optoacoustic
The integrated signal of spectrum, integral breadth should be depending on the wavelength resolutions of used photo-acoustic spectrometer, but minimum Ying Bu little
In the wavelength resolution of 2 times of photo-acoustic spectrometer.
It is quadratured with the photoacoustic spectrum signals size to the wavelength of light, the opposite variation of noise can be reduced, weakened simultaneously
The interference of garbage.
S103, input value of the integrated signal of the N kind standard landwaste optoacoustic spectroscopy as standard BP neural network is chosen,
BP neural network is trained, the BP neural network after being trained.
Above step S103 specifically:
Step 1: every kind of rock sample take (X+Y) organize optoacoustic spectroscopy integrated signal data, wherein X, Y be positive integer, total N ×
(X+Y) optoacoustic spectroscopy integrated signal data are organized；
Step 2: taking X group optoacoustic spectroscopy integrated signal data before every kind of sample, the total N × X group of N kind standard landwaste, as BP
The data training group of neural network；
Step 3: Y group optoacoustic spectroscopy integrated signal data after every kind of sample, the total N × Y group of N kind standard landwaste, as BP mind
Data detection group through network；
Step 4: two groups of data are trained BP neural network, examine, the BP neural network after being trained.
Preferably, BP neural network can use Matlab software realization, and other software realizations can also be used.Except using BP
Neural network method to sample carry out type identification outside, other algorithms can also be used, as least square method, support vector machines,
Principal component analysis, relevant function method etc., but its crucially its input signal is using optoacoustic spectroscopy integrated signal data.
S104, measure optoacoustic spectroscopy of the landwaste to be identified within the scope of preset wavelength using photo-acoustic spectrometer, and by its
Optoacoustic spectroscopy carries out Integral Processing to wavelength, obtains the integrated signal of landwaste optoacoustic spectroscopy to be identified.
The step S104 further include:
Step 1: measuring optoacoustic spectroscopy of the landwaste to be identified within the scope of preset wavelength using photo-acoustic spectrometer.Default wave
Long range should be identical as the wave-length coverage in S101；
Step 2: landwaste optoacoustic spectroscopy to be identified being subjected to Integral Processing to wavelength, obtains landwaste optoacoustic spectroscopy to be identified
Integrated signal.Integral breadth should be identical as the integral breadth in S102.
S105, landwaste to be identified is identified by the BP neural network after the training.
Input value of the integrated signal of landwaste optoacoustic spectroscopy to be identified as BP neural network is chosen, after the training
BP neural network landwaste to be measured is identified, obtain recognition result, judge the affiliated type of the landwaste.
It can guarantee that acquisition time is short by optoacoustic spectroscopy in method provided in an embodiment of the present invention, obtained result
More stable and operation is relatively simple, and by obtaining neural network to identify that landwaste can guarantee to BP neural network training
Identify it is more accurate quickly, especially with optoacoustic spectroscopy integrated signal to landwaste carry out type identification can make its recognition effect more
It is good.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
A kind of landwaste kind identification method is essentially described above, a kind of landwaste identification system will be carried out below detailed
Thin description.
Fig. 2 is the structural schematic diagram of landwaste identification system provided in an embodiment of the present invention, which includes:
Acquisition module 210: for measuring N kind standard landwaste and landwaste to be identified in preset wavelength using photo-acoustic spectrometer
Optoacoustic spectroscopy in range；
Integration module 220: for carrying out at integral the N kind standard landwaste and landwaste optoacoustic spectroscopy to be identified to wavelength
Reason, obtains the integrated signal of N kind standard landwaste and landwaste optoacoustic spectroscopy to be identified；
Training module 230: for choosing the integrated signal of N kind standard landwaste optoacoustic spectroscopy as standard BP neural network
Input value is trained BP neural network, the BP neural network after being trained；
Identification module 240: for identifying landwaste to be identified by the BP neural network after the training.
The identification module 240 chooses input of the integrated signal of landwaste optoacoustic spectroscopy to be identified as BP neural network
Value, identifies landwaste to be measured using the BP neural network after the training, obtains recognition result.
In order to illustrate the effect identified based on optoacoustic spectroscopy integrated signal landwaste, below with reference to specific experiment data and figure
Validity proposed by the present invention to optoacoustic spectroscopy Integral Processing is described further.
By taking white sand rock as an example, using PGS-III3 photo-acoustic spectrometer, which is obtained using method provided by the invention
Wavelength and optoacoustic spectroscopy data, and Integral Processing is carried out to wavelength, table 1 gives integral front and back sample wavelength and optoacoustic spectroscopy
Data, selection wave-length coverage are 700~1200nm, integral breadth 5nm, 5 times of instrumental resolutions.
The integral of table 1 front and back wavelength and optoacoustic modal data
Fig. 3 is original Photoa-counstic spectra, and Fig. 4 is Photoa-counstic spectra after integral, is compared by Fig. 3, Fig. 4 it is found that Integral Processing
After greatly reduce spectral signal noise.
For comparison integral front and back, the situation of change of spectral signal noise, calculating wave-length coverage is the flat of 1000-1195nm
The relative standard deviation of mean value, the relative standard deviation of the average value before integral is calculated are 0.027, the average value after integral
Relative standard deviation be 0.021.It follows that the relative standard deviation of spectral signal average value reduces after integral
0.006, that is, it reduces: 22%.
By above-mentioned experimental data and Fig. 3, Fig. 4 comparison it is found that the wave of photoacoustic spectrum signals before carrying out landwaste identification
The long Integral Processing that carries out can largely reduce the relative standard deviation of spectral signal average value, to reduce noise signal
Opposite variation, improves the accuracy rate of landwaste identification.
Landwaste kind identification method provided by the invention based on optoacoustic spectroscopy integrated signal and other landwaste type identifications
Method, which is compared, has better recognition effect, for example has compared with the landwaste kind identification method based on optoacoustic spectroscopy differential signal
There is higher discrimination, the landwaste kind identification method based on optoacoustic spectroscopy differential signal chooses landwaste optoacoustic spectroscopy to be identified
Input value of the differential signal as BP neural network, to identify landwaste type.Referring to Fig. 5, Fig. 5 is that acquisition is above-mentioned to be identified
The Photoa-counstic spectra after differential process is carried out after chip sample wavelength and optoacoustic spectroscopy data to wavelength.
Table 2 be respectively adopted original photoacoustic spectrum signals, the photoacoustic spectrum signals of Integral Processing, differential process optoacoustic light
Spectrum signal identifies that discrimination when landwaste type compares, and as shown in Table 2 carries out original photoacoustic spectrum signals at differential and integral
After reason, discrimination is all increased, and recognition effect all improves, wherein the discrimination using integrated signal reaches 92%.But it identifies
The reason of effect improves be not identical: (1) signal being carried out Integral Processing, be the opposite variation for reducing noise signal.Such as, wavelength
Range is the relative standard deviation of the average value of 1000-1195nm, according to experimental result before, the phase of the average value before integral
To standard deviation are as follows: 0.027, the relative standard deviation of the average value after integral are as follows: 0.021.After integral, spectral signal average value
Relative standard deviation reduce 0.006, that is, reduce: 22%.(2) signal is subjected to differential process, is to increase useful spectrum
And the opposite variation of noise signal.
2 discrimination of table compares
| Original signal | Integrated signal | Differential signal | |
| Discrimination (%) | 86 | 92 | 88 |
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.Upper
It states in embodiment, all emphasizes particularly on different fields to the description of each embodiment, there is no the part for being described in detail or recording in some embodiment, it can be with
Referring to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that each embodiment described in conjunction with the examples disclosed in this document
Module, unit and/or method and step can be realized with the combination of electronic hardware or computer software and electronic hardware.This
A little functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Specially
Industry technical staff can use different methods to achieve the described function each specific application, but this realization is not
It is considered as beyond the scope of this invention.
In several embodiments provided herein, it should be understood that disclosed system, device and method can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of device or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
The above, the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although referring to before
Stating embodiment, invention is explained in detail, those skilled in the art should understand that: it still can be to preceding
Technical solution documented by each embodiment is stated to modify or equivalent replacement of some of the technical features；And these
It modifies or replaces, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (6)
1. a kind of landwaste kind identification method based on optoacoustic spectroscopy integrated signal characterized by comprising
S1, optoacoustic spectroscopy of the N kind standard landwaste within the scope of preset wavelength is measured using photo-acoustic spectrometer, wherein N is not small
In 5 positive integer；
S2, the N kind standard landwaste optoacoustic spectroscopy is subjected to Integral Processing to wavelength, obtains N kind standard landwaste optoacoustic spectroscopy
Integrated signal, integral breadth should be determined according to the wavelength resolution of used photo-acoustic spectrometer, but should be greater than the light equal to 2 times
The wavelength resolution of acousto-optic spectrometer；
S3, input value of the integrated signal of the N kind standard landwaste optoacoustic spectroscopy as standard BP neural network is chosen, to BP mind
It is trained through network, the BP neural network after being trained；
S4, optoacoustic spectroscopy of the landwaste to be identified within the scope of preset wavelength, and the light that will be measured are measured using photo-acoustic spectrometer
Sound spectrum versus wavelength carries out Integral Processing；
S5, landwaste to be identified is identified by the BP neural network after the training.
2. as described in claim 1 based on the landwaste kind identification method of optoacoustic spectroscopy integrated signal, which is characterized in that described
Step S3 includes:
S31, every kind of rock sample take (X+Y) to organize optoacoustic spectroscopy integrated signal data, and wherein X, Y are positive integer, total N × (X+Y)
Group optoacoustic spectroscopy integrated signal data；
S32, X group optoacoustic spectroscopy integrated signal data before every kind of sample, the total N × X group of N kind standard landwaste, as BP nerve net are taken
The data training group of network；
Y group optoacoustic spectroscopy integrated signal data after S33, every kind of sample, the total N × Y group of N kind standard landwaste, as BP neural network
Data detection group；
S34, two groups of data are trained BP neural network, examine, the BP neural network after being trained.
3. as claimed in claim 2 based on the landwaste kind identification method of optoacoustic spectroscopy integrated signal, which is characterized in that described
BP neural network is realized by MATLAB.
4. as described in claim 1 based on the landwaste kind identification method of optoacoustic spectroscopy integrated signal, which is characterized in that described
Step S4 includes:
S41, optoacoustic spectroscopy of the landwaste to be identified within the scope of preset wavelength is measured using photo-acoustic spectrometer；
S42, landwaste optoacoustic spectroscopy to be identified is subjected to Integral Processing to wavelength, obtains the integral letter of landwaste optoacoustic spectroscopy to be identified
Number, integral breadth should be depending on the wavelength resolution of used photo-acoustic spectrometer, but should be greater than the optoacoustic spectroscopy equal to 2 times
The wavelength resolution of instrument.
5. as described in claim 1 based on the landwaste kind identification method of optoacoustic spectroscopy integrated signal, which is characterized in that described
Step S5 includes: the input value for choosing the integrated signal of landwaste optoacoustic spectroscopy to be identified as BP neural network, utilizes the instruction
BP neural network after white silk identifies landwaste to be measured, obtains recognition result.
6. a kind of landwaste identification system based on optoacoustic spectroscopy integrated signal, which is characterized in that the system comprises:
Acquisition module: for measuring N kind standard landwaste and landwaste to be identified within the scope of preset wavelength using photo-acoustic spectrometer
Optoacoustic spectroscopy；
Integration module: it for the N kind standard landwaste and landwaste optoacoustic spectroscopy to be identified to be carried out Integral Processing to wavelength, obtains
The integrated signal of N kind standard landwaste and landwaste optoacoustic spectroscopy to be identified；
Training module: for choosing input value of the integrated signal of N kind standard landwaste optoacoustic spectroscopy as standard BP neural network,
BP neural network is trained, the BP neural network after being trained；
Identification module: for identifying landwaste to be identified by the BP neural network after the training.
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