TWI595229B - Signal analysis device, signal analysis method and computer program product - Google Patents

Description

Signal analysis device, signal analysis method and computer program product

The present invention relates to a signal analysis device, a signal analysis method, and a computer program product for obtaining a distribution of different combinations of strengths of a plurality of signals based on a signal distribution on a two-dimensional coordinate system.

X-ray analysis is a method of irradiating a sample with radiation such as an electron beam or an X-ray, and analyzing a component contained in the sample based on a characteristic X-ray spectrum generated from the sample. In particular, a method of scanning a radiation beam and irradiating the radiation beam toward a sample to detect characteristic X-rays from respective points on the sample is performed, and a spectrum of the characteristic X-ray is created to correspond to each point on the sample. The spectral distribution and the spectral distribution are used to analyze the components in the sample. An example of X-ray analysis in which radiation to be irradiated to a sample is set as an electron beam is known as Energy Dispersive X-ray Spectroscopy (EDX). In addition, as an example of X-ray analysis in which the radiation to be irradiated to the sample is set as X-ray, there is a fluorescent X-ray analysis. Further, in an analysis method other than X-ray analysis, there is also an analysis method capable of producing a spectral distribution. For example, in Raman spectroscopic analysis, a spectral distribution of a spectrum in which Raman light is recorded can be produced for each point on an image corresponding to each point on the sample.

Since characteristic X-rays of a specific wavelength can be obtained from a specific element, the distribution of a specific element can be obtained by investigating the signal intensity of a specific wavelength in the spectrum of each point on the sample. Due to the inclusion of multiple elements in the sample, Therefore, the distribution of a plurality of elements can be obtained according to the spectral distribution. Usually, a plurality of components are contained in a sample, and a plurality of elements are contained in each component. For example, in the case where the sample is rock, the rock contains a plurality of mineral components, and each mineral component contains a plurality of elements. Since there is a case where the same element is contained even if the components are different, the distribution of the components in the sample generally does not coincide with the distribution of the elements.

Patent Document 1 describes a method of obtaining a plurality of element distributions from a spectral distribution and determining a distribution of components in a sample based on a plurality of element distributions. In this method, the sum of linearity of values in a plurality of element distributions is calculated by using two methods for each point, and a two-dimensional drawing of values of linear sums of points is created. In the scatter plot, points that are concentrated in substantially the same area on the scatter plot are determined as points included in the same component. Principal component analysis is used in the calculation of the appropriate coefficients. The distribution of the components in the sample can be known by correlating the points on the spectral distribution with the components determined to contain the respective points. The method can also be applied to a spectral distribution obtained by an analysis method other than X-ray analysis such as Raman spectroscopic analysis.

[Previous Technical Literature] [Patent Literature]

[Patent Document 1] Japanese Patent No. 3461208

In the technique described in Patent Document 1, the operation of classifying a plurality of points on the scattergram obtained by principal component analysis is performed by the user, and thus there is a problem that the user subjectively affects the classification result. also, Although there is a method of automatically classifying according to the distance on the scatter map or the like, there is a problem that it is difficult to adjust the performance of the analysis.

The present invention has been made in view of the above circumstances, and an object thereof is to provide a signal analysis device, a signal analysis method, and a computer program product that can perform subjective analysis of a user and can perform adjustment of analysis performance.

The signal analysis device of the present invention is based on a spectral distribution obtained by specifying a spectrum including one or more signals for each point on a two-dimensional coordinate system, and determining a distribution of a plurality of spectra different in the combination of the intensities of the plurality of specific signals. The signal analysis device includes: a mechanism for generating an intensity distribution of a plurality of specific signals according to the spectral distribution; and n of the intensity distributions of the plurality of specific signals generated by the memory (n is an integer of 2 or more) specific a mechanism for intensity distribution of signals; for each point included in the spectral distribution, a mechanism for generating n-dimensional coordinate points in n-dimensional space defined by a combination of strengths of n specific signals; specifying the number of clusters The plurality of clusters are mechanisms for classifying the generated plurality of n-dimensional coordinate points according to locations on the n-dimensional space; generating a probability distribution model of n-dimensional coordinate points included in each cluster; a probability calculation mechanism that performs a process of calculating a probability that each of the plurality of n-dimensional coordinate points generated in each cluster is included in each cluster; and a model updating mechanism to obtain a plurality of n from the calculated probability The method of increasing the likelihood of the classification of the coordinate points becomes a process of updating the probability distribution model of each cluster; repeating the mechanism, causing the above-described probability calculation mechanism and the above-mentioned model update mechanism to repeatedly perform processing; and designating by multiple clusters respectively Points corresponding to the n-dimensional coordinate points contained in each cluster within the above spectral distribution The distribution, whereby the combination of the intensity of the n specific signals is generated by a combination of different spectral distributions.

In the signal analysis device of the present invention, the intensity distribution of the plurality of signals measured from the same measurement target is used to obtain a plurality of types of components having different combinations of the intensities of the plurality of signals measured in the portion included in the measurement target. A distributed signal analysis device, comprising: a mechanism for storing an intensity distribution of n signals; and generating, for each point in the measurement object, an n-dimensional coordinate on an n-dimensional space defined by a combination of strengths of n signals A mechanism for specifying a number of clusters for classifying a plurality of generated n-dimensional coordinate points according to positions on an n-dimensional space; generating n included in each cluster The mechanism of the probability distribution model of the dimensionality point; the probability calculation mechanism performs the calculation of the probability that each of the plurality of n-dimensional coordinate points generated in each cluster is included in each cluster; the model updating mechanism, so that the self-calculated probability is obtained The way in which the likelihood of classification of n-dimensional coordinate points becomes larger, the processing of updating the probability distribution model of each cluster is performed; the repetition mechanism is used to make the above-mentioned probability calculation mechanism and The model updating means repeats the processing; and assigns a distribution of the points corresponding to the n-dimensional coordinate points included in each cluster to the measurement target for each of the plurality of clusters, thereby generating the plurality of parts in the measurement target distributed.

In the signal analysis device of the present invention, the repetition means causes the probability calculation means and the model update means to repeatedly perform processing until a predetermined convergence condition is satisfied.

The signal analysis device of the present invention is characterized in that the above-described probability calculation mechanism, the above-described model update mechanism, and the above-described repeating mechanism are executed in compliance with EM. (Expectation-maximization) The processing of the algorithm.

The signal analysis apparatus of the present invention is characterized by further comprising: a mechanism for collecting a plurality of clusters in a plurality of clusters whose mutual distances on the n-dimensional space are less than or equal to a specific distance into one cluster.

The signal analysis device of the present invention is characterized by further comprising: means for accepting an initial value of the number of clusters.

In the signal analysis method of the present invention, a plurality of specific signals are obtained by specifying a spectral distribution of a spectrum including one or more signals for each point on the two-dimensional coordinate system by a computer including a calculation unit and a memory unit. A signal analysis method for combining different intensity distributions of different spectra, comprising the steps of: generating, by the calculation unit, an intensity distribution of a plurality of specific signals according to the spectral distribution; and generating a plurality of The intensity distribution of n specific signals in the intensity distribution of the specific signal; for each point included in the above spectral distribution, the calculation unit generates an n-dimensional coordinate on the n-dimensional space defined by the combination of the intensities of the n specific signals a process of specifying a number of clusters for classifying a plurality of generated n-dimensional coordinate points according to a position on an n-dimensional space; and generating, by the calculation unit, each cluster a probability distribution model of n-dimensional coordinate points included in the calculation; performing a probability calculation process by the calculation unit, wherein the probability calculation process is to calculate a plurality of generated n-dimensional coordinate points a probability included in each cluster; performing a model update process by the calculation unit, the model update process being to increase the likelihood of classification of a plurality of n-dimensional coordinate points obtained from the calculated probability a method of updating a probability distribution model of each cluster; and performing, by the calculation unit, the above-described probability calculation processing and the above-described model update processing, by a plurality of groups The set specifies a distribution within the spectral distribution of points corresponding to the n-dimensional coordinate points included in each cluster, whereby the calculation unit generates a plurality of spectral distributions of different combinations of intensities of the specific signals; and The distribution of the above various spectra generated by the memory.

The computer program product of the present invention is a computer program product for causing a computer to perform processing for determining a plurality of specific signals according to a spectral distribution obtained by specifying a spectrum of one or more signals for each point on a two-dimensional coordinate system. Combining different intensity distributions of different spectra, and the computer program product is characterized in that the computer performs a process comprising: generating an intensity distribution of a plurality of specific signals according to the spectral distribution; for the spectral distribution At each point, among a plurality of specific signals for which an intensity distribution has been generated, an n-dimensional coordinate point on an n-dimensional space defined by a combination of intensities of n specific signals is generated; and a plurality of clusters are specified, the plurality of clusters It is used to classify the generated n-dimensional coordinate points according to the position on the n-dimensional space; generate a probability distribution model of the n-dimensional coordinate points included in each cluster; perform probability calculation processing, and calculate the generated multiple The probability that each n-dimensional coordinate point is included in each cluster; the model update process is performed so that the plurality of n-dimensional coordinate points obtained from the calculated probability are divided The likelihood that the likelihood becomes larger, updating the probability distribution model of each cluster; repeating the above probability calculation processing and the above model update processing; and designating, corresponding to the n-dimensional coordinate points included in each cluster, by a plurality of clusters respectively The distribution of the points within the above spectral distribution, thereby generating a distribution of a plurality of different spectra of the combination of the intensities of the n specific signals.

In the present invention, the intensity of a plurality of specific signals is generated according to the spectral distribution The n-dimensional coordinate points on the n-dimensional space defined by the combination of the intensities of the n specific signals are generated, and the n-dimensional coordinate points are classified into a plurality of clusters, and the distribution of the spectra is respectively generated by the cluster. Thereby, the distribution of the spectrum of the specific shape corresponding to the distribution of the substance components including the plurality of elements and the like can be obtained.

In the present invention, n-dimensional coordinate points in an n-dimensional space defined by a combination of the intensities of n signals are generated based on intensity distributions of a plurality of signals measured from the same measurement object, and n-dimensional coordinate points are classified into multiple Clusters, which generate the distribution of the parts of the measurement object by cluster. Therefore, with respect to the measurement target, it is possible to obtain a distribution of various portions of the measurement target that are different from each other such as the type of the substance component contained or the electronic state.

Further, in the present invention, when the n-dimensional coordinate points are classified into a plurality of clusters, signal analysis is accurately performed by performing processing following the maximum expected algorithm (EM algorithm).

Further, in the present invention, a plurality of clusters which are close to each other in an n-dimensional space are aggregated into one cluster. Therefore, an appropriate number of clusters can be obtained.

Further, in the present invention, the initial value of the number of clusters can be arbitrarily specified. The processing accuracy, processing time, and the like can be adjusted by specifying the number of clusters.

According to the present invention, it is possible to perform analysis for excluding the subjective signal distribution of the user, and it is also possible to achieve an excellent effect such as adjustment of analysis performance by a combination of specific signals for analysis.

Hereinafter, the present invention will be specifically described based on the drawings showing embodiments of the present invention.

(Embodiment 1)

Fig. 1 is a block diagram showing the configuration of a signal analysis device 1 of the present invention. The signal analysis device 1 is constructed using a general-purpose computer such as a personal computer (PC). The signal analysis device 1 includes a central processing unit (CPU) (calculation unit) 11 for performing calculations, and a random access memory (RAM) 12 for memorizing temporary information generated along with the calculation; The drive unit 13 such as a Compact Disc Read-Only Memory Drive reads information from a recording medium 2 such as a compact disc; and a non-volatile memory unit 14. The memory unit 14 is, for example, a hard disk. The CPU 11 causes the drive unit 13 to read the computer program 21 of the present invention from the recording medium 2, and causes the memory unit 14 to memorize the read computer program 21. The CPU 11 can load the computer program 21 from the storage unit 14 to the RAM 12 as needed, and execute the necessary processing in the signal analysis device 1 in accordance with the loaded computer program 21. Further, the signal analysis device 1 includes an input unit 16 such as a keyboard or a pointing device that inputs information such as various processing instructions by a user operation, and a display such as a liquid crystal display that displays various kinds of information. Part 17.

Further, the computer program 21 may be downloaded to and stored in the signal analysis device 1 from an external server server (not shown) connected to the signal analysis device 1 via a communication network (not shown). In the memory unit 14. Further, the signal analysis device 1 may be configured to receive a record such as a read-only memory (ROM) in which the computer program 21 is recorded, without receiving the computer program 21 from the outside. mechanism.

Further, the signal analysis device 1 includes an interfacial portion 15 connected to the measurement device 3 that measures the two-dimensional spectral distribution. The measuring device 3 is, for example, an EDX device, a fluorescent X-ray measuring device, or a Raman spectroscopic device. The EDX device irradiates an electron beam to each point on the sample, detects characteristic X-rays generated from each point on the sample, and measures the spectral distribution of the characteristic X-ray spectral distribution obtained from each point on the two-dimensional coordinate system. . The fluorescent X-ray measuring apparatus irradiates X-rays to each point on the sample, detects fluorescent X-rays generated from each point on the sample, and measures the spectral distribution of the fluorescent X-rays obtained from each point in the two-dimensional coordinate system. The spectral distribution presented above. The Raman spectroscopic device irradiates light to each point on the sample, detects Raman light generated from each point on the sample, and measures the spectrum of the Raman light obtained from each point on the two-dimensional coordinate system. distributed. The measuring device 3 may be another device as long as it is a device capable of measuring a spectral distribution.

Fig. 2 is a schematic characteristic diagram showing an example of a spectrum. In general, the spectrum consists of a combination of multiple signals. In Fig. 2, the horizontal axis represents the wavelength, and the vertical axis represents the intensity of the signal at each wavelength. In Fig. 2, the peak of a signal contained in the spectrum is indicated by an arrow. The signal contained in the spectrum is identified by the wavelength. In the case of a characteristic X-ray spectrum, each signal is caused by an element contained in the sample. The spectral distribution measured by the measuring device 3 is composed of a spectrum obtained for each point on the two-dimensional coordinate system corresponding to the surface of the sample. For each spectrum, the combinations of the intensities of the signals contained are different from each other, and the shapes of the spectra are different from each other. For example, depending on the spectral conditions, there may be a spectrum containing a single number of signals, or there may be a signal strength of Zero spectrum. Further, the horizontal axis of the spectrum is not limited to the wavelength, and may be energy or wave number or the like. Further, the horizontal axis of the spectrum is not limited to an absolute value, and may be a relative value such as a shift of a wavelength from a specific wavelength.

Next, the processing performed by the signal analysis device 1 will be described. 3 and 4 are flowcharts showing the procedure of processing performed by the signal analysis device 1 of the first embodiment. The CPU 11 follows the computer program to perform the following processing. The self-measuring device 3 inputs spectral distribution data to the dielectric surface portion 15, and the CPU 11 stores the spectral distribution data in the memory portion 14 (S1). The spectral distribution data is the data that relates the two-dimensional coordinates of each point on the sample to the data of the spectrum obtained from each point. The spectral data is the data that relates the wavelength to the signal strength. Next, the CPU 11 generates signal distribution data (S2) indicating the intensity distribution of a plurality of specific signals from the spectral distribution data. Specifically, in step S2, the CPU 11 reads the signal strength of the specific signal identified by the predetermined wavelength from the spectrum of each point, and generates the signal intensity corresponding to each point on the two-dimensional coordinate system. Signal distribution data. That is, the signal distribution data is data in which the two-dimensional coordinates of each point on the sample are associated with the signal intensity at a specific wavelength. The memory unit 14 stores a plurality of wavelengths as wavelengths of a specific signal in advance, and the CPU 11 generates signal distribution data for each of a plurality of specific signals. That is, in step S2, a plurality of signal distribution data are generated. In addition, the wavelength of the specific signal can also be included in the computer program 21. Also, specific signals can be identified by energy or wave number. Moreover, the specific signal may not be identified based on the peak position in the spectrum, but based on the signal waveform in the spectrum. Further, the signal analysis device 1 may be configured to execute the processing in and after step S3 by inputting the signal distribution data generated externally.

Then, the CPU 11 displays a signal distribution image indicating the intensity distribution of the specific signal on the two-dimensional coordinate system on the display unit 17 based on the generated signal distribution data (S3). 5A, 5B, 5C, and 5D are schematic diagrams showing an example of a signal distribution image. In FIGS. 5A, 5B, 5C, and 5D, four signal distribution images obtained from one spectral distribution are shown. The portion marked with hatching on the graph indicates the portion of the specific signal whose intensity is greater than zero. That is, the intensity of the signal in the portion where the intensity of the specific signal is greater than 0 is different at each point. The four signal distribution images shown in FIGS. 5A, 5B, 5C, and 5D represent the intensity distributions of the different signals. The intensity distribution shown in FIG. 5A is set as the intensity distribution of the signal a, the intensity distribution shown in FIG. 5B is set as the intensity distribution of the signal b, and the intensity distribution shown in FIG. 5C is set as the intensity distribution of the signal c. The intensity distribution shown in 5D is set to the intensity distribution of the signal d. In the case where the spectrum is a characteristic X-ray spectrum, the signal distribution image indicates the concentration distribution of a specific element contained in the sample. The signal analysis device 1 performs a process of obtaining a distribution of a plurality of spectra after step S4, and the combinations of the intensities of the plurality of specific signals are different in the distribution of the plurality of spectra. The distribution of a plurality of spectra corresponds to the distribution of a plurality of substance components having different contents of a plurality of specific elements in a sample.

Next, the CPU 11 accepts the selection of n specific signals from a plurality of specific signals for displaying the signal distribution image by the user operating the input unit 16 (S4). n is an integer of 2 or more, and the CPU 11 accepts the selection of two or more specific signals in step S4. For example, assume that signals a and d indicating the signal distribution image are selected in FIGS. 5A and 5D. In addition, the CPU 11 can also automatically and appropriately select a specific signal. Then, the CPU 11 makes the selection The n signal distribution data are stored in the memory unit 14 (S5). Next, the CPU 11 generates n-dimensional data formed by a combination of the intensities of the selected n specific signals (S6). Specifically, the CPU 11 generates n-dimensional coordinate points on the n-dimensional space defined by the combination of the strengths of the selected n specific signals for each point on the two-dimensional coordinate system, and generates a two-dimensional coordinate system on the two-dimensional coordinate system. The n-dimensional data associated with the n-dimensional coordinates of each point and the n-dimensional coordinates.

Fig. 6 shows an example of a scattergram obtained by plotting n-dimensional coordinate points on n-dimensional coordinates. In Fig. 6, the case where the signals a and d are selected as n = 2 of the specific signal is shown. In Fig. 6, the horizontal axis represents the intensity of the signal a, and the vertical axis represents the intensity of the signal d. An n-dimensional coordinate point is drawn on the n-dimensional space for each point on the two-dimensional coordinate system corresponding to the surface of the sample. The n-dimensional coordinate points also exist in the n-dimensional space. Further, the signal analysis device 1 may be configured to execute the processing of step S7 and subsequent steps for inputting n-dimensional data generated externally. The signal analysis device 1 performs a process of classifying a plurality of n-dimensional coordinate points into a plurality of clusters by an Expectation-Maximization (EM) algorithm after step S7.

Next, the CPU 11 accepts the initial value of the number of clusters by the user operating the input unit 16 (S7). The CPU 11 may perform a process of specifying an appropriate numerical value as the initial value of the number of clusters in step S7. Then, the CPU 11 performs initial setting of the probability distribution model of the n-dimensional coordinate points included in the cluster for each of the predetermined number of clusters (S8). Specifically, the CPU 11 sets a parameter indicating a probability distribution of the probability that each point on the n-dimensional space is included in each cluster. The parameters of the probability distribution include the central position on the n-dimensional space of each cluster. As a probability distribution, the maximum expected algorithm can be used A mixed Gaussian distribution or a mixed Poisson distribution equal probability distribution utilized in the (EM algorithm).

Next, the CPU 11 calculates the probability that each n-dimensional coordinate point on the n-dimensional space is included in each cluster based on the probability distribution model of each cluster (S9). The processing of step S9 corresponds to the E(Expectation) step in the maximum expected algorithm (EM algorithm). Then, the CPU 11 performs a process of updating the parameters of the probability distribution model of each cluster so that the overall likelihood increases (S10). Specifically, the parameters of the probability distribution of the center position on the n-dimensional space of each cluster are updated. The processing of step S10 corresponds to the M (maximization) step in the maximum expected algorithm (EM algorithm).

Next, the CPU 11 performs convergence determination of the maximum expected algorithm (EM algorithm) (S11). For the index of convergence, the value of the likelihood, the amount of change or the rate of change, or the value of the parameter of the probability distribution model, the amount of change or the rate of change may be equal to the indicator commonly used in the maximum expected algorithm (EM algorithm). For example, the CPU 11 determines that the convergence has occurred when the amount of change in the likelihood is less than or equal to a predetermined value, and determines that the amount of change in the likelihood is greater than the predetermined value. Further, the signal analysis device 1 may be configured to execute processing in steps S9, S10, and S11 using a maximum likelihood method other than the maximum expected algorithm (EM algorithm) or a maximum a posteriori probability estimation method. For example, the signal analysis device 1 can also perform processing using an algorithm having a soft k-means cluster or a Newton-Raphson method.

When the situation has not converged in step S11 (S11: NO), the CPU 11 returns the processing to step S9. When it is determined that the convergence has occurred (S11: YES), the CPU 11 determines whether or not a plurality of clusters exist on the n-dimensional space. The mutual distance is a plurality of clusters of a close distance less than or equal to a predetermined distance (S12). For example, in step S12, the CPU 11 calculates the Mahalanobis distance between the centers between the two clusters, and determines whether the calculated Mahalanobis distance is equal to or smaller than a predetermined value. Further, for example, the CPU 11 calculates the inner product of the vector toward the center between the two clusters, and determines that the mutual distance is equal to or smaller than the predetermined distance when the calculated inner product is closer to 1 than the specific threshold. The CPU 11 performs a process of determining the distance between the two clusters for the combination of all the clusters. In step S12, the CPU 11 can also perform determination using other methods. In the case where there are a plurality of clusters close to each other (S12: YES), the CPU 11 combines the close plurality of clusters (S13). Specifically, the CPU 11 performs a process of specifying the range of the plurality of clusters as the range of the new one cluster. In Fig. 6, the range of the cluster is indicated by a solid line. In the example shown in Figure 6, four clusters are obtained.

After the end of step S13, or when there is no cluster approaching each other in step S12 (S12: NO), the CPU 11 specifies the two-dimensional coordinate system corresponding to the n-dimensional coordinate point included in each cluster. Point, and separately generate a plurality of spectral distribution data of a plurality of different combinations of specific signals (S14). The distribution data of each spectrum is the relationship between the two-dimensional coordinates of each point on the sample and the data indicating the presence or absence of a specific spectrum at each point. The spectral distribution data is generated for each of a plurality of different spectra of a combination of the intensities of a plurality of specific signals. Next, the CPU 11 stores the distribution data of the generated individual spectra in the storage unit 14 (S15), and ends the processing.

7A, 7B and 7C are diagrams showing the distribution of various spectra A schematic diagram of an example of an image. Fig. 7A shows the distribution of the spectrum containing the signal a but not including the signal d, and Fig. 7B shows the distribution of the spectrum containing the signal a but containing the signal d. 7C shows the distribution of the spectrum including both the signal a and the signal d. Thus, a two-dimensional distribution of multiple spectra of different combinations of intensities of a plurality of specific signals can be obtained from the initial spectral distribution data. In the case where the spectrum is a characteristic X-ray spectrum, the distribution of the plurality of spectra corresponds to the distribution of the plurality of substance components having different contents of the plurality of elements in the sample. If it is assumed that the signal a corresponds to the element A and the signal d corresponds to the element D, then FIG. 7A shows the distribution of the substance components containing the element A but not including the element D. 7B shows the distribution of the substance components containing the element A but not including the element D, and FIG. 7C shows the distribution of the substance components including both the element A and the element D.

Further, the distribution of the spectrum obtained in the step S14 is usually a distribution of a plurality of spectra in which the combinations of the intensities of the plurality of signals are different, and therefore, in the present invention, distributions other than the distributions shown in FIGS. 7A, 7B, and 7C can be obtained. . For example, a combination of the intensity of a plurality of signals such as a spectrum in which the intensity of the signal a is 1 and the intensity of the signal d is 2, or the intensity of the signal a is 2 and the intensity of the signal d is 1 is obtained, and FIG. 7A and FIG. The distribution of the different spectra of the examples of 7B and 7C. Further, since there is a certain degree of dispersion in the cluster on the n-dimensional coordinates, the combination of the intensities of the signals included in the distribution of the respective spectra obtained in the step S14 may have a certain extent. For example, it is also possible to generate a distribution of a spectrum in which the intensity of the signal a is 1 or more and less than 2 and the intensity of the signal d is less than 1, the intensity of the signal a is less than 1 and the intensity of the signal d is 1 or more and not The distribution of the spectrum up to 2, and the intensity of the signal a and the signal d are all 2 or more spectral distributions.

In the case where the signal analysis device 1 is in the form of performing the processing in and after step S3 using the input signal distribution data, the CPU 11 generates a plurality of combinations indicating the combinations of the intensities of the plurality of signals in the same manner in step S14. Part of the distribution of the distribution on the sample. Further, in step S15, the CPU 11 causes the generated distribution data to be stored in the storage unit 14. In this form, the same distribution as that shown in FIGS. 7A, 7B, and 7C can be obtained. Similarly, the combination of the intensity of the signal can be obtained differently from the example of FIGS. 7A, 7B, and 7C on the distribution of the portions on the sample, and the combination of the intensities of the signals contained in the respective distributions can also be present. A certain degree of magnitude.

Further, the signal analysis device 1 may be configured to perform the process of determining the number of repetitions of the processes of steps S9 and S10 without performing the convergence determination in step S11. In this aspect, the signal analysis device 1 stores the predetermined number of repetitions of steps S9 and S10 in the memory unit 14 in advance. In step S11, the CPU 11 determines whether or not the number of repetitions of the processing has reached a predetermined number of times, and returns the processing to step S9 when the number of repetitions of the processing has not reached the predetermined number of times, when the number of repetitions of the processing has reached a predetermined number of times. The process proceeds to step S12. As a predetermined number of repetitions of the process, it is defined that the likelihood of the entire plurality of clusters is the number of times that is empirically sufficient. The predetermined number of times is, for example, 100 times. The signal analysis device 1 can shorten the calculation time by repeating the repeated operations of the processing a predetermined number of times regardless of whether or not the convergence condition is satisfied. Further, the signal analysis device 1 may perform both the convergence determination and the number determination, and may proceed to the step when the convergence condition is satisfied until the number of repetitions of the process reaches a predetermined number of times. The form of the processing of S12.

As described above, the signal analysis device 1 generates an intensity distribution of a plurality of specific signals from the spectral distribution measured by the measuring device 3 such as an EDX device, and the intensity of the n specific signals is obtained by the maximum expected algorithm (EM algorithm). The n-dimensional coordinate points on the n-dimensional space defined by the combination are classified into a plurality of clusters. In the points on the spectral distribution corresponding to the n-dimensional coordinate points included in the same cluster, the combinations of the intensities of the plurality of specific signals included in the spectrum are substantially the same, and the shapes of the spectra are substantially equal. In the points on the spectral distribution corresponding to the n-dimensional coordinate points contained in the different clusters, the combinations of the intensities of the plurality of specific signals contained in the spectrum are different, and the shapes of the spectra are different from each other. The distribution of spectra having different shapes is generated one by one by specifying points on the two-dimensional coordinate system corresponding to the n-dimensional coordinate points included in each cluster. The distribution of the spectra of different shapes represents the distribution of the material components produced by the spectra of the various shapes. For example, a distribution of a plurality of components having different compositions contained in a sample measured by an EDX apparatus can be obtained. More specifically, when the sample measured by the EDX apparatus is rock, the distribution of various mineral components contained in the sample can be obtained. In other words, when the measuring device 3 is a device such as a Raman spectroscopic device, the signal analyzing device 1 can obtain the distribution of various substance components contained in the sample.

In the present invention, clustering can be automatically performed by a maximum expected algorithm (EM algorithm). Therefore, the classification of the n-dimensional coordinate points can be performed excluding the subjective subject of the user, so that accurate signal analysis can be performed without subjective influence of the user. Further, in the present invention, the distribution of the substance components can be obtained without identifying the substance components contained in the sample. Also in the present invention, an optional Multiple specific signals used in the analysis. By limiting the signal of the analysis target, it is possible to perform the performance of the analysis of the analysis of the distribution of the substance component that needs to be investigated, such as the distribution of the substance component that is not required to be investigated, from the analysis target. It also shortens the calculation time required for analysis. Also in the present invention, an initial value of the number of clusters can be specified. By performing the detailed analysis by making the initial value of the number of clusters large, the initial value of the number of clusters can be made small, and the type of the substance component to be distributed can be limited, and the initial value of the number of clusters can be specified. Adjust the performance of the analysis. It also adjusts the time required for the analysis. Further, in the present invention, by grouping a plurality of clusters close to each other into one, even when the initial value of the number of clusters is excessive, an appropriate number of distributions of the substance components can be obtained. Further, the signal analysis device 1 may be configured to store the parameters of the maximum expected algorithm (EM algorithm) in the memory unit 14 in the process of the maximum expected algorithm (EM algorithm). In this aspect, when the signal analysis device 1 analyzes the spectral distribution obtained from the same sample, it is possible to shorten the possibility of processing by using the already stored parameters.

(Embodiment 2)

The configuration of the signal analysis device 1 of the second embodiment is the same as that of the first embodiment. 8 and 9 are flowcharts showing the procedure of processing performed by the signal analysis device 1 of the second embodiment. The CPU 11 follows the computer program to perform the following processing. The CPU 11 executes the processing of steps S1 to S8 which are the same as in the first embodiment. After the end of step S8, the CPU 11 calculates the probability that each n-dimensional coordinate point on the n-dimensional space is included in each cluster based on the probability distribution model of the plurality of clusters (S21). Next, the CPU 11 performs a process of updating the parameters of the probability distribution model of each cluster so that the overall likelihood increases (S22). Then, the CPU 11 performs convergence determination of the maximum expected algorithm (EM algorithm) (S23). When it is not yet converged (S23: NO), the CPU 11 returns the processing to step S21. When it is determined that the convergence has occurred (S23: YES), the CPU 11 stores the parameters of the probability distribution model of each cluster in the storage unit 14 (S24), and ends the processing. As described above, the signal analysis device 1 generates parameters of a plurality of clusters in the n-dimensional space in the processes of steps S1 to S8 and steps S21 to S24. Further, similarly to the first embodiment, the CPU 11 may perform the process of determining the number of repetitions of the processes of steps S21 and S22 without performing the convergence determination in step S23.

Further, the signal analysis device 1 performs processing for generating distribution data of a plurality of spectra having different combinations of the intensities of the plurality of specific signals, based on the generated parameters of the plurality of clusters. The CPU 11 first reads out the parameters of the plurality of clustered probability distribution models stored in the storage unit 14 to the RAM 12 (S31). Next, the CPU 11 accepts the threshold for determining whether or not the distance between the clusters in the n-dimensional space is close by the user operating the input unit 16 (S32). In step S32, the CPU 11 inputs, for example, a threshold value of the Mahalanobis distance between the centers of the clusters or a threshold value of the inner product of the vectors toward the center between the clusters. Then, the CPU 11 determines whether or not a plurality of clusters having a distance in which the mutual distance in the n-dimensional space is less than or equal to the distance corresponding to the threshold value exists in the plurality of clusters based on the accepted threshold value (S33). In step S33, the CPU 11 calculates, for example, the Mahalanobis distance between the centers of the clusters, and determines whether or not the calculated Mahalanobis distance is less than or equal to the accepted threshold. For another example, the CPU 11 calculates the inner product of the vector toward the center between the two clusters, and calculates the inner product from the accepted threshold. When the value is closer to 1, it is determined that the mutual distance is close. The CPU 11 performs a process of determining the distance between the two clusters for the combination of all the clusters. In step S33, the CPU 11 can also perform determination using other methods.

In the case where there are a plurality of clusters close to each other (S33: YES), the CPU 11 combines the close plurality of clusters (S34). After the end of step S34, or when there is no cluster that is close to each other in step S33 (S33: NO), the CPU 11 individually generates distribution data of a plurality of spectra different in the combination of the intensities of the plurality of specific signals, and makes individual The distribution data of the spectrum is stored in the memory unit 14 (S35). Next, the CPU 11 displays the distribution of the plurality of spectra on the display unit 17 based on the distribution data of the plurality of spectra generated (S36). For example, as shown in FIGS. 7A to 7C, the CPU 11 displays an image indicating the distribution of the spectrum. After the end of step S36, the CPU 11 ends the processing. The signal analysis device 1 repeats the processing of steps S31 to S36 based on the processing instruction input to the input unit 16 by the user's operation.

As described above, in the present embodiment, the signal analysis device 1 can repeat the processes of steps S31 to S36 a plurality of times based on the parameters obtained by the processes of steps S1 to S8 and steps S21 to S24. In the processing of steps S31 to S36, the results differ depending on the threshold value for determining the distance between the clusters. For example, when the distance corresponding to the threshold is small, the number of clusters increases, and the number of spectrums at which distribution can be obtained increases, so that the number of substance components distributed in the sample can be increased. Conversely, when the distance corresponding to the threshold is large, the number of clusters is reduced, the type of spectrum in which distribution is available is reduced, and the number of substance components that can be distributed in the sample is reduced. Therefore, there is a threshold to adjust in order to obtain a moderate amount of material composition. The need to tailor a moderate value. By repeating the processing of steps S31 to S36 in the signal analysis device 1 while changing the threshold value input by the user, an appropriate result for the user can be obtained. Since the processes of steps S1 to S8 and steps S21 to S24 are separated from the processes of steps S31 to S36 and are not repeated, processing with high load can be avoided, and the processing efficiency of the signal analysis device 1 becomes high. Further, the user can perform the signal analysis of the spectral distribution by the signal analysis device 1 by confirming the processing result corresponding to the input threshold and repeating the operation of changing the threshold.

Further, in the first and second embodiments, the signal analysis device 1 is connected to one measurement device 3. However, the signal analysis device 1 of the present invention may be configured such that a plurality of measurement devices 3 can be connected. Further, the signal analysis device 1 may be configured to perform the same signal analysis process using the intensity distribution of the signals measured by the plurality of measurement devices 3 that are measured by the same sample, and obtain the sample in the sample. The distribution of the various parts of the combination of the intensity of the signal measured by various assays in the contained portion. In this aspect, the signal analysis device 1 can perform detailed analysis that cannot be performed using only the measurement result of the single measurement device 3. For example, by analyzing the measurement results using the EDX apparatus and the Raman spectroscopic apparatus, the distribution of the portion having the crystal structure in which the specific Raman light is generated among the substance components that generate the specific X-ray spectrum can be obtained.

Further, the signal analysis device 1 is not limited to a form in which data is received from the connected measurement device 3, and may be a mode in which signal analysis is performed by inputting an intensity distribution of a signal measured by an unconnected measurement device. Moreover, the intensity distribution of the signal that can be analyzed by the signal analysis device 1 is not limited to being self-contained in the laboratory. The intensity distribution measured by the internally measured sample may also be a more general measurement data. For example, according to the present invention, the distribution of celestial bodies having a large illuminating intensity of both visible light and X-rays can be obtained from the celestial observation results obtained by using visible light and X-rays.

[Industrial availability]

The present invention is for obtaining a distribution of desired components in a measurement target such as a distribution of a specific substance component, based on an intensity distribution of a plurality of signals obtained from a measurement target using a single measurement device or a plurality of measurement devices such as an EDX device.

1‧‧‧Signal analysis device

2‧‧‧Recording media

3‧‧‧Measurement device

11‧‧‧CPU (calculation department)

12‧‧‧RAM

13‧‧‧ Drive Department

14‧‧‧Memory Department

15‧‧‧ face

16‧‧‧ Input Department

17‧‧‧Display Department

21‧‧‧ computer program

S1~S15, S21~S24, S31~S36‧‧‧ steps

Fig. 1 is a block diagram showing the configuration of a signal analysis device of the present invention.

Fig. 2 is a schematic characteristic diagram showing an example of a spectrum.

Fig. 3 is a flow chart showing the procedure of processing performed by the signal analysis device of the first embodiment.

Fig. 4 is a flow chart showing the procedure of processing performed by the signal analysis device of the first embodiment.

Fig. 5A is a schematic diagram showing an example of a signal distribution image.

Fig. 5B is a schematic diagram showing an example of a signal distribution image.

Fig. 5C is a schematic diagram showing an example of a signal distribution image.

Fig. 5D is a schematic diagram showing an example of a signal distribution image.

Fig. 6 shows an example of a scattergram obtained by plotting n-dimensional coordinate points on n-dimensional coordinates.

Fig. 7A is a schematic diagram showing an example of an image representing a plurality of spectral distributions.

Fig. 7B is a schematic diagram showing an example of an image representing a plurality of spectral distributions.

Fig. 7C is a schematic diagram showing an example of an image representing a plurality of spectral distributions.

Fig. 8 is a flow chart showing the procedure of processing performed by the signal analysis device of the second embodiment.

Fig. 9 is a flowchart showing the procedure of processing performed by the signal analysis device of the second embodiment.

1‧‧‧Signal analysis device

2‧‧‧Recording media

3‧‧‧Measurement device

11‧‧‧CPU (calculation department)

12‧‧‧RAM

13‧‧‧ Drive Department

14‧‧‧Memory Department

15‧‧‧ face

16‧‧‧ Input Department

17‧‧‧Display Department

21‧‧‧ computer program

Claims (11)

A signal analysis device is characterized in that a spectral distribution of a spectrum including one or more signals is defined for each point on a two-dimensional coordinate system, and a plurality of spectral distributions of different combinations of specific signals are obtained, and the characteristics thereof are characterized. The signal analysis device includes: a mechanism for generating an intensity distribution of a plurality of specific signals from the spectral distribution; and memorizing the intensity of a specific signal of n (n is an integer of 2 or more) in the intensity distribution of the plurality of specific signals generated. a mechanism for distributing n-dimensional coordinate points in an n-dimensional space defined by a combination of the intensities of n specific signals for each point included in the spectral distribution; a mechanism for specifying the number of clusters The plurality of clusters are used to classify the generated plurality of n-dimensional coordinate points according to locations on the n-dimensional space; generating a probability distribution model for each cluster as a total of n probability density functions defined by the respective dimensions a form of product, wherein the probability distribution model represents a probability of n-dimensional coordinate points contained in each cluster; a probability calculation mechanism performs calculation based on the probability distribution model The processing of the probability that each of the generated n-dimensional coordinate points is included in each cluster; the model updating mechanism, the likelihood that the classification of the plurality of n-dimensional coordinate points obtained from the calculated probability becomes larger And updating the probability distribution model of each cluster; repeating the mechanism, causing the probability calculation mechanism and the model updating mechanism to repeatedly perform processing; and designating, according to the plurality of clusters, the n-dimensional coordinate point pairs included in each cluster The distribution should be based on the distribution within the above spectral distribution, thereby generating a distribution of n different specific signals with different combinations of multiple spectral distributions. The signal analysis device of claim 1, wherein the repeating mechanism causes the probability calculating means and the model updating means to repeatedly perform processing until a predetermined convergence condition is satisfied. The signal analysis device of claim 2, wherein the probability calculation means, the model update means, and the repeating means perform processing following a maximum desired algorithm. A signal analysis device that obtains a distribution of a plurality of types of different combinations of intensities of the plurality of signals measured in a portion included in the measurement target based on intensity distributions of a plurality of signals measured from the same measurement target. The method includes: a mechanism for storing an intensity distribution of n signals; and a mechanism for generating n-dimensional coordinate points in an n-dimensional space defined by a combination of strengths of n signals for each point in the measurement target; a number of clusters of mechanisms for classifying the generated plurality of n-dimensional coordinate points according to locations on the n-dimensional space; generating a probability distribution model for each cluster as defined by each dimension a mechanism for summing the product of n probability density models, wherein the probability distribution model represents a probability of n-dimensional coordinate points included in each cluster; and a probability calculation mechanism performs calculation based on the probability distribution model calculation The processing of the probability that each n-dimensional coordinate point is included in each cluster; the model updating mechanism, the degree of classification of the plurality of n-dimensional coordinate points obtained from the calculated probability Become bigger and update the clusters a process of the probability distribution model; the repetition mechanism causes the probability calculation mechanism and the model update mechanism to repeatedly perform processing; and the points corresponding to the n-dimensional coordinate points included in each cluster are respectively designated by the plurality of clusters in the measurement target Distribution, whereby a mechanism for generating a plurality of distributions among the above-described measurement targets is generated. The signal analysis device of claim 4, wherein the repeating mechanism causes the probability calculating means and the model updating means to repeatedly perform processing until a predetermined convergence condition is satisfied. The signal analysis device of claim 5, wherein the probability calculation means, the model update means, and the repeating means perform processing following a maximum desired algorithm. The signal analysis device according to any one of claims 1 to 6, further comprising a mechanism for collecting a plurality of clusters in the plurality of clusters whose mutual distances in the n-dimensional space are less than or equal to a specific distance into one cluster. The signal analysis device according to any one of claims 1 to 6, further comprising a mechanism for accepting an initial value of the number of clusters. The signal analysis device of claim 7 further includes a mechanism for accepting an initial value of the number of clusters. A signal analysis method for determining the intensity of a plurality of specific signals by specifying a spectral distribution of a spectrum including one or more signals for each point on a two-dimensional coordinate system by a computer including a calculation unit and a memory unit The combination of different spectral distributions, the signal analysis method is characterized by the following steps: And generating, according to the spectral distribution, an intensity distribution of the plurality of specific signals by the calculating unit; and storing, by the memory unit, an intensity distribution of the n specific signals in the intensity distribution of the generated plurality of specific signals; In each of the included points, the calculation unit generates an n-dimensional coordinate point in an n-dimensional space defined by a combination of the intensities of the n specific signals; and the calculating unit executes a process of specifying a number of clusters, The plurality of clusters are configured to classify the generated plurality of n-dimensional coordinate points according to positions on the n-dimensional space; and the calculating unit generates a probability distribution model for each cluster as a total of n defined by each dimension. a form of a product of a probability density function, wherein the probability distribution model represents a probability of an n-dimensional coordinate point included in each cluster; performing a probability calculation process with the calculation unit, the probability calculation process being based on the probability distribution model Calculating a probability that each of the generated n-dimensional coordinate points is included in each cluster; performing a model update process by the calculation unit, the model update process being obtained from the calculated probability Updating the probability distribution model of each cluster in such a manner that the likelihood of classification of the plurality of n-dimensional coordinate points becomes larger; repeating the above-described probability calculation processing and the above-described model update processing by the calculation unit; respectively designating by multiple clusters a distribution corresponding to an n-dimensional coordinate point included in each cluster within the above spectral distribution, thereby generating by the calculation unit The combination of the intensity of the n specific signals is different from the distribution of the plurality of spectra; and the distribution of the plurality of spectra generated by the memory is memorized. A computer program product for causing a computer to perform a process of determining a combination of intensities of a plurality of specific signals according to a spectral distribution of a spectrum including one or more signals for each point on a two-dimensional coordinate system. a plurality of spectral distributions, and the computer program product is characterized by causing the computer to perform a process comprising: generating an intensity distribution of the plurality of specific signals according to the spectral distribution; and generating the intensity for each point included in the spectral distribution In a plurality of specific signals distributed, an n-dimensional coordinate point in an n-dimensional space defined by a combination of strengths of n specific signals is generated; a number of clusters is specified, and the number of the plurality of clusters is used to The generated plurality of n-dimensional coordinate points are classified according to positions on the n-dimensional space; a probability distribution model is generated for each cluster as a form of a product of a total of n probability density functions defined by the respective dimensions, wherein the probability distribution The model represents the probability of the n-dimensional coordinate points contained in each cluster; the probability calculation process is performed, and the generated n-dimension is calculated based on the probability distribution model Probability that each punctuation is included in each cluster; performing model update processing to update the probability distribution model of each cluster in such a manner that the likelihood of classification of a plurality of n-dimensional coordinate points obtained from the calculated probability becomes larger; Repeating the above-described probability calculation processing and the above-described model update processing; and designating distributions of points corresponding to n-dimensional coordinate points included in each cluster in the spectral distribution by a plurality of clusters to generate n specific signals The combination of intensity is different for the distribution of multiple spectra.