CN116415161A - Fitting complementation method for geological drilling detection and different physical wave detection data of string-shaped karst cave - Google Patents
Fitting complementation method for geological drilling detection and different physical wave detection data of string-shaped karst cave Download PDFInfo
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Abstract
The invention provides a fitting complementation method of geological drilling detection and different physical wave detection data of a string-shaped karst cave, which can deeply analyze stratum distribution trend and provide data for the design of foundation pit support and foundation of the string-shaped karst cave; the method comprises the following steps: firstly classifying soil according to the result of geological drilling detection of the string-shaped karst cave, carrying out hierarchical division on the soil classified in the same way, and carrying out physical wave characteristic assignment on the soil of each hierarchy; adopting a physical wave detection instrument to detect physical waves in the area where the drilling hole is positioned; carrying out Gaussian mixture clustering on the detected data samples, and matching the samples to different clusters; bringing the data of the borehole detection assignment into a final model of Gaussian mixture clustering; finally, determining whether the drilling detection result is consistent with the physical wave detection result; the method can embody the trend of the change of the soil quality of the same cluster of the string-shaped karst cave along with the density; the soil layer distribution is well known, and the mechanical analysis is fully performed on single soil.
Description
Technical Field
The invention belongs to the technical field of engineering exploration, and particularly relates to a fitting complementation method of geological drilling detection and different physical wave detection data of a string-shaped karst cave.
Background
String karst cave geology is a relatively special geological morphology that is difficult to effectively detect and understand in geological exploration.
The current common physical detection methods are mainly divided into drilling detection and physical wave detection; wherein, physical wave detection mainly includes: seismic exploration, electromagnetic exploration, and the like.
When the geology of the string-shaped karst cave is detected, the detection is usually carried out by adopting a mode of combining drilling detection and physical wave detection, firstly, the soil texture is adopted through drilling detection, and then the integrity of drilling detection is supplemented through physical wave detection; the integrity of the supplementary borehole detection by physical wave detection often involves fitting of data to achieve three-dimensional imaging of the subsurface cavity.
The existing three-dimensional imaging generated by the physical wave detection data is often simply divided into types of soil, so that progressive change trends of the soil in each type cannot be reflected or the progressive change trends of the soil in each type cannot be described accurately, detailed understanding of soil layer distribution is not facilitated, and full mechanical analysis is performed on single soil in the early stage of construction.
Disclosure of Invention
The invention aims to overcome the problems existing in the prior art and greatly improve the technical effect on the basis of the prior art; for this reason, the invention provides a fitting complementation method of geological drilling detection of a string-shaped karst cave and detection data of different physical waves, which comprises the following steps:
1. analyzing the drilling detection result according to the string-shaped karst cave geological drilling detection result, and classifying the soil detected by the drilling;
2. carrying out hierarchical division on the soil with the same classification, and carrying out physical wave characteristic assignment on the soil of each hierarchy according to the propagation characteristics of different physical waves in the soil; the physical wave characteristic assignment refers to the propagation characteristic value of the physical wave in the corresponding soil hierarchy after the soil hierarchy is divided;
3. according to the region where the drilling is detected, adopting a physical wave detection instrument to detect physical waves of the region where the drilling is detected; and extracting detection data sample { x }, based on the result of physical wave detection 1 ,x 2 ,…x n },x j Representing physical wave characteristic values corresponding to various places of the string-shaped karst cave, wherein j=1, 2 and … n;
4. carrying out Gaussian mixture clustering on the detected data samples, matching the samples to different clusters, and recording posterior probability of sample elements in each cluster and a final model of the Gaussian mixture clustering;
the posterior probability determines the final cluster division of the sample, and the posterior probability formula of the Gaussian mixture cluster is as follows:
wherein alpha is i 、u i Sum sigma i Representing sample x j The parameters of the corresponding ith Gaussian mixture component, k represents the total of k Gaussian mixture component compositions of the Gaussian mixture distribution, alpha h 、u h Sum sigma h Parameters representing any one of the gaussian mixture components in the gaussian mixture distribution; the Gaussian mixture distribution is composed of k Gaussian mixture components, each of the mixture components corresponds to one Gaussian mixture distribution, and the decision formula of the Gaussian mixture distribution is as follows:
wherein x is an element in the sample, alpha h 、u h Sum sigma h Parameters for each gaussian mixture component in the gaussian mixture distribution;
the final model of the Gaussian mixture cluster is obtained by a parameter alpha of posterior probability h 、u h Sum sigma h The final set of parameters alpha is determined by continuous iterative updating of the parameters until the stopping condition is met h 、u h Sum sigma h The determined posterior probability formula is the final model of Gaussian mixture clustering;
5. substituting data of physical wave characteristic assignment for different layers in the same soil property classification of drilling detection into a final model of Gaussian mixture clustering to obtain posterior probability of each layer in all soil property classifications of drilling detection, and solving cluster marks of each layer in all soil property classifications by the posterior probability, wherein a calculation formula of the cluster marks is as follows:
wherein, gamma ji Representing a sample determined by the ith Gaussian mixture component as x j Posterior probability, eta j Representing sample x j A final cluster partition label of (a);
6. the final cluster division marks of all layers in all soil classifications are detected through the obtained drill holes, the cluster division marks of all layers in the same type of soil are respectively matched into the same cluster of Gaussian mixture clusters, and whether the drill hole detection result is consistent with the physical wave detection result is determined; if the probabilities corresponding to the final cluster division marks are consistent, combining the probabilities corresponding to the final cluster division marks obtained by the drilling detection data and the physical wave detection data, and carrying out fitting analysis on the probabilities corresponding to the final cluster division marks of the same cluster of soil to obtain a probability layering curve of the same cluster of soil; and determining the density distribution curve of the geological soil layer of the string-shaped karst cave according to the probability layering curve.
Further, classifying the earth detected by the borehole includes: and classifying the soil detected by the drilling according to the soil condition corresponding to the drilling detection result.
Further, the grading of the same classified soil property includes: hierarchical division is carried out according to the density of the soil of the same class, and the soil of the same class is divided into different layers: (A) 1 ,A 2 ,…A n ) Wherein A is 1 To A n The density of (2) gradually increases.
Further, the assigning physical wave characteristics to the soil of each level includes: according to the intensity change characteristics of the physical wave at the propagation speeds of the soil with different densities, the propagation speed of the physical wave at different soil layers is determined, and the determined propagation speed is taken as a propagation characteristic value of the corresponding soil layer.
Further, the method is characterized in that the result according to the physical wave detection comprises: extracting detection data sample { x }, based on the result of physical wave detection 1 ,x 2 ,…x n -wherein the sample element x 1 To x n And representing the propagation speed corresponding to each point of the string-shaped karst cave extracted from the physical wave detection result, namely the physical wave characteristic value corresponding to each point of the string-shaped karst cave.
Further, the flow of Gaussian mixture clustering includes: a) Input sample set d= { x 1 ,x 2 ,…x n Setting the number k of Gaussian mixture components, and initializing model parameters { (alpha) of Gaussian mixture distribution i ,u i ,∑ i ) I 1 is less than or equal to i is less than or equal to k; b) Calculation of x by posterior probability formula j Posterior probability in each mixed component; c) Sample x by posterior probability j Model parameters { (α) i ,u i ,∑ i ) I1.ltoreq.i.ltoreq.k is updated to { (α) i ',u i ',∑ i ' i 1 is less than or equal to i is less than or equal to k }; d) Bringing the newly updated model parameters into step b) and generating new model parameters, and iterating the process until a stop condition is met; e) Determining x from cluster marking formula j Cluster marking η j And x is taken as j Grouping into corresponding clusters C ηj The method comprises the steps of carrying out a first treatment on the surface of the f) Output cluster division c= { C 1 ,C 2 ,…C k };
Further, the matching the cluster division marks of each level in the same soil texture into the same cluster of the Gaussian mixture cluster comprises the following steps: if the cluster division marks of all layers in the same soil property detected by drilling are completely consistent, verifying that the result of Gaussian mixture clustering is matched with drilling data, and proving that the drilling detection result is consistent with the physical wave detection result; if the cluster division marks of all layers in the same soil texture detected by drilling are not completely consistent, the result of Gaussian mixture clustering is verified to be not matched with drilling data, and the fact that the drilling detection result is inconsistent with the physical wave detection result is proved.
The beneficial effects of the invention are as follows:
the invention provides a fitting complementation method of geological drilling detection and different physical wave detection data of a string-shaped karst cave; the invention divides the same soil sample of drilling data into different layers, and assigns values to the soil samples of different layers; meanwhile, probability distribution analysis is carried out on the detection samples of the physical waves through a Gaussian mixture model, and the detection samples of the physical waves are divided into different clusters; bringing assignment values of different layers of the same soil sample of drilling data into a final Gaussian mixture model posterior probability function, so as to judge whether the drilling data and the physical wave detection data are consistent; finally, combining the probability corresponding to the final cluster division mark obtained by the drilling detection data and the physical wave detection data, performing data fitting to obtain a probability layering curve of the soil quality of the same cluster, and determining a density distribution curve of a geological soil layer of the string-shaped karst cave according to the probability layering curve, wherein the curve can reflect the trend of the soil quality of the same cluster of the string-shaped karst cave along with the change of the density; on the basis of the density distribution curve, the three-dimensional model showing the gradient of the same soil sample can be established, so that the detailed understanding of soil layer distribution and the full mechanical analysis of single soil property are facilitated.
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Fig. 1: the invention discloses a fitting complementation method flow chart of geological drilling detection and different physical wave detection data of a string-shaped karst cave.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings; it should be understood that the particular embodiments presented herein are illustrative and explanatory only and are not restrictive of the invention.
Referring to fig. 1, a flow chart of a complementary fitting method of geological borehole detection and different physical wave detection data of a string-shaped karst cave according to the present invention is shown, the flow chart comprises: step S100, analyzing the drilling detection result according to the string-shaped karst cave geological drilling detection result, and classifying the drilling detection soil; step S101, carrying out hierarchical division on the same classified soil, and carrying out physical wave characteristic assignment on the soil of each hierarchy according to the propagation characteristics of different physical waves in the soil; the physical wave characteristic assignment refers to the propagation characteristic value of the physical wave in the corresponding soil hierarchy after the soil hierarchy is divided; step S102, according to the region where the drilling detection is located, adopting a physical wave detection instrument to detect the physical wave of the region where the drilling is located; and extracting detection data sample { x }, based on the result of physical wave detection 1 ,x 2 ,…x n },x j Representing physical wave characteristic values corresponding to various places of the string-shaped karst cave, wherein j=1, 2 and … n; step S103, performing Gaussian mixture clustering on the detected data samples, matching the samples to different clusters, and recording the posterior probability and Gaussian mixture clustering of the sample elements in each clusterIs a final model of (a); step S104, substituting the data of physical wave characteristic assignment for different layers in the same soil property classification of the drilling detection into a final model of Gaussian mixture clustering to obtain posterior probability of each layer in all soil property classifications of the drilling detection, and solving cluster marks of each layer in all soil property classifications through the posterior probability; step S105, detecting final cluster division marks of all layers in all soil classifications through the obtained drill holes, respectively matching the cluster division marks of all layers in the same soil classification into the same cluster of Gaussian mixture clusters, and determining whether the drill hole detection result is consistent with the physical wave detection result; if the probabilities corresponding to the final cluster division marks are consistent, combining the probabilities corresponding to the final cluster division marks obtained by the drilling detection data and the physical wave detection data, and carrying out fitting analysis on the probabilities corresponding to the final cluster division marks of the same cluster of soil to obtain a probability layering curve of the same cluster of soil; and determining the density distribution curve of the geological soil layer of the string-shaped karst cave according to the probability layering curve.
Specifically, firstly classifying the soil according to the result of geological drilling detection of the string-shaped karst cave, carrying out hierarchical division on the soil classified in the same way, and carrying out physical wave characteristic assignment on the soil of each hierarchy; according to the region where the drilling is detected, adopting a physical wave detection instrument to detect physical waves of the region where the drilling is detected; extracting detection data sample { x } from the result of physical wave detection 1 ,x 2 ,…x n -a }; then, carrying out Gaussian mixture clustering on the detected data samples, matching the samples to different clusters, and recording posterior probability of sample elements in each cluster and a final model of the Gaussian mixture clustering; carrying out physical wave characteristic assignment on the drilling detection data into a final model of Gaussian mixture clustering to obtain posterior probability of each level in all soil classifications of the drilling detection, and solving cluster marks of each level in all soil classifications by the posterior probability; the method comprises the steps of determining whether a drilling detection result is consistent with a physical wave detection result or not by matching cluster division marks of all layers in the same soil in drilling detection into the same cluster of Gaussian mixture clusters; if the two types of the soil are consistent, combining the probability corresponding to the final cluster division mark obtained by the drilling detection data and the physical wave detection data, and carrying out the most analysis on the soil of the same clusterFitting analysis is carried out on probabilities corresponding to the final cluster division marks, and a probability layering curve of the soil quality of the same cluster is obtained; and determining the density distribution curve of the geological soil layer of the string-shaped karst cave according to the probability layering curve.
Step S100, analyzing the drilling detection result according to the string-shaped karst cave geological drilling detection result, and classifying the drilling detection soil; specifically, the method for classifying the soil properties detected by drilling holes comprises the following steps: and classifying the soil detected by the drilling according to the soil condition corresponding to the drilling detection result.
Step S101, carrying out hierarchical division on the same classified soil, and carrying out physical wave characteristic assignment on the soil of each hierarchy according to the propagation characteristics of different physical waves in the soil; the physical wave characteristic assignment refers to the propagation characteristic value of the physical wave in the corresponding soil hierarchy after the soil hierarchy is divided; specifically, the method for hierarchical division of the same classified soil texture comprises the following steps: hierarchical division is carried out according to the density of the soil of the same class, and the soil of the same class is divided into different layers: (A) 1 ,A 2 ,…A n ) Wherein A is 1 To A n The density of (2) gradually increases.
In the foregoing embodiment, specifically, the assigning the physical wave characteristics to the soil of each level includes: according to the intensity change characteristics of the physical wave at the propagation speeds of the soil with different densities, the propagation speeds of the physical wave at different soil layers in the same soil are determined, and the determined propagation speeds are regarded as propagation characteristic values of corresponding soil layers.
In the above embodiment, the physical wave may be a physical wave generated by an instrument survey such as an ultrasonic detector, a seismic prospecting instrument, a tube wave detector, and an electromagnetic prospecting instrument.
Step S102, according to the region where the drilling detection is located, adopting a physical wave detection instrument to detect the physical wave of the region where the drilling is located; and extracting detection data sample { x }, based on the result of physical wave detection 1 ,x 2 ,…x n },x j Representing physical wave characteristic values corresponding to various places of the string-shaped karst cave, wherein j=1, 2 and … n; in particular according toThe result of physical wave detection, a detection data sample { x }, is extracted 1 ,x 2 ,…x n -wherein the sample element x 1 To x n And representing the propagation speed corresponding to each point of the string-shaped karst cave extracted from the physical wave detection result, namely the physical wave characteristic value corresponding to each point of the string-shaped karst cave.
Step S103, carrying out Gaussian mixture clustering on the detected data samples, matching the samples to different clusters, and recording posterior probability of sample elements in each cluster and a final model of the Gaussian mixture clustering; in particular, the different clusters refer to the classification of samples into different classes; the Gaussian mixture clustering process comprises the following steps: a) Input sample set d= { x 1 ,x 2 ,…x n Setting the number k of Gaussian mixture components, and initializing model parameters { (alpha) of Gaussian mixture distribution i ,u i ,∑ i ) I 1 is less than or equal to i is less than or equal to k; b) Calculation of x by posterior probability formula j Posterior probability in each mixed component; c) Sample x by posterior probability j Model parameters { (α) i ,u i ,∑ i ) I1.ltoreq.i.ltoreq.k is updated to { (α) i ',u i ',∑ i ' i 1 is less than or equal to i is less than or equal to k }; d) Bringing the newly updated model parameters into step b) and generating new model parameters, and iterating the process until a stop condition is met; e) Determining x from cluster marking formula j Cluster marking η j And x is taken as j Grouping into corresponding clusters C ηj The method comprises the steps of carrying out a first treatment on the surface of the f) Output cluster division c= { C 1 ,C 2 ,…C k }。
In the above embodiment, specifically, the posterior probability determines the final cluster division of the sample, and the posterior probability formula of the gaussian mixture cluster is:
wherein alpha is i 、u i Sum sigma i Representing sample x j The parameters of the corresponding ith Gaussian mixture component, k represents the total of k Gaussian mixture component compositions of the Gaussian mixture distribution, alpha h 、u h Sum sigma h Parameters representing any one of the gaussian mixture components in the gaussian mixture distribution; the Gaussian mixture distribution is composed of k Gaussian mixture components, each of the mixture components corresponds to one Gaussian mixture distribution, and the decision formula of the Gaussian mixture distribution is as follows:
wherein x is an element in the sample, alpha h 、u h Sum sigma h Parameters for each gaussian mixture component in the gaussian mixture distribution;
in the above embodiment, specifically, the final model of the gaussian mixture cluster is a parameter α of a posterior probability h 、u h Sum sigma h The final set of parameters alpha is determined by continuous iterative updating of the parameters until the stopping condition is met h 、u h Sum sigma h The determined posterior probability formula is the final model of Gaussian mixture clustering;
step S104, carrying data of physical wave characteristic assignment on different layers in the same soil property classification of drilling detection into a final model of Gaussian mixture clustering to obtain posterior probability of each layer in all soil property classifications of drilling detection, and solving cluster marks of each layer in all soil property classifications according to the posterior probability, wherein a calculation formula of the cluster marks is as follows:
wherein, gamma ji Representing a sample determined by the ith Gaussian mixture component as x j Posterior probability, eta j Representing sample x j Is marked by the final cluster division.
Step S105, detecting final cluster division marks of all layers in all soil classifications through the obtained drill holes, respectively matching the cluster division marks of all layers in the same soil classification into the same cluster of Gaussian mixture clusters, and determining whether the drill hole detection result is consistent with the physical wave detection result; if the probabilities corresponding to the final cluster division marks are consistent, combining the probabilities corresponding to the final cluster division marks obtained by the drilling detection data and the physical wave detection data, and carrying out fitting analysis on the probabilities corresponding to the final cluster division marks of the same cluster of soil to obtain a probability layering curve of the same cluster of soil; and determining the density distribution curve of the geological soil layer of the string-shaped karst cave according to the probability layering curve.
In the above embodiment, specifically, the matching the cluster division marks of each level in the same soil property into the same cluster of the gaussian mixture cluster includes: cluster division of Gaussian mixture model is based on physical wave in sample x j Is divided by the propagation speed; the physical wave characteristics of each layer of the same earth quality of the drilling data are assigned, and the physical waves of the same kind are detected according to the physical waves to be converted; therefore, if the cluster division marks of all layers in the same type of soil detected by drilling are completely consistent, the propagation speed of the physical wave in all layers of soil samples in the same type of soil detected by drilling is proved to be completely matched with the speed of data in a certain cluster analyzed by physical wave detection, the result of Gaussian mixture clustering is verified to be matched with drilling data, and the drilling detection result and the physical wave detection result are proved to be consistent; if the cluster division marks of all layers in the same soil texture detected by drilling are not completely consistent, the result of Gaussian mixture clustering is verified to be not matched with drilling data, and the fact that the drilling detection result is inconsistent with the physical wave detection result is proved.
It is to be understood that the above-described embodiments are one or more embodiments of the invention, and that many other embodiments and variations thereof are possible in accordance with the invention; variations and modifications of the invention, which are intended to be within the scope of the invention, will occur to those skilled in the art without any development of the invention.
Claims (7)
1. A method for fitting and complementing geological borehole detection and different physical wave detection data of a string-shaped karst cave, which is characterized by comprising the following steps:
1) Analyzing the drilling detection result according to the string-shaped karst cave geological drilling detection result, and classifying the soil detected by the drilling;
2) Carrying out hierarchical division on the soil with the same classification, and carrying out physical wave characteristic assignment on the soil of each hierarchy according to the propagation characteristics of different physical waves in the soil; the physical wave characteristic assignment refers to the propagation characteristic value of the physical wave in the corresponding soil hierarchy after the soil hierarchy is divided;
3) According to the region where the drilling is detected, adopting a physical wave detection instrument to detect physical waves of the region where the drilling is detected; and extracting detection data sample { x }, based on the result of physical wave detection 1 ,x 2 ,…x n },x j Representing physical wave characteristic values corresponding to various places of the string-shaped karst cave, wherein j=1, 2 and … n;
4) Carrying out Gaussian mixture clustering on the detected data samples, matching the samples to different clusters, and recording posterior probability of sample elements in each cluster and a final model of the Gaussian mixture clustering;
the posterior probability determines the final cluster division of the sample, and the posterior probability formula of the Gaussian mixture cluster is as follows:
wherein alpha is i 、u i Sum sigma i Representing sample x j The parameters of the corresponding ith Gaussian mixture component, k represents the total of k Gaussian mixture component compositions of the Gaussian mixture distribution, alpha h 、u h Sum sigma h Parameters representing any one of the gaussian mixture components in the gaussian mixture distribution; the Gaussian mixture distribution is composed of k Gaussian mixture components, each of the mixture components corresponds to one Gaussian mixture distribution, and the decision formula of the Gaussian mixture distribution is as follows:
wherein x is an element in the sample, alpha h 、u h Sum sigma h Parameters for each gaussian mixture component in the gaussian mixture distribution;
the Gaussian mixtureThe final model of the synthetic cluster is defined by the posterior probability parameter alpha h 、u h Sum sigma h The final set of parameters alpha is determined by continuous iterative updating of the parameters until the stopping condition is met h 、u h Sum sigma h The determined posterior probability formula is the final model of Gaussian mixture clustering;
5) Substituting data of physical wave characteristic assignment for different layers in the same soil property classification of drilling detection into a final model of Gaussian mixture clustering to obtain posterior probability of each layer in all soil property classifications of drilling detection, and solving cluster marks of each layer in all soil property classifications by the posterior probability, wherein a calculation formula of the cluster marks is as follows:
wherein, gamma ji Representing a sample determined by the ith Gaussian mixture component as x j Posterior probability, eta j Representing sample x j A final cluster partition label of (a);
6) The final cluster division marks of all layers in all soil classifications are detected through the obtained drill holes, the cluster division marks of all layers in the same type of soil are respectively matched into the same cluster of Gaussian mixture clusters, and whether the drill hole detection result is consistent with the physical wave detection result is determined; if the probabilities corresponding to the final cluster division marks are consistent, combining the probabilities corresponding to the final cluster division marks obtained by the drilling detection data and the physical wave detection data, and carrying out fitting analysis on the probabilities corresponding to the final cluster division marks of the same cluster of soil to obtain a probability layering curve of the same cluster of soil; and determining the density distribution curve of the geological soil layer of the string-shaped karst cave according to the probability layering curve.
2. The method of fitting and complementing a string-like karst cave geological borehole survey with different physical wave detection data according to claim 1, wherein classifying the borehole detected soil comprises: and classifying the soil detected by the drilling according to the soil condition corresponding to the drilling detection result.
3. The method of fitting and complementing a string-like karst cave geological borehole survey with different physical wave detection data according to claim 1, wherein said hierarchically partitioning the same classified soil comprises: hierarchical division is carried out according to the density of the soil of the same class, and the soil of the same class is divided into different layers: (A) 1 ,A 2 ,…A n ) Wherein A is 1 To A n The density of (2) gradually increases.
4. The method of fitting and complementing a string-like karst cave geological borehole survey with different physical wave detection data according to claim 1, wherein said assigning physical wave characteristics to each level of soil comprises: according to the intensity change characteristics of the physical wave at the propagation speeds of the soil with different densities, the propagation speed of the physical wave at different soil layers is determined, and the determined propagation speed is taken as a propagation characteristic value of the corresponding soil layer.
5. The method of fitting and complementing a string-like karst cave geological borehole survey with different physical wave detection data according to claim 1, wherein the results from the physical wave detection comprise: extracting detection data sample { x }, based on the result of physical wave detection 1 ,x 2 ,…x n -wherein the sample element x 1 To x n And representing the propagation speed corresponding to each point of the string-shaped karst cave extracted from the physical wave detection result, namely the physical wave characteristic value corresponding to each point of the string-shaped karst cave.
6. The method for fitting and complementing a string-shaped karst cave geological borehole detection and different physical wave detection data according to claim 1, wherein the gaussian mixture clustering process comprises the following steps: a) Input sample set d= { x 1 ,x 2 ,…x n Setting the number k of Gaussian mixture components, and initializing model parameters { (alpha) of Gaussian mixture distribution i ,u i ,∑ i ) I 1 is less than or equal to i is less than or equal to k; b) Calculation of x by posterior probability formula j Posterior probability in each mixed component; c) General purpose medicinePosterior probability x of sample j Model parameters { (α) i ,u i ,∑ i ) I1.ltoreq.i.ltoreq.k is updated to { (α) i ',u i ',∑ i ' i 1 is less than or equal to i is less than or equal to k }; d) Bringing the newly updated model parameters into step b) and generating new model parameters, and iterating the process until a stop condition is met; e) Determining x from cluster marking formula j Cluster marking η j And x is taken as j Grouping into corresponding clusters C ηj The method comprises the steps of carrying out a first treatment on the surface of the f) Output cluster division c= { C 1 ,C 2 ,…C k }。
7. The method of fitting and complementing a string-like karst cave geological borehole survey with different physical wave detection data according to claim 1, wherein matching cluster division markers of each level in the same class of soil into the same cluster of gaussian mixture clusters comprises: if the cluster division marks of all layers in the same soil property detected by drilling are completely consistent, verifying that the result of Gaussian mixture clustering is matched with drilling data, and proving that the drilling detection result is consistent with the physical wave detection result; if the cluster division marks of all layers in the same soil texture detected by drilling are not completely consistent, the result of Gaussian mixture clustering is verified to be not matched with drilling data, and the fact that the drilling detection result is inconsistent with the physical wave detection result is proved.
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