CN117970526A - Geological model construction method containing comprehensive multi-source information of geophysics - Google Patents
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Abstract
The invention provides a geological model construction method containing comprehensive multi-source information of geophysics, which comprises the steps of firstly constructing a preliminary geological model and identifying geological abnormal bodies according to laboratory measurement results of geophysics and samples; then combining the preliminary geologic model and the geologic anomaly, and establishing a geophysical multidimensional geologic model; and finally, optimizing the geophysical multidimensional geologic model by combining the drilling data, the geological map and the remote sensing data. Through the fusion interpretation of the comprehensive geophysical, drilling, geological and other multi-source data, the accurate and fine interpretation of the work area to be interpreted is realized, and further powerful help can be provided for distinguishing the geological disasters of the coal field.
Description
Technical Field
The invention belongs to the technical field of mine geology, relates to a geological modeling method, and in particular relates to a geological model construction method containing comprehensive multi-source information of geophysics.
Background
Coal is an energy ballast stone in China, however, due to complex geological conditions in China, hidden geological disasters are seriously threatened in the coal production process, disasters such as gas, rock burst, water damage and the like are aggravated and coupled and overlapped along with the continuous increase of mining depth and mining intensity of a mine, the occurrence risk of coal disasters is also continuously increased, the disaster prevention and treatment difficulty is also greater, and the coal safety production situation is not optimistic. In the prior art, the detection means of the hidden disasters of the coal mine are more, but the detection result is mainly independently interpreted based on a single method, and is limited by the singleness and limitation of the method, and the unified geological-geophysical model is difficult to obtain by comprehensively interpreting the inversion result of the single method. Along with the improvement of the requirements of intelligent coal mine construction on detection precision, a fusion interpretation method among multiple types of detection data is more important.
Regarding the multi-source data comprehensive fusion interpretation method, the prior art mainly focuses on comprehensive processing, for example, an electromagnetic method and a magnetic method adopt cross gradient constraint to realize multi-parameter joint inversion, an L-BFGS method and the like are adopted to carry out multi-parameter joint inversion, and characteristics of coal seam pore water content, coal and rock firmness and the like are utilized to invert by utilizing seismic parameters and resistivity parameters. However, the above prior art mainly solves the problem of how to obtain an accurate geophysical model or a specific parameter feature by using a multi-parameter feature, and there is still a certain difficulty in translating and interpreting the geophysical model into a geological model.
Disclosure of Invention
Aiming at the defects and shortcomings in the prior art, the invention aims to provide a geological model construction method containing comprehensive multi-source information of geophysics, which solves the technical problem that the prior art is difficult to accurately interpret the conversion of the geophysical model into the geological model.
In order to solve the technical problems, the invention adopts the following technical scheme:
a geological model construction method containing comprehensive multi-source information of geophysics comprises the following steps:
Step one, multi-source data of a work area to be explained are obtained:
Establishing a physical property parameter data set, a geological abnormal body parameter data set and a geophysical parameter data set of a coal-rock sample; and acquiring drilling data and geological map.
Step two, normalized pretreatment of multi-source data:
and (3) carrying out normalized preprocessing on the physical property parameter data set, the geological abnormal body parameter data set, the geophysical parameter data set, the drilling data and the remote sensing data of the coal and rock sample obtained in the step one to obtain a normalized physical property parameter data set, a normalized geological abnormal body parameter data set, a normalized geophysical parameter data set, normalized drilling data and normalized remote sensing data of the coal and rock sample.
And thirdly, converting the normalized geophysical parameter data set obtained in the second step into a preliminary geological model by taking the normalized coal rock sample physical parameter data set obtained in the second step as a conversion standard.
And step four, identifying the geological abnormal body according to the normalized geophysical parameter data set and the normalized geological abnormal body parameter data set obtained in the step two.
And fifthly, combining the geological abnormal body identified in the fourth step with the preliminary geological model obtained in the third step to obtain the geophysical multidimensional geological model. If no geological abnormal body is identified, the preliminary geological model is a geophysical multidimensional geological model.
Step six, optimizing the geophysical multidimensional geologic model obtained in the step five:
Step 6.1, combining the drilling data acquired in the step one with a geological map, and drawing a drilling-geological section; drawing a plurality of horizontal and vertical straight lines on the drilling-geological section and the geophysical multidimensional geological model respectively, and dividing the two images into a plurality of grid cells; each grid cell is assigned to obtain two sets of data, which are respectively designated as a borehole geological data set and a geophysical data set.
And 6.2, performing correlation analysis on the drilling geological data group and the geophysical data group obtained in the step 6.1, and calculating and obtaining geological information correlation coefficients.
Step 6.3, if the absolute value of the correlation coefficient of the geological information obtained in the step 6.2 is larger than 0.5 (the correlation is strong), the geophysical multidimensional geological model is not optimized; if the absolute value of the correlation coefficient of the geological information obtained in the step 6.2 is smaller than 0.5 (which indicates weak correlation or uncorrelation), the attribute and the position of the stratum and the geological abnormal body in the geophysical multi-dimensional geological model are corrected according to the borehole-geological profile.
The invention also comprises the following technical characteristics:
Specifically, the step 6.2 includes the following steps:
step 6.2.1, constructing an original variable matrix:
extracting a column in which inconsistent data in the borehole-geological profile and the geophysical multi-dimensional geological model exist, if grid cells of each column in the borehole-geological profile and the geophysical multi-dimensional geological model are n, representing a borehole geological data set of the column as (X 11,x21,x31,……xn1), representing a geophysical data set of the column as (X 12,x22,x32,……xn2), and forming two groups of data into a primary variable matrix X, wherein the primary variable matrix X is represented by the following formula III:
step 6.2.2, carrying out standardization processing on the original variable matrix in the step 6.2.1 to obtain a standardized matrix; the standardized matrix Z is represented by the following formula IV:
Step 6.2.3, obtaining a geological information correlation coefficient:
From the normalized matrix Z, a correlation coefficient r ij is calculated and obtained using the following formula V:
the correlation coefficient matrix R is represented by the following formula vi:
And r 12 and r 21 in the correlation coefficient matrix are the correlation coefficients of the geological information.
Specifically and optionally, the first step further includes obtaining remote sensing data; the sixth step further comprises the following steps: step 6.4, the first step further comprises obtaining remote sensing data; the second step further comprises normalization processing of the remote sensing data to obtain normalized remote sensing data; the sixth step further comprises the following steps: step 6.4, if a geological anomaly exists in the earth surface and shallow layer area of the geophysical multi-dimensional geological model, and the geological anomaly is completely matched with geological anomaly earth surface projection in normalized remote sensing data, the geophysical multi-dimensional geological model is not required to be optimized; if the geological anomaly is inconsistent with the geological anomaly surface projection in the normalized remote sensing data, correcting the attribute and the position of the geological anomaly in the geophysical multidimensional geological model according to the normalized remote sensing data.
Specifically and optionally, if the data amount in the physical property parameter data set of the normalized coal rock sample is more, the step three specifically includes: drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the velocity and resistivity of the longitudinal wave of the elastic wave after the two sections are intersected; and directly classifying the data in the scatter diagram according to the normalized coal rock sample physical property parameter data set, and establishing a preliminary geological model according to the classification result.
Specifically and optionally, if the data amount in the physical property parameter data set of the normalized coal rock sample is smaller, the third step specifically includes the following steps:
and 3.1, drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the elastic wave longitudinal wave velocity and the resistivity after the two sections are intersected, wherein each scatter diagram represents a cluster member in the scatter diagram of the elastic wave longitudinal wave velocity and the resistivity.
Step 3.2, randomly selecting K cluster members from all cluster members to serve as initial cluster centers; then, respectively calculating the distance from each cluster member to each initial cluster center, and distributing the cluster members into the clusters closest to the cluster members one by one; after the distribution of all the cluster members is completed, recalculating a new cluster center in each cluster; and then comparing the cluster center obtained by recalculation with the cluster center obtained by previous calculation.
Step 3.3, if the calculated cluster center is found to change after comparison, repeating the step 3.2; if the clustering center is not changed, stopping and outputting the clustering result.
And 3.4, establishing a preliminary geological model according to the clustering result output in the step 3.3.
Specifically and optionally, if the amount of data in the normalized geological abnormal body parameter data set is large, the fourth step specifically includes: drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the velocity and resistivity of the longitudinal wave of the elastic wave after the two sections are intersected; and directly classifying the data in the scatter diagram according to the normalized geological abnormal body parameter data set, and identifying the geological abnormal body.
Specifically and optionally, if the amount of data in the normalized geological anomaly parameter dataset is small, the fourth step specifically includes the following steps:
and 4.1, drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the elastic wave longitudinal wave velocity and the resistivity after the two sections are intersected, wherein each scatter diagram represents a cluster member in the scatter diagram of the elastic wave longitudinal wave velocity and the resistivity.
Step 4.2, randomly selecting K cluster members from all cluster members to serve as initial cluster centers; then, respectively calculating the distance from each cluster member to each initial cluster center, and distributing the cluster members into the clusters closest to the cluster members one by one; after the distribution of all the cluster members is completed, recalculating a new cluster center in each cluster; and then comparing the cluster center obtained by recalculation with the cluster center obtained by previous calculation.
Step 4.3, if the calculated cluster center is found to change after comparison, repeating the step 3.2; if the clustering center is not changed, stopping and outputting the clustering result.
And 4.4, comparing the clustering result output in the step 4.3 with the normalized geological abnormal body parameter data set, and identifying the geological abnormal body.
Specifically and preferably, the cluster members of each cluster in the cluster result are defined by a dissimilarity decreasing function, and the dissimilarity decreasing function is shown in the following formula ii:
wherein:
N represents a cluster member.
G represents a physical property parameter.
C represents a cluster.
Pc represents the cluster center of a certain cluster.
Delta (g, p c) represents the distance of g from the cluster center of the cluster.
Specifically, in the second step, normalized pretreatment is performed by adopting the following formula I:
In formula I:
h' l represents the normalized value of a particular physical property parameter data; h' l is more than or equal to 0 and less than or equal to 1.
H l represents specific physical property parameter data.
H min represents the minimum value of a particular physical property parameter.
H max represents the maximum value of a particular physical property parameter.
Specifically, in the first step, parameters included in the physical property parameter data set, the geological abnormal property parameter data set and the geophysical property parameter data set of the coal rock sample are specifically density, resistivity, relative dielectric constant, elastic wave longitudinal wave velocity and elastic wave transverse wave velocity.
Compared with the prior art, the invention has the beneficial technical effects that:
According to the geological model construction method containing comprehensive multi-source information of geophysics, a preliminary geological model is constructed and geological anomalies are identified according to laboratory measurement results of geophysics and samples; then combining the preliminary geologic model and the geologic anomaly, and establishing a geophysical multidimensional geologic model; and finally, optimizing the geophysical multidimensional geologic model by combining the drilling data, the geological map and the remote sensing data. Through the fusion interpretation of the comprehensive geophysical, drilling, geological and other multi-source data, the accurate and fine interpretation of the work area to be interpreted is realized, and further powerful help can be provided for distinguishing geological disasters (such as water filling goaf, collapse column and the like) of the coal field.
And (II) the geological model construction method containing the geophysical comprehensive multi-source information can facilitate the explanation of field personnel, so that a great deal of labor cost can be saved for the coal mine, considerable economic benefits are generated, and the method can be popularized to other coal mines and has important potential economic values.
Drawings
FIG. 1 is a cross-sectional view of a seismic migration-time depth transition.
Fig. 2 is a resistivity profile.
Fig. 3 is a scatter plot of elastic wave longitudinal wave velocity and resistivity.
Fig. 4 is a preliminary geologic model.
Fig. 5 is a schematic flow chart of obtaining a correlation coefficient of geological information.
The technical scheme of the invention is further described below by referring to examples.
Detailed Description
In the invention, the following components are added: the multi-source information includes geophysical data, borehole data, telemetry data, and geologic maps.
All methods of exploration and measurement used in the present invention are conventional, unless specifically indicated, as known in the art.
The following specific embodiments of the present application are given according to the above technical solutions, and it should be noted that the present application is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solutions of the present application fall within the protection scope of the present application.
Examples:
the embodiment provides a geological model construction method containing comprehensive multi-source information of geophysics, which comprises the following steps:
Step one, multi-source data of a work area to be explained are obtained:
Step 1.1, establishing a coal rock sample physical property parameter data set: and constructing a borehole in the working area to be interpreted, collecting the existing coal-rock samples (including coal and non-coal rock) of the coal field in the working area to be interpreted through the borehole, analyzing physical properties of the coal-rock samples, and obtaining the density, the resistivity, the relative dielectric constant, the elastic wave longitudinal wave velocity and the elastic wave transverse wave velocity of the coal-rock samples by combining borehole data or a geological manual, and constructing a coal-rock sample physical property parameter data set according to the physical property parameter data.
Step 1.2, establishing a geological abnormal body parameter data set: and selecting a survey point in the work area to be explained and carrying out in-situ measurement, or carrying out laboratory measurement after drilling and sampling to obtain the density, resistivity, elastic wave longitudinal wave speed and elastic wave transverse wave speed of geological anomalies (including water bodies, hollows, fractured coal and rock bodies and the like) of the work area to be explained, and constructing a geological anomaly parameter data set according to the parameter data by combining drilling data or a geological manual.
Step 1.3, establishing a geophysical parameter data set: acquiring physical parameters such as elastic wave longitudinal wave speed, elastic wave transverse wave speed, density and the like of stratum or geological abnormal body of a work area to be explained through seismic exploration data acquisition, processing and migration imaging processing (imaging results are shown in figure 1) or through collecting past seismic migration results; acquiring resistivity parameters of a work area to be explained through electrical exploration; obtaining the relative dielectric constant of the work area to be explained by radar or other high-frequency electromagnetic wave methods; and constructing a geophysical parameter data set according to the parameter data.
Step 1.4, obtaining drilling data: the method comprises the steps of compiling drilling information of a work area to be explained to obtain drilling data of the work area to be explained; the borehole data mainly includes geologic formation information (i.e., information about which geologic formations the borehole and its surrounding areas include, thickness and average thickness of each geologic formation, lithology of each geologic formation, etc.), as well as hydrogeologic information (i.e., information about the position and thickness of the aquifer of the borehole and its surrounding areas).
Step 1.5, obtaining remote sensing data: remote sensing data of the work area to be explained is obtained through a satellite, an airplane, an unmanned aerial vehicle or a ground measuring instrument and the like.
Step 1.6, obtaining a geological map: and drawing a geological map of the work area to be explained through geological exploration means.
In this embodiment, the physical property parameter data set of the coal rock sample and the physical property parameter data set of the geological abnormal body can provide physical property parameter basis for subsequent identification of the geological abnormal body and geological modeling, and the drilling data can provide basis for judging disaster sources, analyzing disaster causes and the like.
Step two, normalized pretreatment of multi-source data:
Carrying out normalized preprocessing on the physical property parameter data set, the geological abnormal body parameter data set, the geophysical parameter data set, the drilling data and the remote sensing data of the coal and rock sample obtained in the step I to obtain a normalized physical property parameter data set, a normalized geological abnormal body parameter data set, a normalized geophysical parameter data set, normalized drilling data and normalized remote sensing data of the coal and rock sample; formula I is shown below:
In formula I:
h' l represents the normalized value of a particular physical property parameter data; h' l is more than or equal to 0 and less than or equal to 1.
H l represents specific physical property parameter data.
H min represents the minimum value of a particular physical property parameter.
H max represents the maximum value of a particular physical property parameter.
In the embodiment, because the geophysical data in the hole and the ground geophysical data have scale differences, the data with different scales can be unified into the same scale through a near point interpolation algorithm, and the interpolation algorithm does not change the data resolution and does not introduce data anomalies; secondly, unifying the same dimension data to a certain range, so as to avoid the influence of magnitude; different source data have different performances in space, such as drilling data are one-dimensional data volumes, remote sensing data are ground surface two-dimensional data, geophysical data are three-dimensional data with different scales, different numbers are required to be converted into a unified coordinate system before fusion, and gridding treatment is carried out on the data, so that the data are completely aligned and the scales are unified.
Step three, taking the normalized coal rock sample physical property parameter data set obtained in the step two as a conversion standard, and converting the normalized geophysical property parameter data set obtained in the step two into a preliminary geological model:
As an optional and specific scheme of this embodiment, if the data amount in the normalized coal rock sample physical property parameter data set is large, the step three specifically includes: drawing a seismic migration-time depth conversion section chart (shown in figure 1) and a resistivity section chart (shown in figure 2) according to the normalized geophysical parameter data set obtained in the step two, and obtaining a scatter chart (shown in figure 3) of the elastic wave longitudinal wave velocity and the resistivity after the two charts are intersected, wherein the ordinate axis in the scatter chart is the elastic wave longitudinal wave velocity, and the abscissa axis is the resistivity; and directly classifying the data in the scatter diagram according to the normalized coal rock sample physical property parameter data set, and establishing a preliminary geological model according to the classification result.
As an optional and specific scheme of the embodiment, if the data volume in the normalized coal rock sample physical property parameter data set is small, the third step adopts a K-means clustering algorithm to perform cluster analysis on the scatter diagram of the elastic wave longitudinal wave velocity and the resistivity, and the specific scheme is as follows:
and 3.1, drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the elastic wave longitudinal wave velocity and the resistivity after the two sections are intersected, wherein each scatter diagram represents a cluster member in the scatter diagram of the elastic wave longitudinal wave velocity and the resistivity.
Step 3.2, randomly selecting K cluster members from all cluster members to serve as initial cluster centers; then, respectively calculating the distance from each cluster member to each initial cluster center, and distributing the cluster members into the clusters closest to the cluster members one by one; after the distribution of all the cluster members is completed, recalculating a new cluster center in each cluster; and then comparing the cluster center obtained by recalculation with the cluster center obtained by previous calculation.
In this embodiment, when the initial clustering center is selected, the initial clustering centers with corresponding numbers may be selected according to the label classification in the physical property parameter dataset and the geological anomaly parameter dataset of the coal rock sample, for example, the initial label classification includes coal, siltstone and shale, and when the initial clustering centers are clustered, three clustering centers may be selected.
Step 3.3, if the calculated cluster center is found to change after comparison, repeating the step 3.2; if the clustering center is not changed, stopping and outputting a clustering result; the cluster members of each cluster in the cluster result are defined by a decreasing function of dissimilarity, which is shown in the following formula II:
wherein:
N represents a cluster member.
G represents a physical property parameter.
C represents a cluster.
Pc represents the cluster center of a certain cluster.
Delta (g, p c) represents the distance of g from the cluster center of the cluster.
And 3.4, establishing a preliminary geological model according to the clustering result output in the step 3.3, as shown in fig. 4.
Step four, identifying a geological abnormal body according to the normalized geophysical parameter data set and the normalized geological abnormal body parameter data set obtained in the step two:
as an optional and specific solution of this embodiment, if the amount of data in the normalized geological abnormal volume parameter dataset is large, the fourth step is specifically as follows: drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the velocity and resistivity of the longitudinal wave of the elastic wave after the two sections are intersected; and directly classifying the data in the scatter diagram according to the normalized geological abnormal body parameter data set, and identifying the geological abnormal body.
As another alternative and specific solution of this embodiment, if the amount of data in the normalized geological anomaly parameter dataset is small, the fourth step is specifically as follows: step 4.1, drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the step two, and obtaining a scatter diagram of elastic wave longitudinal wave velocity and resistivity after the two sections are intersected; step 4.2 and step 4.3 are identical to step 3.2 and step 3.3; and 4.4, comparing the clustering result output in the step 4.3 with the normalized geological abnormal body parameter data set, and identifying the geological abnormal body.
And fifthly, combining the geological abnormal body identified in the fourth step with the preliminary geological model obtained in the third step to obtain the geophysical multidimensional geological model. If no geological abnormal body is identified, the preliminary geological model is a geophysical multidimensional geological model.
Step six, optimizing the geophysical multidimensional geologic model obtained in the step five:
Step 6.1, taking drilling data as vertical direction constraint, taking geological mapping as horizontal plane (transverse and longitudinal) constraint, combining the drilling data with the geological mapping, and drawing a drilling-geological profile; drawing a plurality of horizontal and vertical straight lines on the drilling-geological section and the geophysical multidimensional geological model respectively, and dividing the two images into a plurality of grid cells; each grid cell is assigned to obtain two sets of data, which are respectively designated as a borehole geological data set and a geophysical data set.
In this embodiment, when the borehole-geological section is drawn, the borehole data obtained in step 1.4 may be directly used, or more boreholes may be constructed and richer borehole data may be obtained.
In this embodiment, if the geophysical multidimensional geologic model is a three-dimensional model, it is necessary to cut out a plan view corresponding to the position of the borehole-geologic cross-section, and then perform gridding processing.
In this embodiment, the main composition of the grid cell needs to be analyzed before assignment, if the main composition of a certain grid cell is siltstone, a certain physical parameter value of siltstone can be assigned to the grid cell, and the density of siltstone is 2.60g/cm 3, and the grid cell is assigned to be 2.60; manual assignment may also be performed, such as designating siltstone as 1, and then assigning this grid cell as 1.
Step 6.2, carrying out correlation analysis on the drilling geological data group and the geophysical data group obtained in the step 6.1, and calculating and obtaining geological information correlation coefficients; as shown in fig. 5, the process of calculating the geological information correlation coefficient is as follows:
step 6.2.1, constructing an original variable matrix:
Extracting a column in which inconsistent data in the borehole-geological profile and the geophysical multi-dimensional geological model exist, if grid cells of each column in the borehole-geological profile and the geophysical multi-dimensional geological model are n, the borehole geological data set of the column is denoted as (X 11,x21,x31,……xn1), the geophysical data set of the column is denoted as (X 12,x22,x32,……xn2), and the two sets of data can form an n×2 original variable matrix X, which is denoted by the following formula iii:
Step 6.2.2, carrying out standardization processing on the original variable matrix in the step 6.2.1 to obtain a standardized matrix; the specific process is as follows:
The average value mu i for each column in the above formula III was calculated using the following formula:
Calculating the variance of each column in the above formula III
The data is normalized by the following formula to obtain normalized data z ji:
the normalization matrix Z is represented by the following formula iv:
Step 6.2.3, obtaining a geological information correlation coefficient:
From the normalized matrix Z, a correlation coefficient r ij is calculated and obtained using the following formula V:
the correlation coefficient matrix R is represented by the following formula vi:
In the correlation coefficient matrix R, R 11 and R 22 are all 1, and the numerical values of R 12 and R 21 are completely equal, and the numerical values are the correlation coefficients of the geological information.
Step 6.3, if the absolute value of the correlation coefficient of the geological information obtained in the step 6.2 is larger than 0.5, indicating that the correlation is strong, not optimizing the geophysical multidimensional geological model; and (3) if the absolute value of the correlation coefficient of the geological information obtained in the step (6.2) is smaller than 0.5, and the correlation is weak or irrelevant, correcting the attribute and the position of the stratum and the geological abnormal body in the geophysical multi-dimensional geological model according to the borehole-geological profile.
Step 6.4, if a geological anomaly exists in the earth surface and shallow layer area of the geophysical multi-dimensional geological model, and the geological anomaly is completely matched with geological anomaly earth surface projection in remote sensing data, optimizing the geophysical multi-dimensional geological model is not needed; and if the geological abnormal body is not matched with the geological abnormal surface projection in the remote sensing data, correcting the attribute and the position of the geological abnormal body in the geophysical multi-dimensional geological model according to the remote sensing data.
Claims (10)
1. A geological model construction method containing comprehensive multi-source information of geophysics is characterized by comprising the following steps:
Step one, multi-source data of a work area to be explained are obtained:
Establishing a physical property parameter data set, a geological abnormal body parameter data set and a geophysical parameter data set of a coal-rock sample; acquiring drilling data and a geological map;
step two, normalized pretreatment of multi-source data:
Carrying out standardized pretreatment on the physical property parameter data set, the geological abnormal body parameter data set, the geophysical parameter data set and the drilling data of the coal and rock sample obtained in the first step to obtain a standardized physical property parameter data set, a standardized geological abnormal body parameter data set, a standardized geophysical parameter data set and a standardized drilling data of the coal and rock sample;
step three, converting the normalized geophysical parameter data set obtained in the step two into a preliminary geological model by taking the normalized coal rock sample physical parameter data set obtained in the step two as a conversion standard;
Step four, identifying a geological abnormal body according to the normalized geophysical parameter data set and the normalized geological abnormal body parameter data set obtained in the step two;
Step five, combining the geological abnormal body identified in the step four with the preliminary geological model obtained in the step three to obtain a geophysical multidimensional geological model;
step six, optimizing the geophysical multidimensional geologic model obtained in the step five:
Step 6.1, combining the drilling data acquired in the step one with a geological map, and drawing a drilling-geological section; drawing a plurality of horizontal and vertical straight lines on the drilling-geological section and the geophysical multidimensional geological model respectively, and dividing the two images into a plurality of grid cells; assigning a value to each grid cell to obtain two groups of data, which are respectively recorded as a drilling geological data group and a geophysical data group;
Step 6.2, carrying out correlation analysis on the drilling geological data group and the geophysical data group obtained in the step 6.1, and calculating and obtaining geological information correlation coefficients;
Step 6.3, if the absolute value of the geological information correlation coefficient obtained in the step 6.2 is larger than 0.5, not optimizing the geophysical multidimensional geological model; and (3) if the absolute value of the geological information correlation coefficient obtained in the step (6.2) is smaller than 0.5, correcting the attribute and the position of the stratum and the geological abnormal body in the geophysical multi-dimensional geological model according to the drilling-geological profile.
2. The method for constructing a geologic model containing comprehensive multi-source information of geophysics according to claim 1, wherein said step 6.2 comprises the steps of:
step 6.2.1, constructing an original variable matrix:
extracting a column in which inconsistent data in the borehole-geological profile and the geophysical multi-dimensional geological model exist, if grid cells of each column in the borehole-geological profile and the geophysical multi-dimensional geological model are n, representing a borehole geological data set of the column as (X 11,x21,x31,……xn1), representing a geophysical data set of the column as (X 12,x22,x32,……xn2), and forming two groups of data into a primary variable matrix X, wherein the primary variable matrix X is represented by the following formula III:
step 6.2.2, carrying out standardization processing on the original variable matrix in the step 6.2.1 to obtain a standardized matrix; the standardized matrix Z is represented by the following formula IV:
Step 6.2.3, obtaining a geological information correlation coefficient:
From the normalized matrix Z, a correlation coefficient r ij is calculated and obtained using the following formula V:
the correlation coefficient matrix R is represented by the following formula vi:
And r 12 and r 21 in the correlation coefficient matrix are the correlation coefficients of the geological information.
3. The method of claim 1, wherein the step one further comprises obtaining remote sensing data; the second step further comprises normalization processing of the remote sensing data to obtain normalized remote sensing data;
The sixth step further comprises the following steps: step 6.4, if a geological anomaly exists in the earth surface and shallow layer area of the geophysical multi-dimensional geological model, and the geological anomaly is completely matched with geological anomaly earth surface projection in normalized remote sensing data, the geophysical multi-dimensional geological model is not required to be optimized; if the geological anomaly is inconsistent with the geological anomaly surface projection in the normalized remote sensing data, correcting the attribute and the position of the geological anomaly in the geophysical multidimensional geological model according to the normalized remote sensing data.
4. The method for constructing a geologic model containing comprehensive multi-source information of geophysics according to claim 1, wherein if the data amount in the normalized coal rock sample physical property parameter data set is large, the step three is specifically: drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the velocity and resistivity of the longitudinal wave of the elastic wave after the two sections are intersected; and directly classifying the data in the scatter diagram according to the normalized coal rock sample physical property parameter data set, and establishing a preliminary geological model according to the classification result.
5. The method for constructing a geologic model containing comprehensive multi-source information of geophysics according to claim 1, wherein if the amount of data in the normalized coal rock sample physical property parameter dataset is small, the third step specifically comprises the steps of:
Step 3.1, drawing a seismic migration-time depth conversion section view and a resistivity section view according to the normalized geophysical parameter data set obtained in the step two, and obtaining a scatter diagram of elastic wave longitudinal wave velocity and resistivity after the two views are intersected, wherein each scatter diagram represents a cluster member in the scatter diagram of the elastic wave longitudinal wave velocity and resistivity;
Step 3.2, randomly selecting K cluster members from all cluster members to serve as initial cluster centers; then, respectively calculating the distance from each cluster member to each initial cluster center, and distributing the cluster members into the clusters closest to the cluster members one by one; after the distribution of all the cluster members is completed, recalculating a new cluster center in each cluster; then comparing the cluster center obtained by recalculation with the cluster center obtained by previous calculation;
Step 3.3, if the calculated cluster center is found to change after comparison, repeating the step 3.2; if the clustering center is not changed, stopping and outputting a clustering result;
and 3.4, establishing a preliminary geological model according to the clustering result output in the step 3.3.
6. The method for constructing a geologic model containing comprehensive multi-source information of geophysics according to claim 1, wherein if the amount of data in the normalized geologic anomaly parameter dataset is large, the fourth step is specifically: drawing a seismic migration-time depth conversion section and a resistivity section according to the normalized geophysical parameter data set obtained in the second step, and obtaining a scatter diagram of the velocity and resistivity of the longitudinal wave of the elastic wave after the two sections are intersected; and directly classifying the data in the scatter diagram according to the normalized geological abnormal body parameter data set, and identifying the geological abnormal body.
7. The method of claim 1, wherein if the amount of data in the normalized geological anomaly parameter dataset is small, the fourth step comprises the steps of:
Step 4.1, drawing a seismic migration-time depth conversion section view and a resistivity section view according to the normalized geophysical parameter data set obtained in the step two, and obtaining a scatter diagram of elastic wave longitudinal wave velocity and resistivity after the two views are intersected, wherein each scatter diagram represents a cluster member in the scatter diagram of the elastic wave longitudinal wave velocity and resistivity;
Step 4.2, randomly selecting K cluster members from all cluster members to serve as initial cluster centers; then, respectively calculating the distance from each cluster member to each initial cluster center, and distributing the cluster members into the clusters closest to the cluster members one by one; after the distribution of all the cluster members is completed, recalculating a new cluster center in each cluster; then comparing the cluster center obtained by recalculation with the cluster center obtained by previous calculation;
Step 4.3, if the calculated cluster center is found to change after comparison, repeating the step 3.2; if the clustering center is not changed, stopping and outputting a clustering result;
And 4.4, comparing the clustering result output in the step 4.3 with the normalized geological abnormal body parameter data set, and identifying the geological abnormal body.
8. A method of constructing a geologic model containing comprehensive multi-source information of geophysics as claimed in any of claims 5 to 7, wherein the cluster members of each cluster in the clustered result are defined by a decreasing dissimilarity function of the formula ii:
wherein:
N represents a cluster member;
g represents a physical property parameter;
c represents a cluster;
Pc represents a cluster center of a certain cluster;
Delta (g, p c) represents the distance of g from the cluster center of the cluster.
9. The method for constructing a geologic model containing comprehensive multi-source information of geophysics according to claim 1, wherein in the second step, normalized pretreatment is performed by the following formula i:
In formula I:
h l' represents a normalized value of data of a particular physical property parameter; h l' is more than or equal to 0 and less than or equal to 1;
h l represents certain specific physical property parameter data;
H min represents the minimum value of a particular physical property parameter;
H max represents the maximum value of a particular physical property parameter.
10. The method of claim 1, wherein in the first step, the physical property parameters included in the coal rock sample physical property parameter data set, the geological anomaly parameter data set and the geophysical property parameter data set are density, resistivity, relative dielectric constant, elastic wave longitudinal wave velocity and elastic wave transverse wave velocity.
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