CN112363210B - Coal thickness quantitative prediction method based on transmission groove wave velocity and attenuation coefficient joint inversion - Google Patents
Coal thickness quantitative prediction method based on transmission groove wave velocity and attenuation coefficient joint inversion Download PDFInfo
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Abstract
The invention discloses a coal thickness quantitative prediction method based on joint inversion of transmission channel wave velocity and attenuation coefficient, which utilizes acquired transmission channel wave data to respectively calculate transmission channel wave velocity and transmission channel wave attenuation coefficient, then detects coal thickness data through drilling holes, combines the transmission channel wave velocity and the transmission channel wave attenuation coefficient, establishes a regression analysis model for processing, directly inverts a mathematical model which is related to the coal thickness and velocity and attenuation coefficient, and obtains final coal thickness inversion data through the combination of inverted velocity and attenuation coefficient data and the mathematical model; compared with a single-frequency channel wave speed and a coal thickness quantitative prediction model constructed by a real uncovering geological result, the method can obtain a coal thickness map influenced by a plurality of factors, and has wide applicability; meanwhile, the interference of factors such as speed inversion errors, attenuation coefficient inversion errors and the like on quantitative prediction of the coal thickness can be effectively reduced, and the prediction accuracy is improved; and finally, providing high-precision and quantitative coal thickness information for intelligent mining of the coal mine.
Description
Technical Field
The invention relates to the technical field of coal seam detection, in particular to a coal thickness quantitative prediction method based on joint inversion of wave velocity and attenuation coefficient of a transmission groove.
Background
Along with the increase of the depth and speed of coal mining, mining conditions become more complex, coal mine disaster problems are more and more prominent, and particularly, unknown geological abnormal structures can be suddenly encountered in the stoping process, so that normal production is affected, economic loss is caused, and the life safety of underground workers is threatened. Because the geological abnormal structures are directly related to the coal thickness change, and the coal thickness change condition in the working face has important reference significance for judging the geological abnormal structures, mining area succession deployment and reasonable arrangement of the working face, the detection of the coal layer thickness is of great importance in mine production.
Three main methods for detecting the thickness of the coal seam are: the first type uses ground drilling data as constraint conditions and combines three-dimensional seismic data to detect, the method has higher drilling density requirements on a measuring area, and prediction accuracy is influenced by factors such as quality of the three-dimensional seismic data and the like, so that the prediction accuracy fluctuation is large. The second category is mine geophysical perspective, which mainly includes radio wave transmission and transmission seismic body wave tomography; the radio wave pit penetration can cause abnormal energy attenuation of radio wave signals when the thickness of the coal seam changes, so that an abnormal area of the coal seam is defined; the transmission seismic body wave chromatography can effectively define the range of a coal seam thinning zone in a working surface to a certain extent according to the corresponding relation between the seismic wave velocity and the coal thickness, but the ore mine geophysical perspective method is limited by the detection construction condition of a stope working surface and the inversion method, so that the method has low detection precision on the coal thickness and the structure thereof. The third category is slot wave exploration, which has the characteristic of directly carrying the information of the coal bed and is an important research object in the field of quantitative detection of the thickness of the coal bed at present.
However, in the prior art, a quantitative coal thickness prediction model is generally constructed according to the channel wave speed of a certain frequency and the actual geological result, and is mostly based on an empirical formula of a certain mining area, which is not suitable for other mining areas and is not easy to popularize and use.
Patent publication No. CN111077572A, patent name is a detection method based on quantitative prediction method of coal thickness of inversion of wave dispersion curve of transmission slot, it establishes the detection system of coal seam thickness first, obtain the wave signal of multilayer transmission slot; then extracting a transmission groove wave dispersion curve of the multilayer transmission groove wave signal; establishing a multi-layer horizontal lamellar medium inversion model, and obtaining inversion parameters of the model by carrying out sensitivity analysis on model parameters, wherein the inversion parameters only consider layer thickness and transverse wave speed; and then establishing an initial inversion model, solving a theoretical dispersion curve corresponding to the initial inversion model, performing iterative fitting calculation with an actual data dispersion curve, and finally obtaining a quantitative detection result of the coal seam thickness on the path through which the transmission channel wave signal passes according to the obtained upper and lower extreme values, wherein the detection result is not limited by an empirical formula of each mining area, and has wider applicability. However, the method for quantitatively predicting the coal thickness has little relation with the actual coal thickness of the mining area in the inversion treatment process, and is simply carried out by means of a dispersion curve, so that the method is single in comparison, and the actual coal thickness prediction is obtained according to the physical parameters in multiple aspects of the acquired data.
Disclosure of Invention
In order to solve the problems, the invention aims to provide the coal thickness quantitative prediction method based on the joint inversion of the wave velocity of the transmission slot and the attenuation coefficient, and the coal layer thickness is detected by using the joint inversion method by analyzing the drilling data, the wave velocity of the transmission slot, the attenuation coefficient and other data, so that the prediction accuracy is greatly improved, and the prediction method is not limited by an empirical formula of a mining area and has wide applicability.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the coal thickness quantitative prediction method based on the joint inversion of the wave velocity and the attenuation coefficient of the transmission groove comprises the following steps:
01. according to the existing transmission channel wave data, through seismic travel time tomography, a conjugate gradient method or a stable double conjugate gradient method is adopted to solve the least square solution inversion of the self-adaptive damping factor, and the transmission channel wave speed V= (V) is obtained 1 ,...,V n ) Wherein n is the total number of two-dimensional plane grid divisions;
02. according to the existing transmission channel wave data, solving the least square solution inversion of the self-adaptive damping factor by adopting a conjugate gradient method or a stable double conjugate gradient method through a transmission channel wave attenuation coefficient tomography algorithm to obtain a transmission channel wave attenuation coefficient S= (S) 1 ,...,S n ) Wherein n is the total number of two-dimensional plane grid divisions;
03. obtaining coal thickness data m= (m) at each drilling hole in a detection plane through drilling holes 1 ,...,m n ) Wherein m is the number of drilling holes, and each drilling hole coordinate value (x, y) = (x) is recorded 1 ,y 1 ；…x n ,y n )；
04. Inverting the coordinate value corresponding to each drilling hole and the coal thickness data in the step 01 to obtain a groove wave speed value v= (V) corresponding to the transmission groove wave speed V 1 ,...,v n )；
05. Inverting the coordinate value corresponding to each drilling hole and the coal thickness data in the step 02 to obtain an attenuation coefficient value s= (S) corresponding to the slot wave attenuation coefficient S 1 ,...,s n )；
06. Inputting the groove wave speed V and the attenuation coefficient value S obtained by inversion in the steps 04 and 05 as independent variables, and inputting the coal seam thickness data m corresponding to each drilling hole in the step 03 as the independent variables; regression coefficients are obtained through multiple regression analysis, a coal thickness and channel wave speed and attenuation coefficient prediction model is obtained through the regression coefficients, and then the data in the steps 01 and 02 are combined, so that a whole plane coal thickness distribution map is calculated.
Further, the step 06 includes:
A. the groove wave speed V and the attenuation coefficient value S are input as independent variables, and the coal seam thickness information m is input as the independent variables;
B. establishing a linear and pure quadratic polynomial regression model of the dependent variable, the velocity of the velocity groove wave V and the attenuation coefficient value S, wherein the formula of the regression model is as follows: y=β 0 +β 1 x 1 +…+β m x m (1)；
C. By least square principle, A is obtained T Aβ＝A T b equation, wherein beta is a regression coefficient, b is a variable value, A is an independent variable positive definite matrix;
D. solving equation A T Aβ＝A T b, solving a regression coefficient value beta;
E. obtaining fitting relation between the groove wave speed V and the attenuation coefficient value S and the coal seam thickness information m through regression coefficient values, and respectively obtaining different beta values through different regression model formulas (1) and (2), wherein: linear regression coefficient value β= (β) 0 ,β 1 ,...,β m ) Pure quadratic regression coefficient value β= (β) 0 ，β 1 ，…，β m ，β 11 ，…，β mm )；
The actual calculation fit is as follows:
the linear regression model was calculated as: k (k) i ＝β 0 +β 1 v i +β 2 s i (i=1,., n, n is the number of holes drilled) (1-1);
the pure quadratic regression model was calculated as: k (k) i ′＝β 0 +β 1 v i +β 2 s i +β 11 v i 2 +β 22 s i 2 (i=1,., n, n is the number of holes drilled) (2-1);
G. error analysis is carried out on the prediction model formulas (1-1) and (2-1) respectively, and the calculation formulas are as follows:
selecting whether a final prediction model fitting relation adopts a model formula (1-1) or a model formula (2-1) according to the magnitude of an error analysis value e calculated by the formula;
G. and (3) calculating a prediction model between the coal thickness, the groove wave speed and the attenuation coefficient through a model formula selected in the step F:
when e > e': selecting a model calculation formula (1-1); and when e is less than or equal to e', selecting a model calculation formula (2-1), and substituting V and S in the steps 01 and 02 into calculation one by one in a grid mode to obtain the whole plane coal thickness distribution map.
The invention has the following beneficial effects:
1. the method comprises the steps of respectively calculating the speed of a transmission groove wave and the attenuation coefficient of the transmission groove wave by using acquired transmission groove wave data based on a groove wave speed tomography inversion algorithm and an attenuation coefficient inversion imaging algorithm, detecting the thickness data of coal by using the existing drilling holes, combining the speed of the transmission groove wave and the attenuation coefficient of the transmission groove wave, establishing a regression analysis model, processing the data, directly inverting a mathematical model which is related to the thickness data of the coal and the speed and the attenuation coefficient, and combining the known inversion speed data and the attenuation coefficient data with the mathematical model to obtain final coal thickness inversion data; compared with a single-frequency channel wave speed and a coal thickness quantitative prediction model constructed by a real uncovering geological result, the method can obtain a coal thickness map influenced by a plurality of factors, and has wide applicability; meanwhile, the interference of factors such as speed inversion errors, attenuation coefficient inversion errors and the like on quantitative prediction of the coal thickness can be effectively reduced, and the prediction accuracy is improved; and finally, providing high-precision and quantitative coal thickness information for intelligent mining of the coal mine.
Drawings
FIG. 1 is a plot of the inversion speed of a notch wave in accordance with the present invention;
FIG. 2 is a plot of the inverted attenuation coefficient of the notch wave of the present invention;
FIG. 3 is a graph showing coal thickness data and coordinate values thereof according to the present invention;
FIG. 4 is a graph of the calculated coal thickness profile of the final joint inversion of the present invention;
FIG. 5 is a schematic diagram of the flow steps of the quantitative prediction method of coal thickness according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and specific examples:
referring to fig. 1-5, the coal thickness quantitative prediction method based on the joint inversion of the wave velocity and the attenuation coefficient of the transmission groove comprises the following steps:
01. according to the existing transmission channel wave data, through seismic travel time tomography, a conjugate gradient method or a stable double conjugate gradient method is adopted to solve the least square solution inversion of the self-adaptive damping factor, and the transmission channel wave speed V= (V) is obtained 1 ,...,V n ) Wherein n is the total number of two-dimensional plane grid divisions;
02. according to the existing transmission channel wave data, solving the least square solution inversion of the self-adaptive damping factor by adopting a conjugate gradient method or a stable double conjugate gradient method through a transmission channel wave attenuation coefficient tomography algorithm to obtain a transmission channel wave attenuation coefficient S= (S) 1 ,...,S n ) Wherein n is the total number of two-dimensional plane grid divisions;
03. obtaining coal thickness data m= (m) at each drilling hole in a detection plane through drilling holes 1 ,...,m n ) Wherein m is the number of drilling holes, and each drilling hole coordinate value (x, y) = (x) is recorded 1 ,y 1 ；…x n ,y n ；)；
04. Inverting the coordinate value corresponding to each drilling hole and the coal thickness data in the step 01 to obtain a groove wave speed value v= (V) corresponding to the transmission groove wave speed V 1 ,...,v n )；
05. Inverting the coordinate value corresponding to each drilling hole and the coal thickness data in the step 02 to obtain an attenuation coefficient value s= (S) corresponding to the slot wave attenuation coefficient S 1 ,...,s n )；
06. Inputting the groove wave speed V and the attenuation coefficient value S obtained by inversion in the steps 04 and 05 as independent variables, and inputting the coal seam thickness data m corresponding to each drilling hole in the step 03 as the independent variables; regression coefficients are obtained through multiple regression analysis, a coal thickness and channel wave speed and attenuation coefficient prediction model is obtained through the regression coefficients, and then the data in the steps 01 and 02 are combined, so that a whole plane coal thickness distribution map is calculated.
The step 06 includes:
A. the groove wave speed V and the attenuation coefficient value S are input as independent variables, and the coal seam thickness information m is input as the independent variables;
B. establishing a linear and pure quadratic polynomial regression model of the dependent variable, the velocity of the velocity groove wave V and the attenuation coefficient value S, wherein the formula of the regression model is as follows: y=β 0 +β 1 x 1 +…+β m x m (1)；
C. By least square principle, A is obtained T Aβ＝A T b equation, wherein beta is a regression coefficient, b is a variable value, A is an independent variable positive definite matrix;
D. solving equation A T Aβ＝A T b, solving a regression coefficient value beta;
E. obtaining fitting relation between the groove wave speed V and the attenuation coefficient value S and the coal seam thickness information m through regression coefficient values, and determining thatThe same regression model formulas (1) and (2) respectively obtain different beta values, wherein: linear regression coefficient value β= (β) 0 ,β 1 ,...,β m ) Pure quadratic regression coefficient value β= (β) 0 ，β 1 ，…，β m ，β 11 ，…，β mm )；
The actual calculation fit is as follows:
the linear regression model was calculated as: k (k) i ＝β 0 +β 1 v i +β 2 s i (i=1,., n, n is the number of holes drilled) (1-1);
the pure quadratic regression model was calculated as:(i=1,., n, n is the number of holes drilled) (2-1);
H. error analysis is carried out on the prediction model formulas (1-1) and (2-1) respectively, and the calculation formulas are as follows:
selecting whether a final prediction model fitting relation adopts a model formula (1-1) or a model formula (2-1) according to the magnitude of an error analysis value e calculated by the formula;
G. and (3) calculating a prediction model between the coal thickness, the groove wave speed and the attenuation coefficient through a model formula selected in the step F:
when e > e': selecting a model calculation formula (1-1); and when e is less than or equal to e', selecting a model calculation formula (2-1), and substituting V and S in the steps 01 and 02 into calculation one by one in a grid mode to obtain the whole plane coal thickness distribution map.
The foregoing description is only specific embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the present invention and the accompanying drawings, or direct or indirect application in other related technical fields, are included in the scope of the present invention.
Claims (1)
1. The coal thickness quantitative prediction method based on the joint inversion of the wave velocity and the attenuation coefficient of the transmission groove is characterized by comprising the following steps of: the method comprises the following steps:
01. according to the existing transmission channel wave data, through seismic travel time tomography, a conjugate gradient method or a stable double conjugate gradient method is adopted to solve the least square solution inversion of the self-adaptive damping factor, and the transmission channel wave speed V= (V) is obtained 1 ,...,V n ) Wherein n is the total number of two-dimensional plane grid divisions;
02. according to the existing transmission channel wave data, solving the least square solution inversion of the self-adaptive damping factor by adopting a conjugate gradient method or a stable double conjugate gradient method through a transmission channel wave attenuation coefficient tomography algorithm to obtain a transmission channel wave attenuation coefficient S= (S) 1 ,...,S n ) Wherein n is the total number of two-dimensional plane grid divisions;
03. obtaining coal thickness data m= (m) at each drilling hole in a detection plane through drilling holes 1 ,...,m n ) Wherein m is the number of drilling holes, and each drilling hole coordinate value (x, y) = (x) is recorded 1 ,y 1 ；…x n ,y n )；
04. Inverting the coordinate value corresponding to each drilling hole and the coal thickness data in the step 01 to obtain a groove wave speed value v= (V) corresponding to the transmission groove wave speed V 1 ,...,v n )；
05. Inverting the coordinate value corresponding to each drilling hole and the coal thickness data in the step 02 to obtain an attenuation coefficient value s= (S) corresponding to the slot wave attenuation coefficient S 1 ,...,s n )；
06. Inputting a groove wave velocity value v and an attenuation coefficient value s obtained by inversion in the steps 04 and 05 as independent variables, and inputting coal thickness data m corresponding to each drilling hole in the step 03 as the independent variables; obtaining regression coefficients through multiple regression analysis, obtaining a coal thickness and channel wave speed and attenuation coefficient prediction model through the regression coefficients, and calculating a whole plane coal thickness distribution map by combining the data in the steps 01 and 02;
the step 06 includes:
A. the groove wave velocity value v and the attenuation coefficient value s are input as independent variables, and the coal thickness data m is input as the independent variables;
B. establishing a linear and pure quadratic polynomial regression model of the dependent variable, the velocity value v of the groove wave and the attenuation coefficient value s, wherein the formula of the regression model is as follows: y=β 0 +β 1 x 1 +…+β m x m (1)；
C. By least square principle, A is obtained T Aβ＝A T b equation, wherein beta is a regression coefficient, b is a variable value, A is an independent variable positive definite matrix;
D. solving equation A T Aβ＝A T b, solving a regression coefficient value beta;
E. obtaining fitting relation between the velocity value v of the groove wave and the attenuation coefficient value s and the coal thickness data m through regression coefficient values, and respectively obtaining different beta values through different regression model formulas (1) and (2), wherein: linear regression coefficient value β= (β) 0 ,β 1 ,...,β m ) Pure quadratic regression coefficient value β= (β) 0 ，β 1 ，…，β m ，β 11 ，…，β mm )；
The actual calculation fit is as follows:
the linear regression model was calculated as: k (k) i ＝β 0 +β 1 v i +β 2 s i (1-1), wherein i=1,..n, n is the number of drill holes;
the pure quadratic regression model was calculated as:wherein i=1,..n, n is the number of drill holes;
F. error analysis is carried out on the prediction model formulas (1-1) and (2-1) respectively, so as to obtain a differential analysis value e of the prediction model formula (1-1) and an differential analysis value e' of the prediction model formula (2-1);
G. when e > e': selecting a model calculation formula (1-1); and when e is less than or equal to e', selecting a model calculation formula (2-1), and substituting V and S in the steps 01 and 02 into calculation one by one in a grid mode to obtain the whole plane coal thickness distribution map.
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