CN112363210A - Quantitative coal thickness prediction method based on joint inversion of wave velocity and attenuation coefficient of transmission channel waves - Google Patents
Quantitative coal thickness prediction method based on joint inversion of wave velocity and attenuation coefficient of transmission channel waves Download PDFInfo
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Abstract
The invention discloses a coal thickness quantitative prediction method based on joint inversion of wave velocity and attenuation coefficient of a transmission channel wave, which comprises the steps of respectively calculating the velocity of the transmission channel wave and the attenuation coefficient of the transmission channel wave by utilizing collected data of the transmission channel wave, detecting coal thickness data through drilling, combining the velocity of the transmission channel wave and the attenuation coefficient of the transmission channel wave, establishing a regression analysis model for processing, directly inverting a mathematical model of the coal thickness, the correlation of the velocity and the attenuation coefficient, and combining the inverted velocity and attenuation coefficient data with the mathematical model to obtain final coal thickness inversion data; compared with the method for constructing the coal thickness quantitative prediction model by using the single-frequency trough wave velocity and the actual geological result, the method can obtain the coal thickness map influenced by a plurality of factors, and has wide applicability; meanwhile, the interference of factors such as speed inversion error and attenuation coefficient inversion error on quantitative prediction of coal thickness can be effectively reduced, and the prediction accuracy is improved; finally, high-precision and quantitative coal thickness information is provided 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 channel wave.
Background
With the increase of the mining depth and speed of coal mines, the mining conditions become more complex, the problem of coal mine disasters becomes more and more prominent, and especially, some unknown geological abnormal structures can be suddenly encountered in the stoping process, so that the normal production is influenced, the 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 inside the working face has important reference significance for judging the geological abnormal structures, replacing deployment of mining areas and reasonable arrangement of the working face, the detection of the coal seam thickness is important in mine production.
The method mainly adopted for detecting the thickness of the coal seam comprises three main types: the first type uses ground borehole data as a constraint condition and combines three-dimensional seismic data for detection, the method has higher requirement on the borehole density of a detection area, the prediction precision is influenced by factors such as the quality of the three-dimensional seismic data, and the like, and the prediction precision has large fluctuation. The second type is a mine geophysical perspective method which mainly comprises a radio wave pit penetration and transmission seismic body wave chromatography method; the radio wave penetration causes abnormal energy attenuation of radio wave signals when the thickness of the coal seam changes, and then a coal seam abnormal area is defined; the transmission seismic body wave chromatography can effectively define the coal seam thinning zone range in the working face to a certain extent according to the corresponding relation between the seismic wave velocity and the coal thickness, but the geophysical perspective method of the mine is limited by the detection construction conditions and the inversion method of the stope working face, so that the method has low detection precision on the coal thickness and the structure thereof. The third type is channel wave exploration, which has the characteristic of directly carrying coal seam information and is a key research object in the field of coal seam thickness quantitative detection at present.
However, in the channel exploration coal thickness prediction in the prior art, a coal thickness quantitative prediction model is usually constructed according to the channel velocity of a certain frequency and the actual uncovering geological result, the coal thickness quantitative prediction model is mostly based on an empirical formula of a certain mining area, and the empirical formula of the certain mining area is not suitable for other mining areas and is not easy to popularize and use.
The patent publication No. CN111077572A, entitled a coal thickness quantitative prediction method based on transmission channel wave frequency dispersion curve inversion, discloses a detection method, which comprises the steps of firstly establishing a coal seam thickness detection system and acquiring multilayer transmission channel wave signals; then extracting a transmission slot wave frequency dispersion curve of the multilayer transmission slot wave signals; establishing a multilayer horizontal laminar medium inversion model, and obtaining inversion parameters of the model only by considering the layer thickness and the transverse wave velocity through carrying out sensitivity analysis on model parameters; 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 thickness of the coal seam on a 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 empirical formulas of all mine areas, and has wide applicability. However, the processing method of the coal thickness quantitative prediction method is not greatly connected with the actual coal thickness of the mining area in the inversion processing process, the processing method only depends on a dispersion curve, the processing is single, and the actual coal thickness prediction is obtained according to physical parameters in multiple aspects of collected data.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a coal thickness quantitative prediction method based on joint inversion of transmission channel wave velocity and attenuation coefficient.
In order to achieve the purpose, the 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 channel waves comprises the following steps:
01. according to the existing transmitted channel wave data, through seismic time-lapse tomography, a conjugate gradient method or a stable double-conjugate gradient method is adopted to solve the least square inversion of the adaptive damping factor, and the transmitted channel wave velocity V ═ (V ═ is obtained1,...,Vn) Wherein n is the total number of the two-dimensional plane grid divisions;
02. according to the existing transmission channel wave data, through the transmission channel wave attenuation coefficient tomography algorithm, the least square solution inversion of the self-adaptive damping factor is solved by adopting a conjugate gradient method or a stable double-conjugate gradient method to obtainTo transmission channel wave attenuation coefficient S ═ S (S)1,...,Sn) Wherein n is the total number of the two-dimensional plane grid divisions;
03. obtaining coal thickness data m ═ m (m is m) at each drill hole in the detection plane through drilling1,...,mn) Wherein m is the number of the drill holes, and recording the coordinate value (x, y) of each drill hole as (x)1,y1；…xn,yn)；
04. And (V) inverting the coordinate values corresponding to the drill holes and the coal thickness data in step 01 to obtain a groove wave velocity value V corresponding to the transmission groove wave velocity V (V ═ V-1,...,vn)；
05. The coordinate values and the coal thickness data corresponding to the drill holes are inverted in step 02 to obtain an attenuation coefficient value S corresponding to the channel wave attenuation coefficient S (S ═ S)1,...,sn)；
06. Inputting the trough wave velocity V and the attenuation coefficient value S obtained by the inversion in the steps 04 and 05 as independent variables, and inputting the coal seam thickness data m of each drilling hole corresponding to the step 03 as dependent variables; and (3) obtaining a regression coefficient through multivariate regression analysis, obtaining a coal thickness and trough wave velocity and attenuation coefficient prediction model through the regression coefficient, and calculating a whole plane coal thickness distribution map by combining the data in the steps 01 and 02.
Further, the step 06 includes:
A. the channel wave velocity V and the attenuation coefficient value S are used as independent variables to be input, and the coal seam thickness information m is used as a dependent variable to be input;
B. establishing a linear and pure quadratic multivariate binomial regression model of the dependent variable, the velocity V of the velocity channel wave and the attenuation coefficient value S, wherein the formula of the regression model is as follows: y ═ beta0+β1x1+…+βm xm (1)；
C. Obtaining A by the least square principleTAβ＝ATb, an equation, wherein beta is a regression coefficient, b is a dependent variable value, and A is an independent variable positive definite matrix;
D. solving equation ATAβ＝ATb, solving a regression coefficient value beta;
E. obtaining a fitting relation between the channel wave velocity 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 (β)0,β1,...,βm) The pure quadratic regression coefficient value β ═ β (β)0，β1，…，βm，β11，…，βmm)；
The actual calculation fit is as follows:
the linear regression model was calculated as: k is a radical ofi＝β0+β1vi+β2si(i 1., n, n is the number of drilled holes) (1-1);
the pure quadratic regression model is calculated as: k is a radical ofi′＝β0+β1vi+β2si+β11vi 2+β22si 2(i 1., n, n is the number of drilled holes) (2-1);
G. and (3) respectively carrying out error analysis on the prediction model formulas (1-1) and (2-1), wherein the calculation formulas are respectively as follows:
selecting whether the final prediction model fitting relation adopts a model formula (1-1) or (2-1) according to the magnitude of the error analysis value e calculated by the formula;
G. and (4) calculating to obtain a prediction model between the coal thickness and the trough wave velocity and the attenuation coefficient through the model formula selected from the 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 the grids V and S in the steps 01 and 02 one by one for calculation to obtain the whole plane coal thickness distribution diagram.
The invention has the following beneficial effects:
1. the method comprises the steps of respectively calculating the speed and the attenuation coefficient of a transmission channel wave by utilizing acquired transmission channel wave data based on a channel wave speed chromatographic inversion algorithm and an attenuation coefficient inversion imaging algorithm, detecting coal thickness data through an existing drill hole, combining the speed and the attenuation coefficient of the transmission channel wave, establishing a regression analysis model, processing the data, directly inverting a mathematical model of the coal thickness, the speed and the attenuation coefficient which are correlated with each other, and combining the known inverted speed and attenuation coefficient data with the mathematical model to obtain final coal thickness inversion data; compared with the method for constructing the coal thickness quantitative prediction model by using the single-frequency trough wave velocity and the actual geological result, the method can obtain the coal thickness map influenced by a plurality of factors, and has wide applicability; meanwhile, the interference of factors such as speed inversion error and attenuation coefficient inversion error on quantitative prediction of coal thickness can be effectively reduced, and the prediction accuracy is improved; finally, high-precision and quantitative coal thickness information is provided for intelligent mining of the coal mine.
Drawings
FIG. 1 is a velocity diagram of a trough wave inversion of the present invention;
FIG. 2 is a graph of the inversion attenuation coefficients of the trough waves according to 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 coal thickness distribution plot after the final joint inversion calculation of the present invention;
FIG. 5 is a schematic flow chart illustrating the quantitative coal thickness prediction method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
referring to the attached figures 1-5, the coal thickness quantitative prediction method based on the joint inversion of the wave velocity and the attenuation coefficient of the transmission channel wave comprises the following steps:
01. according to the existing transmitted channel wave data, by seismic time-lapse tomography, the conjugate gradient method or the stable double conjugate gradient method is adopted to solveAnd (V) obtaining the velocity V of the transmission channel wave by the least square solution inversion of the adaptive damping factor1,...,Vn) Wherein n is the total number of the two-dimensional plane grid divisions;
02. according to the existing transmitted channel wave data, through a transmitted channel wave attenuation coefficient tomography algorithm, a conjugate gradient method or a stable double conjugate gradient method is adopted to solve the least square solution inversion of the adaptive damping factor, and the transmitted channel wave attenuation coefficient S ═ (S ═ is obtained1,...,Sn) Wherein n is the total number of the two-dimensional plane grid divisions;
03. obtaining coal thickness data m ═ m (m is m) at each drill hole in the detection plane through drilling1,...,mn) Wherein m is the number of the drill holes, and recording the coordinate value (x, y) of each drill hole as (x)1,y1；…xn,yn；)；
04. And (V) inverting the coordinate values corresponding to the drill holes and the coal thickness data in step 01 to obtain a groove wave velocity value V corresponding to the transmission groove wave velocity V (V ═ V-1,...,vn)；
05. The coordinate values and the coal thickness data corresponding to the drill holes are inverted in step 02 to obtain an attenuation coefficient value S corresponding to the channel wave attenuation coefficient S (S ═ S)1,...,sn)；
06. Inputting the trough wave velocity V and the attenuation coefficient value S obtained by the inversion in the steps 04 and 05 as independent variables, and inputting the coal seam thickness data m of each drilling hole corresponding to the step 03 as dependent variables; and (3) obtaining a regression coefficient through multivariate regression analysis, obtaining a coal thickness and trough wave velocity and attenuation coefficient prediction model through the regression coefficient, and calculating a whole plane coal thickness distribution map by combining the data in the steps 01 and 02.
Said step 06 comprises:
A. the channel wave velocity V and the attenuation coefficient value S are used as independent variables to be input, and the coal seam thickness information m is used as a dependent variable to be input;
B. establishing a linear and pure quadratic multivariate binomial regression model of the dependent variable, the velocity V of the velocity channel wave and the attenuation coefficient value S, wherein the formula of the regression model is as follows: y ═ beta0+β1x1+…+βm xm (1)；
C. Obtaining A by the least square principleTAβ＝ATb, an equation, wherein beta is a regression coefficient, b is a dependent variable value, and A is an independent variable positive definite matrix;
D. solving equation ATAβ＝ATb, solving a regression coefficient value beta;
E. obtaining a fitting relation between the channel wave velocity 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 (β)0,β1,...,βm) The pure quadratic regression coefficient value β ═ β (β)0，β1，…，βm，β11，…，βmm)；
The actual calculation fit is as follows:
the linear regression model was calculated as: k is a radical ofi＝β0+β1vi+β2si(i 1., n, n is the number of drilled holes) (1-1);
the pure quadratic regression model is calculated as:
(i 1., n, n is the number of boreholes) (2-1);
H. and (3) respectively carrying out error analysis on the prediction model formulas (1-1) and (2-1), wherein the calculation formulas are respectively as follows:
selecting whether the final prediction model fitting relation adopts a model formula (1-1) or (2-1) according to the magnitude of the error analysis value e calculated by the formula;
G. and (4) calculating to obtain a prediction model between the coal thickness and the trough wave velocity and the attenuation coefficient through the model formula selected from the 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 the grids V and S in the steps 01 and 02 one by one for calculation to obtain the whole plane coal thickness distribution diagram.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structural changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to other related technical fields, are included in the scope of the present invention.
Claims (2)
1. The coal thickness quantitative prediction method based on the joint inversion of the wave velocity and the attenuation coefficient of the transmission channel waves is characterized by comprising the following steps of: the method comprises the following steps:
01. according to the existing transmitted channel wave data, through seismic time-lapse tomography, a conjugate gradient method or a stable double-conjugate gradient method is adopted to solve the least square inversion of the adaptive damping factor, and the transmitted channel wave velocity V ═ (V ═ is obtained1,...,Vn) Wherein n is the total number of the two-dimensional plane grid divisions;
02. according to the existing transmitted channel wave data, through a transmitted channel wave attenuation coefficient tomography algorithm, a conjugate gradient method or a stable double conjugate gradient method is adopted to solve the least square solution inversion of the adaptive damping factor, and the transmitted channel wave attenuation coefficient S ═ (S ═ is obtained1,...,Sn) Wherein n is the total number of the two-dimensional plane grid divisions;
03. obtaining coal thickness data m ═ m (m is m) at each drill hole in the detection plane through drilling1,...,mn) Wherein m is the number of the drill holes, and recording the coordinate value (x, y) of each drill hole as (x)1,y1；…xn,yn)；
04. And (V) inverting the coordinate values corresponding to the drill holes and the coal thickness data in step 01 to obtain a groove wave velocity value V corresponding to the transmission groove wave velocity V (V ═ V-1,...,vn)；
05. The coordinate values and the coal thickness data corresponding to the drill holes are inverted in step 02 to obtain an attenuation coefficient value S corresponding to the channel wave attenuation coefficient S (S ═ S)1,...,sn)；
06. Inputting the trough wave velocity V and the attenuation coefficient value S obtained by the inversion in the steps 04 and 05 as independent variables, and inputting the coal seam thickness data m of each drilling hole corresponding to the step 03 as dependent variables; and (3) obtaining a regression coefficient through multivariate regression analysis, obtaining a coal thickness and trough wave velocity and attenuation coefficient prediction model through the regression coefficient, and calculating a whole plane coal thickness distribution map by combining the data in the steps 01 and 02.
2. The coal thickness quantitative prediction method based on the joint inversion of the wave velocity and the attenuation coefficient of the transmission channel waves as claimed in claim 1, wherein: said step 06 comprises:
A. the channel wave velocity V and the attenuation coefficient value S are used as independent variables to be input, and the coal seam thickness information m is used as a dependent variable to be input;
B. establishing a linear and pure quadratic multivariate binomial regression model of the dependent variable, the velocity V of the velocity channel wave and the attenuation coefficient value S, wherein the formula of the regression model is as follows: y ═ beta0+β1x1+…+βmxm (1)；
C. Obtaining A by the least square principleTAβ＝ATb, an equation, wherein beta is a regression coefficient, b is a dependent variable value, and A is an independent variable positive definite matrix;
D. solving equation ATAβ＝ATb, solving a regression coefficient value beta;
E. obtaining a fitting relation between the channel wave velocity 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 (β)0,β1,...,βm) Value of pure quadratic regression coefficient
β＝(β0，β1，…，βm，β11，…，βmm)；
The actual calculation fit is as follows:
the linear regression model was calculated as: k is a radical ofi＝β0+β1vi+β2si(i 1., n, n is the number of drilled holes) (1-1);
the pure quadratic regression model is calculated as:
(i 1., n, n is the number of boreholes) (2-1);
F. and (3) respectively carrying out error analysis on the prediction model formulas (1-1) and (2-1), wherein the calculation formulas are respectively as follows:
selecting whether the final prediction model fitting relation adopts a model formula (1-1) or (2-1) according to the magnitude of the error analysis value e calculated by the formula;
G. and (4) calculating to obtain a prediction model between the coal thickness and the trough wave velocity and the attenuation coefficient through the model formula selected from the 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 the grids V and S in the steps 01 and 02 one by one for calculation to obtain the whole plane coal thickness distribution diagram.
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| CN115079270A (en) * | 2022-06-30 | 2022-09-20 | 中国矿业大学 | Fine detection method for channel wave earthquake of water-containing sand layer on upper part of coal seam |
| CN117055115A (en) * | 2023-10-11 | 2023-11-14 | 煤炭科学研究总院有限公司 | Method, device, equipment and medium for detecting abnormal region of coal rock mass structure |
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| CN115079270A (en) * | 2022-06-30 | 2022-09-20 | 中国矿业大学 | Fine detection method for channel wave earthquake of water-containing sand layer on upper part of coal seam |
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| US12111437B1 (en) | 2023-10-11 | 2024-10-08 | Ccteg Chinese Institute Of Coal Science | Method for detecting structural abnormal area of coal and rock mass |
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