CN111077572A - Quantitative coal thickness prediction method based on inversion of transmission groove wave frequency dispersion curve - Google Patents
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Abstract
The invention discloses a coal thickness quantitative prediction method based on transmission channel wave frequency dispersion curve inversion, which comprises the steps of firstly establishing a coal seam thickness detection system so as to obtain multilayer transmission channel wave signals; then extracting a transmission groove wave frequency dispersion curve of the signal; 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; thereby establishing an initial inversion model; and then solving a theoretical dispersion curve corresponding to the initial inversion model, performing iterative fitting calculation with the 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. The method directly inverts the speed characteristics of the coal bed and the surrounding rock of the top and bottom plates by analyzing the data of the frequency dispersion curve of the transmission channel wave, is not limited by a regional empirical formula, and has wide applicability.
Description
Technical Field
The invention relates to a coal thickness quantitative prediction method, in particular to a coal thickness quantitative prediction method based on transmission channel wave dispersion curve inversion.
Background
With the rapid development of scientific technology and the progress of social economy, the intelligent mining technology aiming at realizing less-humanization and unmanned mining becomes the main development trend of coal mining, and the dual guarantee of coal yield and personnel safety is realized. How to accurately and efficiently detect the thickness of a coal seam on a coal face directly ensures the realization of an intelligent mining technology. The method mainly adopted for predicting the thickness of the coal seam comprises three main methods: the first method of using ground borehole data as constraint conditions and performing lateral control on a three-dimensional seismic profile has higher requirements on the borehole density of a measurement area, and meanwhile, the prediction precision is greatly influenced by factors such as the quality of three-dimensional seismic data. The second type is a mine geophysical perspective method, and the existing stage method mainly comprises the steps of radio wave pit penetration and transmission seismic body wave chromatography; the radio wave penetration is mainly characterized in that the energy attenuation abnormality of radio wave signals is caused when the thickness of a coal seam changes, and then a coal seam abnormal area is defined; the seismic wave CT technology can effectively define the coal seam thinning zone range in a working face to a certain extent according to the corresponding relation between the seismic wave velocity and the coal thickness. However, the geophysical perspective method of the mine is limited by the detection construction conditions of the stope face and the inversion method, so that the interpretation precision of the influence of the coal thickness and the structure of the coal thickness is not enough. The third type is channel wave exploration, which is a technical hotspot with higher attention at present, has the characteristic of directly carrying coal seam information, and has the potential to become an effective means for quantitatively exploring the coal seam thickness.
In the current-stage channel wave exploration coal thickness prediction, a coal thickness quantitative prediction model is constructed according to the channel wave speed of a certain frequency and the actual uncovering geological result, and the coal thickness quantitative prediction model is mostly based on an empirical formula of a certain mining area, so that the prediction model established based on the empirical formula of the mining area has poor guidance on the coal thickness detection work of other areas.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a coal thickness quantitative prediction method based on transmission channel wave frequency dispersion curve inversion, which directly inverts the speed characteristics of coal beds and surrounding rocks of top and bottom plates by analyzing the transmission channel wave frequency dispersion curve data, is not limited by regional empirical formulas and has wide applicability; meanwhile, the interference of the lithology change of the top plate and the bottom plate on the quantitative prediction of the coal thickness can be effectively reduced, and the prediction accuracy is improved.
In order to achieve the purpose, the invention adopts the technical scheme that: a coal thickness quantitative prediction method based on transmission channel wave dispersion curve inversion comprises the following specific steps:
A. establishing a coal seam thickness detection system:
arranging a plurality of seismic source points on the side wall of the roadway on one side of the coal seam working surface, arranging a plurality of detectors on the side wall of the roadway on the other side in a row, and enabling the plurality of seismic source points and the plurality of detectors to be located on the same horizontal plane to form a coal seam thickness detection system; transmitting a transmission channel wave signal vertical to the coal wall from the seismic source point to the coal bed, wherein the transmission channel wave signal is received by each detector after passing through the coal bed;
B. extracting a transmission groove wave frequency dispersion curve:
carry out time frequency analysis to the multilayer transmission tank wave signal that receives, according to surveying inaccurate principle, time resolution and frequency resolution are restriction each other, and two kinds of resolutions can be overall considered in the S change among the time frequency analysis to obtain each layer transmission tank wave dispersion curve, wherein every layer transmission tank wave signal extraction transmission tank wave dispersion curve specifically do:
a) according to the coordinates of the detector and the seismic source, the propagation path length L of the channel wave is obtained by ray tracing method, and the received transmitted channel wave signal X (t)i) Performing time frequency analysis to obtain time frequency spectrum H (t)i,fj) (ii) a I is the number of sampling points, J is 1,2,.. J, J is the maximum value of a frequency window of time-frequency analysis;
b) and (3) calculating the group velocity of the transmission channel wave according to the relation between the distance and the time:
vg＝L/ti(1)
c) the transmission channel wave time spectrum H (t) is divided by the formula (1)i,fj) Mapped as H' (L/t)i,fj) (ii) a The specific process is as follows: according to the propagation path length L obtained in the step a), any coordinate (t) in the time spectrumi,fj) Corresponding to an energyValue H (t)i,fj) At this time tiCorresponding to a speed vgi＝L/tiH (t) in the instantaneous spectrumi,fj)＝H'(L/ti,fj) Thereby obtaining the single-channel transmission channel wave group velocity dispersion spectrum H' (v)gi,fj)；
d) In the frequency-group velocity spectrum of the groove wave, according to the energy distribution rule, a groove wave group velocity dispersion curve C (v) is manually picked upgF), according to the relation between group velocity and phase velocity:
so as to obtain the phase velocity dispersion curve C (v) of the transmission slot wave of the layerc,f)；
C. And (3) carrying out inversion on the multilayer transmission groove wave frequency dispersion curve:
establishing a multilayer horizontal laminar medium inversion model, carrying out sensitivity analysis on model parameters, and analyzing the sensitivity analysis result of an influence parameter, wherein the inversion parameters of the model only consider the layer thickness delta h and the transverse wave velocity Vs, namely the inversion model parameters are (delta h, Vs); calculating a theoretical dispersion curve C (v) corresponding to the initial velocity model by adopting a genetic algorithmc,f)1Then, a random universal selection method is adopted to combine the theoretical dispersion curve C (v)c,f)2Curve C (v) with measured datacAnd f) carrying out comparison for multiple times, updating the speed model after each comparison, and finally carrying out minimum error delta epsilonminCorresponding model parameters (H)n,VSn) The result is the inversion calculation result of the dispersion curve; wherein HnIs the relative height of the stratum of the inverse model; since the geophone and the seismic source point are in the same horizontal plane, i.e. the relative height of the geophone and the seismic source point is 0, then
n＝1,2,.....N；
D. Determining the thickness of the coal seam:
according to the inversion result (H) in step Cn,VSn) The speed can be calculated along the depth directionThe rate of change of degree is
In the two-dimensional velocity change rate data body, the velocity change rate at the interface (namely H is a negative value) under the coal bed is a negative value, and the velocity change rate at the interface (namely H is a positive value) on the coal bed is a positive value, so that the H corresponding to the two extreme values respectively is a positive valuenBy subtraction, i.e. | Hnmax-HnminAnd obtaining the quantitative detection result of the thickness of the coal layer on the path passed by the transmission channel wave signal.
Further, the specific steps of the step C are as follows:
①, constructing a two-dimensional multilayer horizontal layered medium inversion model, wherein the longitudinal resolution of the layer thickness and the layer number is (delta h, N), the layer thickness is set to be 1 m, N is 100, the transverse wave velocity of the rock stratum is 2000m/s, the transverse wave velocity of the coal bed is 1000m/s, it should be noted that the velocity parameter and the coal thickness information of the initial model can refer to the transmission seismic velocity chromatography result and geological data, and the initial model is rough but is enough to define the search range of the model parameter needed in the genetic algorithm.
② the stratum thickness variation is 0.1 m, the speed variation is 10 m/s, the population number of thickness and speed is 64, and the first round of 64 x 64 theoretical dispersion curves C (v) can be obtained by genetic algorithmc,f)1Then compared with the measured data curve C (v)cAnd f) comparing to obtain a mean square error set delta epsilon1；
③ selecting the result of step ② by random universal selection method, wherein the selected probability is high, then performing crossover and variation treatment to update the result to the second round of model parameters, and obtaining the theoretical dispersion curve C (v) of the updated modelc,f)2(ii) a Then the curve C (v) is compared with the actually measured datacAnd f) comparing to obtain the mean square difference set delta epsilon2；
④ setting a threshold P, repeating step ③, continuously updating data population through multiple rounds of iterative computation, continuously reducing error until reaching the set threshold P, namely delta epsilon is less than or equal to P, ending computation, and selecting the minimum error delta epsilon in the roundminCorresponding model parametersNumber (H)n,VSn) I.e. the result of the inverse calculation of the dispersion curve, where HnIs the relative height of the stratum of the inverse model; since the geophone and the seismic source point are in the same horizontal plane, i.e. the relative height of the geophone and the seismic source point is 0, then
n＝1,2,.....N。
Compared with the prior art, the method has the advantages that the method collects the wave frequency dispersion curve data of the transmission channel firstly, then carries out time-frequency domain analysis based on the data, directly inverts the speed characteristics of the coal bed and the surrounding rock of the top and bottom plate by establishing an inversion model and processing the data by adopting a genetic algorithm, thereby obtaining the speed of the coal bed and the surrounding rock of the top and bottom plate, and is different from the comprehensive speed response obtained by velocity chromatography of the transmission body wave, so that the method is not limited by a regional empirical formula and has wide applicability; meanwhile, the interference of the lithology change of the top plate and the bottom plate on the quantitative prediction of the 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 schematic layout of a coal seam thickness detection system according to the present invention;
FIG. 2 is a graph of measured transmitted channel data and frequency dispersion spectrum extraction according to the present invention;
wherein, (a) the transmission channel wave signal data is measured; (b) actually measuring a frequency dispersion spectrum extraction diagram of the transmission slot wave;
FIG. 3 is a schematic diagram of a quantitative inversion of coal thickness for a transmitted trough wave dispersion curve according to the present invention;
wherein, (a) a dispersion curve inversion calculation result; (b) comparing the measured frequency dispersion curve with the theoretical frequency dispersion curve; (c) and (5) calculating errors by inverting the dispersion curve.
Detailed Description
The present invention will be further explained below.
As shown in the figure, the method comprises the following specific steps:
A. establishing a coal seam thickness detection system:
arranging a plurality of seismic source points on the side wall of the roadway on one side of the coal seam working surface, arranging a plurality of detectors on the side wall of the roadway on the other side in a row, and enabling the plurality of seismic source points and the plurality of detectors to be located on the same horizontal plane to form a coal seam thickness detection system; transmitting a transmission channel wave signal vertical to the coal wall from the seismic source point to the coal bed, wherein the transmission channel wave signal is received by each detector after passing through the coal bed;
B. extracting a transmission groove wave frequency dispersion curve:
carry out time frequency analysis to the multilayer transmission tank wave signal that receives, according to surveying inaccurate principle, time resolution and frequency resolution are restriction each other, and two kinds of resolutions can be overall considered in the S change among the time frequency analysis to obtain each layer transmission tank wave dispersion curve, wherein every layer transmission tank wave signal extraction transmission tank wave dispersion curve specifically do:
a) according to the coordinates of the detector and the seismic source, the propagation path length L of the channel wave is obtained by ray tracing method, and the received transmitted channel wave signal X (t)i) Performing time frequency analysis to obtain time frequency spectrum H (t)i,fj) (ii) a I is the number of sampling points, J is 1,2,.. J, J is the maximum value of a frequency window of time-frequency analysis;
b) and (3) calculating the group velocity of the transmission channel wave according to the relation between the distance and the time:
vg＝L/ti(1)
c) the transmission channel wave time spectrum H (t) is divided by the formula (1)i,fj) Mapped as H' (L/t)i,fj) (ii) a The specific process is as follows: according to the propagation path length L obtained in the step a), any coordinate (t) in the time spectrumi,fj) Corresponding to an energy value H (t)i,fj) At this time tiCorresponding to a speed vgi＝L/tiH (t) in the instantaneous spectrumi,fj)＝H'(L/ti,fj) Thereby obtaining the single-channel transmission channel wave group velocity dispersion spectrum H' (v)gi,fj)；
d) In the frequency-group velocity spectrum of the groove wave, according to the energy distribution rule, a groove wave group velocity dispersion curve C (v) is manually picked upgF), according to the relation between group velocity and phase velocity:
so as to obtain the phase velocity dispersion curve C (v) of the transmission slot wave of the layerc,f)；
C. And (3) carrying out inversion on the multilayer transmission groove wave frequency dispersion curve:
establishing a multilayer horizontal laminar medium inversion model, carrying out sensitivity analysis on model parameters, and analyzing the sensitivity analysis result of an influence parameter, wherein the inversion parameters of the model only consider the layer thickness delta h and the transverse wave velocity Vs, namely the inversion model parameters are (delta h, Vs); calculating a theoretical dispersion curve C (v) corresponding to the initial velocity model by adopting a genetic algorithmc,f)1Then, a random universal selection method is adopted to combine the theoretical dispersion curve C (v)c,f)2Curve C (v) with measured datacAnd f) carrying out comparison for multiple times, updating the speed model after each comparison, and finally carrying out minimum error delta epsilonminCorresponding model parameters (H)n,VSn) The result is the inversion calculation result of the dispersion curve; wherein HnIs the relative height of the stratum of the inverse model; since the geophone and the seismic source point are in the same horizontal plane, i.e. the relative height of the geophone and the seismic source point is 0, then
n＝1,2,.....N；
D. Determining the thickness of the coal seam:
according to the inversion result (H) in step Cn,VSn) The rate of change of velocity can be calculated along the depth direction, i.e. it is
In the two-dimensional velocity change rate data body, the velocity change rate at the interface (namely H is a negative value) under the coal bed is a negative value, and the velocity change rate at the interface (namely H is a positive value) on the coal bed is a positive value, so that the H corresponding to the two extreme values respectively is a positive valuenBy subtraction, i.e. | Hnmax-HnminTo obtain the thickness of the coal layer on the path passed by the transmission channel wave signalAnd (5) quantitative detection results of the degree.
Further, the specific steps of the step C are as follows:
①, constructing a two-dimensional multilayer horizontal layered medium inversion model, wherein the longitudinal resolution of the layer thickness and the layer number is (delta h, N), the layer thickness is set to be 1 m, N is 100, the transverse wave velocity of the rock stratum is 2000m/s, the transverse wave velocity of the coal bed is 1000m/s, it should be noted that the velocity parameter and the coal thickness information of the initial model can refer to the transmission seismic velocity chromatography result and geological data, and the initial model is rough but is enough to define the search range of the model parameter needed in the genetic algorithm.
② the stratum thickness variation is 0.1 m, the speed variation is 10 m/s, the population number of thickness and speed is 64, and the first round of 64 x 64 theoretical dispersion curves C (v) can be obtained by genetic algorithmc,f)1Then compared with the measured data curve C (v)cAnd f) comparing to obtain a mean square error set delta epsilon1；
③ selecting the result of step ② by random universal selection method, wherein the selected probability is high, then performing crossover and variation treatment to update the result to the second round of model parameters, and obtaining the theoretical dispersion curve C (v) of the updated modelc,f)2(ii) a Then the curve C (v) is compared with the actually measured datacAnd f) comparing to obtain the mean square difference set delta epsilon2；
④ setting a threshold P, repeating step ③, continuously updating data population through multiple rounds of iterative computation, continuously reducing error until reaching the set threshold P, namely delta epsilon is less than or equal to P, ending computation, and selecting the minimum error delta epsilon in the roundminCorresponding model parameters (H)n,VSn) I.e. the result of the inverse calculation of the dispersion curve, where HnIs the relative height of the stratum of the inverse model; since the geophone and the seismic source point are in the same horizontal plane, i.e. the relative height of the geophone and the seismic source point is 0, then
n＝1,2,.....N。
Claims (2)
1. A coal thickness quantitative prediction method based on transmission channel wave dispersion curve inversion is characterized by comprising the following specific steps:
A. establishing a coal seam thickness detection system:
arranging a plurality of seismic source points on the side wall of the roadway on one side of the coal seam working surface, arranging a plurality of detectors on the side wall of the roadway on the other side in a row, and enabling the plurality of seismic source points and the plurality of detectors to be located on the same horizontal plane to form a coal seam thickness detection system; transmitting a transmission channel wave signal vertical to the coal wall from the seismic source point to the coal bed, wherein the transmission channel wave signal is received by each detector after passing through the coal bed;
B. extracting a transmission groove wave frequency dispersion curve:
analyzing the received multilayer transmission slot wave signals to obtain each layer of transmission slot wave frequency dispersion curve, wherein the extraction of the transmission slot wave frequency dispersion curve from each layer of transmission slot wave signals is as follows:
a) according to the coordinates of the detector and the seismic source, the propagation path length L of the channel wave is obtained by ray tracing method, and the received transmitted channel wave signal X (t)i) Performing time frequency analysis to obtain time frequency spectrum H (t)i,fj) (ii) a I is the number of sampling points, J is 1,2,.. J, J is the maximum value of a frequency window of time-frequency analysis;
b) and (3) calculating the group velocity of the transmission channel wave according to the relation between the distance and the time:
vg＝L/ti(1)
c) the transmission channel wave time spectrum H (t) is divided by the formula (1)i,fj) Mapped as H' (L/t)i,fj) (ii) a The specific process is as follows: according to the propagation path length L obtained in the step a), any coordinate (t) in the time spectrumi,fj) Corresponding to an energy value H (t)i,fj) At this time tiCorresponding to a speed vgi＝L/tiH (t) in the instantaneous spectrumi,fj)＝H'(L/ti,fj) Thereby obtaining the single-channel transmission channel wave group velocity dispersion spectrum H' (v)gi,fj)；
d) In the frequency-group velocity spectrum of the groove wave, according to the energy distribution rule, manually picking upChannel group velocity dispersion curve C (v)gF), according to the relation between group velocity and phase velocity:
so as to obtain the phase velocity dispersion curve C (v) of the transmission slot wave of the layerc,f)；
C. And (3) carrying out inversion on the multilayer transmission groove wave frequency dispersion curve:
establishing a multilayer horizontal laminar medium inversion model, carrying out sensitivity analysis on model parameters, and analyzing the sensitivity analysis result of an influence parameter, wherein the inversion parameters of the model only consider the layer thickness delta h and the transverse wave velocity Vs, namely the inversion model parameters are (delta h, Vs); calculating a theoretical dispersion curve C (v) corresponding to the initial velocity model by adopting a genetic algorithmc,f)1Then, a random universal selection method is adopted to combine the theoretical dispersion curve C (v)c,f)2Curve C (v) with measured datacAnd f) carrying out comparison for multiple times, updating the speed model after each comparison, and finally carrying out minimum error delta epsilonminCorresponding model parameters (H)n,VSn) The result is the inversion calculation result of the dispersion curve; wherein HnIs the relative height of the stratum of the inverse model; since the geophone and the seismic source point are in the same horizontal plane, i.e. the relative height of the geophone and the seismic source point is 0, then
D. Determining the thickness of the coal seam:
according to the inversion result (H) in step Cn,VSn) The rate of change of velocity can be calculated along the depth direction, i.e. it is
In the two-dimensional speed change rate data body, the speed change rate at the lower interface of the coal bed is a negative value, and the speed change rate at the upper interface of the coal bed is a positive value, so that two extreme values respectively correspond to HnBy subtraction, i.e. | Hnmax-HnminAnd obtaining the quantitative detection result of the thickness of the coal layer on the path passed by the transmission channel wave signal.
2. The coal thickness quantitative prediction method based on the transmission channel wave dispersion curve inversion of claim 1, wherein the concrete steps of the step C are as follows:
①, constructing a two-dimensional multilayer horizontal layered medium inversion model, wherein the longitudinal resolution of the layer thickness and the layer number is (delta h, N), the layer thickness is set to be 1 meter, N is 100, the transverse wave velocity of the rock stratum is 2000m/s, and the transverse wave velocity of the coal bed is 1000 m/s;
② the stratum thickness variation is 0.1 m, the speed variation is 10 m/s, the population number of thickness and speed is 64, and the first round of 64 x 64 theoretical dispersion curves C (v) can be obtained by genetic algorithmc,f)1Then compared with the measured data curve C (v)cAnd f) comparing to obtain a mean square error set delta epsilon1；
③ selecting the result of step ② by random universal selection method, wherein the selected probability is high, then performing crossover and variation treatment to update the result to the second round of model parameters, and obtaining the theoretical dispersion curve C (v) of the updated modelc,f)2(ii) a Then the curve C (v) is compared with the actually measured datacAnd f) comparing to obtain the mean square difference set delta epsilon2；
④ setting a threshold P, repeating step ③, continuously updating data population through multiple rounds of iterative computation, continuously reducing error until reaching the set threshold P, namely delta epsilon is less than or equal to P, ending computation, and selecting the minimum error delta epsilon in the roundminCorresponding model parameters (H)n,VSn) I.e. the result of the inverse calculation of the dispersion curve, where HnIs the relative height of the stratum of the inverse model; since the geophone and the seismic source point are in the same horizontal plane, i.e. the relative height of the geophone and the seismic source point is 0, then
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140303896A1 (en) * | 2011-11-29 | 2014-10-09 | Kelly Wrobel | Method for Quantitative Definition of Direct Hydrocarbon Indicators |
| CN105549087A (en) * | 2015-12-10 | 2016-05-04 | 北京中矿大地地球探测工程技术有限公司 | Travel time and amplitude combined inversion method for channel wave seismic exploration underground coal mine |
| CN106772557A (en) * | 2016-11-29 | 2017-05-31 | 北京中矿大地地球探测工程技术有限公司 | Using with the pick signal detection colliery architectonic method of digging laneway all directions |
| CN110617404A (en) * | 2019-09-09 | 2019-12-27 | 安徽理工大学 | Gas pipeline leakage point positioning method based on sound wave frequency shift inversion |
-
2019
- 2019-12-17 CN CN201911301781.7A patent/CN111077572A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140303896A1 (en) * | 2011-11-29 | 2014-10-09 | Kelly Wrobel | Method for Quantitative Definition of Direct Hydrocarbon Indicators |
| CN105549087A (en) * | 2015-12-10 | 2016-05-04 | 北京中矿大地地球探测工程技术有限公司 | Travel time and amplitude combined inversion method for channel wave seismic exploration underground coal mine |
| CN106772557A (en) * | 2016-11-29 | 2017-05-31 | 北京中矿大地地球探测工程技术有限公司 | Using with the pick signal detection colliery architectonic method of digging laneway all directions |
| CN110617404A (en) * | 2019-09-09 | 2019-12-27 | 安徽理工大学 | Gas pipeline leakage point positioning method based on sound wave frequency shift inversion |
Non-Patent Citations (1)
| Title |
|---|
| 胡泽安: "煤层工作面透射地震波场特征及其三维成像研究", 《中国博士学位论文全文数据库 基础科学辑》 * |
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