CN114415231A - Microseismic positioning method based on EDT surface probability distribution function of station - Google Patents

Microseismic positioning method based on EDT surface probability distribution function of station Download PDF

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CN114415231A
CN114415231A CN202111579966.1A CN202111579966A CN114415231A CN 114415231 A CN114415231 A CN 114415231A CN 202111579966 A CN202111579966 A CN 202111579966A CN 114415231 A CN114415231 A CN 114415231A
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microseismic
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noise ratio
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曾志毅
韩鹏
杨晓辉
王蕤
缪淼
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Southwest University of Science and Technology
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    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/288—Event detection in seismic signals, e.g. microseismics
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    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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Abstract

The invention discloses a microseism positioning method based on an EDT (enhanced data transmission) surface probability distribution function of a station, belonging to the technical field of microseism positioning, and comprising the steps of filtering and picking up microseism signals of the station to obtain microseism signal first arrival data, and obtaining a microseism signal-to-noise ratio and observed arrival time difference data of any two different stations; carrying out three-dimensional grid division on the microseismic monitoring area to obtain the travel time from each grid point to each station; obtaining calculated travel time difference data; substituting the observed time difference data, the signal-to-noise ratio data and the calculated travel time difference data into an EDT surface probability distribution function to obtain a probability value of a grid point; the maximum probability value is the microseismic event positioning result. According to the method, the station is used for establishing the EDT surface probability distribution function, so that the location of the microseismic event position is realized, the influence of the data with large picking error discrete points on the location precision is effectively reduced, the location precision of the microseismic event is improved, and reliable microseismic event position information is provided for earthquake activity evaluation.

Description

Microseismic positioning method based on EDT surface probability distribution function of station
Technical Field
The invention relates to the technical field of microseismic positioning, in particular to a microseismic positioning method based on an EDT surface probability distribution function of a station.
Background
With the annual reduction and exhaustion of shallow mineral resources in China, mines are gradually mined to deeper parts, so that the stress of the deep environment is obviously increased, severe dynamic disasters such as roof collapse, rock burst and the like are easily induced, and the number of mine microseismic events is greatly increased. Microseismic monitoring is a passive monitoring technology, can carry out long-term monitoring and short-term forecasting on microseismic activity of an underground structure, and is widely applied to metal and nonmetal mining operation at home and abroad. And judging potential mine dynamic disaster activity rules through micro-fracture distribution positions and stress accumulation areas obtained by micro-seismic monitoring, and realizing forecast and early warning according to the mine dynamic disaster activity rules. Therefore, the determination of the microseismic event position is an important and fundamental scientific problem for mine microseismic monitoring, and the high-precision positioning method has important significance for the smooth development of subsequent scientific work.
The distance between a seismic source and a sensor in mine micro-seismic monitoring is close, and the S wave of a monitored signal is mostly unobvious. Direct S waves are susceptible to interference from P-wave subsequent tail waves, and therefore, a source location method based on P, S waves is limited. On the other hand, when the signal energy is weak, the P-wave signal may be submerged by various noises in the well, which may cause the S-wave signal with strong energy to be mistaken as the P-wave, so that the arrival time data contains a small part of arrival time data with large pickup error. In most cases, the inversion objective function for the source location defines the residual with L1 and L2 norms. Although the L1 norm is strongly recommended by many researchers because the L1 norm is relatively insensitive to the timing of the larger error pickups. However, both the L1 and L2 methods are affected when the input data contains large errors in the first arrivals. An objective function based on the station to arrival time difference probability distribution has better robustness to the discrete points with large picking errors, so an equal time difference (EDT) positioning method based on Gaussian distribution and Student's t distribution is proposed in the prior art, but variance parameters in the Gaussian distribution function are not suitable for estimation and are easily influenced by the number of samples and the discrete points, and the Gaussian distribution is non-heavy tail distribution and is sensitive to the discrete arrival time data with large picking errors. While the Student's t distribution is also a heavy-tailed distribution without variance parameters to be estimated, the effect of the arrival time data with small picking errors on the objective function is reduced. Therefore, a new residual objective function form with probability density needs to be constructed, so that the change of the pickup error can be better adapted, the influence of large discrete points of the pickup error is reduced, the effect of the travel time data with high signal-to-noise ratio in the objective function is enhanced, and the positioning accuracy is improved.
Disclosure of Invention
Aiming at the defects existing in the problems, the invention provides a microseismic positioning method based on an EDT surface probability distribution function of a station, which comprises the following steps:
filtering and preprocessing microseismic signals of all stations;
picking up the microseismic signals after the filtering pretreatment to obtain microseismic signal first-arrival data of each station;
calculating and obtaining microseismic signal-to-noise ratios of all the stations according to the first arrival data, and carrying out normalization calculation on the microseismic signal-to-noise ratios;
acquiring observed time difference data of any two different stations in all the stations according to the first arrival data, and reserving signal-to-noise ratio data with a small total signal-to-noise ratio of the microseismic signals of the two different stations;
carrying out three-dimensional grid division on the microseismic monitoring area, and calculating travel time from each grid point to each station to obtain calculated travel time table data;
subtracting any two different pieces of the calculation travel time table data to obtain calculation travel time difference data;
substituting the observed time difference data, the signal-to-noise ratio data with small signal-to-noise ratio of the microseismic signal and the calculated travel time difference data into an EDT surface probability distribution function to obtain a probability value of each grid point;
and setting the grid point position of the maximum probability value as a microseismic event positioning result.
Preferably, calculating and obtaining the signal-to-noise ratios of the microseismic signals of all the stations according to the first arrival data comprises:
the signal-to-noise ratio formula is:
Figure BDA0003426693440000021
in the formula:
Figure BDA0003426693440000022
the signal-to-noise ratio at station k for microseismic event i; w1To be driven from
Figure BDA0003426693440000023
The amplitude value of the microseismic signal in a time window with the length of W is taken forward from time to time; w2To be driven from
Figure BDA0003426693440000024
Taking a background noise amplitude value in a time window with the length of W from time to time;
Figure BDA0003426693440000025
and
Figure BDA0003426693440000026
are respectively represented as W1And W2Root mean square value of the waveform amplitude within the window.
Preferably, normalizing the microseismic signal to noise ratio comprises:
the normalized calculation formula is:
Figure BDA0003426693440000031
in the formula:
Figure BDA0003426693440000032
normalized signal-to-noise ratio at station k for microseismic event i; maxi(SNR) is the maximum value of the signal-to-noise ratio of microseismic event i.
Preferably, obtaining observed time difference data for any two different stations of all stations according to the first arrival data comprises:
Figure BDA0003426693440000033
in the formula:
Figure BDA0003426693440000034
and
Figure BDA0003426693440000035
respectively the first observation arrivals of the microseismic event i at a station k and a station l;
Figure BDA0003426693440000036
the observed time difference of the microseismic event i at the station k and the station l is the station pair time difference of the microseismic event.
Preferably, retaining signal-to-noise ratio data for which the overall microseismic signal to noise ratio is small for two different stations comprises:
Figure BDA0003426693440000037
in the formula: in order to reserve signal-to-noise ratio data with smaller signal-to-noise ratio of the microseismic signal in the station pair k, l of the microseismic event i;
Figure BDA0003426693440000038
to take a smaller value to operate.
Preferably, the step of substituting the observed time difference data, the signal-to-noise ratio data with a small signal-to-noise ratio of the microseismic signal, and the calculated travel time difference data into an EDT surface probability distribution function to obtain the probability value of each grid point includes:
the calculation formula is as follows:
Figure BDA0003426693440000039
Figure BDA00034266934400000310
Figure BDA00034266934400000311
in the formula: l isgA probability value for grid point g; TERR is a tolerance parameter;
Figure BDA00034266934400000312
calculating the difference between the travel time difference of the station pair k and l and the observed time difference;
Figure BDA00034266934400000313
is an EDT surface probability distribution function.
Preferably, the positioning of the grid point with the maximum probability value as the microseismic event positioning result comprises:
S(G)＝max(Lg)；
in the formula: s (G) is the probability maximum, and G is the microseismic event positioning result.
Compared with the prior art, the invention has the beneficial effects that:
the discrete points with large picking errors influence the positioning accuracy in the form of an object function with L2 or L1 norm, the object function based on the station to the residual error probability distribution has better robustness to the discrete points with large picking errors, a new EDT surface probability distribution function is constructed, and the change of the picking errors can be better adapted, so that the residual error object function has the advantages that the low signal-to-noise ratio running time data is the heavy tail distribution and the high signal-to-noise ratio running time data is the non-heavy tail distribution under the condition of the given TERR parameter, the influence of the discrete points with large picking errors is reduced, the effect of the high signal-to-noise ratio running time data in the object function is enhanced, and the positioning accuracy is improved; the parameter TERR has definite physical significance, has certain robustness to certain errors of the velocity model, can eliminate discrete points with large picking errors according to the TERR parameter, and provides arrival time data with small picking errors for subsequent relative positioning.
Drawings
FIG. 1 is a flow chart of the microseismic location method based on the probability distribution function of the station to the EDT surface of the present invention;
FIG. 2 is a simulated microseismic observation system;
FIG. 3 is a simulation of microseismic recording and pickup results;
FIG. 4 is a positioning result of different EDT surface probability distribution functions under different speed model errors;
FIG. 5 is a schematic diagram of an actual mine microseismic observation system and blasting position.
Fig. 6 actual mine blast signal recordings. Wherein the black circle is the first arrival pick-up position.
FIG. 7 is the actual mine blasting location results for different EDT face probability distribution functions;
FIG. 8 is a mine microseismic event localization result for different EDT surface probability distribution functions;
FIG. 9 is a positioning result after an unreliable mine microseismic event is eliminated.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the accompanying drawing 1:
as shown in fig. 1, the present invention provides a microseismic localization method based on EDT surface probability distribution function by a station, which includes:
filtering and preprocessing microseismic signals of all stations;
picking up the microseismic signals after the filtering pretreatment to obtain microseismic signal first-break data of each station;
calculating and obtaining the signal-to-noise ratios of the microseismic signals of all the stations according to the first arrival data, and carrying out normalization calculation on the signal-to-noise ratios of the microseismic signals;
specifically, the signal-to-noise ratio formula is:
Figure BDA0003426693440000051
in the formula:
Figure BDA0003426693440000052
the signal-to-noise ratio at station k for microseismic event i; w1To be driven from
Figure BDA0003426693440000053
The amplitude value of the microseismic signal in a time window with the length of W is taken forward from time to time; w2To be driven from
Figure BDA0003426693440000054
Taking a background noise amplitude value in a time window with the length of W from time to time;
Figure BDA0003426693440000055
and
Figure BDA0003426693440000056
are respectively represented as W1And W2Root mean square value of the waveform amplitude within the window.
Further, normalizing the microseismic signal to noise ratio comprises:
the normalized calculation formula is:
Figure BDA0003426693440000057
in the formula:
Figure BDA0003426693440000058
normalized signal-to-noise ratio at station k for microseismic event i; maxi(SNR) is the maximum value of the signal-to-noise ratio of microseismic event i.
Obtaining observed time difference data of any two different stations in all the stations according to the first arrival data, and reserving signal-to-noise ratio data with small signal-to-noise ratio of the total microseismic signals of the two different stations;
in particular, the amount of the solvent to be used,
Figure BDA0003426693440000059
in the formula:
Figure BDA00034266934400000510
and
Figure BDA00034266934400000511
respectively the first observation arrivals of the microseismic event i at a station k and a station l;
Figure BDA00034266934400000512
the observed time difference of the microseismic event i at the station k and the station l is the station pair time difference of the microseismic event.
Preferably, retaining the small signal-to-noise ratio data of the total microseismic signal to noise ratio of the two different stations comprises:
Figure BDA00034266934400000513
in the formula: in order to reserve signal-to-noise ratio data with smaller signal-to-noise ratio of the microseismic signal in the station pair k, l of the microseismic event i;
Figure BDA00034266934400000514
to take a smaller value to operate.
Carrying out three-dimensional grid division on the microseismic monitoring area, and calculating the travel time from each grid point to each station to obtain the data of the calculated travel time table
Figure BDA0003426693440000061
Denoted as grid point g to the time of calculation of station k.
Subtracting any two different calculation travel time table data to obtain calculation travel time difference data;
specifically, the formula is
Figure BDA0003426693440000062
In the formula:
Figure BDA0003426693440000063
expressed as grid point g to station k;
Figure BDA0003426693440000064
denoted as the calculated time of grid point g to station l.
Substituting the observed time difference data, the signal-to-noise ratio data with small signal-to-noise ratio of the microseismic signal and the calculated travel time difference data into an EDT surface probability distribution function to obtain a probability value of each grid point;
specifically, the calculation formula is:
Figure BDA0003426693440000065
Figure BDA0003426693440000066
Figure BDA0003426693440000067
in the formula: l isgA probability value for grid point g; TERR is a tolerance parameter;
Figure BDA0003426693440000068
calculating the difference between the travel time difference of the station pair k and l and the observed time difference;
Figure BDA0003426693440000069
is an EDT surface probability distribution function.
And setting the grid point position of the maximum probability value as a microseismic event positioning result.
Specifically, s (g) ═ max (L)g)；
In the formula: s (G) is the probability maximum, and G is the microseismic event positioning result.
Example 1
To verify the validity of the positioning algorithm of the present invention, an example model is set as shown in fig. 2. The size of the model is set to be-100 m-500 m in the X direction, to be-100 m-500 m in the Y direction and to be 0 m-600 m in the Z direction, wherein the distances between the grid division X, Y and the Z direction are both 10m, 8 stations are respectively arranged at (0m, 0m, 0m), (400m, 0m, 0m), (0m, 400m, 0m), (0m, 0m, 400m), (400m, 0m, 400m), (0m, 400m, 0m, 0m) and (400m, 400m, 400m, 400m), and microseismic events S are set to be (300m, 300m, 500 m).
Referring to fig. 3, fig. 3 is a diagram of simulated microseismic recording and pickup results; wherein, (a) recording the unnoised microseismic signals; (b) recording the microseismic signal added with noise; (c) recording and picking up results of the microseismic signals after filtering processing, wherein a black circle is an automatic first arrival picking up result, and a black cross is a theoretical first arrival; (d) and (4) recording an automatic pickup curve of the filtered microseismic signal.
Specifically, the microseismic signal records received by 8 stations are shown in fig. 3a, and in order to make the picked-up first arrival data contain small random errors and discrete data with large pickup errors, random noise with different degrees is added to the microseismic signal record in fig. 3a, as shown in fig. 3 b. It can be seen that most of the microseismic signals of the station are submerged by background noise and cannot be manually picked up, and the wavelet threshold denoising method is used for filtering the microseismic signal record containing noise, as shown in fig. 3 c. It can be seen that most of the microseismic signals are identifiable, but the microseismic signals of station 6 and station 7 are still difficult to identify. And (3) utilizing an automatic first arrival picking method to automatically pick the filtered microseismic signal record first arrival, wherein an automatic picking curve is shown as a graph in fig. 3d, and then obtaining a first arrival picking result according to the position information of the maximum value of the picking curve, which is shown as a black circle in fig. 3c, and the theoretical first arrival position is a black cross mark, so that the picking errors of the station 6 and the station 7 are large and are discrete points picked by the first arrival. And simultaneously, calculating to obtain signal-to-noise ratio data corresponding to the first arrival of each station by utilizing the automatically-picked first arrival position information.
FIG. 4 shows the positioning results of different EDT surface probability distribution functions under different speed model errors. (a) The positioning results of Gaussian distribution under the conditions of no speed model error, speed model error of-5 percent and speed model error of +5 percent are respectively obtained; (b) (e) and (h) are respectively positioning results of Student's t distributed under the condition of no speed model error, speed model error of-5% and speed model error of + 5%; (c) and (f) and (i) are positioning results of the positioning method under the conditions of no speed model error, speed model error of-5 percent and speed model error of +5 percent. Wherein the black five-pointed star is the positioning result, and the intersection point of the dotted lines is the theoretical position.
Referring to fig. 4, it can be seen that the positioning method of the present invention has the best imaging resolution (fig. 4d) and the highest positioning accuracy, which indicates that the influence of discrete points with large picking errors on the positioning accuracy can be effectively avoided. In order to verify the positioning effect of the positioning method under a certain speed model error, the-5% and + 5% disturbance quantities are added on the basis of the accurate speed model and are used as the input speed model in the positioning process, and the positioning results are shown in fig. 4d, 4e and 4f, and fig. 4g, 4h and 4 i. It can be seen that the positioning result of the positioning method of the present invention has the highest imaging resolution (fig. 4f and 4i), and the positioning result is less affected by the error of the velocity model, and still can maintain higher positioning accuracy.
Example 2
The microseismic monitoring of the metal mine usually needs high sampling rate recording due to higher requirement on the positioning precision of microseismic events. The recorded seismic signals under the mining environment are quite complex, the microseismic records comprise the mixture of various signals such as microseismic events, explosion, mechanical noise and the like, the signal-to-noise ratio is low, the required microseismic event signal first arrival is difficult to accurately pick up, and the first arrival data inevitably contains discrete point data with large picking errors. Fig. 5 is a schematic diagram of a microseismic monitoring system and one of the blasting locations of a metal mine, wherein 41 stations are arranged in the microseismic monitoring system for receiving microseismic event signals, and the size of the velocity model is 4908 m/s. Fig. 6 shows the microseismic recording and first arrival picking-up results of the blasting signal, wherein the black circle is the first arrival picking-up position. FIG. 7 shows the positioning of the shot signal using different EDT face probability distribution functions, (a) Gaussian distribution, (b) Student's t distribution, and (c) the positioning method of the present invention; wherein, the black five-pointed star is the positioning result, the intersection point of the dotted lines is the theoretical position, and the black inverted triangle is the station. It can be seen that the positioning method of the present invention has the best positioning imaging resolution, easily obtains the positioning result, and has the highest positioning accuracy. FIG. 8 is the results of the location of 218 actual mine microseismic events using different EDT surface probability distribution functions, (a) the method herein, (b) Gaussian distribution, (c) Student's t distribution; wherein the black inverted triangle is the station and the black five-pointed star is the microseismic event. It can be seen that the localization results using the method herein are more aggregative (fig. 8a), better showing the region of local stress variation of the mine, while the localization results for the EDT surface with gaussian distribution and Student's t distribution are more scattered and less aggregative. After the positioning result of fig. 8a is obtained, the positioning result is utilized to further obtain the number of station data with large picking errors contained in each microseismic event, and unreliable positioning results caused by large picking errors can be eliminated by setting a reasonable threshold value of the number of stations, and finally the eliminated positioning results are obtained, as shown in fig. 9, (a) a text method, (b) gaussian distribution, (c) Student' st distribution; wherein the black inverted triangle is the station and the black five-pointed star is the microseismic event. It can be seen that the microseismic number reduction of the method is not large (fig. 9a), and the number of reliable positioning results obtained is 189, while the microseismic number of the positioning method with the EDT surface having gaussian distribution and Student's t distribution is more reduced, wherein the number of final positioning results of gaussian distribution is 151, and the number of final positioning results of Student's t distribution is 104. The test results show that the positioning method has higher microseismic event positioning precision.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. A microseismic positioning method based on EDT surface probability distribution function of a station is characterized by comprising the following steps:
filtering and preprocessing microseismic signals of all stations;
picking up the microseismic signals after the filtering pretreatment to obtain microseismic signal first-arrival data of each station;
calculating and obtaining microseismic signal-to-noise ratios of all the stations according to the first arrival data, and carrying out normalization calculation on the microseismic signal-to-noise ratios;
acquiring observed time difference data of any two different stations in all the stations according to the first arrival data, and reserving signal-to-noise ratio data with a small total signal-to-noise ratio of the microseismic signals of the two different stations;
carrying out three-dimensional grid division on the microseismic monitoring area, and calculating travel time from each grid point to each station to obtain calculated travel time table data;
subtracting any two different pieces of the calculation travel time table data to obtain calculation travel time difference data;
substituting the observed time difference data, the signal-to-noise ratio data with small signal-to-noise ratio of the microseismic signal and the calculated travel time difference data into an EDT surface probability distribution function to obtain a probability value of each grid point;
and setting the grid point position of the maximum probability value as a microseismic event positioning result.
2. The station-to-EDT surface probability distribution function based microseismic location method of claim 1 wherein the step of calculating the signal-to-noise ratio of the microseismic signals of all the stations according to the first arrival data comprises:
the signal-to-noise ratio formula is:
Figure FDA0003426693430000011
in the formula:
Figure FDA0003426693430000012
the signal-to-noise ratio at station k for microseismic event i; w1To be driven from
Figure FDA0003426693430000013
The amplitude value of the microseismic signal in a time window with the length of W is taken forward from time to time; w2To be driven from
Figure FDA0003426693430000014
Taking a background noise amplitude value in a time window with the length of W from time to time;
Figure FDA0003426693430000015
and
Figure FDA0003426693430000016
are respectively represented as W1And W2Root mean square value of the waveform amplitude within the window.
3. The station-to-EDT surface probability distribution function based microseismic location method of claim 2 wherein normalizing the microseismic signal to noise ratio comprises:
the normalized calculation formula is:
Figure FDA0003426693430000017
in the formula:
Figure FDA0003426693430000021
normalized signal-to-noise ratio at station k for microseismic event i; maxi(SNR) is the maximum value of the signal-to-noise ratio of microseismic event i.
4. The method of claim 3 wherein obtaining observed time difference data for any two different stations of all stations based on the first arrival data comprises:
Figure FDA0003426693430000022
in the formula:
Figure FDA0003426693430000023
and
Figure FDA0003426693430000024
respectively the first observation arrivals of the microseismic event i at a station k and a station l;
Figure FDA0003426693430000025
the observed time difference of the microseismic event i at the station k and the station l is the station pair time difference of the microseismic event.
5. The station-to-EDT surface probability distribution function based microseismic location method of claim 4 wherein retaining signal to noise ratio data for two different stations for which the overall microseismic signal to noise ratio is small comprises:
Figure FDA0003426693430000026
in the formula: in order to reserve signal-to-noise ratio data with smaller signal-to-noise ratio of the microseismic signal in the station pair k, l of the microseismic event i;
Figure FDA0003426693430000027
to take a smaller value to operate.
6. The method of claim 5 wherein the step of substituting the observed time difference data, the signal-to-noise ratio data with a low signal-to-noise ratio of the microseismic signal, and the calculated travel time difference data into the EDT surface probability distribution function to obtain the probability value for each grid point comprises:
the calculation formula is as follows:
Figure FDA0003426693430000028
Figure FDA0003426693430000029
Figure FDA00034266934300000210
in the formula: l isgA probability value for grid point g; TERR is a tolerance parameter;
Figure FDA00034266934300000211
calculating the difference between the travel time difference of the station pair k and l and the observed time difference;
Figure FDA00034266934300000212
is an EDT surface probability distribution function.
7. The station-to-EDT surface probability distribution function based microseismic location method of claim 6 wherein the location of the grid point with the maximum probability value as a microseismic event location result comprises:
S(G)＝max(Lg)；
in the formula: s (G) is the probability maximum, and G is the microseismic event positioning result.
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Publication number Priority date Publication date Assignee Title
CN115951403A (en) * 2022-12-29 2023-04-11 南方科技大学 Microseismic offset superposition positioning method and device, electronic equipment and readable storage medium

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115951403A (en) * 2022-12-29 2023-04-11 南方科技大学 Microseismic offset superposition positioning method and device, electronic equipment and readable storage medium
CN115951403B (en) * 2022-12-29 2023-08-18 南方科技大学 Microseism offset superposition positioning method and device, electronic equipment and readable storage medium

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