An earthquake location method, system, device and medium

Abstract

The present invention provides a seismic positioning method, system, device and medium, relating to the technical field of seismic positioning, so as to alleviate the technical problem of inaccurate positioning existing in the prior art. By acquiring a target seismic signal and determining each target station that receives the target seismic signal; combining all the target stations in pairs without repetition to obtain each pair of stations, and based on the waveform amplitude of the target seismic signal and the observed arrival time of the target seismic signal of each pair of stations, determining the correlation intensity and the observed time difference of each pair of stations relative to the target seismic signal; based on the correlation intensity and the observed time difference of each pair of stations relative to the target seismic signal, determining the equal-time difference surface probability distribution function of each pair of stations; and based on the equal-time difference surface probability distribution function of each pair of stations, determining the position of the target seismic signal, so as to achieve the beneficial effects of improving the accuracy and reliability of positioning the seismic position.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake location, and in particular, to an earthquake location method, system, device and medium. Background Art

[0002] Earthquake location is a classical and fundamental problem in seismology. For large-scale destructive natural earthquakes, quickly and accurately locating the occurrence location of an earthquake is crucial for aspects such as earthquake early warning, earthquake disaster assessment, earthquake rescue, and hazard assessment; for small-scale microseisms, microseismic source information can reflect the rock layer fracture location and stress state, and has important application value in mine safety mining, shale gas fracturing effect evaluation, and landslide early warning.

[0003] Currently, the existing earthquake location technologies mainly include two methods: arrival time picking-based and equal differential time (EDT) surface-based. Among them, the arrival time picking-based method is simple and efficient, but is sensitive to abnormal data, which may lead to inaccurate location results; the EDT surface-based location method improves the anti-noise performance of location, but there may be multiple regions with the same maximum number of intersections, resulting in a decrease in location accuracy and an increase in location search difficulty. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an earthquake location method, system, device and medium to improve the accuracy and reliability of earthquake location.

[0005] In a first aspect, the present invention provides an earthquake location method, including:

[0006] Obtain a target earthquake signal and determine each target station that receives the target earthquake signal;

[0007] Combine all target stations pairwise without repetition to obtain each pair of stations, and based on the waveform amplitude of the target earthquake signal and the observed arrival time of the target earthquake signal for each pair of stations, determine the correlation intensity and observed time difference of each pair of stations relative to the target earthquake signal;

[0008] Based on the correlation intensity and observed time difference of each pair of stations relative to the target earthquake signal, determine the equal differential time surface probability distribution function of each pair of stations;

[0009] Based on the equal differential time surface probability distribution function of each pair of stations, determine the location of the target earthquake signal.

[0010] Optionally, based on the waveform amplitude of the target earthquake signal and the observed arrival time of the target earthquake signal for each pair of stations, determining the correlation intensity and observed time difference of each pair of stations relative to the target earthquake signal includes:

[0011] Based on the waveform amplitude of the target seismic signal and the time window length of the station pair, the relative cross-correlation coefficient of each station pair is obtained;

[0012] Based on the relative cross-correlation coefficient of each station pair, the correlation strength of each station pair relative to the target seismic signal is determined;

[0013] Based on the arrival times of the target seismic signals observed by each station pair, the observed time difference of each station pair is determined.

[0014] Optionally, based on the correlation strength and the observed time difference of each station pair relative to the target seismic signal, the probability distribution function of the equal-time difference surface of each station pair is determined, including:

[0015] Based on the observed time difference of each station pair and the source location of the target seismic signal, the residual between the observed time difference of each station pair and the source location is determined;

[0016] Based on the correlation strength and the residual of each station pair, the probability distribution function of the equal-time difference surface of each station pair is determined.

[0017] Optionally, based on the probability distribution function of the equal-time difference surface of each station pair, the location of the target seismic signal is determined, including:

[0018] Group the probability distribution functions of the equal-time difference surfaces of all station pairs;

[0019] Process the probability distribution functions of the equal-time difference surfaces within each group to obtain the target positioning imaging probability distribution;

[0020] Based on the target positioning imaging probability distribution, the location of the target earthquake is determined.

[0021] Optionally, processing the probability distribution functions of the equal-time difference surfaces within each group to obtain the target positioning imaging probability distribution includes:

[0022] Add the probability distribution functions of the equal-time difference surfaces within each group to obtain the positioning imaging probability distribution of each group;

[0023] Multiply the positioning imaging probability distributions of each component to obtain the target positioning imaging probability distribution.

[0024] Optionally, based on the target positioning imaging probability distribution, determining the location of the target seismic signal includes:

[0025] Search the target positioning imaging probability distribution to determine the maximum value position of the target positioning imaging probability distribution; wherein, the maximum value position of the target positioning imaging probability distribution is the location of the target earthquake.

[0026] Optionally, the method further includes:

[0027] Determine the accuracy of the location of the target seismic signal based on the reliability function.

[0028] In a second aspect, the present invention provides a seismic positioning system, comprising:

[0029] An acquisition module, configured to acquire a target seismic signal and determine each target station that receives the target seismic signal;

[0030] A calculation module, configured to perform non-repetitive pairwise combinations of all target stations to obtain each pair of stations, and determine the correlation strength and observed time difference of each pair of stations relative to the target seismic signal based on the waveform amplitude of the target seismic signal and the observed arrival time of the target seismic signal at each pair of stations;

[0031] A construction module, configured to determine the probability distribution function of the equal-time difference surface of each pair of stations based on the correlation strength and observed time difference of each pair of stations relative to the target seismic signal;

[0032] A positioning module, configured to determine the location of the target seismic signal based on the probability distribution function of the equal-time difference surface of each pair of stations.

[0033] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned seismic positioning method is implemented.

[0034] In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and run by a processor, the machine-executable instructions cause the processor to run the above-mentioned seismic positioning method.

[0035] A seismic positioning method, system, device, and medium provided by an embodiment of the present invention have the beneficial effect of improving the accuracy and reliability of seismic location by acquiring a target seismic signal and determining each target station that receives the target seismic signal; performing non-repetitive pairwise combinations of all target stations to obtain each pair of stations, and determining the correlation strength and observed time difference of each pair of stations relative to the target seismic signal based on the waveform amplitude of the target seismic signal and the observed arrival time of the target seismic signal at each pair of stations; determining the probability distribution function of the equal-time difference surface of each pair of stations based on the correlation strength and observed time difference of each pair of stations relative to the target seismic signal; and determining the location of the target seismic signal based on the probability distribution function of the equal-time difference surface of each pair of stations. Description of the Drawings

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0037] Figure 1 Shows the flowchart of a seismic location method provided by an embodiment of the present invention;

[0038] Figure 2 Shows the schematic diagram of the three-dimensional monitoring network structure provided by an embodiment of the present invention;

[0039] Figure 3 Shows the schematic diagram of the positions of two seismic sources S1 and S2 provided by an embodiment of the present invention;

[0040] Figure 4 Shows the schematic diagram of the synthetic waveform of the microseismic events at the seismic source S1 in the model provided by an embodiment of the present invention;

[0041] Figure 5 Shows the schematic diagram of the synthetic microseismic waveform of the seismic signal R1 with high-intensity noise at the seismic source S1 provided by an embodiment of the present invention;

[0042] Figure 6 Shows the schematic diagram of the first arrival time picked up at the maximum position of the automatic picking curve of the seismic signal R1 after denoising at the seismic source S1 provided by an embodiment of the present invention;

[0043] Figure 7 Shows the schematic diagram of the automatic picking curve of the seismic signal R1 after denoising at the seismic source S1 provided by an embodiment of the present invention;

[0044] Figure 8 Shows the schematic diagram of the probability imaging location result of the seismic signal R1 in the addition form at the seismic source S1 provided by an embodiment of the present invention;

[0045] Figure 9 Shows the schematic diagram of the probability imaging location result of the seismic signal R1 in the multiplication form at the seismic source S1 provided by an embodiment of the present invention;

[0046] Figure 10 Shows the schematic diagram of the probability imaging location result of the seismic signal R1 in the form of grouped addition and then multiplication at the seismic source S1 provided by an embodiment of the present invention;

[0047] Figure 11 Shows the schematic diagram of the distribution of geophones and the relative positions of 7 blasting events in the microseismic monitoring of a certain mine provided by an embodiment of the present invention;

[0048] Figure 12 Shows the waveform of the No. 3 blasting event and the schematic diagram of the first arrival time provided by the embodiment of the present invention;

[0049] Figure 13 Shows the schematic diagram of the comparison of the positioning errors of blasting events obtained in the form of addition, multiplication, grouping and superposition and then multiplication provided by the embodiment of the present invention;

[0050] Figure 14 Shows the schematic diagram of the probability imaging positioning result of the No. 3 blasting event in the addition form provided by the embodiment of the present invention;

[0051] Figure 15 Shows the schematic diagram of the probability imaging positioning result of the No. 3 blasting event in the multiplication form provided by the embodiment of the present invention;

[0052] Figure 16 Shows the schematic diagram of the probability imaging positioning result of the No. 3 blasting event in the form of grouping, adding and then multiplying provided by the embodiment of the present invention;

[0053] Figure 17 Shows the schematic diagram of the distribution of the positioning positions of actual mine microseismic events in the addition form provided by the embodiment of the present invention;

[0054] Figure 18 Shows the schematic diagram of the distribution of the positioning positions of actual mine microseismic events in the multiplication form provided by the embodiment of the present invention;

[0055] Figure 19 Shows the schematic diagram of the distribution of the positioning positions of actual mine microseismic events in the form of grouping, adding and then multiplying provided by the embodiment of the present invention;

[0056] Figure 20 Shows the schematic diagram of the comparison of the relationship between the time residual and the reliability of the positioning result obtained in the form of addition, multiplication, grouping, superposition and then multiplication provided by the embodiment of the present invention;

[0057] Figure 21 Shows the schematic diagram of the repositioning result after removing the arrival time data with large picking errors provided by the embodiment of the present invention;

[0058] Figure 22 Shows the schematic diagram of the structure of an electronic device provided by the embodiment of the present invention. Detailed implementation manners

[0059] To make the objectives, 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 accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. The components of the embodiments of the present invention usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0060] To facilitate better understanding of the present application by those skilled in the art, the technical terms related to the present application will be briefly introduced below.

[0061] A station is to receive seismic wave signals from the interior of the earth through sensors and convert physical vibrations into electrical signals; a station pair is to make non-repeating pairwise combinations of all stations that receive seismic signals, where two stations form a station pair; the correlation intensity is a measure of the similarity degree of two seismic signal waveforms; the observed time difference is the time delay between the seismic waveforms received by two stations.

[0062] After introducing the technical terms related to the present application, next, the technical solutions provided by the present application will be described in detail.

[0063] Figure 1 It is a schematic flowchart of a seismic positioning method provided by an embodiment of the present application. As Figure 1 shown, the method at least includes the following steps:

[0064] Step S110: Obtain a target seismic signal and determine each target station that receives the target seismic signal.

[0065] In the present application, synthetic data is used to test the application effect of the seismic positioning method in the present application. First, as Figure 2 shown, a three-dimensional monitoring network of 300m×300m×300m is set up, and a geophone is arranged at each of the 8 corners to receive seismic event signals (i.e., the signals of the target earthquake) with an isotropic and uniform P-wave velocity of 5 km / s. This layout method is relatively common in actual mine microseismic monitoring. As Figure 3As shown, two seismic sources are set simultaneously: Seismic source S1 is located inside the geophone array, with coordinates (240, 240, 200) m; seismic source S2 is located outside the geophone array, and the monitoring azimuth angle is greater than 180°, with coordinates (500, 60, 400) m. The purpose of setting the seismic sources is to test the influence of the observation system on the seismic positioning probability results.

[0066] Figure 4 is the synthetic waveform of the microseismic event of the model. This waveform record is obtained by convolving the seismic source wavelet with the transmission coefficient sequence. The seismic source wavelet uses an exponentially decaying sine wave with a main frequency of 150 Hz, and the amplitude of the received waveform decays with the source distance. Therefore, the maximum amplitudes of the seismic signals received by each geophone are inconsistent, and the maximum amplitude value is 1.

[0067] To simulate the situation where there are multiple outliers in the first arrival time picking data, high-intensity noise with a mean of 0 and a variance in the range of 0 to 1 is added to the noise-free waveform record, as Figure 5 shown in the synthetic microseismic waveform with high-intensity noise. In this case, the microseismic signals on most geophones are severely contaminated by noise, making it difficult to identify the signals. After preprocessing, the denoised microseismic signals gradually appear, as Figure 6 shown, effectively reducing the difficulty of automatic first arrival picking. Then, according to Figure 7 the position of the maximum value of the automatic first arrival picking curve shown, the first arrival times of the microseismic signals on each geophone in Figure 6 are obtained. It can be seen that except for the geophone No. 1 of seismic event E1 and the geophones No. 3, 4, and 5 of S2, the automatic first arrival picking results of other geophones are close to the theoretical first arrival times. Among them, the picking errors of seismic events E1 and E2 on each geophone show that the picking errors of the geophones No. 1 and 7 of microseismic event MS1, and the geophones No. 3, 4, and 5 of MS2 reach 0.142 s, 0.013 s, 0.089 s, 0.187 s, and 0.387 s respectively. These are all outliers with large picking errors in the first arrival picking results.

[0068] It should be noted that in this application, the seismic event generated by seismic source S1 is E1. According to the size of the seismic scale, seismic event E1 is determined to be microseismic event MS1, and the seismic signal generated by the seismic event of seismic source S1 is R1.

[0069] Step S120: Combine all target stations pairwise without repetition to obtain each station pair. Based on the waveform amplitude of the target seismic signal and the observed arrival times of the target seismic signals of each station pair, determine the correlation strength and the observed time difference of each station pair relative to the target seismic signal.

[0070] Among them, it is determined whether the number of target stations is an even number. If not, elimination processing is performed. This elimination processing can be judged according to the intensity of the received target earthquake signal, and the station with the weakest received target earthquake signal intensity is eliminated. It can also be based on the time when the target station receives the target earthquake signal, and the station that takes the longest time to receive the target earthquake signal is eliminated.

[0071] Furthermore, all target stations are combined pairwise without repetition to obtain each pair of stations, and the target earthquake signals received by all target stations are preprocessed by band-pass filtering.

[0072] In an alternative embodiment, in step S120, based on the waveform amplitude of the target earthquake signal and the observed arrival time of the target earthquake signal of each pair of stations, determining the correlation intensity and the observed arrival time difference of each pair of stations relative to the target earthquake signal includes:

[0073] Based on the waveform amplitude of the target earthquake signal and the time window length of the pair of stations, the cross-correlation coefficient of each pair of stations is obtained;

[0074] Based on the cross-correlation coefficient of each pair of stations, the correlation intensity of each pair of stations relative to the target earthquake signal is determined;

[0075] Based on the observed arrival time of the target earthquake signal of each pair of stations, the observed arrival time difference of each pair of stations is determined.

[0076] Furthermore, the cross-correlation coefficient of each pair of stations can be obtained through the following formula:

[0077]

[0078] In the formula, is the cross-correlation coefficient of the pair of stations and at the sampling point , is the time window length of the cross-correlation calculation, is the seismic trace at the sampling point of the waveform amplitude, is the seismic trace at the sampling point of the waveform amplitude.

[0079] Furthermore, according to the magnitude and position of the absolute maximum value of the cross-correlation coefficient of the pair of stations, the correlation intensity and the observed arrival time difference data of the target earthquake signal waveform of the corresponding pair of stations are obtained, where the maximum value of corresponds to the moment , then the correlation intensity of the seismic signal waveform of the pair of stations and the observed arrival time difference It can be expressed as:

[0080]

[0081]

[0082] Wherein, and are respectively the arrival times of the seismic signals observed by the station pair and for the target seismic signal.

[0083] Step S130: Determine the probability distribution function of the equal-time difference surface for each station pair based on the correlation intensity and the observed time difference of each station pair relative to the target seismic signal.

[0084] In an optional embodiment, in step S130, determining the probability distribution function of the equal-time difference surface for each station pair based on the correlation intensity and the observed time difference of each station pair relative to the target seismic signal includes:

[0085] Determine the residual between the observed time difference of each station pair and the source location of the target seismic signal based on the observed time difference of each station pair and the source location of the target seismic signal;

[0086] Determine the probability distribution function of the equal-time difference surface for each station pair based on the correlation intensity and the residual of each station pair; wherein, the probability distribution function of the equal-time difference surface is:

[0087]

[0088] Wherein, is the probability distribution function of the equal-time difference surface with the source location being , is the tolerance parameter, is the residual between the observed time difference of the station pair and the source location at , is the correlation intensity of the seismic signal waveforms of the station pair.

[0089] Step S140: Determine the location of the target seismic signal based on the probability distribution function of the equal-time difference surface for each station pair.

[0090] In an optional embodiment, in step S140, determining the location of the target seismic signal based on the probability distribution function of the equal-time difference surface for each station pair includes:

[0091] Group the probability distribution functions of the equal-time difference surfaces of all station pairs;

[0092] Process the probability distribution functions of the equal-time difference surfaces within each group to obtain the target positioning imaging probability distribution;

[0093] Determine the location of the target earthquake based on the target location imaging probability distribution.

[0094] Among them, processing the equal-time difference surface probability distribution functions within each group to obtain the target location imaging probability distribution includes:

[0095] Add the equal-time difference surface probability distribution functions within each group to obtain the location imaging probability distribution of each group;

[0096] Multiply the location imaging probability distributions of each component to obtain the target location imaging probability distribution.

[0097] Among them, determining the location of the target earthquake signal based on the target location imaging probability distribution includes:

[0098] Search for the target location imaging probability distribution to determine the maximum value position of the target location imaging probability distribution; among them, the maximum value position of the target location imaging probability distribution is the location of the target earthquake signal.

[0099] Specifically, first, reasonably group the equal-time difference surface probability distribution functions of all station pairs. For example, they can be grouped according to the relative positions between station pairs, or according to the characteristics of the target earthquake signals recorded by each station pair, and machine learning algorithms can also be used for automatic grouping;

[0100] Secondly, add the equal-time difference surface probability distribution functions within each group to obtain the location imaging probability part of each group, and multiply the location imaging probability distributions of each component to obtain the target location imaging probability distribution. Among them, the expression of the target location imaging probability distribution is:

[0101]

[0102]

[0103] Among them, is the target location imaging probability distribution at position , means that all station pairs are divided into groups, is the location imaging probability distribution of the th group at position , is the th group, and there are a total of equal-time difference surface probability distribution functions of station pairs in the group, is the th equal-time difference surface probability distribution function corresponding to the th station pair in the th group at position

[0104] Then, search for the position of the maximum value of the target location imaging probability distribution to obtain the position of the target seismic signal. Among them, randomized search methods such as simulated annealing, genetic algorithms, particle swarm optimization, and ant colony algorithms can be used to find the position of the maximum value in the target location imaging probability distribution.

[0105] Furthermore, Figure 8 For the localization probability imaging results in the form of addition, multiplication, and grouped superposition in the multiplicative form, it can be seen from the distribution of the addition-based localization probability imaging results and the true source position. Among them, Figure 8 The "cross" in it represents the positions of the calculated source positions on the Y-X plane, Y-Z plane, and Z-X plane, and the intersection position of the dotted lines represents the positions of the true source positions on the Y-X plane, Y-Z plane, and Z-X plane. Due to the influence of multiple outliers, obvious and severe stripe artifacts appear in the superposition imaging results; Figure 9 For the probability imaging localization results in the multiplicative form. Among them, Figure 9 The "cross" in it represents the positions of the calculated source positions on the Y-X plane, Y-Z plane, and Z-X plane, and the intersection position of the dotted lines represents the positions of the true source positions on the Y-X plane, Y-Z plane, and Z-X plane. It can be seen from Figure 9 that the multiplicative-based localization probability imaging results lead to relatively large localization errors. Although the multiplicative form successfully eliminates the superposition stripe artifacts and improves the probability imaging resolution, the maximum value of the probability fails to concentrate on the true source position, still resulting in relatively large localization errors; Figure 10 For the probability imaging localization results in the form of grouped addition and then multiplication. Among them, Figure 10 The "cross" in it represents the positions of the calculated source positions on the Y-X plane, Y-Z plane, and Z-X plane, and the intersection position of the dotted lines represents the positions of the true source positions on the Y-X plane, Y-Z plane, and Z-X plane. It can be clearly seen from Figure 10 that the form of grouped superposition and then multiplication not only successfully eliminates the superposition stripe artifacts and improves the imaging resolution, but also makes the maximum value of the localization probability concentrate near the theoretical source position, with the smallest localization error. This is because the variable Gaussian probability distribution function has a slow attenuation effect on the first arrival time data with relatively large picking errors, enabling the potential true source position to obtain relatively high probability values, thus enabling the form of grouped superposition and then multiplication to obtain stable imaging resolution and positioning results with excellent positioning accuracy.

[0106] It should be noted that Figure 8 、 Figure 9 and Figure 10 the calculated source positions in are the source positions under Normalized probability.

[0107] In an alternative embodiment, the method further includes the following steps:

[0108] Determine the accuracy of the position of the target seismic signal based on the reliability function.

[0109] Among them, the reliability function is:

[0110]

[0111] In the formula, is the accuracy of the positioning result, is the number of station pairs that satisfy the residual of the observed time difference between stations for and being less than the tolerance , is a function, is the number of all station pairs.

[0112] Furthermore, is the sign function, representing being 1 when

[0113] and 0 otherwise. To ensure the accuracy of the position of the target seismic signal, the accuracy of the position of the target seismic signal is verified through the reliability function. Among them, the accuracy value varies between 0 and 1,

[0114] As Figure 11 shown, it is the microseismic monitoring network of a certain metal mine provided by the embodiment of the present application, which includes 41 single-component geophones deployed in roadways at altitudes of 75 m, 35 m, and -5 m. According to the data recorded by the BSN system, from March 26th to March 31st, 2021, 7 mining blasts were carried out in this mining area. The relative positions of the blast events and the geophones are as follows, Figure 11 the inverted triangles in Figure 12 are geophones, and the hexagons are blast events. It is the original waveform of the No. 3 blast event and the automatically picked first arrival time, showing a low waveform consistency between geophones. The velocity model of this mining area was determined by exploration geological engineers to be a uniform P-wave velocity of 4908 m / s.

[0115] According to the suggestions of on-site microseismic monitoring technicians and the true positions of the blast events, the target area range of the positioning imaging is set as: -100 m to 700 m in the X direction, -100 m to 700 m in the Y direction, -200 m to 200 m in the Z direction, and the spatial step size is 10 m. According to the research area of the mine, during the positioning process, the parameter in the probability distribution function is set to 0.005 s. By comparing Figure 13The positioning errors of the 7 blasting events shown in different combined forms can be seen that the overall positioning error of the grouped superposition and multiplication form is the smallest, the average value of the positioning error is 24.53 m, and its minimum positioning error is only 8.3 m. Specifically, although the positioning error of blasting event No. 2 with the largest positioning error is 50.8 m, this error is still much smaller than the positioning errors of the addition and multiplication forms.

[0116] To compare the positioning probability imaging results of different probability distribution functions, blasting events No. 6, No. 2, and No. 7 were selected in the order of blasting positions. As Figure 14 shows the positioning probability imaging results of the addition form. Since the number of geophones triggered by the blasting events is large, no obvious local probability maximum artifacts and topological structures appear in the positioning imaging probability distribution of the superposition form, but a significant tailing phenomenon appears. From the positioning results of blasting event No. 2, it can be seen that due to the azimuth angle of the geophones in the X-Y plane being greater than 180° and the relatively small number of triggered geophones, a large deviation occurs in the positioning results in the X-Y plane. Although the multiplication form improves the convergence and resolution of the positioning imaging, the positioning accuracy has not been significantly improved, as Figure 15 shown. However, the variable probability distribution function can obtain a higher probability value for the true blasting position by using the grouped superposition and multiplication form, and its positioning result has the smallest error, as Figure 16 shown, Figure 14 , Figure 15 and Figure 16 In, the "cross" is the position of the calculated positioning result in the Y-X plane, Y-Z plane, and Z-X plane, and the position where the dotted lines intersect is the position of the true source in the Y-X plane, Y-Z plane, and Z-X plane.

[0117] These three probability distribution functions were used to locate 432 microseismic events measured in this mining area from March 30 to March 31, 2021. To ensure the quality of the seismic event data, all events in the data set triggered more than 8 geophones. Through the above data preprocessing process, the first arrival times of the geophones triggered by all microseismic events and the corresponding signal-to-noise ratios and other observation data were obtained. Due to the influence of the azimuth angle of some microseismic events relative to the triggered geophones being greater than 180° and the arrival time data with large picking errors, the positioning results may deviate to the boundary of the target area. Therefore, the positioning results after removing the microseismic events located at the boundary of the target area were defined as effective seismic events. Figure 17 shows the positioning results of the addition of the fixed Gaussian distribution function; Figure 18 shows the positioning results of the addition and multiplication forms of the fixed Gaussian distribution function, where the effective seismic events are 338 and 279 respectively. The source positions are relatively matched with the blasting event positions and are distributed near the blasting positions, but the degree of aggregation is not high. In contrast, Figure 19The positioning results obtained by the variable Gaussian probability distribution function shown are in the form of grouped superposition and then multiplication, where the proportion of effective seismic events is the highest, with 361 and 358 respectively; the matching degree between the hypocenter position and the blasting position is relatively high, and the aggregation degree is significantly higher than that of the positioning results in the addition and multiplication forms. These results help to further delineate the microseismic event aggregation area, evaluate the intensity of mine seismic activities, and provide certain guidance for on-site mining and safety production.

[0118] Using the reliability provided by this application, the positioning reliabilities of addition, multiplication, and grouped superposition and then multiplication are quantitatively calculated and compared and analyzed with the travel-time residuals of the positioning results, as Figure 20 shown. It can be seen that when the travel-time residuals of most microseismic events are small, the reliability of the positioning results is relatively high. The average reliability of the positioning results of the variable Gaussian probability distribution function in the form of grouped superposition and then multiplication is 0.7652, which is significantly higher than that of the addition and multiplication forms, indicating that more high-quality arrival-time data participate in the positioning of microseismic events. According to the positioning results of the method of the present invention, after removing the discrete data with large picking errors in the arrival-time data, repositioning is performed, as Figure 21 shown. After repositioning, the hypocenter positions are more concentrated, which is conducive to judging the intensity of seismic activities.

[0119] A seismic positioning method provided by this application has the following beneficial effects:

[0120] 1. Through the correlation degree of waveform cross-correlation between stations and the arrival-time difference information corresponding to the maximum value of the cross-correlation coefficient, when the waveform signal-to-noise ratio is relatively high, the corresponding waveform correlation degree and arrival-time difference accuracy are also relatively high, the width of the equal-time difference surface narrows, showing a non-heavy-tailed distribution characteristic; when the signal-to-noise ratio is low, the corresponding waveform correlation degree and arrival-time difference accuracy are also relatively low, the width of the equal-time difference surface widens, showing a heavy-tailed distribution characteristic, realizing the adaptive adjustment of the probability distribution function of the equal-time difference surface, and automatically reducing the weight of low-signal-to-noise ratio data in the objective function;

[0121] 2. Adopting a grouped form, that is, adding within the group and multiplying between groups, to obtain the target positioning imaging probability distribution, which can not only effectively suppress the local maximum value of the positioning probability generated by superposition, but also improve the probability imaging resolution and positioning accuracy.

[0122] A seismic positioning system provided by an embodiment of this application, the system includes:

[0123] An acquisition module, configured to acquire a target seismic signal and determine each target station that receives the target seismic signal;

[0124] A calculation module, configured to obtain each pair of stations by non-repeatedly combining all target stations in pairs, and determine the correlation strength and observed time difference of each pair of stations relative to the target seismic signal based on the waveform amplitude of the target seismic signal and the observed arrival times of the target seismic signals of each pair of stations;

[0125] A construction module, configured to determine the equal-time difference surface probability distribution function of each pair of stations based on the correlation strength and observed time difference of each pair of stations relative to the target seismic signal;

[0126] A positioning module, configured to determine the position of the target seismic signal based on the equal-time difference surface probability distribution function of each pair of stations.

[0127] In an alternative embodiment, the construction module is further configured to: obtain the cross-correlation coefficient of each pair of stations relative to each other based on the waveform amplitude of the target seismic signal and the time window length of the pair of stations; determine the correlation strength of each pair of stations relative to the target seismic signal based on the cross-correlation coefficient of each pair of stations relative to each other; and determine the observed time difference of each pair of stations based on the observed arrival times of the target seismic signals of each pair of stations.

[0128] In an alternative embodiment, the construction module is further configured to: determine the residual between the observed time difference of each pair of stations and the source position of the target seismic signal; determine the equal-time difference surface probability distribution function of each pair of stations based on the correlation strength and the residual of each pair of stations; where the equal-time difference surface probability distribution function is:

[0129]

[0130] In the formula, is the source position equal-time difference surface probability distribution function, is the tolerance parameter, is the residual between the observed time difference of the pair of stations and the source position at; is the correlation strength of the seismic signal waveforms of the pair of stations.

[0131] In an alternative embodiment, the positioning module is further configured to: group the equal-time difference surface probability distribution functions of all pairs of stations; process the equal-time difference surface probability distribution functions within each group to obtain the target positioning imaging probability distribution; and determine the position of the target earthquake based on the target positioning imaging probability distribution.

[0132] In an alternative embodiment, the positioning module is further configured to: add the equal-time difference surface probability distribution functions within each group to obtain the positioning imaging probability distribution of each group; multiply the positioning imaging probability distributions of each component to obtain the target positioning imaging probability distribution.

[0133] In an alternative embodiment, the positioning module is further configured to: search for the target positioning imaging probability distribution and determine the position of the maximum value of the target positioning imaging probability distribution; wherein, the position of the maximum value of the target positioning imaging probability distribution is the position of the target earthquake.

[0134] In an alternative embodiment, the positioning module is further configured to: determine the accuracy of the position of the target earthquake signal based on the reliability function.

[0135] In an alternative embodiment, the reliability function is:

[0136]

[0137] In the formula, is the accuracy of the positioning result, is the number of station pairs that satisfy the residual of the observed time difference between station pair and being less than the tolerance , is a function, is the number of all station pairs.

[0138] The device provided by the embodiments of the present application has the same implementation principle and the same technical effects as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments.

[0139] As Figure 22 shown, an electronic device 600 provided by an embodiment of the present application includes: a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device runs, communication is carried out between the processor 601 and the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the earthquake positioning method as described above.

[0140] Specifically, the foregoing memory 602 and processor 601 can be general-purpose memory and processor, which are not specifically limited herein. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned earthquake positioning method.

[0141] The processor 601 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 601 or the instructions in the form of software. The above-mentioned processor 601 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0142] Corresponding to the above earthquake location method, an embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium stores machine-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the steps of the above earthquake location method.

[0143] The earthquake location system provided by the embodiments of the present application may be specific hardware on a device or software or firmware installed on the device, etc. For the device provided by the embodiments of the present application, the implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can all refer to the corresponding processes in the above method embodiments, and will not be repeated here.

[0144] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0145] For another example, the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of apparatuses, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0146] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solution of this embodiment.

[0147] In addition, the various functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0148] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0149] Finally, it should be noted that the above embodiments are only specific implementation manners of the present application, used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application. All should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for earthquake location, characterized in that: include: Acquire a target seismic signal, and determine each target station that receives the target seismic signal; All the target stations are combined in pairs without duplication to obtain station pairs, and based on the waveform amplitude of the target seismic signal and the observed time of the target seismic signal of each station pair, the correlation strength and observed time difference of each station pair relative to the target seismic signal are determined; Based on the correlation strength and observed time difference of each station pair relative to the target seismic signal, the probability distribution function of the equal time difference surface of each station pair is determined; wherein the probability distribution function of the equal time difference surface is: In the formula, The earthquake source location is The probability distribution function of the equal time difference surface, is the tolerance parameter, The time difference between the observed station pair and the earthquake source position is The residual at is the correlation strength of the seismic signal waveforms of the station pair; The position of the target seismic signal is determined based on the equal time difference surface probability distribution function of each station pair.

2. The earthquake locating method according to claim 1, characterized in that: Determining the correlation strength and observed time difference of each station pair relative to the target seismic signal based on the waveform amplitude of the target seismic signal and the observed time of the target seismic signal of each station pair, comprising: Based on the waveform amplitude of the target seismic signal and the time window length of the station pair, obtaining the relative mutual correlation coefficient of each of the station pairs; Determining the correlation strength of each station pair relative to the target seismic signal based on the relative mutual correlation coefficient of each station pair; Based on the observation times of the target seismic signals of each of the station pairs, the observed time difference of each of the station pairs is determined.

3. The earthquake locating method according to claim 2, characterized in that: Based on the correlation strength and observed time difference of each station pair relative to the target seismic signal, determining the equal time difference surface probability distribution function of each station pair, including: Based on the observed time difference of each of the station pairs and the hypocenter position of the target seismic signal, determining the residual of the observed time difference of each of the station pairs and the hypocenter position; Based on the correlation strength and the residual of each station pair, a probability distribution function of an equal time difference surface of each station pair is determined.

4. The earthquake locating method according to claim 3, characterized in that: Determining the position of the target seismic signal based on the equal time difference surface probability distribution function of each station pair includes: Grouping the equal time difference surface probability distribution functions of all the station pairs; Processing the probability distribution function of the equal time difference surface in each group to obtain the probability distribution of target positioning imaging; Based on the target positioning imaging probability distribution, the position of the target earthquake is determined.

5. The earthquake locating method according to claim 4, characterized in that: The probability distribution function of the equal time difference surface in each group is processed to obtain the target positioning imaging probability distribution, including: Adding the equal time difference surface probability distribution functions in each group to obtain the positioning imaging probability distribution of each group; The positioning imaging probability distribution of each component is multiplied to obtain the target positioning imaging probability distribution.

6. The earthquake locating method according to claim 5, characterized in that: Determining the position of the target seismic signal based on the target positioning imaging probability distribution includes: The target positioning imaging probability distribution is searched to determine the maximum value position of the target positioning imaging probability distribution; wherein the maximum value position of the target positioning imaging probability distribution is the position of the target earthquake.

7. The earthquake locating method according to claim 1, characterized in that: Also includes: Based on the reliability function, the accuracy of the location of the target seismic signal is determined.

8. An earthquake positioning system, characterized in that: include: An acquisition module, used to acquire a target seismic signal and determine each target station that has received the target seismic signal; A calculation module, used for combining all the target stations in pairs without duplication to obtain each station pair, and determining the correlation strength and observed time difference of each station pair relative to the target seismic signal based on the waveform amplitude of the target seismic signal and the observed time of the target seismic signal of each station pair; A construction module is used to determine the probability distribution function of the equal time difference surface of each station pair based on the correlation strength and the observed time difference of each station pair relative to the target seismic signal; wherein the probability distribution function of the equal time difference surface is: In the formula, The earthquake source location is The probability distribution function of the equal time difference surface, is the tolerance parameter, The time difference between the observed station pair and the earthquake source position is The residual at is the correlation strength of the seismic signal waveforms of the station pair; A positioning module is used to determine the position of the target seismic signal based on the equal time difference surface probability distribution function of each station pair.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to execute the method according to any one of claims 1 to 7.

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