CN110907991A - Seismic source positioning method and system based on data field potential value and readable storage medium - Google Patents
Seismic source positioning method and system based on data field potential value and readable storage medium Download PDFInfo
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Abstract
The invention discloses a seismic source positioning method, a system and a readable storage medium based on a data field potential value, wherein the method comprises the steps of denoising a microseismic signal; then, automatically and manually combined to pick up a P wave first arrival; sampling by adopting a Bootstrap method to obtain a plurality of P wave first-break subdata sets; then, acquiring an initial positioning result of the microseismic event by adopting a double-difference method and a data field potential value; calculating theoretical propagation time from a seismic source to a sensor by using the initial positioning result, and removing P wave first arrival large error pickup signals; and taking the P wave first arrival picked up by removing the large error as new data, and carrying out Bootstrap sampling and data field potential value positioning to obtain the final seismic source position. The method can remove the high frequency and power frequency noise of the microseismic signal, improve the signal to noise ratio and ensure that the P wave first arrival picked by the automatic method is more accurate; and obtaining a point with higher positioning density based on the potential value of the data field, and reducing the influence of the initial iteration center, so that the microseismic positioning result is more stable.
Description
Technical Field
The invention belongs to the field of microseismic monitoring, and particularly relates to a seismic source positioning method and system based on a data field potential value and a readable storage medium.
Background
The microseism monitoring technology is a geophysical technology widely applied to geotechnical engineering, particularly mining engineering and tunnel engineering, and receives microseism signals generated by fault slippage through arranging sensors, so that the characteristics of microseism generation positions, seismic levels, energy changes and the like are analyzed, the mechanical state of rock mass is deduced, and effective prevention and control measures are taken. The microseismic positioning can determine the fracture position of the rock mass and is the basis of subsequent magnitude calculation, seismic source mechanism inversion and risk analysis, so that high-precision seismic source positioning is of great importance.
At present, the microseismic signal denoising is mainly based on low-pass filtering, wavelet threshold denoising and Empirical Mode Decomposition (EMD) denoising. The low-pass filtering can only filter noise of specific frequency; the difficulty in application is how to select wavelet basis and decomposition scale in wavelet analysis, and the wavelet analysis decomposes signals according to a scale fixed frequency band, each frequency band is only related to the sampling frequency of the signals, but not to the information of the signals, and the signals are easily influenced by overlapping of adjacent harmonic components in the signals. EMD has the problems of frequency aliasing, obvious endpoint effect and the like, and reduces the fidelity of signals.
The microseismic positioning is mainly used for determining the position of a seismic source by means of P wave theoretical arrival time difference and monitored arrival time difference between sensors or P wave theoretical propagation time and sensor monitored propagation time, and the microseismic positioning is based on P wave first arrival time or P wave relative arrival time difference. A long-short time window average value ratio method (STA/LTA method), an Alphabet criterion method (AIC method), a high-order statistics method (PAI-S/K method), a fractal dimension method, a neural network method and the like are proposed at home and abroad for P wave first arrival picking, and P wave oscillation starting points are used as P wave first arrival time, but the P wave oscillation starting points are possibly attenuated by propagation, namely the picked P wave first arrival time is not true P wave first arrival time. In view of the above, some researches adopt a cross-correlation method to pick up the relative arrival time of the P-wave instead of the oscillation starting time, and the P-wave has a better effect in the field of natural earthquakes. However, the mine microseismic signals are close in propagation distance, the tail waves are very complex, and the relative arrival time difference of the P waves is difficult to pick up by using a cross-correlation method. In addition, the P-wave first arrival itself may have large errors due to noise, erroneous division of adjacent event signals, and the like.
Microseismic positioning mostly adopts L1 and L2 norms of arrival time differences to define an objective function, but traditional L1 and L2 norms positioning are easily affected by large picking errors, so that the positioning result is not stable. To solve the above problems, many scholars have proposed many new microseismic location methods. The dong longjun et al (2019) studied the problems of wave velocity propagation, signal acquisition and the like in deep complex stopes and proposed a method capable of improving the positioning accuracy of microseismic events, which is a co-positioning method based on theoretical analysis and iterative solution. Leinsmen et al (2016) propose a virtual field optimization method that uses hyperboloid common intersection points determined by stations in three-dimensional space for localization, which uses exponential decay functions to reduce the problem of unstable seismic source localization caused by large pickup errors.
Disclosure of Invention
The invention provides a seismic source positioning method, a seismic source positioning system and a readable storage medium based on data field potential values. The P wave time-arrival data is put back to sample expansion positioning, and the influence of large error picking is reduced. And obtaining a point with higher positioning density based on the potential value of the data field, so that the influence of the initial iteration center can be reduced, and the microseismic positioning result is more stable. In addition, large error pickup signals can be removed after initial positioning, and high-precision positioning can be carried out.
A microseism signal large error picking removal and seismic source positioning method based on a data field potential value comprises the following steps:
step 1: denoising the high-frequency noise and the power-frequency noise of the microseismic signal;
step 2: automatically and manually picking up the P wave first arrival time of the denoised microseismic signals with high signal-to-noise ratio and low signal-to-noise ratio;
and step 3: applying Bootstrap method to microseismic data set S1,S2,…,SnSampling to obtain a plurality of P wave first arrival subdata sets;
Si＝(xi，yi，zi，ti) I is 1,2, …, n is pickup microThe number of seismic signals, (x)i，yi，zi) As three-dimensional coordinate position of the sensor, tiThe P wave first arrival moment picked up in the step 2;
and 4, step 4: the double difference method and the data field potential value realize the initial positioning of the microseismic event;
performing seismic source positioning on each P-wave first-break sub data set by adopting a double-difference method to obtain a positioning position corresponding to each P-wave first-break sub data set;
calculating the data field potential value of each P-wave first-break subdata set at the corresponding positioning position, taking the positioning position mean value corresponding to the first K data field potential values with larger potential value as the initial positioning position of the microseismic event, wherein the number of K is 30-60;
and 5: removing the P wave first arrival large error and picking up a corresponding microseismic data set according to the difference between the theoretical propagation time of the P wave and the propagation time based on the observed time to obtain an updated microseismic data set;
step 6: and repeating the step 3-4 by using the updated microseismic data set to realize microseismic event relocation, and obtaining the relocated event position (x, y, z) as a final location result.
Further, the process of denoising the high-frequency noise and the power frequency noise of the microseismic signal is as follows:
firstly, a microseismic signal x (m) is adaptively decomposed into a plurality of intrinsic mode components IMF by adopting ensemble empirical mode decompositioniAnd 1 residual component rkI is 1,2, …, k, k is the number of empirical mode components, high frequency noise is removed by adopting soft threshold value to the first two intrinsic mode components IMF, and the IMF after denoising1And IMF2With other IMF components IMF3, IMF4, …, IMFkAnd a residual component rkAdding to obtain a microseismic signal without high-frequency noise;
secondly, denoising the power frequency noise of the microseismic signal without the high frequency noise by adopting a sine function fitting mode to obtain the microseismic signal without the high frequency and the power frequency;
x (M) is the microseismic signal, M is 1,2, …, M is the total number of sampling points of the microseismic signal.
Further, toThe denoised microseismic signal with high signal-to-noise ratio is obtained by adopting a long-time window mean ratio method to pick up the P wave approximate first arrival T of the signal with high signal-to-noise ratio1Then, the pool red criterion method AIC is adopted to be in [ T ]1-10ms，T1+10ms]Determining the accurate position t of the first arrival of the P wave within the rangei；
Wherein, the P wave of the signal with high signal-to-noise ratio is picked up by adopting a long-short time window average ratio method1During the process, the amplitude-to-average ratio of the microseismic signals within 0.1s after the first arrival of the P wave is used, and the trigger threshold value is taken as 3.
Further, Bootstrap method is adopted to carry out microseismic data set S1,S2,…,SnThe specific process of sampling to obtain a plurality of P-wave first-break sub-data sets is as follows:
microseismic dataset S using Bootstrap sampling1,S2,…,SnWith put back sampling n times, a new data set D is obtained1,D2,…,Dn(ii) a From a new data set D1,D2,…,DnAll the different data sets are selected and marked as { U1,U2,…,Un1}，n1N is less than or equal to n; repeating the steps for N times to obtain N data sets { U1,U2,…,Un1}，6≤n1Namely N P-wave first-arrival subdata sets, wherein the value range of N is 1000-3000.
Further, the positioning position corresponding to each P-wave first-break sub-data set is calculated by using the following formula:
wherein x is0，y0，z0，t0Corresponding to the three-dimensional position and occurrence time of the microseismic event, respectivelyiFor the distance between each station to the seismic source, tiI is 1,2,3 …, n is the time when the P-wave arrives at each station1，vpIs the P-wave propagation velocity.
The formula is quickly solved by adopting a Nelder-Mead method;
the data field potential value of each P-wave first-break subdata set at the corresponding positioning position is calculated according to the following formula:
wherein (x)i，yi，zi) And (x)j，yj，zj) Positioning positions obtained by the ith P wave first-arrival subdata set and the j P wave first-arrival subdata sets respectively, wherein sigma is a distance influence factor, and sigma is more than or equal to 10 and less than or equal to 200.
Further, according to the difference between the theoretical propagation time of the P-wave and the propagation time based on the observed time, the corresponding microseismic dataset is picked up by removing the first arrival large error of the P-wave, and the specific process of obtaining the updated microseismic dataset is as follows:
calculating sensor position (x)i，yi，zi) P-wave theoretical propagation time T between the microseismic event and the initial location event position (x ', y ', z ')i＝li/vpThen, calculate T againiAnd propagation time (t) based on observed timei-t0) When the difference is less than or equal to T, T is more than or equal to 0.01s and less than or equal to 0.03s, the P wave first arrival pickup is considered to be reliable, and the next step is carried out; when the difference is larger than T, the pickup is considered to have larger error, and the data set S corresponding to the P-wave first arrival pickup is deletedi。
Further, when the high-frequency noise is removed by adopting a soft threshold value for the first two intrinsic mode components IMF, the adopted soft threshold value calculation method comprises one of an unbiased risk estimation threshold value, a fixed threshold value, a heuristic threshold value and a minimum maximum threshold value;
when the power frequency noise of the microseismic signal without the high-frequency noise is reduced by adopting a sine function fitting mode, firstly intercepting a section of microseismic signal (200 us-500 us) before the first arrival of P wave during the sine function fitting so as to obtain the local maximum value and the minimum value point of the intercepted microseismic signal, then determining the period of the sine function according to two adjacent local maximum value points and local minimum value points, determining the phase according to the first maximum value point, and further extending the sine function to a time sequence with the length equal to the length M of the microseismic signal.
Further, the signal-to-noise ratio of the microseismic signal is calculated in a manner of snr (db) ═ 20lg (a)signal/Anoise)，AsignalAnd AnoiseAverage amplitudes of signal and noise, respectively;
the SNR is more than or equal to 6 for the high signal-to-noise ratio, and less than 6 for the low signal-to-noise ratio.
A microseismic signal large error picking removal and seismic source positioning system based on data field potential values comprises:
the microseismic signal denoising unit: denoising the high-frequency noise and the power-frequency noise of the microseismic signal;
p wave first arrival moment picking unit: automatically and manually picking up the P wave first arrival time of the denoised microseismic signals with high signal-to-noise ratio and low signal-to-noise ratio;
p wave first break subdata set extraction unit: applying Bootstrap method to microseismic data set S1,S2,…,SnSampling to obtain a plurality of P wave first arrival subdata sets;
Si＝(xi，yi，zi，ti) Where i is 1,2, …, n, n is the number of microseismic signals picked up, (x)i，yi，zi) As three-dimensional coordinate position of the sensor, tiThe P wave first arrival moment picked up in the step 2;
microseismic event initial positioning unit: performing seismic source positioning on each P-wave first-break sub data set by adopting a double-difference method to obtain a positioning position corresponding to each P-wave first-break sub data set;
calculating the data field potential value of each P-wave first-break subdata set at the corresponding positioning position, taking the positioning position mean value corresponding to the first K data field potential values with larger potential value as the initial positioning position of the microseismic event, wherein the number of K is 30-60;
microseismic dataset updating unit: removing the P wave first arrival large error and picking up a corresponding microseismic data set according to the difference between the theoretical propagation time of the P wave and the propagation time based on the observed time to obtain an updated microseismic data set;
microseismic event final location unit: and (4) with the updated microseismic dataset, repeatedly utilizing the P wave first arrival subdata set extraction unit, the microseismic event first positioning unit and the microseismic dataset updating unit to realize microseismic event relocation, obtaining the relocated event position (x, y, z) and taking the relocated event position (x, y, z) as a final positioning result.
A readable storage medium comprising computer program instructions characterized in that: the computer program instructions, when executed by a processing terminal, cause the processing terminal to perform a method for microseismic signal large error pick-up removal and seismic source location based on data field potential values.
Advantageous effects
The invention provides a seismic source positioning method and system based on a data field potential value and a readable storage medium, which are mainly used for reducing the influence of large P wave picking errors in microseismic event positioning and solving the problems that the traditional positioning method is greatly influenced by an initial iteration center, the microseismic event positioning is unstable and the like. The method comprises the following steps: firstly, removing the high-frequency and power-frequency noises of microseismic signals by combining an Ensemble Empirical Mode Decomposition (EEMD) soft threshold with a sine function; then, automatically picking up the first arrival of the P wave of the microseismic signal with high signal-to-noise ratio by adopting a signal-to-noise ratio, a long-time-window-to-average ratio (STA/LTA) method and an AIC (advanced information center) method, and manually picking up a low signal-to-noise ratio signal; performing playback sampling on the P wave first arrival data set by adopting a Bootstrap method to obtain a plurality of P wave first arrival sub data sets; then, performing seismic source location on each P-wave first-break sub data set by adopting a double-difference method, and taking the position average value of 30-60 points with the largest potential value of the seismic source as a microseismic event first-location result; calculating theoretical propagation time from the seismic source to the sensor by using the primary positioning result, comparing the theoretical propagation time with the monitored propagation time, and removing P wave primary arrival large error pickup signals; and taking the P wave first arrival picked up by removing the large error as new data, and carrying out Bootstrap sampling and data field potential value positioning to obtain the final seismic source position. The method can remove the high frequency and power frequency noise of the microseismic signal, improve the signal to noise ratio, enable the P wave first arrival picked by the automatic method to be more and more accurate, and reduce the overall picking time of the P wave first arrival; the data from P wave to time is put back to sample, spread and positioned, so that the influence of large error pickup can be reduced; obtaining a point with higher positioning density based on the potential value of the data field, and reducing the influence of an initial iteration center, so that the microseismic positioning result is more stable; in addition, large error pickup signals can be removed after initial positioning, and high-precision positioning can be carried out.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of the effects of denoising and automatic picking up microseismic signals, wherein (a) is the original microseismic signal and (b) is the denoising microseismic signal and its AIC sequence picking up;
FIG. 3 is a diagram of sensors generating theoretical data and the location of events to be tested;
FIG. 4 is a diagram of initial positioning and repositioning cases of potential values of a data field, wherein (a) is an initial positioning case of the data field, and (b) is a post-denoising repositioning case;
FIG. 5 is a plot of localization error bins for three different methods, where (a) is a plot of event 1 double-difference localization error bins, (b) is a plot of event 2 double-difference localization error bins, (c) is a plot of event 1 data field localization error bins, (d) is a plot of event 2 data field localization error bins, (e) is a plot of event 1 denoised relocation error bins, and (f) is a plot of event 2 denoised relocation error bins.
Detailed Description
The present invention will be further described with reference to the accompanying drawings 1-5.
The method idea of the invention is as follows: aiming at the problems that the positioning results of the commonly used L1 and L2 norm methods are unstable and may be influenced by large picking errors, and the like, a microseismic signal large error picking removal and seismic source positioning method based on the data field potential value is provided. According to the method, points with high positioning density are obtained by means of a Bootstrap sampling method and a data field theory, the influence of an initial iteration center is reduced, and a good initial positioning result is obtained. And removing the large-error pickup signal by utilizing the difference between the theoretical propagation time and the monitoring propagation time based on the initial positioning, and further repeating the steps to realize high-precision seismic source positioning.
As shown in fig. 1, a method for picking up and removing a microseismic large error and positioning a seismic source based on a data field comprises the following steps:
step 1: denoising the high-frequency noise and the power-frequency noise of the microseismic signal;
the process of denoising the high-frequency noise and the power-frequency noise of the microseismic signal is as follows:
firstly, a microseismic signal x (m) is adaptively decomposed into a plurality of intrinsic mode components IMF by adopting ensemble empirical mode decompositioniAnd 1 residual component rkI is 1,2, …, k, k is the number of empirical mode components, high frequency noise is removed by adopting soft threshold value to the first two intrinsic mode components IMF, and the IMF after denoising1And IMF2With other IMF components IMF3, IMF4, …, IMFkAnd a residual component rkAdding to obtain a microseismic signal without high-frequency noise;
secondly, denoising the power frequency noise of the microseismic signal without the high frequency noise by adopting a sine function fitting mode to obtain the microseismic signal without the high frequency and the power frequency;
x (M) is the microseismic signal, M is 1,2, …, M is the total number of sampling points of the microseismic signal.
When the high-frequency noise of the first two intrinsic mode components IMF is removed by adopting a soft threshold, the adopted soft threshold calculation method comprises one of an unbiased risk estimation threshold, a fixed threshold, a heuristic threshold and a minimum maximum threshold;
when the power frequency noise of the microseismic signal without the high-frequency noise is reduced by adopting a sine function fitting mode, firstly intercepting a section of microseismic signal (200 us-500 us) before the first arrival of P wave during the sine function fitting so as to obtain the local maximum value and the minimum value point of the intercepted microseismic signal, then determining the period of the sine function according to two adjacent local maximum value points and local minimum value points, determining the phase according to the first maximum value point, and further extending the sine function to a time sequence with the length equal to the length M of the microseismic signal.
Step 2: automatically and manually picking up the P wave first arrival time of the denoised microseismic signals with high signal-to-noise ratio and low signal-to-noise ratio;
for the denoised microseismic signal with high signal-to-noise ratio, a long-time window mean ratio method is adopted to pick up the P wave approximate first arrival T of the signal with high signal-to-noise ratio1Then, the pool red criterion method AIC is adopted to be in [ T ]1-10ms，T1+10ms]Range ofInternally determining accurate position t of P wave first arrivali；
Wherein, the P wave of the signal with high signal-to-noise ratio is picked up by adopting a long-short time window average ratio method1During the process, the amplitude-to-average ratio of the microseismic signals within 0.1s after the first arrival of the P wave is used, and the trigger threshold value is taken as 3.
The signal-to-noise ratio of the microseismic signal is calculated in a manner that SNR (dB) is 20lg (A)signal/Anoise)，AsignalAnd AnoiseAverage amplitudes of signal and noise, respectively;
the SNR is more than or equal to 6 for the high signal-to-noise ratio, and less than 6 for the low signal-to-noise ratio.
And step 3: applying Bootstrap method to microseismic data set S1,S2,…,SnSampling to obtain a plurality of P wave first arrival subdata sets;
Si＝(xi，yi，zi，ti) Where i is 1,2, …, n, n is the number of microseismic signals picked up, (x)i，yi，zi) As three-dimensional coordinate position of the sensor, tiThe P wave first arrival moment picked up in the step 2;
applying Bootstrap method to microseismic data set S1,S2,…,SnThe specific process of sampling to obtain a plurality of P-wave first-break sub-data sets is as follows:
microseismic dataset S using Bootstrap sampling1,S2,…,SnWith put back sampling n times, a new data set D is obtained1,D2,…,Dn(ii) a From a new data set D1,D2,…,DnAll the different data sets are selected and marked as { U1,U2,…,Un1}，n1N is less than or equal to n; repeating the steps for N times to obtain N data sets { U1,U2,…,Un1}，6≤n1Namely N P-wave first-arrival subdata sets, wherein the value range of N is 1000-3000.
And 4, step 4: the double difference method and the data field potential value realize the initial positioning of the microseismic event;
performing seismic source positioning on each P-wave first-break sub data set by adopting a double-difference method to obtain a positioning position corresponding to each P-wave first-break sub data set;
calculating the data field potential value of each P-wave first-break subdata set at the corresponding positioning position, taking the positioning position mean value corresponding to the first K data field potential values with larger potential value as the initial positioning position of the microseismic event, wherein the number of K is 30-60;
the positioning position corresponding to each P-wave first-break subdata set is calculated by adopting the following formula:
wherein x is0，y0，z0，t0Corresponding to the three-dimensional position and occurrence time of the microseismic event, respectivelyiFor the distance between each station to the seismic source, tiI is 1,2,3 …, n is the time when the P-wave arrives at each station1，vpIs the P-wave propagation velocity.
The formula is quickly solved by adopting a Nelder-Mead method;
the data field potential value of each P-wave first-break subdata set at the corresponding positioning position is calculated according to the following formula:
wherein (x)i，yi，zi) And (x)j，yj，zj) Positioning positions obtained by the ith P wave first-arrival subdata set and the j P wave first-arrival subdata sets respectively, wherein sigma is a distance influence factor, and sigma is more than or equal to 10 and less than or equal to 200.
And 5: removing the P wave first arrival large error and picking up a corresponding microseismic data set according to the difference between the theoretical propagation time of the P wave and the propagation time based on the observed time to obtain an updated microseismic data set;
according to the difference between the theoretical propagation time of the P wave and the propagation time based on the observed time, the corresponding microseismic data set is picked up by removing the first arrival large error of the P wave, and the specific process of obtaining the updated microseismic data set is as follows:
calculating sensor positionIs arranged (x)i，yi，zi) P-wave theoretical propagation time T between the microseismic event and the initial location event position (x ', y ', z ')i＝li/vpThen, calculate T againiAnd propagation time (t) based on observed timei-t0) When the difference is less than or equal to T, T is more than or equal to 0.01s and less than or equal to 0.03s, the P wave first arrival pickup is considered to be reliable, and the next step is carried out; when the difference is larger than T, the pickup is considered to have larger error, and the data set S corresponding to the P-wave first arrival pickup is deletedi。
Step 6: and repeating the step 3-4 by using the updated microseismic data set to realize microseismic event relocation, and obtaining the relocated event position (x, y, z) as a final location result.
Examples
FIG. 2 is a diagram of the effect of denoising and automatic picking up microseismic signals. As shown in fig. 2(a), the original microseismic signal has a small amount of high frequency signals and a large power frequency signal, which makes P-wave pickup difficult. The signal-to-noise ratio of the microseismic signal denoised by the EEMD soft threshold and the sine function is obviously improved, and the P wave is easier to pick up in the first arrival (figure 2 b). In fig. 2b, the vertical dotted line is the pickup result of the STA/LTA method, and the vertical dotted line is the range where the local minimum of the AIC method is desirable. Compared with manual picking, the AIC method can achieve better picking effect.
FIG. 3 is a diagram of sensors generating theoretical data and the location of an event to be tested. The distribution of the microseismic sensors is wide, wherein 12 sensors are respectively arranged near the height of 930m and 1080m, 4 sensors are arranged near the height of 1120m, and the sampling frequency of the sensors is 6000 Hz. To verify the validity of the method, two typical event test positioning methods were chosen: event to be tested 1 is located inside the sensor array and event to be tested 2 is located outside the sensor array. Considering that in real micro-earthquakes, sensors usually cannot be triggered completely, we selected 15 development studies from the 28 sensors. Theoretical propagation time is generated by P-wave velocity, seismic source position and sensor position, 5% Gaussian noise is added to the P-wave velocity, and 2ms Gaussian noise is added to the theoretical propagation time received by each sensor. In addition, we have also conducted positioning studies on the basis of the above noise, one of the sensors plus large error pick-up.
FIG. 4 is a diagram of an initial location and relocation case for data field potential values. Event 2 is selected as a test, large error noise is added to the first arrival of the P wave of one sensor on the basis of gaussian noise, and the result of the conventional positioning for 2000 times is shown as a small gray dot in fig. 4(a), wherein the darker the gray is, the larger the potential value of the data field is, and obviously, the result of the conventional positioning method is very unstable. The initial positioning of the seismic source based on the potential value of the data field can obtain a more stable positioning result, but the positioning error is still larger. The source relocation based on the source initial positioning to remove the large error pick-up signal is shown in fig. 4(b), and it can be known that the relocation after denoising is closer to the actual position of the event.
FIG. 5 is a block diagram of positioning error for three different methods. Wherein Ex, Ey, Ez and ET are X, Y, Z direction absolute error and total error respectively. As can be seen from the graph, the positioning error of the event inside the sensor array is smaller than that of the event outside the sensor array on the whole, and the initial positioning result based on the potential value of the data field is obviously better than the traditional positioning result. When large errors are picked up in P wave first arrivals, the relocation result after denoising is superior to the initial positioning result, and the necessity of large error denoising and the effectiveness of the patent positioning are shown.
A microseismic signal large error picking removal and seismic source positioning system based on data field potential values comprises:
the microseismic signal denoising unit: denoising the high-frequency noise and the power-frequency noise of the microseismic signal;
p wave first arrival moment picking unit: automatically and manually picking up the P wave first arrival time of the denoised microseismic signals with high signal-to-noise ratio and low signal-to-noise ratio;
p wave first break subdata set extraction unit: applying Bootstrap method to microseismic data set S1,S2,…,SnSampling to obtain a plurality of P wave first arrival subdata sets;
Si＝(xi，yi，zi，ti) Where i is 1,2, …, n, n is the number of microseismic signals picked up, (x)i，yi，zi) Is a sensor IIIPosition of dimensional coordinate, tiThe P wave first arrival moment picked up in the step 2;
microseismic event initial positioning unit: performing seismic source positioning on each P-wave first-break sub data set by adopting a double-difference method to obtain a positioning position corresponding to each P-wave first-break sub data set;
calculating the data field potential value of each P-wave first-break subdata set at the corresponding positioning position, taking the positioning position mean value corresponding to the first K data field potential values with larger potential value as the initial positioning position of the microseismic event, wherein the number of K is 30-60;
microseismic dataset updating unit: removing the P wave first arrival large error and picking up a corresponding microseismic data set according to the difference between the theoretical propagation time of the P wave and the propagation time based on the observed time to obtain an updated microseismic data set;
microseismic event final location unit: and (4) with the updated microseismic dataset, repeatedly utilizing the P wave first arrival subdata set extraction unit, the microseismic event first positioning unit and the microseismic dataset updating unit to realize microseismic event relocation, obtaining the relocated event position (x, y, z) and taking the relocated event position (x, y, z) as a final positioning result.
It should be understood that the functional unit modules in the embodiments of the present invention may be integrated into one processing unit, or each unit module may exist alone physically, or two or more unit modules are integrated into one unit module, and may be implemented in the form of hardware or software.
A readable storage medium comprising computer program instructions characterized in that: the computer program instructions, when executed by a processing terminal, cause the processing terminal to perform a method for microseismic signal large error pick-up removal and seismic source location based on data field potential values.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A microseism signal large error picking removal and seismic source positioning method based on a data field potential value is characterized by comprising the following steps:
step 1: denoising the high-frequency noise and the power-frequency noise of the microseismic signal;
step 2: automatically and manually picking up the P wave first arrival time of the denoised microseismic signals with high signal-to-noise ratio and low signal-to-noise ratio;
and step 3: applying Bootstrap method to microseismic data set S1,S2,…,SnSampling to obtain a plurality of P wave first arrival subdata sets;
Si＝(xi，yi，zi，ti) Where i is 1,2, …, n, n is the number of microseismic signals picked up, (x)i，yi，zi) As three-dimensional coordinate position of the sensor, tiThe P wave first arrival moment picked up in the step 2;
and 4, step 4: the double difference method and the data field potential value realize the initial positioning of the microseismic event;
performing seismic source positioning on each P-wave first-break sub data set by adopting a double-difference method to obtain a positioning position corresponding to each P-wave first-break sub data set;
calculating the data field potential value of each P-wave first-break subdata set at the corresponding positioning position, taking the positioning position mean value corresponding to the first K data field potential values with larger potential value as the initial positioning position of the microseismic event, wherein the number of K is 30-60;
and 5: removing the P wave first arrival large error and picking up a corresponding microseismic data set according to the difference between the theoretical propagation time of the P wave and the propagation time based on the observed time to obtain an updated microseismic data set;
step 6: and repeating the step 3-4 by using the updated microseismic data set to realize microseismic event relocation, and obtaining the relocated event position (x, y, z) as a final location result.
2. The method of claim 1, wherein denoising the high frequency noise and the power frequency noise of the microseismic signal is performed by:
firstly, a microseismic signal x (m) is adaptively decomposed into a plurality of intrinsic mode components IMF by adopting ensemble empirical mode decompositioniAnd 1 residual component rkI is 1,2, …, k, k is the number of empirical mode components, high frequency noise is removed by adopting soft threshold value to the first two intrinsic mode components IMF, and the IMF after denoising1And IMF2With other IMF components IMF3, IMF4, …, IMFkAnd a residual component rkAdding to obtain a microseismic signal without high-frequency noise;
secondly, denoising the power frequency noise of the microseismic signal without the high frequency noise by adopting a sine function fitting mode to obtain the microseismic signal without the high frequency and the power frequency;
x (M) is the microseismic signal, M is 1,2, …, M is the total number of sampling points of the microseismic signal.
3. The method as claimed in claim 1, wherein the P-wave of the denoised microseismic signal with high snr is picked up by long-short time window average ratio method to approximate first arrival T of the P-wave of the signal with high snr1Then, the pool red criterion method AIC is adopted to be in [ T ]1-10ms，T1+10ms]Determining the accurate position t of the first arrival of the P wave within the rangei；
Wherein, the P wave of the signal with high signal-to-noise ratio is picked up by adopting a long-short time window average ratio method1During the process, the amplitude-to-average ratio of the microseismic signals within 0.1s after the first arrival of the P wave is used, and the trigger threshold value is taken as 3.
4. A method according to claim 1, wherein the microseismic dataset S is processed using a bootstrapping method1,S2,…,SnThe specific process of sampling to obtain a plurality of P-wave first-break sub-data sets is as follows:
microseismic dataset S using Bootstrap sampling1,S2,…,SnWith put back sampling n times, a new data set D is obtained1,D2,…,Dn(ii) a From a new data set D1,D2,…,DnAll the different data sets are selected and marked as { U1,U2,…,Un1}，n1N is less than or equal to n; repeating the steps for N times to obtain N data sets { U1,U2,…,Un1}，6≤n1Namely N P-wave first-arrival subdata sets, wherein the value range of N is 1000-3000.
5. The method of claim 4, wherein the location corresponding to each P-wave first arrival sub data set is calculated using the following formula:
wherein x is0，y0，z0，t0Corresponding to the three-dimensional position and occurrence time of the microseismic event, respectivelyiFor the distance between each station to the seismic source, tiI is 1,2,3 …, n is the time when the P-wave arrives at each station1，vpIs the P-wave propagation velocity.
The formula is quickly solved by adopting a Nelder-Mead method;
the data field potential value of each P-wave first-break subdata set at the corresponding positioning position is calculated according to the following formula:
wherein (x)i，yi，zi) And (x)j，yj，zj) Positioning positions obtained by the ith P wave first-arrival subdata set and the j P wave first-arrival subdata sets respectively, wherein sigma is a distance influence factor, and sigma is more than or equal to 10 and less than or equal to 200.
6. The method according to claim 1, wherein the specific process of removing the P-wave first arrival large error to pick up the corresponding microseismic dataset according to the difference between the theoretical propagation time of the P-wave and the propagation time based on the observed time to obtain the updated microseismic dataset is as follows:
calculating sensor position (x)i，yi，zi) P-wave theoretical propagation time T between the microseismic event and the initial location event position (x ', y ', z ')i＝li/vpThen, calculate T againiAnd propagation time (t) based on observed timei-t0) When the difference is less than or equal to T, T is more than or equal to 0.01s and less than or equal to 0.03s, the P wave first arrival pickup is considered to be reliable, and the next step is carried out; when the difference is larger than T, the pickup is considered to have larger error, and the data set S corresponding to the P-wave first arrival pickup is deletedi。
7. The method according to claim 2, wherein when soft threshold values are used for removing high-frequency noise for the first two intrinsic mode components IMF, the soft threshold values are calculated by one of unbiased risk estimation threshold values, fixed threshold values, heuristic threshold values and minimum maximum threshold values;
when the power frequency noise of the microseismic signal without the high-frequency noise is reduced by adopting a sine function fitting mode, firstly intercepting a section of microseismic signal (200 us-500 us) before the first arrival of P wave during the sine function fitting so as to obtain the local maximum value and the minimum value point of the intercepted microseismic signal, then determining the period of the sine function according to two adjacent local maximum value points and local minimum value points, determining the phase according to the first maximum value point, and further extending the sine function to a time sequence with the length equal to the length M of the microseismic signal.
8. The method of claim 1, wherein the signal-to-noise ratio of the microseismic signal is calculated as snr (db) -20 lg (a)signal/Anoise)，AsignalAnd AnoiseAverage amplitudes of signal and noise, respectively;
the SNR is more than or equal to 6 for the high signal-to-noise ratio, and less than 6 for the low signal-to-noise ratio.
9. A microseismic signal large error picking removal and seismic source positioning system based on data field potential values is characterized by comprising:
the microseismic signal denoising unit: denoising the high-frequency noise and the power-frequency noise of the microseismic signal;
p wave first arrival moment picking unit: automatically and manually picking up the P wave first arrival time of the denoised microseismic signals with high signal-to-noise ratio and low signal-to-noise ratio;
p wave first break subdata set extraction unit: applying Bootstrap method to microseismic data set S1,S2,…,SnSampling to obtain a plurality of P wave first arrival subdata sets;
Si＝(xi，yi，zi，ti) Where i is 1,2, …, n, n is the number of microseismic signals picked up, (x)i，yi，zi) As three-dimensional coordinate position of the sensor, tiThe P wave first arrival moment picked up in the step 2;
microseismic event initial positioning unit: performing seismic source positioning on each P-wave first-break sub data set by adopting a double-difference method to obtain a positioning position corresponding to each P-wave first-break sub data set;
calculating the data field potential value of each P-wave first-break subdata set at the corresponding positioning position, taking the positioning position mean value corresponding to the first K data field potential values with larger potential value as the initial positioning position of the microseismic event, wherein the number of K is 30-60;
microseismic dataset updating unit: removing the P wave first arrival large error and picking up a corresponding microseismic data set according to the difference between the theoretical propagation time of the P wave and the propagation time based on the observed time to obtain an updated microseismic data set;
microseismic event final location unit: and (4) with the updated microseismic dataset, repeatedly utilizing the P wave first arrival subdata set extraction unit, the microseismic event first positioning unit and the microseismic dataset updating unit to realize microseismic event relocation, obtaining the relocated event position (x, y, z) and taking the relocated event position (x, y, z) as a final positioning result.
10. A readable storage medium comprising computer program instructions characterized in that: the computer program instructions, when executed by a processing terminal, cause the processing terminal to perform the method of any of claims 1-8.
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