CN113534236A - Microseism first arrival picking method based on geophone spacing constraint - Google Patents
Microseism first arrival picking method based on geophone spacing constraint Download PDFInfo
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Abstract
The invention discloses a microseism first arrival picking method based on geophone spacing constraint, which relates to the technical field of geophysical exploration. According to the method, the spacing constraint condition of the detectors is introduced into the cross-correlation operation between channels, so that the influence of the space spread of the detectors on the waveform similarity of signals between the channels and the phenomenon of alignment difference of the waveforms after time difference correction are greatly reduced, the influence of the seismic detection distance on the first arrival time is reasonably and effectively avoided, and the accuracy of the picked result is improved.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a microseism first-break pickup method based on geophone spacing constraint.
Background
With the rapid development of information technology in recent years, the microseism monitoring technology is widely applied to underground engineering, such as oil and gas field development, mine safety production, tunnel construction, geological disaster monitoring and other fields, and the reliability and effectiveness of the microseism monitoring technology are greatly recognized.
In the exploration and development process of unconventional oil and gas reservoirs, the micro-seismic monitoring technology is an important technical means for knowing the distribution rule of fracturing fractures and evaluating the fracturing effect, and is based on acoustic emission and geology, and the stress distribution condition of an underground stress field and the development process and the development state of artificial fractures are monitored by observing and analyzing micro-seismic events caused by rock fracture or fault fracture. The identification and first arrival picking of the micro-seismic events are key steps of micro-seismic monitoring data processing, the accurate identification of the micro-seismic events directly influences the precision of the first arrival picking of the micro-seismic, and the error of the first arrival picking has a large influence on the positioning accuracy of the seismic source in the later period, so that the method has a key and important significance on how to improve the accuracy of the identification of the micro-seismic events and the precision of the first arrival picking.
The essence of first arrival picking of micro-seismic events is to identify and analyze the characteristic differences between the effective signal and the background noise from the aspects of polarization, spectrum, waveform, statistical characteristics, and energy of the event signal. Due to the fact that micro-seismic signals generated by artificial fracturing are weak in energy and high in frequency, compared with seismic exploration signals, signal noise is low, background noise and continuous waves generated by events have strong influences on seismic phase identification and first arrival picking of micro-seismic events, the positioning result of later events is influenced, and in addition, the accuracy of seismic phase identification can be influenced by the spatial distribution of a micro-seismic detector. The traditional artificial microseism seismic facies identification and first arrival picking mainly depend on artificial identification, the workload is extremely large, the picking standards are inconsistent, artificial errors are generated due to artificial data processing experience interference, and the requirement of actual production efficiency is difficult to meet. Many researchers have studied and proposed various methods for automatically identifying the seismographic phase and first arrival picking of the microseismic event, and common first arrival picking methods can be divided into a single-channel feature method, a multi-channel cross-correlation method and a template matching method. The single-channel characteristic method comprises a long-short time average ratio method (STA/LTA method) and an AIC method; polarization analysis, PAI-S/K method, fractal dimension method, etc. In addition, some scholars combine the method and the characteristic difference of the micro-seismic single-channel signal to research a multi-algorithm, multi-characteristic-attribute seismic facies identification and first arrival picking method. Through similar characteristic analysis research on the micro-seismic event inter-trace records, students successively put forward a multi-trace cross-correlation algorithm for optimizing micro-seismic facies recognition and first arrival picking results, such as a cross-correlation algorithm based on iteration, a cross-correlation algorithm based on waveform similarity and the like. Compared with single-channel pickup, the multi-channel cross-correlation method has low sensitivity to low signal-to-noise ratio events, effectively improves the pickup precision of low signal-to-noise ratio time, and greatly reduces the missing pickup/mistaken pickup rate. The template matching method is to pick up the signals by utilizing the similar characteristics of the waveforms of different events of adjacent sub seismic sources, and detect and pick up the signals with low signal to noise ratio by taking the signals with high signal to noise ratio as a template.
Microseism events with similar seismic source mechanisms show similar waveform characteristics on records, and according to the characteristics, a microseism seismic phase identification and first arrival picking method based on waveform cross correlation is proposed. The basic idea is to select a series of major earthquake events, perform cross-correlation processing on the major earthquake events and continuous sectional records, judge and select high-correlation record information, and assume that the section of records has micro earthquake events. The STA/LTA method is generally used to determine the major seismic events, but is insensitive to low signal-to-noise ratio events and tends to miss-pick effective major seismic events, which in turn results in missed picking of microseismic events with a source mechanism similar to that of the major seismic events. In order to effectively avoid the condition that major earthquake events are missed to be picked up due to low signal-to-noise ratio recording, Weimengyu 31054and the like propose a method for identifying microseism events by utilizing inter-channel recording waveform similarity, the method comprises the steps of calculating a global inter-channel cross-correlation function, carrying out time difference correction on microseism recording, identifying the microseism events by utilizing inter-channel similarity coefficients, then carrying out superposition on a plurality of channels of records after time difference correction and carrying out first arrival picking, and obtaining the first arrival time of the microseism events by combining time difference information among the channels. The similarity degree of the inter-channel signal waveforms depends on the space spread and the event distance of the detector, meanwhile, the first arrival picking of the micro-seismic event is influenced by comprehensive factors such as a propagation path, energy, background noise and the like, the waveforms are aligned and different after time difference correction, the global waveform cross-correlation method is utilized to identify the seismic phase of the micro-seismic event, and the error factors are often ignored during the first arrival picking, so that the influence of individual inter-channel information on the whole micro-seismic event picking result is caused.
Disclosure of Invention
In order to solve the technical problems, the invention discloses a microseism first arrival picking method based on geophone spacing constraint, which introduces geophone spacing constraint conditions in cross-correlation operation between channels, greatly reduces the influence of space spread of geophones on waveform similarity of signals between channels and the phenomenon of alignment difference of waveforms after time difference correction, thereby reasonably and effectively avoiding the influence of geophone spacing on first arrival time and improving the precision of picking results.
In order to achieve the purpose, the invention adopts the following technical scheme:
a microseism first arrival picking method based on geophone spacing constraint specifically comprises the following steps:
step one, constructing a global cross-correlation function;
step two, performing one-time moveout correction on the microseism record by using the global cross-correlation function;
thirdly, introducing a spacing constraint condition of the detectors, and reconstructing a local cross-correlation function;
step four, performing secondary time difference correction on the record after the primary time difference correction by using a local cross-correlation function;
step five, carrying out superposition operation on the multiple records after the secondary time difference correction to obtain a superposition channel;
step six, carrying out first arrival picking on the superposed channel by adopting an STA/LTA method to obtain a first arrival time;
and step seven, performing reverse time difference correction on the relative arrival time of each channel and the first arrival time of the superposed channel calculated by combining the local cross-correlation function, so that the first arrival time of the microseism event can be obtained.
Further, in step one, the cross-correlation function of the two inter-channel signals is:
wherein N is the number of sampling points, xi(n) and xj(n) respectively representing two data; when | ci，j(k) When | is maximum, it is recorded as
At this time xi(n) and xj(n) the waveforms have the greatest similarity, x of the two signalsi(n) and xj(n) time difference Deltati，jThe first arrival time difference between the two lanes is considered.
Time difference between tracks Deltati，jAnd the first arrival time t of the two recordsi，tjThe relationship between them is as follows:
ti-tj＝Δti，j (2)。
further, in the second step and the fourth step, the time difference correction adopts the following two modes:
method one, selecting record with high signal-to-noise ratio as reference track xi(n) any other lane is xj(n) calculating the cross-correlation function of the reference track and each of the other tracks according to Δ ti，jAdjusting the time window position of each channel, and then correcting the time difference of the microseism record;
method two, according to delta t without selecting reference channeli，jRelative arrival time t to each trackiThe relationship between the two sets of linear equations to solve tiAnd according to tiThe time difference correction is performed for each recording.
Furthermore, the time difference correction of the multi-channel recording is realized by establishing an over-determined linear equation set solution method; the time difference correction of the multi-channel recording can be realized by establishing an over-determined linear equation system for solving.
The following linear equation set (taking 5 records as an example) is established according to equation (2):
in order to avoid instability in the solution process, a constraint equation sigma t can be added at the bottom of the equation seti0. For M-channel recording, M (M-1)/2 cross-correlation operations are required to be performed in formula (3) to obtain M (M-1)/2 groups of inter-channel time difference information. Equation (3) is simplified to the following form:
At＝Δt (4)
where A is the sparse coefficient matrix and Δ t is the observation data vector (from Δ t)i，jIs formed), t is the parameter vector to be solved (consisting of t)iMake up); the least squares solution of equation (4) is:
t＝(ATA)-1ATΔt (5)
solving t by formula (5) to obtain the first-arrival information of each track record, and obtaining the relative arrival time t of each trackiThe time difference correction is performed for each recording.
Further, in step six, the calculation formula of the STA/LTA method is as follows:
wherein, x (N) is the seismic record, M is the long time window length, N is the short time window length, and the maximum point of R is generally considered as the initial point of the seismic wave.
Effective microseism events can be identified through a formula (6), and the first-arrival homophase axis leveling record can be obtained after time difference correction. Random noise exists in the original single-channel record, the random noise can be effectively suppressed through superposition, the recording signal-to-noise ratio is improved, the first arrival pickup is more accurate, and the leveled records are superposed to obtain superposed channels. And (3) performing first arrival picking on the superposed traces by using an STA/LTA method (long-short time energy ratio method). The basic idea of the method is to select a group of long and short sliding time windows, and reflect the change of signal amplitude or energy by using the ratio of the average values of signals in the two time windows. STA (short time window average) reflects the amplitude level of the local signal and LTA (long time window average) reflects the amplitude level of the background noise. Near the first arrival, the STA changes faster than the LTA, and the corresponding STA/LTA ratio exhibits a significant extremum.
The superposed track is the set of all tracks, so that the first arrival picking of the track is equivalent to the first arrival picking of all tracks, and the first arrival time of the superposed track is T0Combining the relative arrival time t of each track in the formula (3)iAnd then reversely correcting the time difference to obtain the actual first arrival time T of each tracki。
The calculation formula is as follows:
Ti＝T0+ti (7)
the degree of waveform similarity of microseismic events is reflected in two aspects: firstly, the waveforms of the same microseism event record received by the similar detectors are similar; the second is that microseismic events with similar source locations and fracture mechanisms exhibit similar waveforms on the recording. In actual recording, the wavefields from the same microseismic event, after traversing different propagation paths, have differences in the waveforms received by the different detectors, which increases with increasing distance between the detectors. The conventional cross-correlation microseism first arrival picking method needs to carry out uniform time difference correction on the records of all detectors so as to carry out subsequent stack trace calculation. If the difference is too large to find the relative arrival times of the microseismic signals by the cross-correlation function, then the method will have significant errors.
The distance between the detectors is considered to be limited, namely the microseismic signal arrival time difference is obtained only for the detector pairs with the distance within a certain range. For this problem, the distance between the detectors is limited, that is, the arrival time difference of the microseismic signals is obtained only for the pair of detectors with a certain distance. I.e. the detector spacing constraint introduced by the present invention.
As shown in fig. 1, a plan view of a microseismic monitor geophone arrangement is shown. When the spacing limit of the detectors is not added, the receiving records of all the detectors need to be subjected to cross-correlation operation, but the detectors with different colors are far away from each other, and the received waveforms of the same microseismic event possibly have large difference, so that the relative arrival time of each channel cannot be accurately calculated, and therefore, the cross-correlation operation is only performed on the records received by the detectors with the same color.
Taking the spacing of the detectors in 4 channels as an example of a constraint condition, the linear equation system of the moveout correction of the local cross-correlation multi-channel recording obtained according to the formula (3) is as follows:
in summary, the geophone spacing constraint condition is introduced into the flow of cross-correlation first arrival picking, when the microseism record is subjected to global cross-correlation calculation and time difference correction, a first arrival wave homophase axis leveling record is obtained, then the record is subjected to local cross-correlation calculation based on geophone spacing constraint and time difference correction to obtain a local cross-correlation leveling record, and subsequent seismic phase identification, multi-channel superposition calculation and first arrival picking are carried out by utilizing the record.
The beneficial effect of the invention is that,
the method comprises the steps of firstly utilizing a global cross-correlation algorithm with similar waveforms to correct time difference of records, utilizing a local cross-correlation algorithm with introduced wave detector spacing constraint conditions to correct time difference of a once-leveled record, utilizing an STA/LTA method to obtain the first arrival time of a superposed channel, and utilizing reverse time difference correction to obtain the first arrival picking result of the microseism event.
The method is effective through processing the record of the actual microseism event, wherein the condition that effective waveform information is completely corrected due to unreasonable selection of the length of a time window is avoided by a global cross-correlation algorithm, the signal-to-noise ratio is improved by multi-channel superposition of the flattened records, the influence of space distribution of the detector on a picked result is effectively reduced by introducing space constraint of the detector, and the accuracy of a first arrival picked result is effectively improved.
Drawings
FIG. 1 is a schematic diagram of the spacing constraint of detectors according to the present invention;
FIG. 2 is a schematic diagram of a cross-correlation first arrival picking process based on detector spacing constraints according to the present invention;
FIG. 3 shows the layout of the geophone design according to the present invention;
(a) a test area aerial view, (b) a test area actual detector arrangement position diagram;
FIG. 4 is a record of an actual microseismic event of the present invention;
FIG. 5 is a maximum value of the cross-correlation function between traces according to the present invention;
FIG. 6 is a computed inter-track time difference for global cross-correlation in accordance with the present invention;
FIG. 7 is a cross-track time difference after global cross-correlation time difference correction in accordance with the present invention;
FIG. 8 is a plot of the inter-trace time difference after cross-correlation computation (partial cross-correlation) based on detector spacing constraint according to the present invention;
FIG. 9 is a plot of the inter-trace time difference after correction of the time difference calculated (local cross-correlation) based on the detector spacing constrained cross-correlation of the present invention;
FIG. 10 is a record of events after the global cross-correlation moveout correction of the present invention;
FIG. 11 is a record of events after cross-correlation computation (partial cross-correlation) based on detector spacing constraints according to the present invention;
FIG. 12 is a diagram of the trace record and pickup results after the global cross-correlation moveout correction of the present invention;
FIG. 13 is a record and pickup of a superimposed trace after cross-correlation computation (local cross-correlation) based on geophone spacing constraint according to the present invention;
FIG. 14 is a first arrival picking result of a microseismic event according to the global cross-correlation method of the present invention;
FIG. 15 is a first arrival picking result of a microseismic event based on the geophone spacing constrained cross-correlation method (local cross-correlation) of the present invention.
FIG. 16 is a diagram of the first arrival picking result of a microseismic event (event 1) according to the global cross-correlation method of the present invention;
FIG. 17 is a first arrival picking result (event 1) of a microseismic event based on the geophone spacing constrained cross-correlation method (local cross-correlation) of the present invention;
FIG. 18 is a diagram of the first arrival picking result of a microseismic event (event 2) of the global cross-correlation method of the present invention;
FIG. 19 is a first arrival picking result (event 2) of a microseismic event based on the geophone spacing constrained cross-correlation method (local cross-correlation) of the present invention;
FIG. 20 is a diagram of the first arrival picking result of a microseismic event (event 3) of the global cross-correlation method of the present invention;
FIG. 21 is a first arrival picking result (event 3) of a microseismic event based on the geophone spacing constrained cross-correlation method (local cross-correlation) of the present invention;
FIG. 22 is a diagram of the first arrival picking result of a microseismic event (event 4) of the global cross-correlation method of the present invention;
FIG. 23 is a microseismic event first arrival pickup (event 4) based on the geophone spacing constrained cross-correlation method (local cross-correlation) of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a microseism first arrival picking method based on geophone spacing constraint, which introduces geophone spacing constraint conditions in cross-correlation operation between channels, greatly reduces the influence of space spread of geophones on the similarity of signal waveforms between channels and the phenomenon of alignment difference of waveforms after time difference correction, thereby reasonably and effectively avoiding the influence of seismic detection spacing on first arrival time and improving the precision of picking results.
A microseism first arrival picking method based on geophone spacing constraint specifically comprises the following steps:
(1) constructing a global cross-correlation function;
(2) performing one-time moveout correction on the microseism record by utilizing the global cross-correlation function;
(3) introducing a spacing constraint condition of a detector, and reconstructing a local cross-correlation function;
(4) performing secondary time difference correction on the record after the primary time difference correction by using a local cross-correlation function;
(5) performing superposition operation on the multiple records after the secondary time difference correction to obtain a superposition channel;
(6) performing first arrival picking on the superposed channel by adopting an STA/LTA method to obtain a first arrival time;
(7) and performing reverse time difference correction on the relative arrival time of each channel and the first arrival time of the superposed channel calculated by combining the local cross-correlation function, so as to obtain the first arrival time of the microseism event.
Firstly, performing cross-correlation operation and time difference correction on a global effective channel, then performing local cross-correlation operation based on detector spacing constraint on records after time difference correction and performing time difference correction, then stacking multiple records after secondary time difference correction to form a stacked channel, performing first arrival pickup on the stacked channel by adopting an STA/LTA method, and finally combining first arrival information of the stacked channel and a time difference relative correction value to obtain the first arrival time of a microseism event.
To test the effectiveness of the method herein, we processed the actual data and compared the results with conventional methods.
FIG. 2 is a schematic diagram of cross-correlation first arrival picking process based on detector spacing constraint of the present invention, which includes:
(a) is a waveform similarity diagram, wherein black asterisks represent a seismic source, black triangles represent a detector, and curves represent similar microseismic event signals (longitudinal is time, and transverse is amplitude));
(b) the time difference and the picking principle are shown schematically, wherein the vertical axis of the coordinate axis is time, and the horizontal axis is a detection point number; the two black curves represent the signals received by the two detectors, and the circles represent the in-phase positions on the two signals, i.e. the positions desired to be picked up;
(c) calculating a time difference corrected recording schematic diagram for cross-correlation based on detector spacing constraints, and showing the corrected recording form in the step (b);
(d) corrected schematic for all records;
(e) performing first arrival picking on the superposed channel by using a conventional first arrival picking method (a long-time energy ratio method), wherein a dot is the first arrival time of the superposed channel;
(f) performing first arrival picking on the step (e), namely performing first arrival picking on all tracks;
(g) and (f) carrying out time difference correction once on the reverse direction to obtain the actual arrival time of each track.
FIG. 3 shows the layout of detectors according to the present invention, each dot representing a detector; 71 detectors are arranged on the north-south direction measuring lines, the distance between the detectors is 2m, and the length of the measuring lines is 140 m; wherein, one hammering is carried out near the position of (60,60) as the target event of the first arrival picking.
FIG. 4 is a record of an actual microseismic event of the present invention with time in ms on the abscissa and distance in m on the ordinate. The cross-correlation function is used for calculating the records to obtain the correlation maximum value among the channels as shown in fig. 5, the correlation maximum value in the graph represents the waveform similarity of the two records, the larger the numerical value is, the higher the waveform similarity of the two records is, and the influence of the detector spacing on the correlation of the two records is obvious.
The inter-channel time difference and the corrected inter-channel time difference obtained by performing the cross-correlation operation on the records by using the conventional waveform cross-correlation method (global cross-correlation) are shown in fig. 6-7, and the time difference and the corrected time difference obtained by performing the local cross-correlation operation on the leveled records by introducing the spacing constraint condition of the detectors (the adjacent 4 channels are constraint) are shown in fig. 8-9. It can be seen through comparison that the records after the calculation based on the space constraint condition of the detectors are only subjected to cross-correlation calculation, the interference of far-end records on the cross-correlation calculation is ignored, and the influence of the space distribution of the detectors on the waveform similarity of the signals between the channels is reasonably avoided.
Compared with the original seismic record (figure 4), the waveforms of the microseism event time correction of the two methods respectively shown in figure 10 and figure 11 are obvious, the event axis after time difference correction can be basically leveled, the event axis after time difference correction of the new method is more stable, the rectangular frame is the record after correction of a measuring line of 30-70 meters, the event axis slightly fluctuates by the conventional method, and the new method does not exist.
The records after time difference correction are superposed, and the first arrival time of the superposed channels is obtained by using an STA/LTA method, and the results are shown in FIGS. 12-13. The actual first arrival times of each lane can be obtained by performing the reverse moveout correction according to the formula (7), and the results are 14-15. Compared with the pickup results of the two methods, the conventional waveform cross-correlation first arrival pickup method has obvious discontinuity between the first arrival point and the subsequent first arrival point on the measuring line of about 2-10 meters, but the detectors are continuously arranged in space, so that errors appear in the pickup, the discontinuity existing in the conventional method is suppressed by the novel method, and the pickup result is more accurate.
Other micro-seismic events are picked up by the method, and the picking result is shown in FIGS. 16-22. By comparing the pickup results of the events 1-4, the fact that the space spread of the detectors has obvious influence on the pickup results can be easily found, the conventional cross-correlation first arrival pickup method has the advantages that the accuracy of the pickup results becomes lower and the continuity becomes worse along with the increase of the seismic detection distance, meanwhile, the reliability of the cross-correlation first arrival pickup method based on the spacing constraint of the detectors is verified, compared with the conventional waveform cross-correlation first arrival pickup method, the overall continuity of the pickup results is better, the results are more accurate, and the influence of the space spread of the detectors on the pickup results is reasonably avoided.
The method utilizes the local cross-correlation algorithm that the microseism signals have higher similarity characteristics among different detectors and introduces the space constraint condition of the detectors to calculate the first arrival time of the microseism event. The method comprises the steps of performing time difference correction on records by using a global cross-correlation algorithm with similar waveforms, performing time difference correction on a one-time leveling record by using a local cross-correlation algorithm with introduced wave detector spacing constraint conditions, solving the arrival time of a stacking channel by using an STA/LTA method, and obtaining a first arrival picking result of a microseism event by using reverse time difference correction. The method is proved to be effective by processing the record of the actual microseism event, wherein the condition that effective waveform information is completely corrected due to unreasonable selection of the length of a time window is avoided by a global cross-correlation algorithm, the signal-to-noise ratio is improved by multi-channel superposition of the leveled record, the influence of space distribution of a detector on a picked result is effectively reduced by introducing space constraint of the detector, and the accuracy of the first arrival picked result is effectively improved.
The method is mainly suitable for the micro-seismic events with the same seismic source or the same seismic source mechanism and high similarity of similar waveforms. In order to ensure the effect of the overall processing of the record in actual production, a plurality of events in the record can be extracted and processed independently, so that only a single event is processed and analyzed. In actual production data, microseism events of different seismic source mechanisms can be classified according to waveform differences, and first arrival information of different types of events is obtained respectively. The method effectively suppresses the influence of factors such as background noise, low signal-to-noise ratio recording, seismic-isolation distance and the like on first arrival pickup, but regular interferences such as refracted waves, reflected waves, continuous waves and the like exist in actual data, pickup errors are caused by the existence of the signals, and the method is a new direction for subsequent research of the invention even if the first arrival pickup accuracy is improved by reducing the errors.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (6)
1. A microseism first arrival picking method based on geophone spacing constraint is characterized by comprising the following steps:
step one, constructing a global cross-correlation function;
step two, performing one-time moveout correction on the microseism record by using the global cross-correlation function;
thirdly, introducing a spacing constraint condition of the detectors, and reconstructing a local cross-correlation function;
step four, performing secondary time difference correction on the record after the primary time difference correction by using a local cross-correlation function;
step five, carrying out superposition operation on the multiple records after the secondary time difference correction to obtain a superposition channel;
step six, carrying out first arrival picking on the superposed channel by adopting an STA/LTA method to obtain a first arrival time;
and step seven, performing reverse time difference correction on the relative arrival time of each channel and the first arrival time of the superposed channel calculated by combining the local cross-correlation function, so that the first arrival time of the microseism event can be obtained.
2. The method for picking up the first arrival of the microseism based on the spacing constraint of the detectors as claimed in claim 1, wherein in the step one, the cross-correlation function of the signals between two channels is as follows:
wherein N is the number of sampling points, xi(n) and xj(n) respectively representing two data; when | ci，j(k) When | is maximum, it is recorded as
At this time xi(n) and xj(n) the waveforms have the greatest similarity, x of the two signalsi(n) and xj(n) time difference Deltati，jThe first arrival time difference between the two channels is considered;
time difference between tracks Deltati，jAnd the first arrival time t of the two recordsi，tjThe relationship between them is as follows:
ti-tj＝Δti，j。
3. the method for picking up the first arrival of the microseism based on the spacing constraint of the detectors as claimed in claim 1, wherein in the second step and the fourth step, the time difference correction adopts the following two modes:
method one, selecting record with high signal-to-noise ratio as reference track xi(n) any other lane is xj(n) calculating the cross-correlation function of the reference track and each of the other tracks according to Δ ti，jAdjustment ofThe time difference correction can be carried out on the microseism record at the time window position of each channel;
method two, according to delta t without selecting reference channeli，jRelative arrival time t to each trackiThe relationship between the two sets of linear equations to solve tiAnd according to tiThe time difference correction is performed for each recording.
4. The method as claimed in claim 3, wherein the time difference correction of the multiple traces is implemented by solving an overdetermined system of linear equations, and the following system of linear equations is established:
At＝Δt；
where A is the sparse coefficient matrix and Δ t is the observation data vector (from Δ t)i，jIs formed), t is the parameter vector to be solved (consisting of t)iMake up); its least squares solution is:
t＝(ATA)-1ATΔt；
that is, the first arrival information of each track record is obtained according to the relative arrival time t of each trackiThe time difference correction is performed for each recording.
5. The microseism first arrival picking method based on the geophone spacing constraint of claim 1, wherein in the sixth step, the calculation formula of the STA/LTA method is as follows:
wherein, x (N) is seismic record, M is long time window length, N is short time window length, and the maximum point of R is considered as the first arrival point of seismic wave;
the first arrival time of the superposed tracks is T0Combining the relative arrival times t of the tracksiAnd then reversely correcting the time difference to obtain the actual first arrival time T of each trackiThe calculation formula is as follows:
Ti＝T0+ti。
6. the microseism first arrival picking method based on the geophone spacing constraint is characterized in that the geophone spacing constraint condition is as follows: only the geophone pair with a certain distance range is subjected to time difference calculation;
introducing a geophone spacing constraint condition into a cross-correlation first arrival picking process, performing global cross-correlation calculation and time difference correction on microseism records to obtain a first arrival wave in-phase axis leveling record, performing local cross-correlation calculation based on geophone spacing constraint on the records to perform time difference correction to obtain a local cross-correlation leveling record, and performing subsequent seismic phase identification, multi-channel superposition calculation and first arrival picking by using the record.
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