CN116047604A - Deep seismic phase rapid pickup method based on amplitude statistics and time-frequency analysis - Google Patents
Deep seismic phase rapid pickup method based on amplitude statistics and time-frequency analysis Download PDFInfo
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
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- G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
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Abstract
The application relates to the technical field of seismic data analysis, in particular to a depth seismic phase rapid pickup method based on amplitude statistics and time-frequency analysis, which comprises the following steps: picking up the actual first arrival time of the direct wave; transforming the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals; determining the corresponding time of the time spectrum of each energy group in the vibration phase time spectrum; and determining the actual occurrence time of each deep vibration phase energy group according to the time difference value of the time spectrum correspondence of the deep vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave by taking the first energy group in the vibration phase time spectrum as the direct wave energy group and the subsequent energy groups as the deep vibration phase energy groups. According to the technical scheme, a time domain and a frequency domain are combined, the actual occurrence time of each deep seismic phase energy group is determined through the seismic phase time spectrum of the seismic waveform signal, and the final position of the deep seismic phase is finally picked up.
Description
Technical Field
The application relates to the technical field of seismic analysis, in particular to a depth seismic phase rapid pickup method based on amplitude statistics and time-frequency analysis.
Background
The first arrival picking is a key step in the seismic exploration data processing process, and at present, the main method of the seismic wave first arrival automatic picking technology comprises the following steps: long and short time window ratio method, fractal theory, SPS (Shell Processing Support ) information constraint first-arrival picking algorithm, first-arrival picking method based on AIC (Akaike Information Criterion, red pool information amount criterion) information criterion, and the like, but when the data signal-to-noise ratio is low, the picking precision is affected, so that many students adopt the thought of firstly denoising through a time-frequency domain and then carrying out first-arrival picking work. The time-frequency analysis is an effective method for analyzing local characteristics of non-stationary signals, and is mainly divided into two major categories, namely a frequency-selecting reconstruction method and a denoising reconstruction method, but the two time-frequency domain methods relate to forward and reverse operation of signal conversion between a time domain and a time-frequency domain, and have the problem of large calculation amount.
In order to improve the calculation efficiency and the pickup precision, a first arrival pickup algorithm based on time-frequency analysis is provided based on the characteristic that the amplitude, the phase and the frequency of a first arrival wave can be changed in the prior art, and the algorithm extracts an instantaneous phase spectrum of a signal through wavelet transformation under the constraint of a noise discrimination coefficient, and then carries out first arrival pickup by utilizing the zero crossing attribute of the instantaneous phase spectrum. The first arrival picking method based on the time-frequency spectrum entropy is also provided, and the first arrival of the seismic signals with low signal-to-noise ratio can be effectively picked up. The method also provides a new method for denoising and picking up the first arrival only through the forward transformation in the time-frequency domain, thereby not only adapting to the environment with low signal-to-noise ratio, but also saving the calculation cost. However, these methods have difficulty in picking up the deep seismic facies.
The reliability of deep seismic phase pickup can be improved by automatic extraction or superposition techniques (e.g., fang and van der Hilst,2019; craig, 2019). But the methods are only suitable for the high-magnitude long-distance vibration with obvious depth vibration phase.
In the prior art, pP and sP are also identified by a semi-automatic statistical method, however, the method may misjudge the mojohnsonian converted wave (S-to-P) from below the station as pP or sP. To overcome this problem, a source depth localization technique capable of automatically identifying pP and sP is provided in the prior art, but is difficult to apply in cases where the data contains only pP or sP. For this purpose, a velocity spectrum analysis may be performed by using the station array to obtain accurate pP and P travel time differences, and then match with a theoretical value to solve for an accurate source depth, however, this method assumes that the identified depth seismic phase is always pP, which is often not true in practice.
In summary, the existing deep vibration phase picking method has the problem that the accuracy and precision of deep vibration phase picking are not high enough.
Disclosure of Invention
In order to overcome the problem that the picking accuracy and precision are not high enough in the deep vibration phase picking related technology to at least a certain extent, the application provides a deep vibration phase quick picking method based on amplitude statistics and time-frequency analysis.
The scheme of the application is as follows:
a depth vibration phase rapid pickup method based on amplitude statistics and time-frequency analysis comprises the following steps:
picking up the actual first arrival time of the direct wave;
transforming the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals;
determining the corresponding time of the time spectrum of each energy group in the vibration phase time spectrum;
and taking the first energy group in the vibration phase time spectrum as a direct wave energy group, taking the subsequent energy groups as depth vibration phase energy groups, and determining the actual occurrence time of each depth vibration phase energy group according to the time difference value corresponding to the time spectrum of the depth vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave.
Preferably, before the actual first arrival time of the direct wave is picked up, the method further comprises:
and carrying out amplitude normalization processing on the seismic waveform signals.
Preferably, before transforming the seismic waveform, the method further comprises:
weak amplitude seismic facies in the seismic waveform signals are filtered.
Preferably, filtering out weak amplitude seismic phases in the seismic waveform includes:
dividing the amplitude value range of the seismic waveform signal into a plurality of amplitude intervals, and counting the number of amplitude extremum values in each amplitude interval;
determining the mean value and standard deviation of the amplitude extremum;
the amplitude value lying within the mean ± standard deviation is updated to 0.
Preferably, picking up the actual first arrival time of the direct wave includes:
setting a long sliding time window and a short sliding time window, and respectively calculating the average ratio of the sum of the absolute values of the amplitudes of the seismic waveform signals in the long time window and the short time window;
adding the length of a long time window to the moment when the ratio of the mean ratio exceeds the preset ratio threshold value as the actual first arrival moment of the direct wave;
the long time window and the short time window both adopt rolling time windows, the rolling direction is shown to slide along the horizontal direction on the waveform curve, and the relative positions of the long time window and the short time window are kept unchanged in the sliding process;
the formula for calculating the average ratio of the sum of the absolute values of the amplitudes of the seismic waveform signals in the long time window and the short time window is as follows:
wherein R is the average value ratio, A STA A is the average value of the sum of the absolute values of the amplitudes of the seismic waveform signals in a short time window LTA The average value of the sum of the amplitude absolute values of the seismic waveform signals in the long time window is that x (i) is the absolute value of the ith amplitude in the short time window, N is the number of sampling points in the short time window, x (j) is the absolute value of the jth amplitude in the long time window, and M is the number of sampling points in the long time window.
Preferably, transforming the seismic waveform signal to obtain a seismotemporal spectrum of the seismic waveform signal includes:
performing wavelet transformation on the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals; the transformation formula of the wavelet transformation is as follows:
wherein a is a scale factor, b is a translation parameter, W (a, b) is a seismic phase time spectrum coefficient obtained after wavelet transformation, x (t) is a seismic waveform signal,
as complex conjugate of the parent wavelet, dt is a micro-variable with t as a variable.
Preferably, determining the time spectrum corresponding time of each energy cluster in the seismic phase time spectrum includes:
determining an index position of an amplitude extremum of the seismic waveform in the seismic phase time spectrum;
carrying out statistical analysis on the amplitude extremum of the seismic waveform in the seismic phase time spectrum, and determining an amplitude threshold according to an analysis result;
screening the amplitude extremum of the seismic waveform in the seismic phase time spectrum according to the amplitude threshold, and taking the index position of the amplitude extremum of the seismic waveform reaching the amplitude threshold as the index position of the energy cluster;
and calculating the product of the index position of the energy cluster and the preset sampling rate to obtain the time spectrum corresponding time of the energy cluster.
Preferably, determining the actual occurrence of each deep seismic energy bolus comprises:
and taking the sum of the time difference value corresponding to the time frequency spectrum of the deep vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave as the actual appearance time of the deep vibration phase energy group.
The technical scheme that this application provided can include following beneficial effect: the method for quickly picking up the deep vibration phase based on amplitude statistics and time-frequency analysis comprises the following steps: picking up the actual first arrival time of the direct wave; transforming the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals; determining the corresponding time of the time spectrum of each energy group in the vibration phase time spectrum; and determining the actual occurrence time of each deep vibration phase energy group according to the time difference value of the time spectrum correspondence of the deep vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave by taking the first energy group in the vibration phase time spectrum as the direct wave energy group and the subsequent energy groups as the deep vibration phase energy groups. According to the technical scheme, a time domain and a frequency domain are combined, the actual occurrence time of each deep seismic phase energy group is determined through the seismic phase time spectrum of the seismic waveform signal, and the final position of the deep seismic phase is finally picked up. Compared with the method for analyzing the depth vibration phase by only using a single time domain or frequency domain signal in the prior art, the technical scheme of the method greatly improves the picking efficiency and accuracy of the depth vibration phase.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic flow chart of a method for rapid picking up of deep seismic facies based on amplitude statistics and time-frequency analysis according to one embodiment of the present application;
FIG. 2 is a flow chart of another method for rapid pickup of deep seismic facies based on amplitude statistics and time-frequency analysis according to one embodiment of the present application;
FIG. 3 is a schematic flow chart of filtering weak amplitude seismic facies in a seismic waveform signal according to one embodiment of the present disclosure;
FIG. 4 is a schematic flow chart of determining corresponding time of each energy cluster in a seismophase time spectrum according to an embodiment of the present application;
FIG. 5 is a seismogram identified with the actual first arrival of a direct wave provided by one embodiment of the present application;
FIG. 6 is a statistical histogram of amplitude extrema for a plurality of amplitude bins of a seismic waveform signal provided in one embodiment of the present application;
FIG. 7 is a plot of a seismogram after filtering weak amplitude seismograms in a seismic waveform signal provided in one embodiment of the present application;
FIG. 8 is a graph of a seismophase time-frequency spectrum with strong amplitude characteristics based on continuous wavelet transform according to one embodiment of the present application;
fig. 9 is a seismogram identifying the actual first arrival time of the direct wave and the actual occurrence time of the deep seismogram according to one embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
Fig. 1 is a flow chart of a method for quickly picking up a deep vibration phase based on amplitude statistics and time-frequency analysis according to an embodiment of the present invention, referring to fig. 1, the method for quickly picking up a deep vibration phase based on amplitude statistics and time-frequency analysis includes:
s11: picking up the actual first arrival time of the direct wave;
it should be noted that, referring to fig. 2, before the actual first arrival time of the direct wave is picked up, the method further includes:
and carrying out amplitude normalization processing on the seismic waveform signals.
It can be understood that, before implementation, the technical solution in this embodiment needs to perform amplitude normalization processing on the seismic waveform signal, where the normalization processing is to normalize the seismic waveform signal, so as to facilitate subsequent processing.
It should be noted that, picking up the actual first arrival time of the direct wave includes:
setting a long sliding time window and a short sliding time window, and respectively calculating the average ratio of the sum of the absolute values of the amplitudes of the seismic waveform signals in the long time window and the short time window;
adding the length of the long time window to the moment when the ratio of the average ratio exceeds the preset ratio threshold value as the actual first arrival moment of the direct wave;
the long time window and the short time window both adopt rolling time windows, the rolling direction is shown to slide along the horizontal direction on the waveform curve, and the relative positions of the long time window and the short time window are kept unchanged in the sliding process;
the formula for calculating the average ratio of the sum of the absolute values of the amplitudes of the seismic waveform signals in the long time window and the short time window is as follows:
wherein R is the average value ratio, A STA A is the average value of the sum of the absolute values of the amplitudes of the seismic waveform signals in a short time window LTA The average value of the sum of the amplitude absolute values of the seismic waveform signals in the long time window is that x (i) is the absolute value of the ith amplitude in the short time window, N is the number of sampling points in the short time window, x (j) is the absolute value of the jth amplitude in the long time window, and M is the number of sampling points in the long time window.
It can be understood that in this embodiment, the actual first arrival time of the direct wave is automatically picked up by the long and short time window technology.
S12: transforming the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals;
with reference to fig. 2, before transforming the seismic waveform, the method further includes:
filtering weak amplitude seismic phases in the seismic waveform signals;
filtering out weak amplitude seismic facies in a seismic waveform signal, referring to fig. 3, includes:
s21: dividing the amplitude value range of the seismic waveform signal into a plurality of amplitude intervals, and counting the number of amplitude extremum values in each amplitude interval;
s22: determining the mean value and standard deviation of the amplitude extremum;
s23: the amplitude value lying within the mean ± standard deviation is updated to 0.
It can be understood that the amplitude value of a waveform containing random noise and seismic signals can be considered to conform to the normal distribution rule, so that most of the amplitude located near the average value of the amplitude belongs to weak amplitude, and the strong amplitude is small, so that in order to obtain high-quality seismic phases, the accuracy of the identification of the seismic phases is improved, and in this embodiment, the weak amplitude on the seismic waveform is filtered based on the statistical characteristics of the maximum value and the minimum value of the amplitude, specifically: firstly, carrying out amplitude extremum statistics on an amplitude normalized seismic waveform signal, namely dividing an amplitude value range [ -1,1] of the seismic waveform signal into a certain number of amplitude intervals, and counting the number of amplitude extremums in each amplitude interval; then calculating to obtain the mean value (mu) and standard deviation (sigma) of the amplitude extremum; and finally, updating amplitude values (weak amplitude seismic phases in the seismic waveform signals) within the range of mu+/-sigma to 0, and reserving to obtain high-quality seismic phases with strong amplitude characteristics.
The method for transforming the seismic waveform signal to obtain the seismic phase time spectrum of the seismic waveform signal includes:
performing wavelet transformation on the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals; the transform formula of the wavelet transform is:
wherein a is a scale factor, b is a translation parameter, W (a, b) is a seismic phase time spectrum coefficient obtained after wavelet transformation, x (t) is a seismic waveform signal,
as complex conjugate of the parent wavelet, dt is a micro-variable with t as a variable.
The complex conjugate is an operation for changing the imaginary part of the complex number.
It can be understood that, in this embodiment, based on the above formula, the strong amplitude seismic waveform signal obtained in S23 is subjected to continuous wavelet transformation to obtain the seismic-phase time spectrum of the strong amplitude seismic waveform signal.
S13: determining the corresponding time of the time spectrum of each energy group in the vibration phase time spectrum;
determining the time spectrum corresponding time of each energy cluster in the seismic phase time spectrum, referring to fig. 4, includes:
s131: determining an index position of an amplitude extremum of the seismic waveform in the seismic phase time spectrum;
s132: carrying out statistical analysis on the amplitude extremum of the seismic waveform in the seismic phase time spectrum, and determining an amplitude threshold according to an analysis result;
s133: screening the amplitude extremum of the seismic waveform in the seismic phase time spectrum according to the amplitude threshold value, and taking the index position of the amplitude extremum of the seismic waveform reaching the amplitude threshold value as the index position of the energy cluster;
s134: and calculating the product of the index position of the energy cluster and the preset sampling rate to obtain the time spectrum corresponding time of the energy cluster.
S14: and determining the actual occurrence time of each deep vibration phase energy group according to the time difference value of the time spectrum correspondence of the deep vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave by taking the first energy group in the vibration phase time spectrum as the direct wave energy group and the subsequent energy groups as the deep vibration phase energy groups.
In specific practice, the time difference value corresponding to the time spectrum of the deep vibration phase energy group and the direct wave energy group and the sum of the actual first arrival time of the direct wave are taken as the actual occurrence time of the deep vibration phase energy group.
The sampling rate is a value set manually in advance.
The technical scheme in this embodiment is illustrated:
example seismic event parameters: month 1 of 2010, 22:28:22; mw 6.2, 154.35 DEG E,6.39 DEG S, centroid depth 40.2km.
In specific implementation, firstly, an original seismic waveform signal received by a station MY.IPM in the seismic event is normalized, then, the actual first arrival time of a direct wave is picked up based on a long-short time window ratio method of 'amplitude mean', the adopted long-short time window length can be 1000 sampling points but not limited to 240 sampling points, when the mean ratio of the sum of the amplitude absolute values of a long sliding time window and a short sliding time window is calculated, the ratio of the mean ratio is suddenly increased (namely, the time when the ratio of the mean ratio exceeds a preset ratio threshold value) and the length of the long-short time window is added as the first arrival time of the direct wave (as shown by a first longitudinal straight line in fig. 5); then, calculating the mean value (mu) and standard deviation (sigma) of the amplitude extremum of the seismic signal, dividing the amplitude value range of the seismic waveform signal into 11 amplitude intervals according to the statistical analysis of the amplitude extremum, wherein the statistical histogram is shown in fig. 6, and updating the amplitude value in the mu+/-sigma range to 0 according to the statistical result to obtain a high-quality seismic phase with strong amplitude characteristics as shown in fig. 7; the time spectrum of the earthquake phase with the strong amplitude characteristic is obtained based on continuous wavelet transformation, as shown in figure 8, three strong energy groups (ellipses) corresponding to the strong amplitude earthquake phase on the earthquake waveform can be seen from figure 8; and selecting an amplitude threshold value with the value of 0.75 according to the amplitude extremum statistical analysis, screening the amplitude extremum of the seismic waveform in the seismic phase time spectrum according to the amplitude threshold value, taking the index position of the amplitude extremum of the seismic waveform reaching the amplitude threshold value as the index position of the energy cluster, and multiplying the index position of the energy cluster by the sampling rate to obtain the time spectrum corresponding time of the energy cluster. Then taking the first energy group in the vibration phase time spectrum as a direct wave energy group, taking the last two energy groups as depth vibration phase energy groups, respectively subtracting the corresponding time of the time spectrum of the direct wave energy group from the corresponding time of the time spectrum of the last two depth vibration phase energy groups, and obtaining the corresponding time difference value of the time spectrums of the depth vibration phase energy groups and the direct wave energy groups, namely the time difference between the direct wave and the two subsequent depth vibration phases; finally, the actual appearance time of the two depth vibration phases can be obtained by adding the time difference to the first arrival time of the previously picked direct wave (as shown by the two longitudinal straight lines in fig. 9).
It can be understood that the method for quickly picking up the deep seismic phase based on amplitude statistics and time-frequency analysis in the embodiment includes: picking up the actual first arrival time of the direct wave; transforming the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals; determining the corresponding time of the time spectrum of each energy group in the vibration phase time spectrum; and determining the actual occurrence time of each deep vibration phase energy group according to the time difference value of the time spectrum correspondence of the deep vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave by taking the first energy group in the vibration phase time spectrum as the direct wave energy group and the subsequent energy groups as the deep vibration phase energy groups. The technical scheme of the embodiment combines the time domain and the frequency domain, determines the actual occurrence time of each deep seismic phase energy group through the seismic phase time spectrum of the seismic waveform signal, and finally picks up the final position of the deep seismic phase. Compared with the method for analyzing the depth vibration phase by only using a single time domain or frequency domain signal in the prior art, the technical scheme of the embodiment greatly improves the picking efficiency and accuracy of the depth vibration phase.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (8)
1. A depth vibration phase rapid pickup method based on amplitude statistics and time-frequency analysis is characterized by comprising the following steps:
picking up the actual first arrival time of the direct wave;
transforming the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals;
determining the corresponding time of the time spectrum of each energy group in the vibration phase time spectrum;
and taking the first energy group in the vibration phase time spectrum as a direct wave energy group, taking the subsequent energy groups as depth vibration phase energy groups, and determining the actual occurrence time of each depth vibration phase energy group according to the time difference value corresponding to the time spectrum of the depth vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave.
2. The method of claim 1, wherein prior to the actual first arrival time of the direct wave is picked up, the method further comprising:
and carrying out amplitude normalization processing on the seismic waveform signals.
3. The method of claim 1, wherein prior to transforming the seismic waveform, the method further comprises:
weak amplitude seismic facies in the seismic waveform signals are filtered.
4. A method according to claim 3, wherein filtering out weak amplitude seismic facies in the seismic waveform comprises:
dividing the amplitude value range of the seismic waveform signal into a plurality of amplitude intervals, and counting the number of amplitude extremum values in each amplitude interval;
determining the mean value and standard deviation of the amplitude extremum;
the amplitude value lying within the mean ± standard deviation is updated to 0.
5. The method of claim 1, wherein picking up the actual first arrival time of the direct wave comprises:
setting a long sliding time window and a short sliding time window, and respectively calculating the average ratio of the sum of the absolute values of the amplitudes of the seismic waveform signals in the long time window and the short time window;
adding the length of a long time window to the moment when the ratio of the mean ratio exceeds the preset ratio threshold value as the actual first arrival moment of the direct wave;
the long time window and the short time window both adopt rolling time windows, the rolling direction is shown to slide along the horizontal direction on the waveform curve, and the relative positions of the long time window and the short time window are kept unchanged in the sliding process;
the formula for calculating the average ratio of the sum of the absolute values of the amplitudes of the seismic waveform signals in the long time window and the short time window is as follows:
wherein R is the average value ratio, A STA A is the average value of the sum of the absolute values of the amplitudes of the seismic waveform signals in a short time window LTA The average value of the sum of the amplitude absolute values of the seismic waveform signals in the long time window is that x (i) is the absolute value of the ith amplitude in the short time window, N is the number of sampling points in the short time window, x (j) is the absolute value of the jth amplitude in the long time window, and M is the number of sampling points in the long time window.
6. The method of claim 1, wherein transforming the seismic waveform signal to obtain a seismotemporal spectrum of the seismic waveform signal comprises:
performing wavelet transformation on the seismic waveform signals to obtain a seismic phase time spectrum of the seismic waveform signals; the transformation formula of the wavelet transformation is as follows:
wherein a is a scale factor, b is a translation parameter, W (a, b) is a seismic phase time spectrum coefficient obtained after wavelet transformation, x (t) is a seismic waveform signal,
as complex conjugate of the parent wavelet, dt is a micro-variable with t as a variable.
7. The method of claim 1, wherein determining the time spectrum corresponding time instants for each energy bin in the seismo-phase time spectrum comprises:
determining an index position of an amplitude extremum of the seismic waveform in the seismic phase time spectrum;
carrying out statistical analysis on the amplitude extremum of the seismic waveform in the seismic phase time spectrum, and determining an amplitude threshold according to an analysis result;
screening the amplitude extremum of the seismic waveform in the seismic phase time spectrum according to the amplitude threshold, and taking the index position of the amplitude extremum of the seismic waveform reaching the amplitude threshold as the index position of the energy cluster;
and calculating the product of the index position of the energy cluster and the preset sampling rate to obtain the time spectrum corresponding time of the energy cluster.
8. The method of claim 1, wherein determining the actual time of occurrence of each deep seismic energy bolus comprises:
and taking the sum of the time difference value corresponding to the time frequency spectrum of the deep vibration phase energy group and the direct wave energy group and the actual first arrival time of the direct wave as the actual appearance time of the deep vibration phase energy group.
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| JPH11118939A (en) * | 1997-10-17 | 1999-04-30 | Toshiba Corp | Earthquake observation system |
| CN108596327A (en) * | 2018-03-27 | 2018-09-28 | 中国地质大学（武汉） | A kind of seismic velocity spectrum artificial intelligence pick-up method based on deep learning |
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| CN112904412A (en) * | 2019-12-03 | 2021-06-04 | 北京矿冶科技集团有限公司 | Mine microseismic signal P wave first arrival moment extraction method and system |
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