CN110007342B - Time-frequency domain direct pickup first arrival method and system for seismic signals with low signal-to-noise ratio - Google Patents
Time-frequency domain direct pickup first arrival method and system for seismic signals with low signal-to-noise ratio Download PDFInfo
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Abstract
The invention relates to a time-frequency domain direct pickup first arrival method and a time-frequency domain direct pickup first arrival system for seismic signals with low signal-to-noise ratio, and belongs to the technical field of seismic exploration. Firstly, carrying out time-frequency forward transformation on a low signal-to-noise ratio signal; then, noise self-adaptive attenuation is carried out in a time-frequency domain; and finally, according to the characteristic that the coefficient superposition response and the time domain of the seismic signals have the same waveform representation in the time-frequency domain, carrying out first arrival pickup directly in the time-frequency domain by using a statistical-based method. The method has the self-adaptive characteristics of high running speed and less manual intervention, and the accuracy of seismic processing results of subsequent passive source seismic source positioning, velocity analysis, tomography and the like of a target area with poor acquisition quality is improved through the accurate first arrival position of the acquired low signal-to-noise ratio data.
Description
Technical Field
The invention relates to a direct first arrival picking method and a direct first arrival picking system, belongs to the technical field of seismic exploration, and particularly relates to a time-frequency domain direct first arrival picking method and a time-frequency domain direct first arrival picking system for seismic signals with low signal-to-noise ratio.
Background
The seismic signal first arrival picking is the arrival time picking of seismic signal first arrival waveforms, is one of the most basic data processing methods in the field of seismic exploration, and the accuracy of seismic processing results such as passive source seismic source positioning, velocity analysis, tomography and the like is determined by the picking accuracy. The traditional energy ratio method and the first arrival picking method based on statistical information can obtain higher picking precision under the condition of higher signal-to-noise ratio of signals, but when the signal-to-noise ratio of the signals is reduced, the picking precision of the traditional method is correspondingly reduced, and in order to improve the picking precision of the first arrival of the signals with low signal-to-noise ratio, a plurality of new methods are introduced, wherein the first arrival picking method based on time-frequency analysis is also widely applied.
At present, the low signal-to-noise ratio signal first arrival picking method based on time-frequency analysis is mainly divided into two main categories, which are as follows:
the prior method 1:
repeating the steps (1) to (3) channel by channel for the seismic signals collected by each detector:
(1) converting the original data into a time-frequency domain, and carrying out time-frequency analysis on the original data;
(2) picking out a signal with higher signal-to-noise ratio from signals of a plurality of frequency bands (scales) decomposed from a time-frequency domain;
(3) and (4) carrying out first arrival picking on the selected high signal-to-noise ratio signal by adopting a traditional method.
The prior method 1 has the following defects:
(1) selecting seismic data corresponding to a scale with higher signal-to-noise ratio and relying on artificial intervention too much;
(2) time-frequency forward and inverse transformation needs to be carried out on signals at the same time for each picking, and time is consumed.
The prior method 2 comprises the following steps:
repeating the steps (1) to (3) channel by channel for the seismic signals collected by each detector:
(1) converting the original data into a time-frequency domain through time-frequency transformation;
(2) selecting a proper threshold value to attenuate noise in a time-frequency domain by utilizing the characteristic that the signal and the noise have coefficient difference in the time-frequency domain, and inversely transforming to the time domain;
(3) and performing first arrival pickup on the signal subjected to noise suppression by adopting a traditional method.
The prior method 2 has the following defects:
(1) the noise suppression process of each signal needs to perform time-frequency forward transformation and time-frequency inverse transformation simultaneously, which is time-consuming;
(2) the threshold selection in the denoising process depends on human intervention too much.
In summary, a fast and adaptive first arrival picking method for seismic signals with low signal-to-noise ratio is provided to solve the above problems, which is a problem to be solved urgently in the technical field of seismic exploration.
Disclosure of Invention
In order to solve the problems in the prior art, the invention discloses a time-frequency domain direct pickup first arrival method for seismic signals with low signal-to-noise ratio. Firstly, carrying out time-frequency forward transformation on a low signal-to-noise ratio signal; then, noise self-adaptive attenuation is carried out in a time-frequency domain; and finally, according to the characteristic that the coefficient superposition response and the time domain of the seismic signals have the same waveform representation in the time-frequency domain, carrying out first arrival pickup directly in the time-frequency domain by using a statistical-based method.
The purpose of the invention is realized by the following technical scheme:
a time-frequency domain direct pickup first arrival method for seismic signals with low signal-to-noise ratio comprises the following steps:
and 3, directly carrying out first arrival picking in a time-frequency domain by using a method based on statistics.
Preferably, the time-frequency forward transform in step 1 includes but is not limited to wavelet transform, S transform and other time-frequency forward transform methods, and is based on formula 1:
wherein d (t) represents a time domain signal, Xs(a, b) are corresponding wavelet coefficients, y represents the complex conjugate of a mother wavelet y (t), wherein the mother wavelet is a Ricker wavelet similar to a seismic wavelet signal; b represents the time shift measure of the mother wavelet; a represents the scale transformation metric of the mother wavelet.
Preferably, the noise attenuation in step 2 comprises the following substeps:
step 2.1, randomly selecting one channel from the time-frequency domain data converted in the step 1, and calculating a threshold value by using a formula 2:
λ ═ α · s formula 2;
in the formula, the threshold value lambda is represented as the product of alpha and s, s represents the ratio of the area of a probability density function curve to the total area in the range of three standard deviations around the average in the standard normal distribution, alpha is a calibration parameter, and the value of alpha can be determined by taking any one seismic signal;
and 2.2, traversing coefficient values of all the gathers in the time-frequency domain, if the coefficient values are larger than the threshold value lambda calculated in the step 2.1, reserving the gather, and otherwise, carrying out zero setting or certain proportion of attenuation processing on the gather.
Preferably, the statistical-based first arrival picking in step 3 comprises the following sub-steps:
step 3.1, solving the QAIC vector of each trace set by an improved aic (akaike information criterion) method, as shown in formula 3:
QAIC(i)＝1/{log10[var(tw1)]/log10[var(tw2)]k } formula 3;
in the formula, tw1And tw2Respectively representing the data of two adjacent time windows in the to-be-processed trace set, var (·) represents the calculation variance, and k is a constant.
And 3.2, calculating the maximum value in the QAIC vector corresponding to each gather, namely the first arrival time position corresponding to each gather.
A time-frequency domain direct pickup first-arrival system for low signal-to-noise ratio seismic signals, comprising the following modules:
the time-frequency forward transformation module is used for transforming the original data into a time-frequency domain through time-frequency forward transformation;
the noise attenuation module is used for carrying out noise suppression by utilizing a self-adaptive noise attenuation method;
and the first arrival picking module directly carries out first arrival picking in a time-frequency domain by using a method based on statistics.
Preferably, when the time-frequency forward transform module performs time-frequency forward transform, a time-frequency forward transform method including but not limited to wavelet transform, S transform, and the like is adopted, and is based on formula 1:
wherein d (t) represents a time domain signal, Xs(a, b) are the corresponding wavelet coefficients, y*Representing the complex conjugate of a mother wavelet y (t), wherein the mother wavelet is a Ricker wavelet similar to a seismic wavelet signal; b represents the time shift measure of the mother wavelet; a represents the scale transformation metric of the mother wavelet.
Preferably, the noise attenuation module includes:
a threshold acquisition unit: randomly selecting one channel from time-frequency domain data converted by a time-frequency forward transform module, and calculating a threshold value by using a formula 1:
λ ═ α · s formula 2;
in the formula, the threshold value lambda is represented as the product of alpha and s, s represents the ratio of the area of a probability density function curve to the total area in the range of three standard deviations around the average in the standard normal distribution, alpha is a calibration parameter, and the value of alpha can be determined by taking any one seismic signal;
a data processing unit: and traversing coefficient values of all the trace sets in a time-frequency domain, if the coefficient values are larger than a threshold lambda calculated by the threshold acquisition unit, reserving the coefficient values, and otherwise, carrying out zero setting or certain proportion of attenuation processing on the coefficient values.
Preferably, the first arrival picking module comprises:
QAIC vector acquisition unit: solving a QAIC vector of each trace set based on an improved AIC (akaike information criterion) method, as shown in formula 3:
QAIC(i)＝1/{log10[var(tw1)]/log10[var(tw2)]k }, formula 3;
in the formula, tw1And tw2Respectively representing the data of two adjacent time windows in the to-be-processed trace set, var (·) represents the calculation variance, and k is a constant.
First arrival time position acquisition unit: the maximum value in the QAIC vector corresponding to each gather, i.e., the first arrival time position corresponding to each gather, is calculated.
Therefore, the invention has the following advantages: 1. the method has the self-adaptive characteristics of high running speed and less manual intervention, and the accuracy of seismic processing results of subsequent passive source seismic source positioning, speed analysis, tomography and the like of a target area with poor acquisition quality is improved through the accurate first arrival position of the acquired low signal-to-noise ratio data; 2. the method converts the traditional first-arrival picking calculation domain from the time domain to the time-frequency domain, and avoids the time-frequency inverse transformation process in the traditional low signal-to-noise ratio signal first-arrival picking method based on time-frequency transformation, thereby reducing the calculation cost to a great extent and improving the algorithm efficiency; 3. in the threshold denoising step, the method can be applied to all gather data only by analyzing and calculating the threshold of any one of the data, thereby reducing manual intervention to a great extent.
Description of the drawings:
FIG. 1 is a flow chart of a time-frequency domain direct pick-up first arrival method of the present invention;
FIG. 2 is a graph of the superposition response of a simulated noise-free signal in the time-frequency domain versus the time-domain signal;
FIG. 3a shows the pick-up result of a conventional aic method applied to simulate a noise-free signal;
FIG. 3b shows the result of the method of the present invention applied to simulate a noise-free signal;
FIG. 3c shows the first arrival pick-up comparison results of conventional aic and the method of the present invention applied to a simulated noise-free signal;
FIG. 4a shows the pickup result of the conventional aic method applied to simulate a signal with low SNR;
FIG. 4b shows the result of the method of the present invention applied to simulate the pickup of a signal with a low SNR;
FIG. 4c shows the comparison result of the first arrival pick-up of the conventional aic and the method of the present invention applied to simulate a low SNR signal;
FIG. 5a shows the result of the method of the present invention applied to the actual low SNR signal;
FIG. 5b is a comparison graph of calibration results of first arrival picking up of actual low SNR signals according to the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in FIG. 1, the present invention provides a time-frequency domain direct pickup first-arrival method and system for seismic signals with low SNR, which comprises the following basic steps:
1) converting the original data into a time-frequency domain through time-frequency forward transformation;
2) carrying out noise suppression by using a self-adaptive noise attenuation method;
3) carrying out first arrival picking directly in a time-frequency domain by using a method based on statistics;
the above steps are described in detail below:
the step 01 of transferring the original data into the time-frequency domain can be realized by time-frequency transformation such as wavelet transformation, and the like, as shown in formula (1):
wherein d (t) represents a time domain signal, Xs(a, b) are the corresponding wavelet coefficients, y*Representing the complex conjugate of a mother wavelet y (t), wherein the mother wavelet is a Ricker wavelet similar to a seismic wavelet signal; b represents the time shift measure of the mother wavelet; a represents the scale transformation metric of the mother wavelet.
And step 02, performing noise suppression by using a self-adaptive noise attenuation method:
the noise suppression method comprises the following steps:
(A) randomly selecting one way from the time-frequency domain data converted by equation (1), and calculating a threshold value using equation (2):
λ＝α·s， (2)
in the formula, the threshold lambda is expressed as the product of alpha and s, s represents the ratio of the area of a probability density function curve to the total area in the range of three standard deviations around the mean in the standard normal distribution, alpha is a calibration parameter, and the value of alpha can be determined through 3-5 times of trial calculation.
(B) Traversing the coefficient values of all gathers in the time-frequency domain, if greater than the threshold λ calculated by step (a) above, retaining, otherwise, taking a zeroing or proportional decay process on them.
Step 03, the first arrival picking based on statistics comprises the following steps:
(A) solving the QAIC vector of each trace set in the time-frequency domain by an improved aic (akaike information criterion) method, wherein the calculation process is as shown in formula (3):
QAIC(i)＝1/{log10[var(tw1)]/log10[var(tw2)]·k}， (3)
in the formula, tw1And tw2Respectively representing data of two adjacent time windows in a channel set to be processed, and var (phi) represents solving the variance, wherein the size of the time window can be selected according to the main frequency width of the seismic signal, and k is a constant.
(B) Calculating the maximum value in the QAIC vector corresponding to each gather in the time-frequency domain, and according to the characteristics that the coefficient superposition response of the seismic signal in the time-frequency domain and the time domain have the same waveform representation, as shown in FIG. 2 (in the figure, the long-segment dotted line is the original time domain signal, and the short-segment dotted line represents the superposed time-frequency coefficient response), the maximum value of the QAIC vector of each gather in the time-frequency domain corresponds to the first-arrival time position thereof.
Thus, the direct first arrival pickup of the seismic signals with low signal-to-noise ratio in the time-frequency domain is completed.
The invention is verified below by means of the model and the actual data, respectively.
Firstly, carrying out numerical simulation to carry out verification on the method of the invention:
to calibrate and subsequently verify the accuracy of the inventive method, first-break pickups were performed on simulated noiseless data using the conventional aic method and the inventive method, respectively. The number of data channels adopted by simulation is 49, the sampling rate is 0.001s, and the sampling time is 0.9 s. The comparative results are shown in FIG. 3.
FIGS. 3a and 3b are plots of the first arrival pick-up calibration of a conventional aic and the method of the present invention applied to a simulated noise-free signal, and the plus sign in FIG. 3a is the pick-up result of the conventional aic method; in FIG. 3b, the plus sign is the result picked up by the method of the present invention; fig. 3c shows a comparison of the two, where only the pick-up results are retained for clarity of comparison, where the plus sign and the circle represent the pick-up positions of the present method and the conventional aic method, respectively, and it can be seen that the pick-up results of the two methods are identical in a noise-free signal.
Fig. 4a, 4b and 4c are first arrival picking contrast graphs of conventional aic and the method of the present invention applied to simulate low snr data constructed by adding 1 snr noise energy to fig. 3a and 3 b. The plus sign in FIG. 4a is the pickup result of the conventional aic method; in FIG. 4b, the plus sign is the result picked up by the method of the present invention; fig. 4c is a comparison graph of the calibration results of the two methods and fig. 3a and 3b, wherein for clarity of comparison, only the picking results are retained, the plus sign represents the calibration picking results, and the open circles and the filled circles represent the picking results of the method and the conventional aic method, respectively, and comparison shows that the method and the picking results in the case of no noise are basically coincident, while the conventional method has a picking error of about 5/49.
Secondly, the method designed by the invention is verified through actually acquired low signal-to-noise ratio data:
fig. 5a shows the pick-up result of the method of the present invention applied to the actual low snr signal, wherein the plus sign indicates the first arrival pick-up position.
In order to further verify the picking effect of the method of the present invention, noise attenuation processing is performed on the same actual data, and the picking result is plotted in the signal after noise attenuation, as shown in fig. 5b, fig. 5b is a calibration comparison diagram of the first arrival picking result of the method of the present invention applied to the actual low snr signal, and the robustness of the method of the present invention applied to the actual low snr signal first arrival picking can be reflected to a certain extent according to the comparison result of fig. 5 b.
Finally, it should be noted that the above numerical simulation and actual data collection calculation examples provide further verification for the purpose, technical solution and advantages of the present invention, which only belong to the specific embodiment examples of the present invention, and are not intended to limit the scope of the present invention, and any modification, improvement or equivalent replacement made within the spirit and principle of the present invention should be within the scope of the present invention.
Claims (6)
1. A time-frequency domain direct pickup first arrival method for seismic signals with low signal-to-noise ratio comprises the following steps:
step 1, converting original data into a time-frequency domain through time-frequency forward transformation;
step 2, carrying out noise suppression by using a self-adaptive noise attenuation method;
step 3, directly carrying out first arrival picking in a time-frequency domain by using a method based on statistics;
the noise attenuation in step 2 comprises the following substeps:
step 2.1, randomly selecting one channel from the time-frequency domain data converted in the step 1, and calculating a threshold value by using a formula 2:
λ ═ α · s formula 2;
in the formula, the threshold value lambda is represented as the product of alpha and s, s represents the ratio of the area of a probability density function curve to the total area in the range of three standard deviations around the average in the standard normal distribution, alpha is a calibration parameter, and the value of alpha can be determined by taking any one seismic signal;
and 2.2, traversing coefficient values of all the gathers in the time-frequency domain, if the coefficient values are larger than the threshold value lambda calculated in the step 2.1, reserving the gather, and otherwise, carrying out zero setting or certain proportion of attenuation processing on the gather.
2. The time-frequency domain direct pickup first arrival method for seismic signals with low signal-to-noise ratio as claimed in claim 1, wherein the time-frequency domain forward transform in step 1 includes but is not limited to wavelet transform, S transform time-frequency forward transform method, based on formula 1:
wherein d (t) represents a time domain signal, Xs(a, b) are the corresponding wavelet coefficients,. psi*Representing complex conjugates of a mother wavelet ψ (t) where the mother wavelet is chosen to be similar to the seismic wavelet signalRicker wavelets; b represents the time shift measure of the mother wavelet; a represents the scale transformation metric of the mother wavelet.
3. The time-frequency domain direct first arrival picking method for seismic signals with low signal to noise ratio as claimed in claim 1, wherein said step 3 of statistically based first arrival picking comprises the sub-steps of:
step 3.1, solving the QAIC vector of each trace set by an improved aic (akaike information criterion) method, as shown in formula 3:
QAIC(i)＝1/{log 10[var(tw1)]/log 10[var(tw2)]k } formula 3;
in the formula, tw1And tw2Respectively representing data of two adjacent time windows in a to-be-processed gather, var (phi) represents solving variance, and k is a constant;
and 3.2, calculating the maximum value in the QAIC vector corresponding to each gather, namely the first arrival time position corresponding to each gather.
4. A time-frequency domain direct pickup first-arrival system for low signal-to-noise ratio seismic signals, comprising the following modules:
the time-frequency forward transformation module is used for transforming the original data into a time-frequency domain through time-frequency forward transformation;
the noise attenuation module is used for carrying out noise suppression by utilizing a self-adaptive noise attenuation method;
the first arrival picking module is used for directly carrying out first arrival picking in a time-frequency domain by using a method based on statistics;
wherein the noise attenuation module comprises:
a threshold acquisition unit: randomly selecting one channel from time-frequency domain data converted by a time-frequency forward transform module, and calculating a threshold value by using a formula 1:
λ ═ α · s formula 2;
in the formula, the threshold value lambda is represented as the product of alpha and s, s represents the ratio of the area of a probability density function curve to the total area in the range of three standard deviations around the average in the standard normal distribution, alpha is a calibration parameter, and the value of alpha can be determined by taking any one seismic signal;
a data processing unit: and traversing coefficient values of all the trace sets in a time-frequency domain, if the coefficient values are larger than a threshold lambda calculated by the threshold acquisition unit, reserving the coefficient values, and otherwise, carrying out zero setting or certain proportion of attenuation processing on the coefficient values.
5. The time-frequency domain direct pickup first-arrival system for seismic signals with low signal-to-noise ratio as claimed in claim 4, wherein the time-frequency forward transform module performs time-frequency forward transform by using a method including but not limited to wavelet transform and S transform, based on equation 1:
wherein d (t) represents a time domain signal, Xs(a, b) are the corresponding wavelet coefficients,. psi*Representing the complex conjugate of a mother wavelet ψ (t), where the mother wavelet is a Ricker wavelet similar to the seismic wavelet signal; b represents the time shift measure of the mother wavelet; a represents the scale transformation metric of the mother wavelet.
6. The time-frequency domain direct pickup first arrival system for low signal-to-noise ratio seismic signals as claimed in claim 4, wherein said first arrival pickup module comprises:
QAIC vector acquisition unit: solving a QAIC vector of each trace set based on an improved AIC (akaike information criterion) method, as shown in formula 3:
QAIC(i)＝1/{log 10[var(tw1)]/log 10[var(tw2)]k }, formula 3;
in the formula, tw1And tw2Respectively representing data of two adjacent time windows in a to-be-processed gather, var (phi) represents solving variance, and k is a constant;
first arrival time position acquisition unit: the maximum value in the QAIC vector corresponding to each gather, i.e., the first arrival time position corresponding to each gather, is calculated.
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