CN112925020B - Parametric array type shallow profile intelligent energy compensation method - Google Patents
Parametric array type shallow profile intelligent energy compensation method Download PDFInfo
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- 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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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/362—Effecting static or dynamic corrections; Stacking
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Abstract
The invention provides a parametric array type shallow-profile intelligent energy compensation method, which relates to the field of ocean high-resolution shallow-profile seismic data processing, and realizes correction of high-energy abnormal amplitude trace amplitude by counting inter-trace energy amplitude and then performing normalization processing; firstly, determining the seabed time, the delay time of a system and the length of a measurement time window, determining a statistical amplitude time window according to the three parameters, then calculating the root mean square amplitude of each channel in the range of the statistical amplitude time window, and obtaining an amplitude energy correction factor of each channel according to the calculated root mean square amplitude; and finally, correcting the data by the obtained amplitude energy correction factor of each channel of data, namely completing the energy correction between channels. The method is simple and quick, has obvious effect, has obvious application effect in the seismic data processing of large-batch shallow profile data, has strong adaptability, and is the best processing method for solving the problems at present.
Description
Technical Field
The invention relates to the field of ocean high-resolution shallow-profile seismic data processing, in particular to a parametric array type shallow-profile intelligent energy compensation method.
Background
A shallow profiler (sub-bottom profiler) is an instrument that uses sound waves to detect the structure of a shallow profile. The shallow stratum profiler is an improved ultra-wideband submarine profiler, and is a device for profile display of the bottom strata of oceans, rivers and lakes, and can detect the geological structure condition below the water bottom by combining geological interpretation. The method is widely applied to marine geological survey, geophysical exploration and marine engineering, marine observation, submarine resource exploration and development, channel bay engineering and submarine pipeline laying.
The types of shallow profiles commonly used are many, and for a parametric array type shallow profile system, it is a system in which a source and a receiver are combined in a single transducer array, and typical devices are as ParaSound P70, Kongsberg TOPAS series, Innomar SES series, and the like. These types of shallow-profile systems tend to record seismic profiles with significant lateral energy differences in deep sea environments (fig. 1). It is characterized by that the transverse energy difference is obvious, and on the cross section it is made into the form of strip, its energy intensity is not constant, and its energy intensity is not regular. Such an energy difference exists in the entire frequency band with respect to the abnormal amplitude appearing in the band shape, and cannot be distinguished by the frequency characteristic. If such energy difference is not significant or the band-shaped energy is weak, there is a case where the processing is not required, but if the sea state is poor, the energy difference is too large as a whole in the profile, and if not, it is difficult to satisfy the requirement of the subsequent explanation.
If the difference of the strip energy is strong, the quantity is small and the distribution is concentrated, an abnormal amplitude denoising method can be adopted for removing the energy. However, according to the data statistics actually acquired, the abnormal amplitude of the strip has basically no obvious rule, that is, the abnormal energy is actually any combination, so that the method for denoising the abnormal amplitude cannot adapt to other conditions, for example, the abnormal energy of the interval strip or a large amount of transverse abnormal energy is difficult to remove, or even has no effective effect.
Disclosure of Invention
The invention provides a parametric array type shallow-dissection intelligent energy compensation method aiming at the problem of large difference of transverse energy of data acquired by a parametric array type shallow-dissection system.
The invention is realized by adopting the following technical scheme: a parametric array type shallow profile intelligent energy compensation method comprises the following steps:
step A, determining a starting point of a statistical amplitude time window according to the seabed time, the delay time of a shallow profile system and the length of a measurement time window;
setting the top time of a measurement time window to be T1, the seabed time to be T2, the bottom time of the measurement time window to be T3, and counting the size of an amplitude time window to be T4; when T1+ T4< T2, the starting point of the statistical amplitude time window is T1, otherwise, the starting point of the statistical amplitude time window is T3-T4;
b, calculating the root mean square amplitude of each acquired data in the range of the statistical amplitude time window, calculating the root mean square amplitude of each data in the range of the measurement time window, and determining the average amplitude energy of the measurement time window;
step C, obtaining an amplitude energy correction factor of each data channel according to the root-mean-square amplitude obtained by calculation in the step B;
and D, finishing the correction of the energy between the tracks according to the obtained amplitude energy correction factor.
Further, the step B specifically includes the following steps:
(1) the following method is adopted for calculating the root mean square amplitude of each channel in the statistical amplitude time window range:
wherein, X j Is the root mean square amplitude in the jth statistical amplitude time window; n is the number of sampling points in the statistical amplitude time window, L is the maximum number of tracks, A ij Is the amplitude energy of the ith sampling point of the jth track;
(2) the following method is adopted for calculating the root mean square amplitude of each channel in the measurement time window range:
wherein, Y j Is the root mean square amplitude in the jth measurement window; m is the number of sampling points in the measurement window, L is the maximum number of tracks, A ij Is the amplitude energy of the ith sampling point of the jth track;
(3) the average amplitude energy of the measurement time window is obtained by the following method:
wherein Z is the average amplitude energy of the measurement time window.
Further, in the step C, a correction factor is obtained according to the root-mean-square amplitude obtained in the step B and the average amplitude energy of the measurement time window:
wherein, F j Is the amplitude energy correction factor for the jth trace; z is the measured time window average amplitude energy, X j Is the root mean square amplitude of each channel within the statistical amplitude window, L is the maximum number of channels, and C is the scaling factor.
Further, in the step D, the inter-track energy correction is completed according to the obtained amplitude energy correction factor, that is:
B ij ＝A ij F j (i＝1,2,....M,j＝1,2,....L) (5)
wherein, B ij Is the amplitude energy after energy correction of the ith sampling point of the jth channel, A ij Is the amplitude energy of the ith sample point of the jth channel, F j Is the amplitude energy correction factor for the jth trace.
Further, in the step A, the statistical amplitude window size T4 is in
Selecting the raw materials.
Further, the proportionality coefficient C is selected from 0.1-1.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the scheme, the correction of the high-energy abnormal amplitude channel amplitude is realized by counting the inter-channel energy amplitude and then carrying out normalization processing; the method is simple, quick and obvious in effect, has obvious application effect in large-batch shallow profile data seismic data processing, has extremely strong adaptability, and is a better processing method for solving the problems.
Drawings
FIG. 1 is a schematic seismic section of the lateral energy difference recorded by Kongsberg P70;
FIG. 2 is a schematic diagram illustrating a statistical time window calculation principle according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an actual cross-section of a parametric array of the Western Pacific according to an embodiment of the present invention;
FIG. 4 is a graph of data corresponding to the actual cross-sectional view of FIG. 3;
FIG. 5 is a diagram illustrating the calculated RMS amplitude of the statistical time window and the measured RMS amplitude of the measurement time window according to an embodiment of the invention;
FIG. 6 is a schematic diagram of each trace amplitude correction factor calculated according to an embodiment of the present invention;
FIG. 7 is a schematic diagram showing the comparison between before and after energy calibration according to the embodiment of the present invention, wherein the left image is a cross section before calibration and the right image is a cross section after calibration;
FIG. 8 is a schematic diagram illustrating comparison between before and after processing of a parametric array shallow section in a certain work area in south China sea according to an embodiment of the present invention, wherein the left diagram is original data, and the right diagram is data after correction;
fig. 9 is a schematic flow chart of a compensation method according to an embodiment of the present invention.
Detailed Description
In order to make the above objects, features and advantages of the present invention more clearly understood, the present invention will be further described with reference to the accompanying drawings and examples. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and thus, the present invention is not limited to the specific embodiments disclosed below.
For a parametric array type shallow profile system, aiming at the problem of energy imbalance among channels caused by difference of amplitude energy, the embodiment provides an inter-channel energy correction method based on intelligent statistics, and a basic idea of the method is to correct the amplitude of a high-energy abnormal amplitude channel by a method of counting the energy amplitude among channels and then performing normalization processing, as shown in fig. 9, the method for intelligent energy compensation of the parametric array type shallow profile comprises the following steps:
step A, determining seabed time, delay time of a shallow profile system and measurement time window length to obtain a statistical amplitude time window starting point;
b, calculating the root mean square amplitude of each acquired data in the range of the statistical amplitude time window, calculating the root mean square amplitude of each data in the range of the measurement time window, and determining the average amplitude energy of the measurement time window;
step C, obtaining an amplitude energy correction factor of each data channel according to the root-mean-square amplitude calculated in the step B;
and D, finishing the correction of the energy between the tracks according to the obtained amplitude energy correction factor.
Specifically, the method comprises the following steps:
when the starting point of the statistical amplitude time window is determined in step a, the effective waves at the bottom of the sea and below the bottom of the sea need to be avoided due to the statistical amplitude time window, that is, the environmental noise should be taken as the standard in the statistical amplitude time window, as shown in fig. 2. Because the measurement time window is limited, when the seabed is close to the top of the measurement time window, if the amplitude statistics time window takes the top of the measurement time window as a starting point to determine the size of the amplitude statistics time window downwards, the amplitude statistics time window contains effective signals of the seabed and cannot meet the requirement, and at the moment, the amplitude statistics time window takes the bottom of the measurement time window as the starting point to determine the size of the amplitude statistics time window upwards; and when the seabed is close to the bottom of the measurement time window, if the statistical amplitude time window takes the bottom of the measurement time window as a starting point to upwards determine the size of the statistical amplitude time window, the statistical amplitude time window also contains effective signals of the seabed and cannot meet the requirement, and at the moment, the statistical amplitude time window takes the top of the measurement time window as the starting point to upwards determine the size of the statistical amplitude time window.
That is, in the scheme, the time of the top of a measurement time window is T1, the time of the bottom of a seabed is T2, the time of the bottom of the measurement time window is T3, the size of a statistical amplitude time window is T4, and when T1+ T4 is less than T2, the starting point of the statistical amplitude time window is T1; otherwise, the starting point of the statistical amplitude time window is T3-T4.
In the step B:
(1) the following method is adopted for calculating the root mean square amplitude of each channel in the statistical amplitude time window range:
wherein, X j Is the root mean square amplitude in the jth statistical amplitude time window; n is the number of sampling points in the statistical amplitude time window, L is the maximum number of tracks, A ij Is the amplitude energy of the ith sampling point of the jth track;
(2) the following method is adopted for calculating the root mean square amplitude of each channel in the measurement time window range:
wherein, Y j Is the root mean square amplitude in the jth measurement window; m is the number of sampling points in the measurement window, L is the maximum number of tracks, A ij Is the amplitude energy of the ith sampling point of the jth channel;
(3) the average amplitude energy of the measurement time window is obtained by the following method:
in the step C, a correction factor is obtained according to the root-mean-square amplitude obtained in the step B and the average amplitude energy of the measurement time window:
wherein, F j Is the amplitude energy correction factor for the jth trace; z is the measured time window average amplitude energy, X j Is the root mean square amplitude of each trace within the statistical amplitude window, L is the maximum number of traces, and C is a scaling factor, typically between 0.1 and 1.
Finally, according to the obtained amplitude energy correction factor, completing inter-track energy correction;
B ij ＝A ij F j (i＝1,2,....M,j＝1,2,....L) (5)
wherein, B ij Is the amplitude energy after energy correction of the ith sampling point of the jth channel, A ij Is the amplitude energy of the ith sample point of the jth channel, F j Is the amplitude energy correction factor for the jth trace.
In order to further verify the effectiveness of the method, the following describes the scheme in detail by taking the actual data of a certain parameter array of the western pacific as an example:
1. as shown in fig. 3, the top time of the measurement time window is T1-140 ms, the bottom time of the measurement time window is T2 range [200ms-350ms ], the bottom time of the measurement time window is T3-500 ms, the size of the statistical amplitude time window is T4-100 ms, and the starting point of the statistical time window is T3-T4-400 ms because T1+ T4> T2; in the scheme, the measurement time window top time T1 and the measurement time window bottom time T3 can be directly obtained according to collected data, similarly, the corresponding seabed time T2 can also be known, for the statistical amplitude time window size T4, T4 is generally selected from 1/2-1/3 of | T3-T1|, generally about 100ms, and is specifically adjusted according to actual conditions, which is not described in detail herein.
2. Fig. 3 is an image display of actual seismic data, where the original data, i.e., amplitude data, is shown in fig. 4, the number of sampling points N and M can be respectively determined according to a determined statistical amplitude time window and a measurement time window, and then the root mean square amplitude of each channel in the statistical amplitude time window and the root mean square amplitude of each channel in the measurement time window range are respectively calculated according to formula 2 and formula 3, which is specifically shown in fig. 5.
3. The average amplitude energy Z of the measurement time window is calculated according to formula 3, and the amplitude correction factor for each channel is calculated according to formula 4, referring to fig. 6. It can be seen that each trace amplitude correction factor has an inverse relationship with the seismic banding energy, i.e., where the banding energy is large, the correction factor is smaller, and where the banding energy is small, the correction factor is larger.
4. The inter-track energy amplitude correction is done according to equation 5:
the left graph of fig. 7 is a cross section before energy correction between partial data tracks of a certain measuring line, it can be seen that energy imbalance between tracks on the cross section is manifested as strong amplitude energy in a large area and weak energy in a partial track, and the energy difference has no specific rule to follow, and the right graph of fig. 7 is a cross section after energy correction between tracks, it can be seen that the amplitude energy difference is corrected, and energy between tracks tends to be consistent.
In addition, in fig. 8, the left graph is parametric array shallow section original data acquired by a certain working area in south sea by using Kongsberg TOPAS P70, wherein the strip abnormal energy is obvious, and after the energy correction by using the method, as shown in the right graph of fig. 8, the energy recovery is good, the actual geological condition is met, and the effectiveness of the scheme is further verified.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.
Claims (5)
1. A parametric array type shallow profile intelligent energy compensation method is characterized by comprising the following steps:
step A, determining a starting point of a statistical amplitude time window according to the seabed time, the delay time of a shallow profile system and the length of a measurement time window;
setting the top time of a measurement time window to be T1, the seabed time to be T2, the bottom time of the measurement time window to be T3, and counting the size of an amplitude time window to be T4; when T1+ T4< T2, the starting point of the statistical amplitude time window is T1, otherwise, the starting point of the statistical amplitude time window is T3-T4;
b, calculating the root mean square amplitude of each acquired data in the range of the statistical amplitude time window, calculating the root mean square amplitude of each data in the range of the measurement time window, and determining the average amplitude energy of the measurement time window;
(1) the following method is adopted for calculating the root mean square amplitude of each channel in the statistical amplitude time window range:
wherein, X j Is the root mean square amplitude in the jth statistical amplitude time window; n is the number of sampling points in the statistical amplitude time window, L is the maximum number of tracks, A ij Is the amplitude energy of the ith sampling point of the jth track;
(2) the following method is adopted for calculating the root mean square amplitude of each channel in the measurement time window range:
wherein the content of the first and second substances,Y j is the root mean square amplitude in the jth measurement window; m is the number of sampling points in the measurement window, L is the maximum number of tracks, A ij Is the amplitude energy of the ith sampling point of the jth track;
(3) the average amplitude energy of the measurement time window is obtained by the following method:
wherein Z is the average amplitude energy of the measurement time window;
step C, obtaining an amplitude energy correction factor of each channel of data according to the root mean square amplitude of each channel of data calculated in the statistical amplitude time window range and the average amplitude energy of the measurement time window obtained in the step B;
and D, finishing the correction of the energy between the tracks according to the obtained amplitude energy correction factor.
2. The parametric array type shallow profile intelligent energy compensation method as claimed in claim 1, wherein: in the step C, a correction factor is obtained according to the root mean square amplitude of each data calculated in the statistical amplitude time window range obtained in the step B and the average amplitude energy of the measurement time window:
wherein, F j Is the amplitude energy correction factor for the jth trace; z is the measured time window average amplitude energy, X j Is the root mean square amplitude of each channel within the statistical amplitude window, L is the maximum number of channels, and C is the scaling factor.
3. The parametric array type shallow profile intelligent energy compensation method as claimed in claim 1, wherein: in the step D, inter-track energy correction is completed according to the obtained amplitude energy correction factor, that is:
B ij ＝A ij F j (i＝1,2,...M,j＝1,2,...L) (5)
wherein, B ij Is the amplitude energy after energy correction of the ith sampling point of the jth channel, A ij Is the amplitude energy of the ith sample point of the jth channel, F j Is the amplitude energy correction factor for the jth trace.
4. The parametric array type shallow profile intelligent energy compensation method as claimed in claim 1, wherein: in step A, the amplitude window size T4 is counted
Selecting the raw materials.
5. The parametric array type shallow profile intelligent energy compensation method as claimed in claim 2, wherein: the proportionality coefficient C is selected between 0.1 and 1.
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| CN102707317A (en) * | 2010-10-27 | 2012-10-03 | 中国石油化工股份有限公司 | Method of using absorption and attenuation characteristics of seismic wave for reservoir analysis |
| CN110389378A (en) * | 2019-04-26 | 2019-10-29 | 中国石油化工股份有限公司 | Seismic data zero phase bearing calibration |
| CN111078953A (en) * | 2019-11-27 | 2020-04-28 | 滨州学院 | Method for directly reconstructing SEGY data coordinate based on navigation file |
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