CN112198553A - Man-machine interactive deep sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation - Google Patents
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Abstract
The invention relates to a man-machine interactive deep sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation. And performing three-dimensional velocity analysis by using the processed data to obtain the root mean square velocity distribution of the deep sea water body. Under the condition that the original information of the data is not damaged, the method realizes accurate speed analysis aiming at water layer reflection by extracting and compensating the weak useful information in the original data and the like, and provides a relatively accurate initial model for sea water sound velocity profile inversion, final offset imaging and full waveform.
Description
Technical Field
The invention belongs to the technical field of marine seismic exploration, and particularly relates to a deep sea water body velocity analysis method aiming at smooth gradient change, in particular to a man-machine interactive deep sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation.
Background
In recent years, seismic oceanography is used as an emerging interdiscipline of geophysics and physical oceanography, a conventional seismic reflection method is applied to ocean data processing, and the advantages of high resolution, short-time reflection imaging of a large-range water body and the like are obviously embodied. In the marine seismology method, the processing of the reflection seismic section is particularly important, which is the basis for researching the physical property difference of seawater by using the AVO technology. However, the conventional marine seismic method is used for processing the reflection of the seabed stratum, and the research and processing on the weak reflection of seawater are few.
Due to the influence of the smooth gradient change of the seawater speed and the multi-effect of the seawater, seismic waves form weak reflection in the water body, and the observation of the wave field propagation rule and the speed information carried by the analysis data are influenced, which is a difficult point of water body data processing. This effect is mainly manifested in two ways: 1. compared with the strong reflection of the seabed stratum, the characteristic difference of the water body reflection and the stratum reflection amplitude can reach 60dB, and the good effect is difficult to achieve when the conventional technology is applied to water body treatment; 2. the difference between background noise and weak reflection caused by bubble effect is small, and aliasing effect is generated to make the reflection in-phase axis of the marine seismic record difficult to identify. When processing seawater data, errors in velocity parameter inversion inevitably occur due to the above-mentioned effects. Such errors will have a fatal effect on the processing and interpretation of seismic data.
With respect to the seismic reflection profile of seawater, a great deal of research has been done by scholars at home and abroad. Brandt in the OCEAN 75Conference published "Acoustic time from dense structures in structural surveys" which first clearly suggested the possibility of Acoustic imaging to study the motions and fine structures of marine bodies of water. Ocean variance & acoustics Propagation discloses the "Multichannel adaptive reflection profiling of Ocean water temperature/salinity interfaces" of phillips.j.d and dean.d.f, which states that shoal seismic data reflection may reflect the possibility of seawater temperature and salinity structure, i.e. the structure of seawater velocity. Geophanic Research Letters 2005, 15, discloses "Ocean wave induced from semiconductor reflection transforms" of Holbrook. W.S, which shows that when seismic profile data of a certain place in Finland are reprocessed, a sea water layer fine structure is found, and sea water motion can be studied by using reflection seismic. The 'research on processing method for marine water layer reflection' of lutongxiang et al was published in volume 33, 3 of 2018, the geophysical progress, which proposes a water layer weak signal protection technology to achieve a fidelity effect by effectively separating water layer reflection signals.
However, no method for performing accurate velocity analysis on seawater by using seawater man-machine interactive amplitude compensation aiming at seawater weak reflection has been reported so far.
Disclosure of Invention
The invention aims to provide a man-machine interactive deep-sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation, aiming at overcoming the defects of the prior art, and capable of performing effective amplitude compensation on deep-water reflection and performing effective velocity analysis on processed data.
The purpose of the invention is realized by the following technical scheme:
a man-machine interactive deep sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation comprises the following steps:
a. reading in shallow interference information such as direct waves and the like, and deep water multi-seismic-source common-center-point reflection seismic data and parameters of an observation system after stratum reflection is removed;
b. after data and parameters are read in, carrying out weighted average calculation on absolute values of each deep water seismic record, taking the absolute values as a reference, extending a window upwards, and extracting signals within the window range;
c. carrying out man-machine interaction in-phase axis pickup on the integrally filtered deepwater seismic record, eliminating in-phase axis weakening influence caused by smooth gradient change of seawater speed, selecting a proper amplitude value according to pickup coordinates and establishing a Fourier series fitting formula, and carrying out weak amplitude compensation on the deepwater reflection seismic record, wherein the Fourier series fitting form is as follows:
f(x)＝a0+a1·cos(x·w)+b1·sin(x·w)
wherein a0, a1, b1 and w are fitting coefficients;
d. and c, performing superposition velocity spectrum calculation on the compensation data obtained after the step c is performed, and obtaining a two-phase screen with the optimal values of vertical two-pass travel time and superposition velocity, wherein a travel time function is defined as:
if the travel time curve is no longer close to a hyperbola, the quartic term of the travel time polynomial should be considered, with similarity defined by:
in the formula, n is the number of nonzero sampling points after direct wave excision;
e. d, carrying out velocity cluster picking on the superposed velocity spectrum obtained after the step d is carried out, and carrying out amplitude enhancement treatment in the step c to obtain a better root mean square velocity distribution characteristic of the seawater;
f. and (4) repeatedly processing the steps a-e to obtain a three-dimensional deepwater speed analysis result for overcoming the weak reflection of the seawater.
Further, in step a, the parameters further include NMO stretch return to zero values, similar length smoothing window values, and similar value weights.
Further, in step d, before calculation, the numerator and the denominator are respectively smoothed, and then the similarity relation is set as the power of the parameter similarity value weight. When the similarity value weight is >1, the difference of the similarity values is stretched in the upper half part of the similarity range [0,1] and is compressed in the lower half part, and a few larger appearance values are enhanced; many small values are enhanced when the similarity value weight < 1.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a basic idea of man-machine interactive fitting compensation amplitude for finely analyzing the deep water reflection speed. On the basis of applying weighted integer filtering, three-dimensional velocity analysis of controllable correlation coefficients is used, and the algorithm flow is detailed. Secondly, the man-machine interaction amplitude compensation method provided by the invention can effectively strengthen the reflection in-phase axis for any fitting or real deepwater data, which is an important precondition for speed inversion. On the basis of the effective amplitude compensation method, the invention analyzes the superposition velocity of the deep water layer, effectively analyzes the deep water root mean square velocity change, and provides relatively accurate initial conditions for the inversion of the sea water sound velocity profile, the final offset imaging and the full waveform.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, compensation function values are extracted from multi-shot common-center-point reflection seismic data in which direct waves and shallow layer interference are removed, energy compensation is performed on seawater reflection in reflection seismic records by using the compensation function values, and background noise interference such as bubble scattering and the like is eliminated through filtering calculation;
2. carrying out three-dimensional velocity analysis by using the processed data to obtain the root mean square velocity distribution of the deep sea water body;
3. under the condition that the original information of the data is not damaged, the method realizes accurate speed analysis aiming at water layer reflection by extracting and compensating the weak useful information in the original data and the like, and provides a relatively accurate initial model for sea water sound velocity profile inversion, final offset imaging and full waveform.
Drawings
FIG. 1 is a flow chart of a seawater weak reflection interaction amplitude compensation accurate three-dimensional velocity analysis;
FIG. 2 a-FIG. 2b raw deep sea water body fraction data for use in the present invention;
FIG. 3 is a graph of the weighted average shaped filter applied to the raw data, FIG. 3a record after weighted average filtering; FIG. 3b partially records track values; FIG. 3c is a phase axis discontinuity event;
FIG. 4 is a schematic diagram of interactive in-phase axis coordinate picking, and FIG. 4a is a schematic diagram of discontinuous in-phase axis coordinates picking; FIG. 4b sets up a complete in-phase axis function; FIG. 4c shows the values of the compensated amplitude functions;
FIG. 5 overlays the fitting function values over the raw data, FIG. 5a interactively compensating the seismic recording; FIG. 5b amplitude enhancement; FIG. 5c contrasts with background differences;
FIG. 6a shows the results of conventional superimposed velocity spectra of unprocessed data compared to velocity analysis of data processed by the deep sea water weak reflection interaction amplitude compensation technique; FIG. 6b human-computer interactive amplitude compensation superimposed velocity spectrum results;
Detailed Description
The following further explanation is made in conjunction with the drawings and the embodiments.
The invention discloses a man-machine interactive deep sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation, which comprises the following steps:
a. reading in shallow layer interference information such as direct waves and the like, deep water multi-seismic source common center point seismic data reflected by a stratum and parameters of an observation system; and parameters such as an NMO stretching return-to-zero value, a similar length smooth window value, a similar value weight and the like are given. These data and parameters constitute the starting conditions;
b. b, according to the known conditions read in the step a, carrying out weighted average calculation on the absolute value of the wave field amplitude value in each channel of deep water seismic data, taking the absolute value as a reference, extending a window upwards, and extracting signals in the window range to obtain a graph 3 a;
c. man-machine interaction event pickup is carried out on the deepwater seismic record after integral filtering (figure 4a), and the event weakening influence caused by smooth gradient change of seawater speed is eliminated. And selecting a proper amplitude value according to the picked coordinates, establishing a Fourier series fitting function (figure 4b), and performing weak amplitude compensation on the deep water reflection seismic record (figure 5 a). The Fourier series fitting form is:
f(x)＝a0+a1·cos(x·w)+b1·sin(x·w)
wherein a0, a1, b1 and w are fitting coefficients;
d. and c, calculating the superposition root mean square velocity spectrum of the compensation data after the step c (figure 6b) to obtain a two-phase screen with the optimal values of the vertical two-stroke travel time and the superposition velocity. The travel time function is defined as:
the coefficients ais1, ais2 are small values, indicating that the non-hyperbolic property is small. It is more accurate to use a quartic polynomial expansion, when the function is no longer in polynomial form. Similarity is defined by the following formula:
n is the number of non-zero sampling points after the direct resection wave. The numerator and denominator were smoothed separately before calculation. The similarity relationship is then set to the power of the parameter similarity value weights. When the similarity value weight >1, the difference in similarity value is stretched in the upper half of the similarity range [0,1] and contracted in the lower half thereof, and therefore, a small number of larger appearance values are enhanced. When the similarity value weight is <1, many small values are enhanced and therefore more easily discernable under background noise. This is achieved within a wide range of other features.
e. And d, carrying out speed pickup on the superposed root mean square speed spectrum obtained in the step d. And (c) performing amplitude enhancement treatment in the step c to obtain a better seawater speed distribution characteristic.
f. And (4) according to requirements, carrying out repeated treatment on the steps a-e to obtain a three-dimensional seawater compensation amplitude accurate speed analysis result.
In order to better explain the implementation effect of the above specific algorithm process, a specific set of examples is given below:
examples
a. Reading in the multi-shot-domain common-center-point deep water reflection seismic data and observation system parameters which are subjected to the processing processes of direct wave cutting, shallow interference information suppression, formation reflection removal and the like. In addition, parameters such as an NMO stretching return-to-zero value, a similar length smoothing window value, a similar value weight and the like need to be given. In a specific embodiment: the data of the common center point has 125 tracks, the distance between the guns is 10m, and the distance between the receiving tracks is 10 m; sampling 1000 time sampling points in each channel at sampling intervals of 5 ms; the value of NMO stretching return to zero is 1.5, the value of a similar length smooth window is 3, and the weight of the similar value is 1;
b. and b, according to the known conditions read in the step a, carrying out weighted average calculation on the absolute value of the wave field amplitude value in each channel of deep water seismic data, taking the weighted average calculation as a reference, extending a window upwards, and extracting a signal in the window range (fig. 3 a). A, b in fig. 2 are raw seawater data. If the raw data is subjected to velocity analysis, a superimposed velocity spectrum shown in fig. 6a is obtained;
c. and (4) carrying out human-computer interaction in-phase axis function pickup on the integral filtered deepwater seismic record to obtain a coordinate set shown in figure 4 a. Eliminating the influence of weakening of the same phase axis. And establishing a Fourier series fitting formula according to the picked coordinates, wherein the Fourier series fitting formula is as follows:
f(x)＝a0+a1·cos(x·w)+b1·sin(x·w)
where a0, a1, b1, w are suitable fitting coefficients. Fitted (fig. 4 b):
Zs1＝337.2-175.8*cos(x1*0.01411)+45.11*sin(x1*0.01411)
Zs2＝430.3-134.1*cos(x1*0.01508)-14.34*sin(x1*0.01508)
Zs3＝505.5-106.1*cos(x1*0.0169)-24.72*sin(x1*0.0169)
Zs4＝568.4-73.7*cos(x1*0.01856)-12.14*sin(x1*0.01856)
FIG. 3b shows the original deep sea water data with a small difference between the background noise and the effective signal. FIG. 3c shows the appearance of the event of the discontinuity in the raw data. Fig. 5b shows the signal after amplitude compensation according to the present invention, with a significant enhancement of the in-phase axis. The effective signal in fig. 5c is effectively distinguished from the background signal.
d. And c, calculating the superposition root mean square velocity spectrum of the compensation data (shown in figure 5a) processed in the step c to obtain a two-phase screen with the optimal values of the vertical two-way travel time and the superposition velocity. The travel time function is defined as:
if the travel time curve is no longer close to a hyperbola, the quartic term of the travel time polynomial should be considered. In the simple form (i.e., anis2 ═ 0), optimization of all parameters requires a three-phase screen. Similarity is defined by the following formula:
n is the number of non-zero sampling points after the direct resection wave. The numerator and denominator were smoothed separately before calculation. The similarity relationship is then set to the power of the parameter similarity value weights. When the similarity value weight >1, the difference in similarity value is stretched in the upper half of the similarity range [0,1] and contracted in the lower half thereof, and therefore, a small number of larger appearance values are enhanced. When the similarity value weight is <1, many small values are enhanced and therefore more easily discernable under background noise. This is achieved within a wide range of other features.
e. And d, carrying out speed pickup on the superposed root mean square speed spectrum obtained in the step d. And (c) performing amplitude enhancement treatment in the step c to obtain a better seawater speed distribution characteristic.
The velocity spectrum energy blob is weak in the raw data velocity analysis in fig. 6 a. The sharp velocity cluster and root mean square velocity distribution trends were obtained from analysis of the weak reflection enhanced recordings using human-machine interaction compensated amplitude techniques in fig. 6 b.
f. And (4) repeatedly processing the steps a-e to obtain a three-dimensional deep water accurate speed analysis result.
3a, 3b, 3c are normal deep sea water seismic data. Fig. 5a, 5b and 5c show interactive value-added amplitude-preserved deep-sea water body data, and the interference of weak reflection and background noise effects such as bubble effect and the like is effectively improved. Fig. 6a and 6b are comparison of the superposition velocity spectrum of the original deep sea water body data and the processed sea water data, the distribution trend of the superposition velocity is obviously reflected after the processing of the technology, and a solid foundation is laid for inversion and imaging of a sea water section.
The invention fully utilizes the interactive thought, and fully utilizes the interactive thought in order to realize the amplification and value preservation and accurate speed analysis of the water layer. And performing weighted average integer filtering on the deepwater seismic signals of the common center points of all the channels to preliminarily restore the water layer reflection characteristics. And then, the water layer reflection in-phase axis coordinate information is picked up by human-computer interaction, the Fourier series fitting is used for expecting in-phase axis curve functions to perform coverage compensation, the influence of in-phase axis blurring and discontinuity caused by background effects such as bubble scattering and the like is eliminated, the reflection energy information is enhanced, and the speed analysis is laid. In addition, the three-dimensional velocity analysis can control the change of the correlation coefficient to respectively highlight a small number of large appearance values or small values under background noise, and further enhances the accuracy of the three-dimensional velocity analysis. Through an effective treatment process, accurate root mean square speed of the seawater fine structure can be obtained.
Claims (3)
1. A man-machine interactive deep sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation is characterized by comprising the following steps:
a. reading in shallow interference information such as direct waves and the like, and deep water multi-seismic-source common-center-point reflection seismic data and parameters of an observation system after stratum reflection is removed;
b. after data and parameters are read in, carrying out weighted average calculation on absolute values of each deep water seismic record, taking the absolute values as a reference, extending a window upwards, and extracting signals within the window range;
c. carrying out man-machine interaction in-phase axis pickup on the integrally filtered deepwater seismic record, eliminating in-phase axis weakening influence caused by smooth gradient change of seawater speed, selecting a proper amplitude value according to pickup coordinates and establishing a Fourier series fitting formula, and carrying out weak amplitude compensation on the deepwater reflection seismic record, wherein the Fourier series fitting form is as follows:
f(x)＝a0+a1·cos(x·w)+b1·sin(x·w)
in the formula: a0, a1, b1 and w are fitting coefficients;
d. and c, performing superposition velocity spectrum calculation on the compensation data obtained after the step c is performed, and obtaining a two-phase screen with the optimal values of vertical two-pass travel time and superposition velocity, wherein a travel time function is defined as:
if the travel time curve is no longer close to a hyperbola, the similarity is defined by:
in the formula, n is the number of nonzero sampling points after direct wave excision;
e. d, carrying out velocity cluster picking on the superposed velocity spectrum obtained after the step d is carried out;
f. and (4) repeatedly processing the steps a-e to obtain a three-dimensional deepwater speed analysis result for overcoming the weak reflection of the seawater.
2. The human-computer interactive deep-sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation, as claimed in claim 1, wherein: step a, the parameters also comprise an NMO stretching return-to-zero value, a similar length smooth window value and a similar value weight.
3. The human-computer interactive deep-sea water body three-dimensional velocity analysis method integrating reflection amplitude compensation, as claimed in claim 1, wherein: and d, smoothing the numerator and the denominator before calculation, and setting the similarity relation as the power of the parameter similarity value weight.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001080945A1 (en) * | 2000-04-21 | 2001-11-01 | Doctors Tech Co., Ltd. | Method for transdermal administration of ascorbic acid |
| CN101276000A (en) * | 2008-05-26 | 2008-10-01 | 张文坡 | Method for performing waveform stretching compensation for seismic signal |
| CN104977615A (en) * | 2015-07-01 | 2015-10-14 | 中国海洋石油总公司 | Model-statistics-pickup-based multiple suppression method of deep-sea OBC data |
| JP2019045789A (en) * | 2017-09-06 | 2019-03-22 | 旭化成株式会社 | Structure, and sound insulation material, vibration damping material, material and various members including the structure |
| CN111323821A (en) * | 2020-04-16 | 2020-06-23 | 吉林大学 | Shaping filtering method for sea surface fluctuation effect correction in marine seismic data |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001080945A1 (en) * | 2000-04-21 | 2001-11-01 | Doctors Tech Co., Ltd. | Method for transdermal administration of ascorbic acid |
| CN101276000A (en) * | 2008-05-26 | 2008-10-01 | 张文坡 | Method for performing waveform stretching compensation for seismic signal |
| CN104977615A (en) * | 2015-07-01 | 2015-10-14 | 中国海洋石油总公司 | Model-statistics-pickup-based multiple suppression method of deep-sea OBC data |
| JP2019045789A (en) * | 2017-09-06 | 2019-03-22 | 旭化成株式会社 | Structure, and sound insulation material, vibration damping material, material and various members including the structure |
| CN111323821A (en) * | 2020-04-16 | 2020-06-23 | 吉林大学 | Shaping filtering method for sea surface fluctuation effect correction in marine seismic data |
Non-Patent Citations (1)
| Title |
|---|
| 王宇翔等: "基于梯度平方结构张量算法的高密度二维立体层析反演", 《地球物理学报》 * |
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