CN117148432A - Shallow profile data space interpolation method based on multi-scale component extraction - Google Patents
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Abstract
The invention relates to a shallow profile data spatial interpolation method based on multi-scale component extraction, which belongs to the field of marine geophysical data processing and analysis, and comprises curve pickup during travel with high signal to noise ratio and same phase axis, and shallow profile data is extracted by using curvelet transformations(x,t) Is used for identifying data channels to be interpolated or enhanced in amplitude for shallow profile datas(x,t) Is defined by the respective scale component of (a)s n (x,t) And (3) carrying out seismic channel interpolation and amplitude enhancement processing based on a convex set projection algorithm, and synthesizing each scale data after interpolation or amplitude enhancement into a section. The method can strengthen the weak amplitude data channel in the shallow-profile data, and construct a missing empty channel by utilizing the adjacent data channel, so that the processed shallow-profile data can accurately reflect the actual underground structure and horizon information, and can provide high precision for the subsequent multiple pressing processIs a data of (a) a data of (b).
Description
Technical Field
The invention relates to the field of marine geophysical data processing and analysis, in particular to a shallow profile data spatial interpolation method based on multi-scale component extraction.
Background
The shallow profile survey method is a geophysical method for continuously sailing to detect underwater shallow stratum structures and structures based on an acoustic principle, is one of common means for performing marine geophysical surveys, and continuously detects the submarine sediment structures and structures by utilizing the propagation and reflection characteristics and rules of sound waves in seawater and submarine sediment so as to obtain visual submarine shallow stratum structure profiles. The shallow stratum profile method can rapidly detect the geological features and the distribution of the underwater stratum and has higher longitudinal resolution, so that the method is widely applied to marine investigation.
In the process of collecting shallow profile data in the field, the observation ship is influenced by waves to fluctuate, so that the towed observation equipment is suddenly changed in position and posture, and the amplitude of the collected partial data is weaker and even a channel exists. Compared with the conventional seismic data, the longitudinal resolution of the shallow-profile data is high, and the signal-to-noise ratio of the data in different frequency bands is greatly different, so that the conventional seismic data interpolation method is invalid. When more weak amplitude seismic traces or empty trace data exist in shallow profile data, the effect of multiple matching and subtraction of the free interface multiple attenuation (SRME) method through the inter-channel phase axis similarity is seriously affected, and meanwhile interpretation and analysis of underground horizons and structures by data interpretation personnel are misled.
Disclosure of Invention
The invention aims to provide a shallow-profile data spatial interpolation method based on multi-scale component extraction, which aims to enhance a weak amplitude data channel in shallow-profile data and construct a missing empty channel by utilizing an adjacent data channel, so that the processed shallow-profile data can accurately reflect actual underground structure and horizon information and can provide high-precision data for a subsequent multiple pressing process.
The invention is realized by the following technical scheme:
a shallow profile data spatial interpolation method based on multi-scale component extraction comprises (1) high signal-to-noiseThe curve pickup is carried out during travel compared with the same phase axis, (2) each scale component of shallow-profile data s (x, t) is extracted by utilizing curvelet transformation, (3) the identification of a data channel to be interpolated or enhanced in amplitude, (4) each scale component s of shallow-profile data s (x, t) n (x, t) carrying out seismic trace interpolation and amplitude enhancement processing based on a convex set projection algorithm, and (5) synthesizing each scale data after interpolation or amplitude enhancement into a section.
Further, the curve pickup during travel of the high signal-to-noise ratio phase axis obtains the strong reflection phase axis with the highest signal-to-noise ratio in the section by a manual pickup mode, and the strong reflection phase axis can be expressed as a coordinate x i Varied travel time curve t max (x) I.e.
（1）
Wherein s (x, t) represents shallow profile data, and x represents the space coordinates of each channel of data; t represents travel time; t is t max (x) Representing travel time of the same phase shaft with highest signal-to-noise ratio in the data; max_snr represents the process of obtaining the highest signal-to-noise ratio event by means of manual pick-up.
Further, the method utilizes the curvelet transformation to extract each scale component of the shallow-profile data s (x, t), and the curvelet transformation is realized by the shallow-profile data s (x, t) and a curvelet function phi j,l,m The inner product of (x, t) is implemented, so the curvelet transform can be expressed as
（2）
Wherein,a curvelet basis function representing a scale j, a direction l and a position m, c (j, l, m) represents a curvelet coefficient obtained by curvelet transformation, and s (x, t) represents shallow-profile data;
the curvelet transform is a reversible mathematical transform, by which shallow-profile data s (x, t) can be reconstructed using curvelet coefficients c (j, l, k), i.e.
（3）
The shallow data s (x, t) is decomposed in multiple scales, and the inverse transformation component with the scale n (n is more than or equal to 1 and less than or equal to J) is as follows
（4）
Wherein s is n (x, t) represents shallow profile data for the nth scale.
The identification of the data tracks to be interpolated or amplitude enhanced for each scale component s n Any one data in (x, t) is respectively represented by t max (x) The delta t length is extended upwards and downwards, so that high signal-to-noise ratio local records including strong reflection phase shafts are intercepted; then, for each track of data recorded locally with high signal-to-noise ratio, the maximum absolute value A is found by comparing the absolute values of the sample points max The final composition can characterize the component profile s n Extremum list A of signal-to-noise ratios of data of each track in (x, t) max (x)；
After the reference channel number N and the judgment threshold value a are given, firstly, an extremum list A of N channels is shared at two sides of the target channel max (x) If the local extremum is found, the criterion for judging the local extremum as the channel to be interpolated or the amplitude enhancement channel is
（5）
The value range of the judging threshold value a is 0< a <1, and the more the value is, the more seismic traces are selected.
Further, each scale component s for shallow profile data s (x, t) n (x, t) performing seismic trace interpolation and amplitude enhancement processing based on convex set projection algorithm, and setting total iteration times Q and threshold parameter lambda of each iteration q (Q is more than or equal to 1 and less than or equal to Q), the requirement is that
（6）
Wherein Max is a component profile s n Maximum absolute value of (x, t);epsilon is a small value close to zero; threshold parameter lambda q The value of (2) is linearly decreasing;
in the q-th iteration, the processing result of the last iteration is converted by utilizing two-dimensional Fourier transformConversion to FK domain, i.e
（7）
Where q represents the number of iterations, when q=1, letF represents frequency, k represents wave number, x represents space coordinates of each channel of data; t represents travel time, e represents natural logarithm, i is imaginary unit;
threshold filtering is performed on single-scale records of FK domain, i.e. the absolute value of amplitude is smaller than lambda q Is set to a zero value, and the process can be expressed as
（8）
Wherein,representing the processing result after threshold filtering;
inverse fourier transform using two dimensionsTransformed back to time-space domain, with
（9）
In the method, in the process of the invention,e represents natural logarithm, i is imaginary unit;
holdingThe data of the channel to be interpolated or the amplitude enhancement channel is unchanged, and the effective channel in the data is utilized to input the profile s n The data tracks in (x, t) are replaced to obtain the interpolation or amplitude enhancement result of the q-th iteration, and then the threshold lambda of the next iteration is introduced q+1 See equation (6), input profileAnd (3) sequentially carrying out seismic interpolation or amplitude enhancement and effective channel replacement processing of the formulas (7) - (9) until a final optimization result is obtained after Q times of iteration.
Further, the interpolation or amplitude enhancement of each scale data is synthesized into a section, thereby obtaining the final interpolation or amplitude enhancement result, namely
（10）
In the method, in the process of the invention,the data is shallow profile data after multi-scale component interpolation or amplitude enhancement, and J is the number of scales of curvelet transformation. Profile compared to the original profile s (x, t)The signal-to-noise ratio of the circuit is obviously improved, and the continuity of the same phase axis is obviously improved.
Compared with the prior art, the invention has the beneficial effects that:
the method can strengthen the weak amplitude data channel in the shallow-profile data, and construct a missing empty channel by utilizing the adjacent data channel, so that the processed shallow-profile data can accurately reflect the actual underground structure and horizon information, and can provide high-precision data for the subsequent multiple pressing process.
Drawings
FIG. 1 is H sea Z 2 Measuring original shallow profile data;
FIG. 2 is a graph of the picked-up high signal-to-noise ratio event travel;
FIG. 3 is a shallow cut data component with a scale number of 1;
FIG. 4 is a graph of interpolation of shallow profile data components with scale number 1;
FIG. 5 is a shallow cut data component with a scale number of 2;
FIG. 6 is a graph of interpolation of shallow profile data components with scale number 2;
FIG. 7 is a shallow cut data component with a scale number of 3;
FIG. 8 is H sea Z 2 The shallow profile of the line after the multi-component interpolation.
Detailed Description
The technical scheme of the present invention is further explained below by means of examples in combination with the accompanying drawings, but the scope of the present invention is not limited in any way by the examples.
The embodiment is a shallow profile data spatial interpolation method based on multi-scale component extraction, and the specific implementation process mainly comprises the following four steps: 1) The curve is picked up when traveling of the same phase axis with high signal-to-noise ratio; 2) Extracting each scale component of shallow profile data by using curvelet transformation; 3) Identification of the data tracks to be interpolated or amplitude enhanced; 4) Seismic channel interpolation and amplitude enhancement processing based on convex set projection algorithm; 5) And (3) synthesizing the data of each scale after interpolation (or amplitude enhancement).
Example 1
The H sea area is a hard seabed area, the sea bottom is severely fluctuated, and Z 2 The measuring line is that the gun interval is 1m, the number of receiving channels is 1, and the channel interval is 1m; the recorded shallow stratum section record with the sampling interval of 0.1ms is characterized in that in the process of collecting shallow stratum section data in the open air, the observation ship is influenced by waves to be fluctuated up and down, so that obvious weak amplitude data and air channel data exist in the collected data.
The following describes in detail the implementation of the invention with reference to the accompanying drawings:
(1) High signal to noise ratio and on-trip curve pickup of the event. The shallow profile data s (x, t) shown in fig. 1 is characterized by continuous multiple channels of weak amplitude and null data whose amplitudes are characterized by strong weakening and then weak strengthening, under the influence of the spatial positions of the excitation and receiving devices. To determine the value to be interpolated (or amplitudeEnhanced) data tracks, which require the acquisition of the strongly reflected phase axis (typically the sea bottom phase) with the highest signal-to-noise ratio in the profile by means of manual pick-up, can be expressed as a piece of co-ordinate x i Varied travel time curve t max (x) I.e.
（1）
Wherein s (x, t) represents shallow profile data, and x represents the space coordinates of each channel of data; t represents travel time; t is t max (x) Representing travel time of the same phase shaft with highest signal-to-noise ratio in the data; max_snr represents the process of obtaining the highest signal-to-noise ratio event by means of manual pick-up.
For the original shallow profile data shown in fig. 1, the seabed reflection event is picked up, and the signal to noise ratio is highest, so that the method is used for identifying the weak amplitude channel and the empty channel data.
(2) And extracting each scale component of the shallow-profile data s (x, t) by using curvelet transformation. The curvelet transformation is performed by shallow profile data s (x, t) and curvelet function phi j,l,m The inner product of (x, t) is implemented, so the curvelet transform can be expressed as
（2）
Wherein,a curvelet basis function representing a scale j, a direction l and a position m, c (j, l, m) represents a curvelet coefficient obtained by curvelet transformation, and s (x, t) represents shallow-profile data;
the curvelet transform is a reversible mathematical transform, by which shallow-profile data s (x, t) can be reconstructed using curvelet coefficients c (j, l, k), i.e.
（3）
As can be seen from formulas (2) and (3), the curvelet transform actually decomposes the signal into curvelet coefficients of different scales and directions, and the inverse transform is implemented by weighted summation of the curvelet coefficients.
If only one scale of curvelet coefficient is used for inverse transformation, the shallow profile data s (x, t) can be conveniently subjected to multi-scale decomposition, and then the inverse transformation component with the scale of n (n is more than or equal to 1 and less than or equal to J) is
（4）
Wherein s is n (x, t) represents shallow profile data for the nth scale.
The original shallow-profile data shown in fig. 1 are input, the scale components are extracted by means of curvelet transformation, and the obtained results are shown in fig. 3, fig. 5 and fig. 7 respectively. According to analysis, as the scale sequence number increases, the resolution of the profile component is gradually improved, and the weak amplitude channel and the empty channel of different scale components are obviously different.
(3) Identification of the data tracks to be interpolated or amplitude enhanced. In order for the weak amplitude track data to be enhanced and for the empty data tracks to be effectively reconstructed, it is necessary to accurately identify the data tracks to be interpolated or amplitude enhanced. For each scale component s n Any one data in (x, t) is respectively represented by t max (x) The delta t length is extended upward and downward, respectively, so that a high signal-to-noise ratio local record including the strong reflection phase axis is intercepted. Then, for each track of data recorded locally with high signal-to-noise ratio, the maximum absolute value A is found by comparing the absolute values of the sample points max The final composition can characterize the component profile s n Extremum list A of signal-to-noise ratios of data of each track in (x, t) max (x)。
To determine whether the current track is a valid track, reference is made to extremum A of the adjacent tracks max . After the reference channel number N and the judgment threshold value a are given, firstly, an extremum list A of N channels is shared at two sides of the target channel max (x) If the local extremum is found, the criterion for determining it as the track to be interpolated (or amplitude enhancement track) is
（5）
The value range of the judging threshold value a is 0< a <1, and the more the value is, the more seismic traces are selected.
The decision threshold values of the scale components are respectively 0.25, 0.35 and 0.45, and the channel (or the amplitude enhancement channel) to be interpolated in the cross sections shown in fig. 3, 5 and 7 is decided according to the formula (5). By processing, a plurality of tracks (or amplitude enhancement tracks) to be interpolated are identified in the first two sections, whereas no tracks to be interpolated or amplitude enhancement tracks are present in the section shown in fig. 7.
(4) For each scale component s of shallow data s (x, t) n And (x, t) carrying out seismic trace interpolation and amplitude enhancement processing based on a convex set projection algorithm. Setting total iteration times Q and threshold parameter lambda of each iteration q (Q is more than or equal to 1 and less than or equal to Q), the requirement is that
（6）
Wherein Max is a component profile s n Maximum absolute value of (x, t); epsilon is a small value close to zero, typically 0.001; threshold parameter lambda q The value of (2) is linearly decreasing.
In the q-th iteration, the processing result of the last iteration is converted by utilizing two-dimensional Fourier transformConversion to FK domain, i.e
（7）
Where q represents the number of iterations, when q=1, letF represents frequency, k represents wave number, x represents space coordinates of each channel of data; t represents travel time, e represents natural logarithm, and i is imaginary unit.
Threshold filtering is performed on single-scale records of FK domain, i.e. the absolute value of amplitude is smaller than lambda q Is set to a zero value, and the process can be expressed as
（8）
Wherein,representing the processing result after threshold filtering.
Inverse fourier transform using two dimensionsTransformed back to time-space domain, with
（9）
In the method, in the process of the invention,the processing result of the q-th interpolation is that e represents a natural logarithm and i is an imaginary unit.
HoldingThe data of the channel to be interpolated (or the amplitude enhanced) is unchanged, and the effective channel is utilized to input the profile s n The data tracks in (x, t) are replaced, resulting in an interpolation (or amplitude enhancement) result for the q-th iteration. Subsequently, a threshold lambda of the next iteration is introduced q+1 (see equation (6)), input profileAnd (3) sequentially carrying out seismic interpolation (or amplitude enhancement) and effective channel replacement processing in the formulas (7) - (9) until a final optimization result is obtained after Q times of iteration.
The sections shown in fig. 3 and 5 are interpolated to obtain the results shown in fig. 4 and 6, in which the weak amplitude data tracks in the original components are significantly enhanced and the missing empty tracks are constructed.
(5) Synthesizing the interpolated (or amplitude-enhanced) scale data into a section, thereby obtaining the final interpolation (or amplitude-enhanced) result, namely
（10）
In the method, in the process of the invention,the data is represented by shallow profile data after multi-scale component interpolation (or amplitude enhancement), and J is represented by the number of scales of curvelet transformation. Profile compared to the original profile s (x, t)The signal-to-noise ratio of the circuit is obviously improved, and the continuity of the same phase axis is obviously improved.
The component data shown in fig. 4, 5 and 7 are input and synthesized into a cross section according to formula (10) (see fig. 8). Compared to the original profile shown in fig. 1, the interpolated shallow profile data more accurately reflects the actual subsurface structure and horizon information and provides high-accuracy input data for the subsequent multiple suppression process.
Claims (6)
1. A shallow-profile data spatial interpolation method based on multi-scale component extraction is characterized in that the method comprises (1) curve pickup during travel with high signal-to-noise ratio and phase axis, (2) extraction of each scale component of shallow-profile data s (x, t) by means of curvelet transformation, (3) identification of data tracks to be interpolated or enhanced in amplitude, (4) identification of each scale component s of shallow-profile data s (x, t) n (x, t) carrying out seismic trace interpolation and amplitude enhancement processing based on a convex set projection algorithm, and (5) synthesizing each scale data after interpolation or amplitude enhancement into a section.
2. The method for spatial interpolation of shallow profile data based on multi-scale component extraction as claimed in claim 1, wherein the curve of the high signal-to-noise ratio phase axis during travel is picked up, and the strong reflection phase axis with the highest signal-to-noise ratio in the profile is obtained by manual pick-up, which can be expressed as a coordinate x i Varied travel time curve t max (x) I.e.
（1）
Wherein s (x, t) represents shallow profile data, and x represents the space coordinates of each channel of data; t represents travel time; t is t max (x) Representing travel time of the same phase shaft with highest signal-to-noise ratio in the data; max_snr represents the process of obtaining the highest signal-to-noise ratio event by means of manual pick-up.
3. The method for spatial interpolation of shallow-profile data based on multi-scale component extraction as claimed in claim 2, wherein each scale component of the shallow-profile data s (x, t) is extracted by a curvelet transform, which is a combination of the shallow-profile data s (x, t) and a curvelet function phi j,l,m The inner product of (x, t) is implemented, so the curvelet transform can be expressed as
（2）
Wherein,a curvelet basis function representing a scale j, a direction l and a position m, c (j, l, m) represents a curvelet coefficient obtained by curvelet transformation, and s (x, t) represents shallow-profile data;
the curvelet transform is a reversible mathematical transform, by which shallow-profile data s (x, t) can be reconstructed using curvelet coefficients c (j, l, m), i.e.
（3）
The shallow data s (x, t) is decomposed in multiple scales, and the inverse transformation component with the scale n (n is more than or equal to 1 and less than or equal to J) is as follows
（4）
Wherein s is n (x, t) represents shallow profile data for the nth scale.
4. A method of spatial interpolation of shallow-profile data based on multi-scale component extraction as claimed in claim 3, wherein the shallow-profile data s (x, t) are extracted by means of a curvelet transformation, and the data tracks to be interpolated or amplitude-enhanced are identified for each scale component s n Any one data in (x, t) is respectively represented by t max (x) The delta t length is extended upwards and downwards, so that high signal-to-noise ratio local records including strong reflection phase shafts are intercepted; then, for each track of data recorded locally with high signal-to-noise ratio, the maximum absolute value A is found by comparing the absolute values of the sample points max The final composition can characterize the component profile s n Extremum list A of signal-to-noise ratios of data of each track in (x, t) max (x)；
After the reference channel number N and the judgment threshold value a are given, firstly, an extremum list A of N channels is shared at two sides of the target channel max (x) If the local extremum is found, the criterion for judging the local extremum as the channel to be interpolated or the amplitude enhancement channel is
（5）
The value range of the judging threshold value a is 0< a <1, and the more the value is, the more seismic traces are selected.
5. The method of spatial interpolation of shallow-profile data based on multi-scale component extraction as set forth in claim 4, wherein each scale component s for shallow-profile data s (x, t) n (x, t) performing seismic trace interpolation and amplitude enhancement processing based on convex set projection algorithm, and setting total iteration times Q and threshold parameter lambda of each iteration q (Q is more than or equal to 1 and less than or equal to Q), the requirement is that
（6）
Wherein Max is a component profile s n Maximum absolute value of (x, t); epsilon is a small value close to zero; threshold parameter lambda q The value of (2) is linearly decreasing;
in the q-th iteration, the processing result of the last iteration is converted by utilizing two-dimensional Fourier transformConversion to FK domain, i.e
（7）
Where q represents the number of iterations, when q=1, letF represents frequency, k represents wave number, x represents space coordinates of each channel of data; t represents travel time, e represents natural logarithm, i is imaginary unit;
threshold filtering is performed on single-scale records of FK domain, i.e. the absolute value of amplitude is smaller than lambda q Is set to a zero value, and the process can be expressed as
（8）
Wherein,representing the processing result after threshold filtering;
inverse fourier transform using two dimensionsTransformed back to time-space domain, with
（9）
In the method, in the process of the invention,for the processing result of the q-th interpolation, e represents the natural logarithm, i is the imaginary unit；
HoldingThe data of the channel to be interpolated or the amplitude enhancement channel is unchanged, and the effective channel in the data is utilized to input the profile s n The data tracks in (x, t) are replaced to obtain the interpolation or amplitude enhancement result of the q-th iteration, and then the threshold lambda of the next iteration is introduced q+1 See formula (6), input section +.>And (3) sequentially carrying out seismic interpolation or amplitude enhancement and effective channel replacement processing of the formulas (7) - (9) until a final optimization result is obtained after Q times of iteration.
6. The method of spatial interpolation of shallow-profile data based on multi-scale component extraction as set forth in claim 5, wherein said interpolating or amplitude-enhancing each scale data is combined into a profile to obtain a final interpolation or amplitude enhancement result
（10）
In the method, in the process of the invention,the data is shallow profile data after multi-scale component interpolation or amplitude enhancement, and J is the number of scales of curvelet transformation.
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