CN112162318A - Multi-channel deconvolution processing method based on dip angle constraint - Google Patents

Multi-channel deconvolution processing method based on dip angle constraint Download PDF

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CN112162318A
CN112162318A CN202011045519.3A CN202011045519A CN112162318A CN 112162318 A CN112162318 A CN 112162318A CN 202011045519 A CN202011045519 A CN 202011045519A CN 112162318 A CN112162318 A CN 112162318A
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reflection coefficient
deconvolution
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dip angle
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CN112162318B (en
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王磊
刘春强
马雄
黄保
赵妮
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Earth Pulse Wuxi Technology Co ltd
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    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
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Abstract

The invention relates to the technical field of seismic information processing in physical exploration, and discloses a multi-channel deconvolution processing method based on dip angle constraint
Figure DDA0002707839230000011
And then carrying out L2 norm constraint on the derivative of the reflection coefficient r (t, x) along the dip angle direction, constructing a multi-channel deconvolution target function J (r) with the dip angle constraint, and finally solving the deconvolution target function J (r) to obtain a reflection coefficient iterative solution formula, thereby protecting the spatial continuity of a deconvolution result, improving the precision of deconvolution processing, and providing high-quality data for later-stage seismic interpretation and seismic inversion.

Description

Multi-channel deconvolution processing method based on dip angle constraint
Technical Field
The invention relates to the technical field of seismic information processing in physical exploration, in particular to a multi-channel deconvolution processing method based on dip angle constraint.
Background
Seismic exploration is an exploration method for exploring subsurface structures using artificial seismic techniques. The method adopts a certain mode to artificially excite and receive seismic waves, and obtains the reflection coefficient by processing the reflection signals, namely the obtained seismic data, wherein the reflection coefficient can represent the impedance difference between the stratums, or the place with the reflection coefficient is the interface of the underground stratums.
In reflection seismic exploration, a seismic record may generally be viewed as a convolution of a seismic wavelet with a sequence of reflection coefficients. The main purpose of seismic deconvolution is to eliminate the effect of seismic wavelets from the recorded seismic data, obtain an ideal sequence of reflection coefficients, and then probe the subsurface geological structure. Therefore, deconvolution processing has been the core research content of seismic data processing.
Since the frequency bandwidth of seismic wavelets is limited, there is a multiplicity of solutions in the process of eliminating seismic wavelets. In order to obtain reasonable deconvolution results, it is necessary to add as much a priori information as possible in the deconvolution algorithm. Among them, Berkhout (1977) achieves pulse deconvolution by Winner filtering on the assumption that the reflectivity sequence follows a Gaussian distribution and that the seismic wavelets have minimum phase. Wiggins (1978) introduced the concept of entropy to the deconvolution process, proposing minimum entropy deconvolution. Taylor (1979) et al assume that the earth's reflection coefficient is composed of a superposition of several sharp pulses and propose sparse pulse deconvolution based on the L1 norm constraint based on this assumption. Although sparse pulse deconvolution can significantly improve the seismic resolution of the original data, the method is based on a single-trace convolution model, that is, each trace of seismic data is processed independently, so that the connection between seismic data traces is neglected, and the lateral discontinuity problem often occurs in the single-trace processing result.
Disclosure of Invention
In view of the defects of the background art, the invention provides a multi-channel deconvolution processing method based on dip angle constraint, and aims to solve the technical problem that the deconvolution processing of seismic data is based on a single-channel convolution model at present, the connection between seismic data channels is neglected, and the finally obtained reflection coefficient is discontinuous in the transverse direction.
In order to solve the technical problems, the invention provides the following technical scheme: a multi-channel deconvolution processing method based on dip angle constraint comprises the following steps:
s1: calculating a formation dip angle theta (t, x) according to the original seismic data d (t, x);
s2: calculating the derivative of the reflection coefficient r (t, x) along the dip direction according to the dip angle theta (t, x) of the stratum
Figure BDA0002707839210000021
S3: performing L2 norm constraint on the derivative of the reflection coefficient r (t, x) along the inclination angle direction, and constructing a multi-channel deconvolution target function J (r) with inclination angle constraint;
s4: and solving the deconvolution target function J (r) to obtain a reflection coefficient iterative solution formula.
Wherein, step S1 is specifically as follows:
s10: calculating partial derivatives of raw seismic data d (t, x) along time direction
Figure BDA0002707839210000022
S11 calculating partial derivatives of the raw seismic data d (t, x) along the spatial direction
Figure BDA0002707839210000023
S12, according to the formula:
Figure BDA0002707839210000024
the formation dip angle θ (t, x) is calculated.
Wherein, step S2 is specifically as follows:
s20, calculating the projection of the partial derivative of the reflection coefficient r (t, x) along the time direction in the dip angle direction according to the dip angle theta (t, x) of the stratum
Figure BDA0002707839210000031
S21: calculating the projection of the partial derivative of the reflection coefficient r (t, x) along the space direction in the dip angle direction according to the stratum dip angle theta (t, x)
Figure BDA0002707839210000032
S22: projection of the partial derivative of the reflection coefficient r (t, x) in the time direction in the direction of the inclination angle
Figure BDA0002707839210000033
And projection of the partial derivative in the spatial direction in the direction of the inclination
Figure BDA0002707839210000034
Calculating the derivative of the reflection coefficient r (t, x) in the direction of the inclination
Figure BDA0002707839210000035
In step S20, the projection of the partial derivative of the reflection coefficient r (t, x) in the time direction in the tilt direction
Figure BDA0002707839210000036
In step S21, the projection of the partial derivative of the reflection coefficient r (t, x) in the spatial direction in the direction of the tilt angle
Figure BDA0002707839210000037
In step S22, the derivative of the reflection coefficient r (t, x) in the direction of the inclination angle
Figure BDA0002707839210000038
In the formula
Figure BDA0002707839210000039
Is the derivative in the direction of the tilt angle (theta direction).
Wherein the deconvolution objective function
Figure BDA00027078392100000310
λ is the adjustment factor in the longitudinal direction and μ is the adjustment factor in the tilt direction. Optionally, the value range of λ is0.01-0.5, and the value range of mu is 0.01-1.
In addition, the solution process for the deconvolution objective function is as follows: firstly, setting an initial value of a reflection coefficient r (t, x) as 0, then solving an objective function J (r) by using an iterative reweighting algorithm to obtain a reflection coefficient iterative solution formula:
Figure BDA00027078392100000311
wherein r isk+1Is a discrete reflection coefficient sequence after the iteration of the (k +1) th step, W is a multi-channel seismic wavelet matrix,
Figure BDA00027078392100000312
for iterative updating of operators, DθIs a dip angle constraint operator.
Compared with the prior art, the invention has the beneficial effects that: the method comprehensively considers the spatial dip angle information of the underground stratum, constructs the dip angle constraint regularization item, protects the spatial continuity of the deconvolution result by adding the dip angle constraint item into the deconvolution algorithm, improves the precision of deconvolution processing, provides high-quality data for later seismic interpretation and seismic inversion, and has important guiding significance and reference value for seismic exploration.
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The invention has the following drawings:
FIG. 1 is a flow chart of a multi-pass deconvolution processing method based on tilt angle constraints in an embodiment;
FIG. 2 is a schematic illustration of a noise-free synthetic seismic record;
FIG. 3 is a schematic illustration of a synthetic seismic record incorporating noise;
FIG. 4 is a schematic diagram of deconvolution results after addition of dip constraints to multi-channel seismic data;
FIG. 5 is a schematic illustration of deconvolution processing without dip constraints added to single trace seismic data;
FIG. 6 is a schematic diagram of deconvolution processing to add dip constraints to multi-channel seismic data.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
For a two-dimensional seismic section, the expression for the multi-pass convolution model:
Figure BDA0002707839210000041
wherein d (t, x) is two-dimensional seismic data, i.e. raw seismic data, w (t) is seismic wavelets, r (t, x) is a two-dimensional reflection coefficient model,
Figure BDA0002707839210000042
is the convolution operator.
As shown in fig. 1, the multi-pass deconvolution processing method based on tilt angle constraint includes the following steps:
s1: calculating a formation dip angle theta (t, x) according to the original seismic data d (t, x);
s2: calculating the derivative of the reflection coefficient r (t, x) along the dip direction according to the dip angle theta (t, x) of the stratum
Figure BDA0002707839210000051
S3: performing L2 norm constraint on the derivative of the reflection coefficient r (t, x) along the inclination angle direction, and constructing a multi-channel deconvolution target function J (r) with inclination angle constraint;
s4: and solving the deconvolution target function J (r) to obtain a reflection coefficient iterative solution formula.
And solving the reflection coefficient of the stratum according to a reflection coefficient iteration solving formula.
Wherein, step S1 is as follows:
s10: calculating partial derivatives of raw seismic data d (t, x) along time direction
Figure BDA0002707839210000052
Wherein the content of the first and second substances,
Figure BDA0002707839210000053
Δ t is the time sampling interval in seconds;
s11 calculating partial derivatives of the raw seismic data d (t, x) along the spatial direction
Figure BDA0002707839210000054
Wherein the content of the first and second substances,
Figure BDA0002707839210000055
Δ x is the time sampling interval in meters;
s12, according to the formula:
Figure BDA0002707839210000056
calculating a stratum inclination angle theta (t, x), wherein the theta (t, x) is the stratum inclination angle of each underground point and the unit is radian;
Figure BDA0002707839210000057
as partial derivative in the horizontal direction (x-direction),
Figure BDA0002707839210000058
is the partial derivative in the vertical direction (direction t).
Wherein, step S2 is specifically as follows:
s20, calculating the projection of the partial derivative of the reflection coefficient r (t, x) along the time direction in the dip angle direction according to the dip angle theta (t, x) of the stratum
Figure BDA0002707839210000059
S21: calculating the projection of the partial derivative of the reflection coefficient r (t, x) along the space direction in the dip angle direction according to the stratum dip angle theta (t, x)
Figure BDA00027078392100000510
S22: projection of the partial derivative of the reflection coefficient r (t, x) in the time direction in the direction of the inclination angle
Figure BDA0002707839210000061
And projection of the partial derivative in the spatial direction in the direction of the inclination
Figure BDA0002707839210000062
Calculating the derivative of the reflection coefficient r (t, x) in the direction of the inclination
Figure BDA0002707839210000063
In step S20, the projection of the partial derivative of the reflection coefficient r (t, x) in the time direction in the tilt direction
Figure BDA0002707839210000064
In step S21, the projection of the partial derivative of the reflection coefficient r (t, x) in the spatial direction in the direction of the tilt angle
Figure BDA0002707839210000065
In step S22, the derivative of the reflection coefficient r (t, x) in the direction of the inclination angle
Figure BDA0002707839210000066
In the formula
Figure BDA0002707839210000067
Is the derivative in the direction of the tilt angle (theta direction).
Wherein the deconvolution objective function
Figure BDA0002707839210000068
The first term of the deconvolution objective function from left to right is a data matching term, the second term is a longitudinal sparse constraint term, the third term is a dip constraint term, lambda is a longitudinal adjustment factor, and mu is an adjustment factor of a dip direction.
Optionally, the value range of λ is 0.01-0.5, and the value range of μ is 0.01-1.
In addition, the solution process for the deconvolution objective function is as follows: firstly, setting an initial value of a reflection coefficient r (t, x) as 0, then solving an objective function J (r) by using an iterative reweighting algorithm to obtain a reflection coefficient iterative solution formula:
Figure BDA0002707839210000069
wherein r isk+1Is a discrete reflection coefficient sequence after the iteration of the (k +1) th step, W is a multi-channel seismic wavelet matrix,
Figure BDA00027078392100000610
for iterative updating of operators, DθAnd d is discrete seismic record data and is an adjusting factor, and the value range is 0.1-0.01.
As shown in fig. 2-4, in fig. 4, by adding dip constraints when deconvoluting multi-channel seismic data, the spatial continuity of the deconvolution result is protected, and the precision of the deconvolution processing is improved.
As shown in fig. 5-6, when deconvolution is performed on multi-channel seismic data, dip angle constraint is added, so that the longitudinal resolution of the seismic data is effectively improved, and the coaxial line becomes thin; on the other hand, the method also effectively protects the spatial continuity of the deconvolution result, improves the seismic data processing precision, and provides high-quality data support for later seismic interpretation and seismic inversion.
In light of the foregoing, it is to be understood that various changes and modifications may be made by those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (7)

1. The multi-channel deconvolution processing method based on the dip angle constraint is characterized by comprising the following steps of: the method comprises the following steps:
s1: calculating a formation dip angle theta (t, x) according to the original seismic data d (t, x);
s2: calculating the derivative of the reflection coefficient r (t, x) along the dip direction according to the dip angle theta (t, x) of the stratum
Figure FDA0002707839200000011
S3: performing L2 norm constraint on the derivative of the reflection coefficient r (t, x) along the inclination angle direction, and constructing a multi-channel deconvolution target function J (r) with inclination angle constraint;
s4: and solving the deconvolution target function J (r) to obtain a reflection coefficient iterative solution formula.
2. The tilt constraint-based multi-pass deconvolution processing method of claim 1, wherein: step S1 is specifically as follows:
s10: calculating partial derivatives of raw seismic data d (t, x) along time direction
Figure FDA0002707839200000012
S11 calculating partial derivatives of the raw seismic data d (t, x) along the spatial direction
Figure FDA0002707839200000013
S12: according to the formula:
Figure FDA0002707839200000014
the formation dip angle θ (t, x) is calculated.
3. The tilt constraint-based multi-pass deconvolution processing method of claim 1, wherein: step S2 is specifically as follows:
s20, calculating the projection of the partial derivative of the reflection coefficient r (t, x) along the time direction in the dip angle direction according to the dip angle theta (t, x) of the stratum
Figure FDA0002707839200000015
S21: calculating the projection of the partial derivative of the reflection coefficient r (t, x) along the space direction in the dip angle direction according to the stratum dip angle theta (t, x)
Figure FDA0002707839200000016
S22: projection of the partial derivative of the reflection coefficient r (t, x) in the time direction in the direction of the inclination angle
Figure FDA0002707839200000017
And projection of the partial derivative in the spatial direction in the direction of the inclination
Figure FDA0002707839200000018
Calculating the derivative of the reflection coefficient r (t, x) in the direction of the inclination
Figure FDA0002707839200000019
4. The tilt constraint-based multi-pass deconvolution processing method of claim 3, wherein: in step S20, the projection of the partial derivative of the reflection coefficient r (t, x) in the time direction in the tilt direction
Figure FDA0002707839200000021
In step S21, the projection of the partial derivative of the reflection coefficient r (t, x) in the spatial direction in the direction of the tilt angle
Figure FDA0002707839200000022
In step S22, the derivative of the reflection coefficient r (t, x) in the direction of the inclination angle
Figure FDA0002707839200000023
5. The tilt constraint-based multi-pass deconvolution processing method of claim 1, wherein: the deconvolution objective function
Figure FDA0002707839200000024
Wherein, λ is the longitudinal adjustment factor, μ is the inclination angle adjustment factor.
6. The tilt constraint-based multi-pass deconvolution processing method of claim 5, wherein: the value range of lambda is 0.01-0.5, and the value range of mu is 0.01-1.
7. The tilt constraint-based multi-pass deconvolution processing method of claim 5, wherein: the solution to the deconvolution objective function is as follows: first of all the reflection coefficient r (t, x)Setting the initial value as 0, then solving an objective function J (r) by using an iterative reweighting algorithm to obtain a reflection coefficient iterative solution formula:
Figure FDA0002707839200000025
wherein r isk+1Is a discrete reflection coefficient sequence after the iteration of the (k +1) th step, W is a multi-channel seismic wavelet matrix,
Figure FDA0002707839200000026
for iterative updating of operators, DθIs a dip angle constraint operator.
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CN114859409A (en) * 2022-04-11 2022-08-05 中山大学 Method and device for acquiring information of rock ring discontinuity of oceanic rock

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