CN111766626A - Algorithm for realizing three-dimensional space fitting of seismic data - Google Patents

Algorithm for realizing three-dimensional space fitting of seismic data Download PDF

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CN111766626A
CN111766626A CN202010646487.6A CN202010646487A CN111766626A CN 111766626 A CN111766626 A CN 111766626A CN 202010646487 A CN202010646487 A CN 202010646487A CN 111766626 A CN111766626 A CN 111766626A
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fitting
seismic data
seismic
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李�浩
张曼莉
林畅松
梁鹰
宁振虎
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China University of Geosciences Beijing
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • G01V2210/612—Previously recorded data, e.g. time-lapse or 4D
    • G01V2210/6122—Tracking reservoir changes over time, e.g. due to production
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • G01V2210/616—Data from specific type of measurement
    • G01V2210/6161—Seismic or acoustic, e.g. land or sea measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/63—Seismic attributes, e.g. amplitude, polarity, instant phase

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  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geology (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
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  • Geophysics And Detection Of Objects (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

The invention discloses an algorithm for realizing three-dimensional space fitting of seismic data, which comprises the following steps: constructing a three-dimensional fitting polynomial; calculating the amplitude of the fitted seismic traces; a method for determining a best fit coefficient in three dimensional space. The method for realizing the three-dimensional space fitting of the seismic data can perform rapid fitting on the three-dimensional space of the three-dimensional seismic data in a specific area, and improves the fitting effect of the seismic data in all directions.

Description

Algorithm for realizing three-dimensional space fitting of seismic data
Technical Field
The invention relates to the technical field of big data, in particular to an algorithm for realizing three-dimensional space fitting of seismic data.
Background
The manual excitation of seismic waves by a vibroseis is a main mode for petroleum exploration. The fitting calculation can effectively enhance the seismic signals and improve the signal-to-noise ratio of the seismic data, so the fitting method is frequently used in the seismic data processing process, and the fitting calculation is more widely applied to processing the carbonate seismic data because the carbonate stratum has more obvious transverse anisotropy. The fitting method adopted at present mostly uses orthogonal polynomials in quadratic polynomials to perform fitting calculation based on two-dimensional space, and the method can fit the superposed seismic section in a single direction (comprehensive survey line or transverse survey line), so that the homophase axis is smoother, and the signal-to-noise ratio of the seismic section is improved.
The existing two-dimensional fitting method can process the seismic event in one direction, but can cause the incoordination of the seismic event in other directions to form a vertical breakpoint, thereby influencing the explanation of the whole seismic profile.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the existing defects, provide an algorithm for realizing three-dimensional space fitting of seismic data, perform rapid fitting on the three-dimensional space of the three-dimensional seismic data in a specific area, improve the fitting effect of the seismic data in all directions, and effectively solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention provides an algorithm for realizing three-dimensional space fitting of seismic data, which comprises the following steps:
s1: constructing a three-dimensional fitting polynomial;
s2: calculating the amplitude of the fitted seismic traces;
s3: a method for determining a best fit coefficient in three dimensional space.
As a preferable scheme, the step S1 includes:
when the vertical coordinate of the three-dimensional seismic data represents the two-way travel of seismic waves, for any area needing fitting on the three-dimensional seismic data, a series of time windows are divided according to a longitudinal time axis, and if 2N +1 seismic channels are horizontally arranged in a certain time window D and 2M +1 seismic channels are arranged in the vertical measuring line direction, a discrete coordinate system can be established for the time windows:
d { (x, y) | x ∈ [ -M, M ], y ∈ [ -N, N ]; x, y are integers }
Taking the quadratic orthogonal polynomials in [ -M, M ] and [ -N, N ], respectively:
Figure BDA0002573229690000021
Figure BDA0002573229690000022
then for any 0 ≦ i, j ≦ 2 has
Figure BDA0002573229690000023
Figure BDA0002573229690000024
Order to
F＝{Fij|Fij＝piqj,0≤i,j≤2}
For any 0 ≦ i, j, k, l ≦ 2
Figure BDA0002573229690000031
F is therefore the set of orthogonal polynomials on D;
let G be { G ═ GiI is less than or equal to 0 and less than or equal to 8, F, G is known as an orthogonal polynomial set with the highest order of four on D, and the elements in G are expanded as follows:
G0＝1,G1＝x,G2＝y,G3＝xy,
Figure BDA0002573229690000032
Figure BDA0002573229690000033
Figure BDA0002573229690000034
the polynomial fit over the time window D is as follows:
Figure BDA0002573229690000035
as a preferable scheme, the step S2 includes:
the fitted amplitudes of the points in each time window are obtained by weighted superposition of the original amplitudes, and the specific formula is as follows:
Figure BDA0002573229690000036
wherein A isijOutput amplitude, W, expressed in coordinates of (i, j) pointijklThe weighting factor for a point (k, l) to a point (i, j), is generally a function of the distance d ((k, l), (i, j)) between the two points, BklRepresenting the original amplitude of point (k, l).
As a preferred scheme, the initial scanning range and the scanning step determine the scanning speed, and the initial scanning range can be given according to the variation trend of the fitting data, wherein the specific scanning method is as follows:
the first step is as follows: adopting a manual interaction method to pick up the inclination angle along the in-phase axis of the data signal to be fitted or the change direction of the coherent interference;
the second step is that: establishing a tilt field of the signal or coherent interference, i.e. establishing an initial central value of the initial scanning range and then expanding a small range to both sides of the central value to generate an initial scanning range of the whole processed data, or
The maximum possible initial range may be taken to be given by the apparent velocity of the steepest region of seismic data change or the moveout of the adjacent traces to be fitted.
As a preferable scheme, the step S3 includes:
let the correlation time window length be 2L and the sampling time interval be t0Then, the space where the related sampling points are located is:
Figure BDA0002573229690000041
is provided with G0Coefficient c0Has a scanning range of [ N1,N2]Scanning step length of G0Then the scanning range is:
c0(i)＝N1+i×c0(i＝0，1，...，(N2-N1)/c0)
from similar criteria are:
Figure BDA0002573229690000042
r (i) fitting the sum of cross-correlations, s (j, l, c)0(i) + t) denotes a spatial coordinate of (j, l, c)0(i) + t) sample values;
for different c0(i) C to maximize R (i)0(i) I.e. is G0The best fit coefficient of (a);
for GpCoefficient c ofpLet us order
Figure BDA0002573229690000043
cp(i) As defined above, then cpSum of cross-correlation of fitted traces
Figure BDA0002573229690000051
For different cp(i) C to maximize R (i)p(i) I.e. is GpThe best fit coefficient of (3).
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
the method can perform rapid fitting on the three-dimensional space of the three-dimensional seismic data of a specific area, and improves the fitting effect of the seismic data in all directions.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The seismic channel data generated by artificially exciting seismic waves received by the detector has transverse coherence and similar waveforms, the transverse change of seismic recording phase time is described by constructing a quadratic polynomial, each seismic data (amplitude) changing along the phase time can also be represented by a polynomial with a coefficient to be determined, then three-dimensional space fitting is carried out on the polynomial, the seismic signal phase time, the standard waveform and the amplitude weighting coefficient are calculated, and then the fitting seismic channel is formed by the polynomial and the standard waveform and the amplitude weighting coefficient, so that the aim of optimizing the seismic data is fulfilled.
In order to better understand the technical solutions, the technical solutions will be described in detail with reference to specific embodiments.
Example (b):
the embodiment provides an algorithm for realizing three-dimensional space fitting of seismic data, which comprises the following steps:
s1: constructing a three-dimensional fitting polynomial;
s2: calculating the amplitude of the fitted seismic traces;
s3: a method for determining a best fit coefficient in three dimensional space.
In the algorithm for implementing three-dimensional space fitting of seismic data provided by this embodiment, step S1 includes:
the vertical coordinate of the three-dimensional seismic data represents the two-way travel time of seismic waves (the seismic waves are artificially excited to propagate downwards and then are reflected back to be received by a detector), for any area needing to be fitted on the three-dimensional seismic data, a series of time windows are divided according to a longitudinal time axis, a certain time window D is assumed to have 2N +1 seismic channels in the transverse direction, 2M +1 seismic channels are arranged in the longitudinal measuring line direction, and a discrete coordinate system can be established for the time windows:
d { (x, y) | x ∈ [ -M, M ], y ∈ [ -N, N ]; x, y are integers }
Taking the quadratic orthogonal polynomials in [ -M, M ] and [ -N, N ], respectively:
Figure BDA0002573229690000061
Figure BDA0002573229690000062
then for any 0 ≦ i, j ≦ 2 has
Figure BDA0002573229690000063
Figure BDA0002573229690000064
Order to
F＝{Fij|Fij＝piqj,0≤i,j≤2}
For any 0 ≦ i, j, k, l ≦ 2
Figure BDA0002573229690000071
F is therefore the set of orthogonal polynomials on D;
let G be { G ═ GiI is less than or equal to 0 and less than or equal to 8, F, G is known as an orthogonal polynomial set with the highest order of four on D, and the elements in G are expanded as follows:
G0＝1,G1＝x,G2＝y,G3＝xy,
Figure BDA0002573229690000072
Figure BDA0002573229690000073
Figure BDA0002573229690000074
the polynomial fit over the time window D is as follows:
Figure BDA0002573229690000075
since G is an orthogonal function set, the fitting coefficient c can be obtained by an independent scanning methodiTherefore, the calculation is greatly simplified, and the polynomial coefficient in each time window can be quickly solved by sliding the time window according to a certain step length.
In the algorithm for implementing three-dimensional space fitting of seismic data provided by this embodiment, step S2 includes:
the fitted amplitudes of the points in each time window are obtained by weighted superposition of the original amplitudes, and the specific formula is as follows:
Figure BDA0002573229690000076
wherein A isijOutput amplitude, W, expressed in coordinates of (i, j) pointijklThe weighting factor for a point (k, l) to a point (i, j), is generally a function of the distance d ((k, l), (i, j)) between the two points, BklRepresenting the original amplitude of point (k, l).
According to the algorithm for realizing the three-dimensional space fitting of the seismic data, the initial scanning range and the scanning step length determine the scanning speed, and the initial scanning range can be given according to the variation trend of the fitting data, wherein the specific scanning method comprises the following steps:
the first step is as follows: adopting a manual interaction method to pick up the inclination angle along the in-phase axis of the data signal to be fitted or the change direction of the coherent interference;
the second step is that: establishing a tilt field of the signal or coherent interference, i.e. establishing an initial central value of the initial scanning range and then expanding a small range to both sides of the central value to generate an initial scanning range of the whole processed data, or
The maximum possible initial range may be taken to be given by the apparent velocity of the steepest region of seismic data change or the moveout of the adjacent traces to be fitted.
In the algorithm for implementing three-dimensional space fitting of seismic data provided by this embodiment, step S3 includes:
let the correlation time window length be 2L and the sampling time interval be t0Then, the space where the related sampling points are located is:
Figure BDA0002573229690000081
is provided with G0Coefficient c0Has a scanning range of [ N1,N2]Scanning step length of G0Then the scanning range is:
c0(i)＝N1+i×c0(i＝0，1，...，(N2-N1)/c0)
from similar criteria are:
Figure BDA0002573229690000082
r (i) fitting the sum of cross-correlations, s (j, l, c)0(i) + t) denotes a spatial coordinate of (j, l, c)0(i) + t) sample values;
for different c0(i) C to maximize R (i)0(i) I.e. is G0The best fit coefficient of (a);
for GpCoefficient c ofpLet us order
Figure BDA0002573229690000091
cp(i) As defined above, then cpSum of cross-correlation of fitted traces
Figure BDA0002573229690000092
For different cp(i) C to maximize R (i)p(i) I.e. is GpThe best fit coefficient of (3).
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. An algorithm for realizing three-dimensional space fitting of seismic data is characterized in that: the method comprises the following steps:
s1: constructing a three-dimensional fitting polynomial;
s2: calculating the amplitude of the fitted seismic traces;
s3: a method for determining a best fit coefficient in three dimensional space.
2. The algorithm for performing three-dimensional spatial fitting of seismic data according to claim 1, wherein: step S1 includes:
when the vertical coordinate of the three-dimensional seismic data represents the two-way travel of seismic waves, for any area needing fitting on the three-dimensional seismic data, a series of time windows are divided according to a longitudinal time axis, and if 2N +1 seismic channels are transversely arranged in a certain time window D and 2M +1 seismic channels are arranged in the direction of a vertical measuring line, a discrete coordinate system can be established for the time windows:
d { (x, y) | x ∈ [ -M, M ], y ∈ [ -N, N ]; x, y are integers }
Taking the quadratic orthogonal polynomials in [ -M, M ] and [ -N, N ], respectively:
p0(x)＝1,p1(x)＝x,
Figure FDA0002573229680000011
x∈[-M,M]
q0(y)＝1,q1(y)＝y,
Figure FDA0002573229680000012
y∈[-N,N]
then for any 0 ≦ i, j ≦ 2 has
Figure FDA0002573229680000013
Figure FDA0002573229680000014
Order to
F＝{Fij|Fij＝piqj,0≤i,j≤2}
For any 0 ≦ i, j, k, l ≦ 2
Figure FDA0002573229680000021
F is therefore the set of orthogonal polynomials on D;
let G be { G ═ GiI is less than or equal to 0 and less than or equal to 8, F, G is known as an orthogonal polynomial set with the highest order of four on D, and the elements in G are expanded as follows:
G0＝1,G1＝x,G2＝y,G3＝xy,
Figure FDA0002573229680000022
Figure FDA0002573229680000023
Figure FDA0002573229680000024
the polynomial fit over the time window D is as follows:
Figure FDA0002573229680000025
3. the algorithm for performing three-dimensional spatial fitting of seismic data according to claim 2, wherein: step S2 includes:
the fitted amplitudes of the points in each time window are obtained by weighted superposition of the original amplitudes, and the specific formula is as follows:
Figure FDA0002573229680000026
wherein A isijOutput amplitude, W, expressed in coordinates of (i, j) pointijklThe weighting factor for a point (k, l) to a point (i, j), is generally a function of the distance d ((k, l), (i, j)) between the two points, BklRepresenting the original amplitude of point (k, l).
4. The algorithm for performing three-dimensional spatial fitting of seismic data according to claim 3, wherein: the initial scanning range and the scanning step length determine the scanning speed, and the initial scanning range can be given according to the variation trend of the fitting data, wherein the specific scanning method comprises the following steps:
the first step is as follows: adopting a manual interaction method to pick up the inclination angle along the in-phase axis of the data signal to be fitted or the change direction of the coherent interference;
the second step is that: establishing a tilt field of the signal or coherent interference, i.e. establishing an initial central value of the initial scanning range and then expanding a small range to both sides of the central value to generate an initial scanning range of the whole processed data, or
The maximum possible initial range may be taken to be given by the apparent velocity of the steepest region of seismic data change or the moveout of the adjacent traces to be fitted.
5. The algorithm for performing three-dimensional spatial fitting of seismic data according to claim 4, wherein: step S3 includes:
let the correlation time window length be 2L and the sampling time interval be t0Then, the space where the related sampling points are located is:
Figure FDA0002573229680000031
Figure FDA0002573229680000032
let G0Coefficient c0Has a scanning range of [ N1,N2]Scanning step length of G0Then the scanning range is:
c0(i)＝N1+i×c0(i＝0，1，...，(N2-N1)/c0)
from similar criteria are:
Figure FDA0002573229680000033
r (i) fitting the sum of cross-correlations, s (j, l, c)0(i) + t) denotes a spatial coordinate of (j, l, c)0(i) + t) samplePoint values;
for different c0(i) C to maximize R (i)0(i) I.e. is G0The best fit coefficient of (a);
for GpCoefficient c ofpLet us order
Figure FDA0002573229680000041
cp(i) As defined above, then cpSum of cross-correlation of fitted traces
Figure FDA0002573229680000042
For different cp(i) C to maximize R (i)p(i) I.e. is GpThe best fit coefficient of (3).
CN202010646487.6A 2020-07-07 2020-07-07 Algorithm for realizing three-dimensional space fitting of seismic data Pending CN111766626A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112305606A (en) * 2020-10-16 2021-02-02 宁夏回族自治区地震局 Earthquake activity field analysis method based on natural orthogonal function expansion

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115142829B (en) * 2022-05-30 2023-12-01 中煤科工开采研究院有限公司 Ground horizontal well staged fracturing monitoring method and vibration combined monitoring system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102681014A (en) * 2012-05-23 2012-09-19 中国石油集团川庆钻探工程有限公司地球物理勘探公司 Regular linear interference suppressing method based on polynomial fitting
CN102736108A (en) * 2012-05-31 2012-10-17 中国石油集团川庆钻探工程有限公司地球物理勘探公司 True three-dimensional earthquake data noise suppressing method based on spline fitting

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102681014A (en) * 2012-05-23 2012-09-19 中国石油集团川庆钻探工程有限公司地球物理勘探公司 Regular linear interference suppressing method based on polynomial fitting
CN102736108A (en) * 2012-05-31 2012-10-17 中国石油集团川庆钻探工程有限公司地球物理勘探公司 True three-dimensional earthquake data noise suppressing method based on spline fitting

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李浩 等: "利用正交多项式对三维地震资料拟合算法的研究", 《西北地震学报》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112305606A (en) * 2020-10-16 2021-02-02 宁夏回族自治区地震局 Earthquake activity field analysis method based on natural orthogonal function expansion

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