CN114002739B - Edge detection method, device and medium based on geometric non-parallel statistical attribute - Google Patents
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Abstract
The invention relates to an edge detection method, device, medium and equipment based on geometric non-parallel statistical properties, wherein the detection method comprises the following steps: collecting seismic data of a seismic phase to be analyzed; calculating the structural dip angle deviation of the earthquake; calculating amplitude or energy gradient deviation of the earthquake; based on the structural inclination angle deviation and the amplitude or energy gradient deviation, calculating the total earthquake deviation, wherein the total earthquake deviation is the geometric non-parallel statistical attribute, and utilizing the geometric non-parallel statistical attribute to carry out the edge detection of the earthquake phase. According to the method, the inclination angle deviation attribute, the amplitude energy gradient attribute and the total deviation attribute are sequentially calculated according to the seismic data, so that an algorithm based on geometric nonparallel statistical attributes is obtained and applied to edge detection of actual earthquakes, and the effectiveness of the method is verified.
Description
Technical Field
The invention relates to an edge detection method, device, medium and equipment based on geometric non-parallel statistical properties, and belongs to the technical field of seismic analysis.
Background
Pattern recognition based on computer-aided seismic classification speeds up conventional seismic interpretation, which requires automatic extraction of the effective seismic attributes of the destination layer from a large amount of data, requiring considerable time and effort by the interpretation personnel. Some special seismic phases such as salt dome or karst seismic characteristics can be distinguished from surrounding sedimentary rock with stratification by lateral and vertical changes in dip angle, energy and continuity.
For seismic phase analysis, the type of seismic phase is often assisted by a number of seismic attributes, some of which do not indicate the seismic phase well during the analysis, and which need to be analyzed in combination with a plurality of attributes. Attributes commonly used for seismic phase analysis are dip, amplitude, energy, discontinuity and structural attributes of the reflecting surface, so selecting the appropriate attribute that characterizes the seismic phase is a huge effort. In order to better ensure the transverse resolution of the seismic attribute, the calculation of the seismic attribute is usually constrained by the local dip attribute, and the calculation method for calculating the local dip of the stratum is numerous, wherein the calculation method comprises various algorithms based on single-channel cross correlation, multi-channel analysis, discrete surface scanning, gradient structure tensor, planar waveform analysis, the ratio of smooth instantaneous wave number to smooth instantaneous frequency and the like. The first statistical measurement of the tilt angle properties of the reflecting surface was proposed by Barnes, and the measured deviation of parallelism was estimated from the standard deviation of the vector tilt angle. Lateral changes in the tilt angle properties of the reflecting surface may be measured by curvature or energy properties, while vertical changes in tilt angle may be measured by the convergence of the reflecting surface.
Conventional window-based calculated amplitude class attributes include an average amplitude attribute and a root mean square amplitude attribute. Through Hilbert transform, instantaneous envelope attribute, instantaneous frequency attribute and other attribute sensitive to vertical variation of seismic amplitude can also be calculated. In contrast, the co-directional and transverse components of the amplitude gradient are more sensitive to transverse variations in amplitude and to seismic reflection. Marfurt represents a coherent amplitude gradient by the gradient of the first eigenvector, which gradient is most representative of the seismic data within the analysis window, and then weights the results by the first eigenvalue. The coherent amplitude gradient highlights the subtle lateral amplitudes caused by the layer thickness lateral variations, including those below 1/4 wavelength tuning thickness, which can be further enhanced by calculating the second derivative of the amplitude, resulting in a measure of the amplitude curvature.
If the seismic reflection pattern of one or more targets is cluttered, such as under salt dome and karst seismic characteristics, such cluttered statistical measurement attributes aid in multi-attribute classification. In the traditional phase analysis, an interpreter can combine a plurality of attribute information by means of RGB fusion or intersection analysis and the like to finally obtain a set of discontinuous attribute bodies aiming at a target area, but the method is limited to two or three different attributes.
Disclosure of Invention
Aiming at the technical problems, the invention provides an edge detection method, device, medium and equipment based on geometric non-parallel statistical properties.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an edge detection method based on geometric non-parallel statistical properties comprises the following steps:
collecting seismic data of a seismic phase to be analyzed;
calculating the structural dip angle deviation of the earthquake;
calculating amplitude or energy gradient deviation of the earthquake;
based on the structural inclination angle deviation and the amplitude or energy gradient deviation, calculating the total deviation of the earthquake, wherein the total deviation of the earthquake is the geometric non-parallel statistical attribute, and the edge detection of the earthquake phase is carried out by utilizing the geometric non-parallel statistical attribute.
The edge detection method preferably includes the steps of: the inline dip, crossline dip, energy or root mean square attribute, inline amplitude gradient, crossline amplitude gradient of the dip is constructed.
In the edge detection method, preferably, the construction inclination deviation of the earthquake is calculated, and the method comprises the following steps:
converting the conventional time domain seismic data from a time domain t to a depth domain z by utilizing speed data, and constructing a main line component p and a longitudinal line component q of the dip angle, wherein the components of the dip angle are represented by the angle of the dip angle along the horizontal direction;
for some areas with large construction amplitude, the inclination angle is represented by a unit normal direction n;
where the energy is strongest, the three normal components n are measured at appropriate metrics by the energy attribute, root mean square amplitude attribute, or envelope attribute on the jth seismic trace k To calculate the unit of the whole seismic volume J within the analysis windowAn average component of the normal;
from these given values, the energy weighted standard deviation of the vector tilt angle with respect to its mean is obtained and characterized in terms of angle.
Preferably, the edge detection method calculates the amplitude or energy gradient deviation of the earthquake, and comprises the following steps:
defining amplitude variations of the phase reflections within the analysis window along the formation dip, using a covariance matrix to form a first eigenvector of the window, weighting the derivatives of the main and crosslines of this eigenvector by the first eigenvalue or the square root of the first eigenvalue, for steep dip areas, the gradient g needs to be compensated by increasing the seismic reflection length between adjacent traces;
two average gradient components are calculated to obtain an amplitude gradient bias.
The method for detecting the edge preferably calculates the total deviation of the earthquake based on the deviation of the inclination angle and the deviation of the amplitude or the energy gradient, and comprises the following steps:
after the amplitude gradient deviation is calculated, the covariance matrix C is utilized to compare the change in an analysis window of the unit normal inclination angle vector n and the amplitude gradient vector g;
defining the total deviation as a first eigenvalue lambda of the covariance matrix C 1 Using the first characteristic value lambda 1 And calculating to obtain the total deviation, namely the geometric non-parallel statistical attribute, and carrying out the edge detection of the seismic phase by utilizing the geometric non-parallel statistical attribute.
A second aspect of the present invention provides a detection apparatus for the above edge detection method, including:
a first processing unit for collecting seismic data of a seismic phase to be analyzed;
the second processing unit is used for calculating the construction dip angle deviation of the earthquake;
a third processing unit for calculating an amplitude or energy gradient deviation of the earthquake;
the fourth processing unit is used for calculating total deviation of the earthquake based on the deviation of the structural inclination angle and the deviation of the amplitude or the energy gradient, wherein the total deviation of the earthquake is the geometric non-parallel statistical attribute, and the edge detection of the earthquake phase is carried out by utilizing the geometric non-parallel statistical attribute.
A third aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the edge detection method described above.
A fourth aspect of the invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above-described edge detection method when executing the computer program.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. to better exploit the differences between stratified and chaotic waveform reflected seismic features, the present invention introduces three-dimensional geometric non-parallel statistical properties to statistically quantify the lateral and longitudinal changes in dip and amplitude gradients. When encountering salt dome or karst earthquake response, the method has good predictability for earthquake data with higher signal-to-noise ratio by utilizing coherence attribute, texture attribute and the like, but when the signal-to-noise ratio for the earthquake data is low, the simple attribute cannot perform good edge detection on the special earthquake phases. In order to solve the problem, the invention utilizes the seismic data as input, obtains the inclination angle deviation attribute, the amplitude energy gradient attribute and the total deviation attribute of the seismic data respectively, and develops an algorithm based on geometric non-parallel statistical attribute on the basis to be applied to the edge detection of the actual earthquake so as to verify the effectiveness of the invention.
2. The invention uses the seismic data as input, calculates the statistics attributes of main line and cross line of the seismic structure dip angle attribute and amplitude gradient vector attribute, and defines the dip angle deviation as the energy weighting standard deviation of the vector dip angle about the mean value, and the energy gradient deviation as the standard deviation of the amplitude gradient vector in the analysis window. We then use these new geometric non-parallel statistical properties to develop edge detection of special lithofacies.
Drawings
FIG. 1 is a flowchart of an algorithm for calculating geometric non-parallel attributes using seismic data according to an embodiment of the present invention;
FIG. 2 is a three-dimensional seismic data seismic root mean square amplitude time slice and corresponding three-dimensional seismic profile provided by this embodiment of the invention;
FIG. 3 is a slice of tilt deviation attribute and corresponding three-directional profile provided by this embodiment of the present invention;
FIG. 4 is a cross-section of the energy gradient bias property slice and corresponding three directions provided by this embodiment of the invention;
FIG. 5 is a cross section of the total deviation attribute slice and corresponding three directions provided by this embodiment of the present invention;
FIG. 6 is a time slice and three-dimensional cross-section of the multi-attribute classification provided by this embodiment of the invention;
FIG. 7 is a planar time slice represented by the Pasteur distance provided by this embodiment of the invention;
fig. 8 is a three-dimensional perspective view of a discontinuous geologic volume characterized by a pasteurization distance according to this embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
The invention uses the seismic data as input, calculates the statistics attributes of main line and cross line of the seismic structure dip angle attribute and amplitude gradient vector attribute, and defines the dip angle deviation as the energy weighting standard deviation of the vector dip angle about the mean value, and the energy gradient deviation as the standard deviation of the amplitude gradient vector in the analysis window. These new geometric non-parallel statistical properties are then used to develop edge detection of specific lithofacies in the seismic phase.
The overall calculation and implementation process can be divided into three steps in total:
the first step: calculating the structural dip angle deviation of the earthquake:
since most three-dimensional seismic data is time-domain, most dip calculations (including calculation methods in Radon transforms) are calculated in the time domain. But subsequent curvature calculations require characterization of these dip angles in the depth domain. Therefore, the conventional time domain seismic data needs to be converted from the time domain t to the depth domain z by using the velocity data, and the component of the dip angle is characterized by the angle of the dip angle along the horizontal direction by constructing the main line component p and the inline component q of the dip angle:
wherein x represents the direction along the inline, y represents the direction along the crossline, z represents the downward direction along the vertical direction, θ x Represents the viewing angle, θ, along the x-direction of the main line y The values of p and q are applicable to medium tilt values, representing apparent tilt along the crossline y direction. For areas where the formation is particularly large in amplitude, however, the steep dip of the formation may become very large. In this case, the inclination angle can be generally represented by a unit normal direction n, and the specific expression is:
for seismic data, the tilt angle is most difficult to calculate where the energy is strongest. Thus, the three normal components n can be measured appropriately by the energy attribute, root mean square amplitude attribute, or envelope attribute, etc. on the jth seismic trace k (k=x, y, z) to calculate the average component per unit normal of the whole seismic volume J within the analysis window:
wherein mu k The average component of unit normal line of earthquake in analysis window is j, the number of earthquake channels is e j Energy of jth seismic trace, n kj From these given values, the energy weighted standard deviation of the vector tilt with respect to its mean can be obtained for the normal component of the jth seismic trace, namely:
the unit of angle can be characterized as:
and a second step of: calculating amplitude or energy gradient deviation of the earthquake:
for seismic waveforms, the amplitude or energy gradient is typically indicative of a change in the thickness of the formation, or a change in the formation lateral with a significant difference in impedance above or below the formation. This lateral variation is different from the energy variation of the clutter which is typically seen in complex formations such as salt domes, karst landforms or superpressure shale gas.
The vertical variation in seismic amplitude is a combination of lithology, formation thickness and seismic wavelets. In contrast, seismic wavelets are relatively constant along the formation dip, so lateral variations in dip amplitude along the formation are variations in lithology and formation thickness. Along this dip, the first eigenvector of the covariance matrix formation window is used to define the amplitude variation of the phase reflections within the analysis window, and the derivatives of the inline and crossline of this eigenvector can be weighted by the square root of the first eigenvalue (energy weighted coherent amplitude gradient) or the first eigenvalue (root mean square amplitude weighted coherent amplitude gradient). For steep dip areas, the gradient g needs to be compensated by increasing the seismic reflection length between adjacent traces. Let ζ be the distance in the x-z plane and η be the distance in the y-z plane, then there is:
wherein g ξ Representing the gradient in the z-plane along the inline x-direction and the vertical direction, g η Representing the gradient in the z-plane along the crossline y-direction and the vertical direction g x Represents the gradient along the inline x-direction, g y Representing the gradient along the y-direction of the crossline.
As with the tilt deviation calculation, two average gradient components are first calculated:
wherein g kj A gradient of the jth seismic trace along the directions of the main line and the cross-line, v k Representing the average gradient component along the inline and crossline, this does not require weighting of the energy at this time, as the calculated gradient is a measure of the lateral change in energy. The amplitude gradient bias is therefore:
wherein sigma gradient Representing the amplitude gradient deviation amount;
and a third step of: calculating the total deviation:
after the amplitude gradient bias is calculated, the covariance matrix C is used to compare the variation in the analysis window of the unit normal tilt angle vector n and the amplitude gradient vector g as shown in the following equation:
wherein R is n ＝sin(R dip )，R n Represents the sum of normal components, R dip Represents the sum of the inclination angles R g Representing the sum of gradient components, m being the number of different seismic traces, e m Representing the corresponding energies of the different seismic trace numbers. The upper left element of the covariance matrix C in the above equation is the energy-weighted dip bias, while the lower right element is the amplitude gradient bias. Defining the total deviation as the covariance matrix C first eigenvalue, (equation 9) can be re-characterized as follows:
thus, the first eigenvalue λ of equation (10) 1 Can be expressed as:
substituting formula (9) into formula (11) to obtain a first characteristic value lambda 1 The specific expression form of (2) is as follows:
by using lambda in the above 1 The calculated total deviation is the geometric nonparallel statistical attribute, the geometric nonparallel statistical attribute in the formula is utilized to carry out the edge detection of the special lithofacies, and the application of actual data shows that the algorithm has obvious recognition effect on the edge detection of the special lithofacies.
FIG. 1 shows a flowchart of an algorithm for calculating geometrical non-parallel properties using seismic data, wherein dip deviation properties, energy gradient deviation properties, and total deviation properties are mathematically independent properties, expressed by what are (5), (8), and (12), respectively. The dip deviation attribute computes the lateral variation of the dip of the structure, while the energy gradient deviation attribute characterizes the lateral variation of the seismic amplitude. The total deviation attribute calculates the covariance of the formation dip and energy, representing not only the edges of the formation dip changes, but also the internal reflection amplitude changes of the karst collapsed seismic phase.
The invention also provides a practical application embodiment, which comprises the following steps:
the invention is used in a 3D earthquake work area with a certain wide azimuth angle at sea to prove the practicability of the geometrical nonparallel statistical attribute in earthquake phase analysis. FIG. 2 is a three-dimensional seismic section and a time slice of 3D seismic data of the region, where white polygons represent interpreted karst seismic facies and arrows represent major large faults.
FIGS. 3, 4 and 5 show sections and time slices of dip angle deviation, energy gradient deviation and total deviation properties, respectively, from which it can be seen that the dip angle deviation properties shown in FIG. 3 can highlight both karst seismic phase characteristics and fault properties, and the dip angle deviation properties on the sections can also reflect collapsed edge characteristics of the karst seismic phase; the energy gradient bias attribute corresponding to fig. 4 also shows low value anomalies at the dip edges of the karst seismic facies, and the overall bias attribute (fig. 5) is more distinct in karst dip characteristics and more spatially delineated than the dip bias attribute (fig. 3), but the depiction of the overall bias attribute on faults is somewhat weaker than the dip bias attribute or the energy gradient bias attribute.
To further demonstrate the ability of the geometric non-parallel attributes in seismic facies analysis, the geometric non-parallel attributes with coherence and dip amplitude were characterized using multi-attribute classification, as shown in fig. 6, from which it can be seen that karst edge features are represented in grey-white, and karst collapse features can be distinguished from the background by color. Fig. 7 shows the same time slices represented by the pasteurization distances, with higher pasteurization distances representing karst collapse characteristics. For 3D visualization, three-dimensional engraving with a papanicolaou distance less than 0.5 as a threshold value, as shown in fig. 8, gives an excellent three-dimensional visualization attempt of karst collapse characteristics.
Seismic geometry attributes (e.g., seismic coherence attributes and dip amplitude attributes, etc.) may be used to map discontinuous bodies in different directions. However, these properties do not effectively enable edge detection directly for special geologic bodies such as karsts. In the present invention we propose tilt deviation properties, energy gradient deviation properties and total deviation properties based on statistical measurements of structural tilt components and energy gradients. The dip angle deviation attribute is from the lamellar change of the shaped stratum, and the energy gradient deviation attribute is independent of the dip angle deviation mathematically, so that the edge characteristics of the special geologic body are more clear. In conventional seismic data, reflected waves along the fault surface exhibit strong amplitude and structural dip changes, which also lead to edge property anomalies in dip deviation properties and energy gradient deviation properties. The total deviation attribute of the calculated dip angle and the energy lateral variation covariance better distinguishes the unconnected geologic volume from faults. And inputting the three non-parallel attribute bodies together with the dip angle amplitude attribute and the coherence attribute, performing classification work based on multiple attributes, adding three statistical attributes to remarkably improve the distinguishing degree of the discontinuous geologic body and other background geologic bodies, and finally calibrating through drilled wells to give a threshold value of the Pasteur distance, thereby performing three-dimensional spatial characterization on the discontinuous geologic body. Compared with the traditional discontinuous geologic body based on manual interpretation, the non-parallel attribute and the automatic interpretation workflow provided by the invention not only greatly quicken the interpretation process of the discontinuous geologic body, but also greatly improve the interpretation precision.
A second aspect of the present invention provides a detection apparatus for the above edge detection method, including:
a first processing unit for collecting seismic data of a seismic phase to be analyzed;
the second processing unit is used for calculating the construction dip angle deviation of the earthquake;
a third processing unit for calculating an amplitude or energy gradient deviation of the earthquake;
the fourth processing unit is used for calculating total deviation of the earthquake based on the deviation of the structural inclination angle and the deviation of the amplitude or the energy gradient, wherein the total deviation of the earthquake is the geometric non-parallel statistical attribute, and the edge detection of the earthquake phase is carried out by utilizing the geometric non-parallel statistical attribute.
A third aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the edge detection method described above.
A fourth aspect of the invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above-described edge detection method when executing the computer program.
The present invention is described in terms of flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. An edge detection method based on geometric non-parallel statistical properties is characterized by comprising the following steps:
collecting seismic data of a seismic phase to be analyzed;
calculating the structural dip angle deviation of the earthquake;
calculating amplitude or energy gradient deviation of the earthquake;
calculating total deviation of the earthquake based on the deviation of the structural dip angle, the deviation of the amplitude or the deviation of the energy gradient, wherein the total deviation of the earthquake is the geometric non-parallel statistical attribute, and the edge detection of the earthquake phase is carried out by utilizing the geometric non-parallel statistical attribute;
the total deviation of the earthquake is calculated as follows:
wherein lambda is 1 Is a first characteristic value; j is the whole seismic body; mu (mu) k The average component of unit normal in the analysis window for the earthquake; j is the number of seismic traces; e, e j Energy for the jth seismic trace; n is n kj Is the normal component of the jth seismic trace; g kj Gradients along the inline and crossline directions for the jth seismic trace; v k Representing the average gradient component along the inline and crossline.
2. The edge detection method of claim 1, wherein collecting seismic data for a seismic phase to be analyzed comprises: the inline dip, crossline dip, energy or root mean square attribute, inline amplitude gradient, and crossline amplitude gradient of the dip are constructed.
3. The edge detection method according to claim 1, wherein calculating the structural dip deviation of the earthquake comprises the steps of:
converting the conventional time domain seismic data from a time domain t to a depth domain z by utilizing speed data, and constructing a main line component p and a longitudinal line component q of the dip angle, wherein the components of the dip angle are represented by the angle of the dip angle along the horizontal direction;
for some areas with large construction amplitude, the inclination angle is represented by a unit normal direction n;
where the energy is strongest, the three normal components n are measured at appropriate metrics by the energy attribute, root mean square amplitude attribute, or envelope attribute on the jth seismic trace k Weighting is carried out to calculate the average component of unit normal of the whole seismic body J in an analysis window;
from these given values, the energy weighted standard deviation of the vector tilt angle with respect to its mean is obtained and characterized in terms of angle.
4. The edge detection method according to claim 1, wherein calculating the amplitude or energy gradient deviation of the earthquake comprises the steps of:
defining amplitude variations of the phase reflections within the analysis window along the formation dip, using a covariance matrix to form a first eigenvector of the window, weighting the derivatives of the main and crosslines of this eigenvector by the first eigenvalue or the square root of the first eigenvalue, for steep dip areas, the gradient g needs to be compensated by increasing the seismic reflection length between adjacent traces;
two average gradient components are calculated to obtain an amplitude gradient bias.
5. The edge detection method according to claim 1, wherein the calculation of the total deviation of the earthquake based on the deviation of the construction inclination and the deviation of the amplitude or energy gradient comprises the steps of:
after the amplitude gradient deviation is calculated, the covariance matrix C is utilized to compare the change in an analysis window of the unit normal inclination angle vector n and the amplitude gradient vector g;
defining the total deviation as a first eigenvalue lambda of the covariance matrix C 1 Using the first characteristic value lambda 1 And calculating to obtain the total deviation, namely the geometric non-parallel statistical attribute, and carrying out the edge detection of the seismic phase by utilizing the geometric non-parallel statistical attribute.
6. A detecting device for an edge detecting method according to any one of claims 1 to 5, comprising:
a first processing unit for collecting seismic data of a seismic phase to be analyzed;
the second processing unit is used for calculating the construction dip angle deviation of the earthquake;
a third processing unit for calculating an amplitude or energy gradient deviation of the earthquake;
the fourth processing unit is used for calculating total deviation of the earthquake based on deviation of the structural dip angle, the amplitude or the energy gradient, wherein the total deviation of the earthquake is a geometric non-parallel statistical attribute, and the edge detection of the earthquake phase is carried out by utilizing the geometric non-parallel statistical attribute;
the total deviation of the earthquake is calculated as follows:
wherein lambda is 1 Is a first characteristic value; j is the whole seismic body; mu (mu) k The average component of unit normal in the analysis window for the earthquake; j is the number of seismic traces; e, e j Energy for the jth seismic trace; n is n kj Is the normal component of the jth seismic trace; gk j Gradients along the inline and crossline directions for the jth seismic trace; v k Representing the average gradient component along the inline and crossline.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the edge detection method according to any one of claims 1-5.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the edge detection method according to any one of claims 1-5 when the computer program is executed by the processor.
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