CN114002739A - Edge detection method, device and medium based on geometric non-parallel statistical attributes - Google Patents
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Abstract
The invention relates to an edge detection method, a device, a medium and equipment based on geometric non-parallel statistical attributes, wherein the detection method comprises the following steps: collecting seismic data of a seismic facies to be analyzed; calculating the deviation of the structure inclination angle of the earthquake; calculating amplitude or energy gradient deviation of the earthquake; and calculating the total earthquake deviation based on the structural inclination deviation and the amplitude or energy gradient deviation, wherein the total earthquake deviation 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 method sequentially calculates the structural dip angle deviation attribute, the amplitude energy gradient attribute and the total deviation attribute according to the seismic data, so that the algorithm based on the geometric non-parallel statistical attribute is obtained and applied to the edge detection of the actual earthquake, 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 attributes, and belongs to the technical field of seismic analysis.
Background
Pattern recognition based on computer-aided seismic classification accelerates the speed of traditional seismic interpretation, which requires automatic extraction of effective seismic attributes of a target layer from a large amount of data and consumes a large amount of time and effort of interpreter personnel. Some special seismic facies, such as salt domes or karst seismic features, can often be distinguished from surrounding sedimentary rocks with stratifications by lateral and vertical changes in dip, energy, and continuity.
For seismic facies analysis, the type of seismic facies is usually analyzed by some seismic attributes, and in the analysis process, some attributes do not well indicate the seismic facies, and analysis needs to be performed by combining a plurality of attributes. The properties commonly used for seismic facies analysis are the dip angle properties, amplitude properties, energy properties, discontinuity properties, structural properties, etc. of the reflecting surfaces, and therefore it is a huge task to select suitable properties that can characterize the seismic facies. In order to better ensure the lateral resolution of the seismic attributes, the local dip attributes are usually used for constraint in seismic attribute calculation, and there are many methods for calculating the local dip of the formation, including various algorithms based on single-pass cross-correlation, multi-pass analysis, discrete surface scanning, gradient structure tensor, plane waveform analysis, and the ratio of smooth instantaneous wave number to smooth instantaneous frequency. The first statistical measurement of the reflection surface tilt attribute was proposed by Barnes, which measures the deviation in parallelism, which is estimated from the standard deviation of the vector tilt. Lateral variation of the tilt angle properties of the reflective surfaces can be measured by curvature or energy properties, while vertical variation of the tilt angle can be measured by reflective surface convergence.
Conventional window-based computed amplitude class attributes include an average amplitude attribute and a root-mean-square amplitude attribute. The instantaneous envelope attribute, instantaneous frequency attribute and other attributes sensitive to the vertical variation of seismic amplitude can be calculated through Hilbert transform. In contrast, the homodromous and lateral components of the amplitude gradient are sensitive to lateral variations in amplitude and seismic reflectors. Marfurt represents the coherent amplitude gradient through the gradient of the first eigenvector, which most closely represents the seismic data within the analysis window, and then weights the results by the first eigenvalue. The coherent amplitude gradient highlights subtle lateral amplitudes caused by lateral variations in layer thickness, including those below the 1/4 wavelength tuning thickness, which can be further enhanced by calculating the second derivative of the amplitude, resulting in a measure of amplitude curvature.
If the seismic reflection pattern of one or more targets is chaotic, such as under salt dome and karst seismic signatures, the chaotic statistical measurement of attributes facilitates multi-attribute classification. In the traditional phase analysis, an interpreter combines 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 only limited to two or three different attributes.
Disclosure of Invention
In order to solve the technical problems, the invention provides an edge detection method, a device, a medium and equipment based on geometric non-parallel statistical attributes.
In order to achieve the purpose, the invention adopts the following technical scheme:
an edge detection method based on geometric non-parallel statistical attributes comprises the following steps:
collecting seismic data of a seismic facies to be analyzed;
calculating the deviation of the structure inclination angle of the earthquake;
calculating amplitude or energy gradient deviation of the earthquake;
and calculating the total deviation of the earthquake based on the constructed dip angle deviation and the amplitude or energy gradient deviation, 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 collects seismic data of a seismic facies to be analyzed, and includes: constructing a inline dip, a tie line dip, an energy or root mean square attribute, an inline amplitude gradient, a tie line amplitude gradient for the dip.
The edge detection method preferably calculates the structural dip angle deviation of the earthquake, and comprises the following steps:
converting seismic data of a conventional time domain from a time domain t to a depth domain z by using velocity data, and representing the components of the constructed dip angle by using the angle of the dip angle along the horizontal direction through a main measuring line component p and a longitudinal measuring line component q of the constructed dip angle;
for some regions with large structural amplitude, the unit normal direction n is used for representing the inclination angle;
where the energy is strongest, the three normal components n are measured by energy property, root mean square amplitude property or envelope property on the jth seismic tracekTo calculate the average component of the unit normal of the entire seismic volume J within the analysis window;
from these given values, the energy weighted standard deviation of the vector dip with respect to its mean is obtained and characterized in units of angles.
The edge detection method preferably calculates the amplitude or energy gradient deviation of the earthquake, and comprises the following steps:
along the construction dip, forming a first eigenvector of the window by using a covariance matrix to define amplitude changes of coherent reflections in the analysis window, weighting derivatives of a main line and a crossline of the eigenvector by the first eigenvalue or a square root of the first eigenvalue, wherein for a steep dip region, the gradient g needs to be compensated by increasing the length of seismic reflections between adjacent traces;
two average gradient components are calculated to obtain the amplitude gradient bias.
The edge detection method preferably calculates the total deviation of the earthquake based on the construction dip angle deviation and the amplitude or energy gradient deviation, and comprises the following steps:
after the amplitude gradient deviation is calculated, comparing the change in the analysis window of the unit normal inclination angle vector n and the amplitude gradient vector g by using a covariance matrix C;
defining the total deviation as a first eigenvalue λ of the covariance matrix C1Using the first characteristic value lambda1And calculating to obtain a total deviation which is a geometric non-parallel statistical attribute, and carrying out edge detection on the seismic facies by using the geometric non-parallel statistical attribute.
A second aspect of the present invention provides a detection apparatus for the edge detection method, including:
the first processing unit is used for collecting seismic data of a seismic facies to be analyzed;
the second processing unit is used for calculating the tectonic dip angle deviation of the earthquake;
a third processing unit for calculating amplitude or energy gradient bias of the earthquake;
and the fourth processing unit is used for calculating the total deviation of the earthquake based on the constructed dip angle deviation and the amplitude or energy gradient deviation, 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 invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above-mentioned edge detection method.
A fourth aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the 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 seismic waveform reflections and clutter waveform reflection seismic signatures, the present invention introduces three-dimensional geometric non-parallel statistical properties to quantify the lateral and longitudinal variations of dip and amplitude gradients. When salt dome or karst earthquake response is met, for earthquake data with high signal-to-noise ratio, good predictability is achieved by utilizing coherence attributes, texture attributes and the like, but for earthquake data with low signal-to-noise ratio, the simple attributes cannot perform good edge detection on the special earthquake phases. In order to solve the problem, the invention uses the seismic data as input, and verifies the effectiveness of the invention by respectively obtaining the dip angle deviation attribute, the amplitude energy gradient attribute and the total deviation attribute of the seismic data and then developing an algorithm based on the geometric non-parallel statistical attribute to be applied to the edge detection of the actual earthquake.
2. The invention uses earthquake data as input, calculates the statistical properties of the main measuring line, the cross measuring line and the like of the structure dip angle property and the amplitude gradient vector property of the earthquake, defines the dip angle deviation as the energy weighted standard deviation of the vector dip angle relative to the mean value thereof, and defines the energy gradient deviation as the standard deviation of the amplitude gradient vector in the analysis window. Then, we use these new geometric non-parallel statistical properties to perform edge detection of special facies bodies.
Drawings
FIG. 1 is a flowchart of an algorithm for computing geometric non-parallelism based on seismic data, according to an embodiment of the invention;
FIG. 2 is a three-dimensional seismic data seismic RMS amplitude time slice and corresponding three-dimensional seismic sections provided in accordance with this embodiment of the invention;
FIG. 3 is a slice of the dip attribute and a cross section corresponding to three directions provided by the embodiment of the present invention;
FIG. 4 is a slice of the energy gradient bias property provided by this embodiment of the invention and a cross-section corresponding to three directions;
FIG. 5 is a total deviation attribute slice and cross-sections corresponding to 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 present invention;
FIG. 7 is a planar time slice represented by Papanicolaou distance provided by this embodiment of the present invention;
fig. 8 is a three-dimensional perspective view of a discrete plastid characterized by the pasteur distance provided by this embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention are described clearly and completely below, and it is obvious that the described embodiments are some, not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention uses earthquake data as input, calculates the statistical properties of the main measuring line, the cross measuring line and the like of the structure dip angle property and the amplitude gradient vector property of the earthquake, defines the dip angle deviation as the energy weighted standard deviation of the vector dip angle relative to the mean value thereof, and defines the energy gradient deviation as the standard deviation of the amplitude gradient vector in the analysis window. Then, edge detection of special lithofacies bodies in the seismic phases is carried out by utilizing the new geometric non-parallel statistical attributes.
The whole calculation and implementation process can be divided into three steps in total:
the first step is as follows: calculating the structural dip deviation of the earthquake:
since most three-dimensional seismic data is time-domain, most dip calculations (including the calculation methods in the Radon transform) are calculated in the time domain. But subsequent curvature calculations require characterization of these dip angles in the depth domain. Therefore, the seismic data in the conventional time domain needs to be converted from the time domain t to the depth domain z by using the velocity data, and the component of the structural dip is characterized by the angle of the structural dip along the horizontal direction through the main line component p and the longitudinal line component q of the structural dip, and then:
where x denotes along the inline direction, y denotes along the crossline direction, z denotes down along the vertical direction, θxRepresenting the apparent inclination angle, theta, along the inline x directionyIndicating the apparent tilt along the crossline y, the values of p and q apply to the medium tilt values. However, for some regions of particularly large formation amplitude, the steep dip of the formation may become very large. At this time, the inclination angle can also be generally characterized by a unit normal direction n, and the specific expression is as follows:
for seismic data, the dip is most difficult to calculate at the locations where the energy is strongest. Thus, the three normal components n may be measured in appropriate measures by an energy property, a root mean square amplitude property, or an envelope property on the jth seismic trace, etck(k x, y, z) to calculate the average component of the unit normal within the analysis window for the entire seismic volume J:
wherein, mukIs the average component of unit normal of earthquake in the analysis window, j is the number of seismic channels, ejIs the energy of the jth seismic trace, nkjFor the normal component of the jth seismic trace, given these values, the energy weighted standard deviation of the vector dip with respect to its mean can be found, namely:
in degrees, can be characterized as:
the second step is that: calculating amplitude or energy gradient bias of the earthquake:
for seismic waveforms, amplitude or energy gradients can generally indicate changes in the thickness of the formation, or lateral changes in the formation with significant differences in impedance at the upper or lower portions of the formation. This lateral variation is different from the energy variation of the clutter, which is often seen in complex formations such as salt domes, karst landforms, or overpressured shale gas.
The vertical variation in seismic amplitude is a combination of lithology, thickness of the formation, and seismic wavelets. In contrast, seismic wavelets are relatively constant along formation dip, so the lateral variation in amplitude along formation dip is a variation in lithology and formation thickness. Along this dip, the amplitude variation of coherent reflections within the analysis window is defined by forming a first eigenvector of the window using a covariance matrix, the derivative of the principal and crossline of this eigenvector being weighted by the first eigenvalue (energy-weighted coherent amplitude gradient) or the square root of the first eigenvalue (root-mean-square amplitude-weighted coherent amplitude gradient). For steeply dipping regions, the gradient g needs to be compensated by increasing the seismic reflection length between adjacent traces. Defining ξ as the distance in the x-z plane and η as the distance in the y-z plane, then there are:
wherein, gξDenotes the gradient in the z-plane, g, along the inline x-direction and the perpendicular directionηDenotes the gradient in the z-plane along the crossline y-direction and the vertical direction, gxDenotes the gradient along the inline x direction, gyRepresenting the gradient along the crossline y direction.
As with the dip deviation calculation, two average gradient components are first calculated:
wherein, gkjFor the gradient of the jth seismic trace along the inline and crossline directions, vkRepresenting the average gradient component along the inline and crossline, which does not require weighting of the energy at this point, since the gradient is calculated as a measure of the lateral variation of the energy. The amplitude gradient bias is therefore:
wherein sigmagradientRepresenting an amplitude gradient deviation amount;
the third step: calculating the total deviation:
after calculating the amplitude gradient bias, the variance matrix C is used to compare the variation in the analysis window for unit normal tilt angle vector n and amplitude gradient vector g as shown in the following equation:
wherein R isn＝sin(Rdip)，RnDenotes the sum of the normal components, RdipDenotes the sum of the angles of inclination, RgRepresenting the sum of the gradient components, m being the number of different seismic traces, emRepresenting the corresponding energies for different numbers of seismic traces. The top left element of the covariance matrix C in the above equation is the energy weighted tilt deviation and the bottom right element is the amplitude gradient deviation. Defining the total deviation as the first eigenvalue of the covariance matrix C, (equation 9) can be re-characterized as follows:
therefore, the first characteristic value λ of equation (10)1Can be expressed as:
substituting equation (9) into equation (11) can obtain a first eigenvalue λ1The specific expression form of (A) is as follows:
using the above formula for λ1The calculated total deviation is the geometric non-parallel statistical attribute, the edge detection of the special lithofacies is carried out by utilizing the geometric non-parallel statistical attribute in the formula, and the application of actual data shows that the algorithm has obvious identification effect on the edge detection of the special lithofacies.
FIG. 1 shows a flow chart of an algorithm for computing geometric non-parallelism using seismic data, where dip bias, energy gradient bias, and total bias are mathematically independent attributes, and the mathematical expressions are respectively which of equations (5), (8), and (12). The dip bias attribute calculates the lateral variation of the structure dip, while the energy gradient bias attribute characterizes the lateral variation of the seismic amplitude. The total deviation attribute calculates the covariance of the formation dip and energy, and thus represents not only the edges of the formation dip changes, but also the internal reflection amplitude changes of the litho-collapsed seismic facies.
The invention also provides a practical application embodiment, which is as follows:
the method is used in a 3D earthquake work area with a certain wide azimuth angle on the sea to prove the practicability of the geometric non-parallel statistical attribute provided by the invention in earthquake phase analysis. FIG. 2 is a seismic section and a time slice of three directions of 3D seismic data for the region, in which white polygons represent interpreted litho seismic facies and arrows represent major large faults.
FIG. 3, FIG. 4 and FIG. 5 show the section and time slice of the dip deviation, energy gradient deviation and total deviation attribute, respectively, and it can be seen from the figure that the dip deviation attribute shown in FIG. 3 can highlight the karst seismic facies feature and fault attribute at the same time, and the dip deviation attribute on the section can also reflect the collapse edge feature of the karst seismic facies; the energy gradient bias attribute corresponding to fig. 4 also shows low-value anomaly at the collapse edge of the litho seismic facies, and the total bias attribute (fig. 5) is more distinct in litho collapse characteristics and more clear in spatial delineation than the dip bias attribute (fig. 3), but the total bias attribute is slightly weaker in delineation than the dip bias attribute or the energy gradient bias attribute.
To further demonstrate the ability of the geometric non-parallel property in seismic facies analysis, the geometric non-parallel property with coherence and dip amplitude was characterized by multi-property classification, as shown in fig. 6, where the karst edge features are represented in gray-white and the karst collapse features can be distinguished from the background by color. Fig. 7 shows the same time slice represented by the pap distance, with higher pap distances representing the karst collapse feature. For 3D visualization, three-dimensional sculpting with a barth distance less than 0.5 as a threshold, as shown in fig. 8, gives an excellent three-dimensional visualization attempt of the karst collapse feature.
Seismic geometry attributes (e.g., seismic coherence attributes and dip amplitude attributes, etc.) may be used to map discontinuous geobodies in different directions. However, these properties are not effective in performing edge detection directly on specific geological bodies such as karsts. In the present invention, we propose dip bias properties, energy gradient bias properties and total bias properties based on statistical measurements of the structural dip component and energy gradient. The dip deviation attribute comes from the lamellar variation of the conformal stratum, and the energy gradient deviation attribute is mathematically independent of the dip deviation, so that the edge characteristics of a special geologic body are more definite. In conventional seismic data, reflected waves along the fault surface exhibit strong amplitude and structural dip variations, which also result in edge property anomalies in dip bias properties and energy gradient bias properties. Calculating the total deviation attribute of the dip and energy lateral variation covariance better distinguishes the unconnected geologic volume from faults. Inputting the three non-parallel attribute bodies together with the inclination angle amplitude attribute and the coherence attribute to carry out classification work based on multiple attributes, remarkably improving the distinguishing degree of the discontinuous body and other background bodies by adding the three statistical attributes, and finally carrying out calibration through drilled wells to give a threshold value of the Pasteur distance so as to carry out three-dimensional space characterization on the discontinuous body. Compared with the traditional discontinuous geologic body based on manual interpretation, the non-parallel attribute and automatic interpretation workflow provided by the invention not only greatly quickens the interpretation process of the discontinuous geologic body, but also greatly improves the interpretation precision.
A second aspect of the present invention provides a detection apparatus for the edge detection method, including:
the first processing unit is used for collecting seismic data of a seismic facies to be analyzed;
the second processing unit is used for calculating the tectonic dip angle deviation of the earthquake;
a third processing unit for calculating amplitude or energy gradient bias of the earthquake;
and the fourth processing unit is used for calculating the total deviation of the earthquake based on the constructed dip angle deviation and the amplitude or energy gradient deviation, 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 invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the above-mentioned edge detection method.
A fourth aspect of the present invention provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the 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 specific embodiments. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. An edge detection method based on geometric non-parallel statistical attributes is characterized by comprising the following steps:
collecting seismic data of a seismic facies to be analyzed;
calculating the deviation of the structure inclination angle of the earthquake;
calculating amplitude or energy gradient deviation of the earthquake;
and calculating the total deviation of the earthquake based on the constructed dip angle deviation and the amplitude or energy gradient deviation, 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.
2. The edge detection method of claim 1, wherein collecting seismic data for a seismic facies to be analyzed comprises: constructing a inline dip, a tie line dip, an energy or root mean square attribute, an inline amplitude gradient, a tie line amplitude gradient for the dip.
3. The edge detection method of claim 1, wherein calculating the tectonic dip bias of the earthquake comprises the steps of:
converting seismic data of a conventional time domain from a time domain t to a depth domain z by using velocity data, and representing the components of the constructed dip angle by using the angle of the dip angle along the horizontal direction through a main measuring line component p and a longitudinal measuring line component q of the constructed dip angle;
for some regions with large structural amplitude, the unit normal direction n is used for representing the inclination angle;
where the energy is strongest, the three normal components n are measured by energy property, root mean square amplitude property or envelope property on the jth seismic tracekTo calculate the average component of the unit normal of the entire seismic volume J within the analysis window;
from these given values, the energy weighted standard deviation of the vector dip with respect to its mean is obtained and characterized in units of angles.
4. The edge detection method of claim 1, wherein calculating the amplitude or energy gradient bias of the earthquake comprises the steps of:
along the construction dip, forming a first eigenvector of the window by using a covariance matrix to define amplitude changes of coherent reflections in the analysis window, weighting derivatives of a main line and a crossline of the eigenvector by the first eigenvalue or a square root of the first eigenvalue, wherein for a steep dip region, the gradient g needs to be compensated by increasing the length of seismic reflections between adjacent traces;
two average gradient components are calculated to obtain the amplitude gradient bias.
5. The edge detection method of claim 1, wherein calculating the total deviation of the earthquake based on the formation dip deviation and the amplitude or energy gradient deviation comprises the steps of:
after the amplitude gradient deviation is calculated, comparing the change in the analysis window of the unit normal inclination angle vector n and the amplitude gradient vector g by using a covariance matrix C;
defining the total deviation as a first eigenvalue λ of the covariance matrix C1Using the first characteristic value lambda1And calculating to obtain a total deviation which is a geometric non-parallel statistical attribute, and carrying out edge detection on the seismic facies by using the geometric non-parallel statistical attribute.
6. A detection apparatus according to any one of claims 1 to 5, comprising:
the first processing unit is used for collecting seismic data of a seismic facies to be analyzed;
the second processing unit is used for calculating the tectonic dip angle deviation of the earthquake;
a third processing unit for calculating amplitude or energy gradient bias of the earthquake;
and the fourth processing unit is used for calculating the total deviation of the earthquake based on the constructed dip angle deviation and the amplitude or energy gradient deviation, 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.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the edge detection method according to any one of claims 1 to 5.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the edge detection method according to any one of claims 1 to 5 when executing the computer program.
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