CN111025394A - Depth domain-based seismic data fine fault detection method and device - Google Patents
Depth domain-based seismic data fine fault detection method and device Download PDFInfo
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Abstract
The embodiment of the invention provides a depth domain-based seismic data fine fault detection method and a device, wherein the method comprises the following steps: acquiring depth domain seismic data in the area to be identified by using a prestack depth domain processing technology, and collecting detection data in the area to be identified; acquiring seismic wavelets of a target interval in an area to be identified; acquiring depth domain seismic horizon interpretation of a target interval; acquiring a corresponding seismic attribute body based on the seismic wavelet; extracting an attribute of an edge layer in a depth domain attribute body by using depth domain seismic horizon interpretation; and analyzing the attribute of the bedding by using a principal component analysis method to obtain a classified result, and delineating the minor fault according to the classified result. The embodiment of the invention is applied to analyze and explain more small faults and can be more accurate.
Description
Technical Field
The invention relates to seismic data fine fault detection based on a depth domain, in particular to a seismic data fine fault detection method and device based on the depth domain.
Background
With the increasing demand of coal mine safety production, the identification of a small fault of 3-5m becomes one of important indexes of coal mine safety and high-efficiency production.
The invention patent with the application number of CN201610143328.8 in the prior art discloses a micro-amplitude structure fine prediction method, which comprises the steps of 1) correcting the depth of an extended reach well; 2) analyzing the cycle response characteristics on the logging and seismic data, and finely calibrating the target interval by utilizing VSP data; 3) calculating a seismic coherent data volume, performing fault fine interpretation by combining the seismic coherent data volume, and determining whether a small fault exists; 4) according to well seismic calibration and seismic profile comparison analysis, determining seismic reflection event axes corresponding to the top surface of the reservoir and performing fine horizon interpretation; 5) selecting the time-depth relationship of each well after calibration, establishing an isochronous stratigraphic framework by adopting a stable seismic sequence interpretation interface, and adding the time-depth relationship interpolation extrapolation of each well under the constraint of the isochronous stratigraphic framework to establish a three-dimensional velocity field; 6) gridding the depth domain layer, and drawing a depth contour line; 7) adding well point geological stratification correction depth to the depth grid after the grid in the step 6) to obtain a depth structure diagram. By combining the seismic coherence body and adopting the thought of true three-dimensional interpretation, the fault is finely interpreted, the minor fault is more accurately interpreted, and the fault interpretation precision is improved.
However, in the prior art, the true three-dimensional interpretation cannot fully utilize attribute information such as coherent bodies, and the detection result of the small fault is not accurate enough.
Disclosure of Invention
The invention aims to provide a method and a device for detecting a fine fault based on depth domain seismic data to improve the detection accuracy of the small fault.
The invention solves the technical problems through the following technical means:
the embodiment of the invention provides a depth domain-based seismic data fine fault detection method, which comprises the following steps:
acquiring depth domain seismic data in a region to be identified by utilizing a prestack depth domain processing technology, and collecting detection data in the region to be identified, wherein the detection data comprises: drilling well position, layering, logging data and histogram;
acquiring seismic wavelets of a target interval in an area to be identified; acquiring depth domain seismic horizon interpretation of a target interval;
acquiring a corresponding seismic attribute body based on the seismic wavelets, wherein the seismic attribute body comprises: variance, coherence, curvature, and ant;
extracting an attribute of an edge layer in a depth domain attribute body by using depth domain seismic horizon interpretation;
and analyzing the attribute of the bedding by using a principal component analysis method to obtain a classified result, and delineating the minor fault according to the classified result.
By applying the depth domain seismic data fine detection method and device provided by the embodiment of the invention, the abnormal response information of attribute bodies such as variance, coherence, curvature, ants and the like is utilized, and the abnormal information of the small fault is finely depicted on the basis, so that the depth domain interpretation is more accurate and clear, and more small faults are analyzed and interpreted more accurately by the method.
Optionally, the obtaining of depth domain seismic data in the region to be identified by using the prestack depth domain processing technology includes:
and (3) establishing an accurate three-dimensional speed model by combining drilling, roadway exposure and working face extraction data as constraints, and performing high-precision depth domain imaging processing by adopting a matched prestack depth migration technology to obtain prestack depth migration data.
Optionally, the acquiring the seismic wavelet of the target interval in the region to be identified includes:
selecting Hamon-Russell software, and loading a depth domain seismic data body, a logging curve and drilling hole layering data;
extracting Rake wavelets to perform first depth domain synthetic seismic record calibration, and firstly calibrating and identifying wave group characteristics of a target interval;
and then, finely calibrating the depth domain synthetic seismic record of the wave group characteristics of the target interval by using the well side channel wavelets, and matching with the well side channel according to the wave signal similarity principle to obtain the seismic wavelets of the wave group characteristics of the target interval.
Optionally, when extracting the attribute along the layer, the method further includes: and (5) carrying out attribute sensitivity analysis and research on the development of the micro crack structure.
Optionally, the analyzing the attribute of the edge layer by using a principal component analysis method to obtain a classified result includes:
carrying out data standardization processing on the attribute data along the layer;
establishing a correlation coefficient array of variables: r ═ Rij)nxnWherein R is a correlation coefficient matrix; rijIs the correlation between the ith attribute and the jth attribute;
and determining the number of the main components.
The embodiment of the invention provides a depth domain seismic data-based fine minor fault detection device, which comprises:
the collecting module is used for acquiring depth domain seismic data in the area to be identified by utilizing a prestack depth domain processing technology and collecting detection data in the area to be identified, wherein the detection data comprises: drilling well position, layering, logging data and histogram;
the acquisition module is used for acquiring seismic wavelets of a target interval in the area to be identified; acquiring depth domain seismic horizon interpretation of a target interval;
acquiring a corresponding seismic attribute body based on the seismic wavelets, wherein the seismic attribute body comprises: one or a combination of variance, coherence, curvature and ant;
an extraction module for extracting an attribute of an edge layer in a depth domain attribute body by using depth domain seismic horizon interpretation;
and the analysis module is used for analyzing the attribute of the bedding by utilizing a principal component analysis method to obtain a classified result and outlining the minor fault according to the classified result.
Optionally, the collecting module is configured to:
and (3) establishing an accurate three-dimensional speed model by combining drilling, roadway exposure and working face extraction data as constraints, and performing high-precision depth domain imaging processing by adopting a matched prestack depth migration technology to obtain prestack depth migration data.
Optionally, the obtaining module is configured to:
selecting Hamon-Russell software, and loading a depth domain seismic data body, a logging curve and drilling hole layering data;
extracting Rake wavelets to perform first depth domain synthetic seismic record calibration, and firstly calibrating and identifying wave group characteristics of a target interval;
and then, finely calibrating the depth domain synthetic seismic record of the wave group characteristics of the target interval by using the well side channel wavelets, and matching with the well side channel according to the wave signal similarity principle to obtain the seismic wavelets of the wave group characteristics of the target interval.
Optionally, the extracting module is configured to: and (5) carrying out attribute sensitivity analysis and research on the development of the micro crack structure.
Optionally, the analysis module is configured to:
carrying out data standardization processing on the attribute data along the layer;
establishing a correlation coefficient array of variables: r ═ Rij)nxnWherein R is a correlation coefficient matrix; rijIs the correlation between the ith attribute and the jth attribute;
and determining the number of the main components.
The invention has the advantages that:
by applying the depth domain seismic data fine detection method and device provided by the embodiment of the invention, the abnormal response information of attribute bodies such as variance, coherence, curvature, ants and the like is utilized, and the abnormal information of the small fault is finely depicted on the basis, so that the depth domain interpretation is more accurate and clear, and the small fault is analyzed and interpreted more accurately by the method.
Drawings
FIG. 1 is a schematic flow chart of a depth domain-based seismic data fine fault detection method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the coherence properties along a layer according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the variance attribute along a layer according to an embodiment of the present invention;
FIG. 4 is a schematic view of the curvature property along a layer provided by an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating the attributes of ants along a layer according to an embodiment of the present invention;
FIG. 6 is a basic flowchart of a principal component analysis method according to an embodiment of the present invention;
fig. 7 is a schematic view of a fault distribution obtained in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a depth domain-based seismic data fine fault detection method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
s101, acquiring depth domain seismic data in a region to be identified by utilizing a prestack depth domain processing technology, and collecting detection data in the region to be identified, wherein the detection data comprises: well location, layering, logging data, and histogram.
Illustratively, the acquisition of the depth domain seismic data and the borehole data in the area to be identified is to collect pre-stack depth migration seismic data and borehole well position, layering, logging data, histogram and other data after the area to be identified is processed.
Due to the fact that the post-stack time migration seismic data are influenced by various interference factors on the identification of the minor fault, and the production requirement is difficult to meet through an interpretation technology. The embodiment of the invention obtains the depth domain seismic data by using the pre-stack depth migration processing method which belongs to the relatively accurate seismic data processing technology at present.
The prestack depth domain interpretation has real and intuitive underground structure, can truly reflect the form of the underground structure, has more accurate geological significance compared with a time domain data body, and has more rationality for the horizon after the synthetic seismic record is corrected.
S102, acquiring seismic wavelets of a target interval in an area to be identified; and acquiring depth domain seismic horizon interpretation of the target interval.
Illustratively, direct calibration of depth domain synthetic recording is performed hierarchically according to the borehole data, and depth domain well-seismic calibration is performed on depth domain seismic data in the region by using logging data so as to correspond the seismic data with the detection data. And then determining an interval corresponding to the target layer, and further extracting seismic wavelets corresponding to the target layer according to the corresponding relation between the target layer and the seismic data, wherein the seismic wavelets comprise reflection features and wave group features. Specifically, on the basis of prestack time migration processing, a kirchhoff migration method is used for processing seismic data to obtain depth domain seismic data, Hamon-Russell software is used for loading a depth domain seismic data body and a logging curve and drilling layering, rake wavelets are extracted for first depth domain synthetic seismic record calibration, and wave group characteristics of a target interval are calibrated and identified; and then extracting well side channel wavelets to perform fine calibration of the depth domain synthetic seismic record, and matching with the well side channels according to the principle of similarity of wavelets. This process may also be referred to as a simplified one-dimensional forward process.
During fine calibration, according to the reflection characteristics of a target interval of a region to be identified, the wave group characteristics of a target layer are determined through a synthetic record calibration method on a depth domain seismic section, point, line and surface repeated cyclic comparison is carried out on the log-calibrated target layer homophasic axis through a marker layer and a target layer position mark on the seismic section, a layer position with the same wave group characteristics of the seismic homodromous axis at a drilling hole is found, then points with the drilling hole depth corresponding to the same layer position are connected into a line, a surface corresponding to the layer position is obtained according to a plurality of connecting lines corresponding to the same layer position, and the distribution of the stratum on the depth domain can be obtained, wherein the target layer is usually a coal seam.
In the embodiment of the invention, the prestack depth migration profile is a depth domain, and needs to be more detailed when interpreting and picking up data, so that artificial errors are prevented; after analyzing the change relation of a target layer, carrying out depth domain structure mapping according to a depth domain interpretation result, determining the development conditions of a formation dip angle, a backward dip and a fold through a contour map of a research area, and extracting the seismic attribute of the depth domain as layer interpretation by utilizing a depth domain interpretation horizon.
S103, acquiring a corresponding seismic attribute body based on the seismic wavelet, wherein the seismic attribute body comprises: variance, coherence, curvature, and ant.
FIG. 2 is a schematic diagram of the coherence properties along a layer according to an embodiment of the present invention; FIG. 3 is a schematic diagram of the variance attribute along a layer according to an embodiment of the present invention; FIG. 4 is a schematic view of the curvature property along a layer provided by an embodiment of the present invention; fig. 5 is a schematic diagram of the attribute of an ant body along a layer according to an embodiment of the present invention, which may extract attribute bodies such as variance, coherence, curvature, ant body, and the like by using depth domain seismic data.
In practical application, the ant body is sensitive to fine fractures, but the recognition effect on large faults is poor, and the variance, coherence and curvature bodies are sensitive to large faults. Thus, large faults can be identified by variance, coherence, and curvature.
It should be emphasized that the methods for extracting attribute bodies such as variance, coherence, curvature, ant body, etc. are the prior art, and the attribute bodies are seismic data bodies including measurement coordinates and seismic wave amplitude values. And will not be described in detail herein. In the field of coal field exploration geophysical prospecting, a small fault refers to a fault between 3 and 5 meters in length.
And S104, extracting the attribute of the along-layer in the depth domain attribute body by using the depth domain seismic horizon interpretation.
The seismic attribute of the depth domain seismic interpretation horizon in the seismic attribute body of the depth domain can be used for extracting the attribute of the boundary layer, and the attribute of the boundary layer is a tangent plane parallel to the stratum in the attribute body. The attribute sensitivity analysis and research are carried out on the structural development of the micro fracture by extracting the attribute of the edge layer from each attribute body aiming at the development micro fault in the region to be identified, and the identification capability of the micro fault is enhanced.
For example, depth domain interpretation may be used to extract seismic attribute edge slices along 5m edges above and below the target horizon; and comparing and analyzing the preliminarily obtained layer attributes with the interpretation fault of the region to be identified, and adjusting the position of the fault of the region to be identified and the breakpoint combination mode.
Furthermore, the planar attributes of the edge layers can be extracted along the upper and lower 5m of the target layer on the seismic attribute data of the coherence, variance, curvature and ant body depth domain, and the planar attributes sensitive to the small fault structure are selected preferably by utilizing the edge layer attributes.
Then, in step S105, the developing minor fault in the region to be identified is extracted from each attribute body, and the principal component analysis and research are carried out on the development of the micro fracture structure according to the layer attributes, so that the identification capability of the minor fault is enhanced.
S105, analyzing the attribute of the edge layer by using a principal component analysis method, namely comprehensively considering the geophysical significance under the guidance of a certain geological rule, selecting the attribute capable of representing the fault characteristics, carrying out mathematical operation transformation on a plurality of attributes, simultaneously considering the influence factors of each attribute on the fault, amplifying the dominant characteristics of the attributes, combining the influence factors to obtain the classified result, sketching out the small fault according to the classified result, analyzing the attribute of the edge layer of the coherence, variance, curvature and ant body by using a principal component analysis algorithm, automatically classifying, and outputting the classified result.
For example, fig. 6 is a basic flow chart of a principal component analysis method provided in an embodiment of the present invention, and as shown in fig. 6, firstly, a principal component analysis method is used to fuse attributes of extracted 4 edge layers, an attribute fusion technique is based on attribute optimization, attributes representing different representative fault features are selected, and a plurality of attributes are fused together after a certain mathematical operation, and the fused attributes can simultaneously consider the influence of each attribute on a fault, so that the principal fault is clearer after fusion, and some fine faults on a depth domain attribute body are well depicted.
Principal component analysis is a dimension reduction method, and corresponding features are extracted for a plurality of seismic attributes, because the seismic attributes are not really independent of each other, and information exists in any M groups of seismic attributes. The data may be divided into M groups using principal component analysis, each group representing an attribute of the classification. The data of the first main control assembly has the largest variation range, and each subsequent component is regarded as the variation as possible. The lower order components contain most of the variation of the seismic attribute and the higher order components contain most of the redundancy. Principal component attribute analysis is mainly used for reducing the dimensionality of a data set, preserving the change of an original data set as much as possible and determining basic variables.
Principal component analysis is to find the most dominant aspects in the data, replacing the original data with the most dominant ones.
① write the extracted m seismic attribute data (each attribute having n tracks of data) into a data matrix X.
Setting M along-layer attributes as samples, using M categories of the along-layer attributes as parameters, and writing the original data into a matrix X (X)ij)M×m(wherein xijFor the raw data of the jth sample in the ith along-layer attribute, i.e. seismic wavesStrength); the method comprises the following specific steps:
② the data matrix X is normalized by scaling the data to fall within a small specified interval to yield a normalized data array where R is (R)ij)m×m；
③ calculating the correlation coefficient matrix R of the data matrix, and calculating its eigenvalue and corresponding eigenvector by Jacobi iteration method (only one multiplication of matrix and vector is calculated once per iteration, and the original matrix X is always unchanged during calculation).
④ sorting the eigenvalues in descending order by a sorting algorithm to obtain lambda1≥λ2≥λ3≥...≥λmAnd corresponding feature vectors v1,v2,v3,...,vm。
⑤ feature vector v is orthogonalized by Schmidt1,v2,v3,...,vmPerforming unit orthogonalization to obtain u1,u2,u3,...,um。
⑥ determining the number of principal components, calculating the contribution rate and the cumulative contribution rate of the characteristic values, extracting p principal components, and F1,F2,F3,...,FpThe cumulative contribution rate g of the first p principal componentspNot less than a given extraction efficiency t (e.g. 85%), i.e. gp>t；
⑦ calculating the projection F of the normalized data X on the extracted p feature vectorsi＝ui1x1+ui2x2+...+uimxmI is 1,2, …, n. The obtained F is the main component finally extracted, namely the comprehensive variable after dimensionality reduction.
⑧ the contribution rate of the extracted principal component is used as a weighting coefficient, and the principal component is weighted and fused to obtain a principal component analysis fusion attribute map which can be directly used for displaying a small fault and a large fault.
The principal component analysis method is based on the classification of the amplitude and waveform of the input along-layer properties. The classification result is a classified attribute plane graph; each class contains a phase axis with a unique characteristic, and all phase axes are classified according to the degree of correlation with a particular classification characteristic. And performing weighted fusion processing on the principal component rows by taking the extracted principal component contribution rate as a weighting coefficient to obtain a principal component analysis fusion attribute, and finally delineating a small fault and using the small fault for fault verification. The principal component analysis method by extracting the attribute horizon of the fault layer analyzes the attribute of the fault layer which is sensitive to and independent from the minor fault by preferential identification, can reserve the fault information of the attribute information of the original depth domain to the maximum extent, and achieves the purpose of identifying fault abnormal information more effectively by extracting a plurality of principal components. Fig. 7 is a schematic diagram of a fault distribution obtained in the embodiment of the present invention, as shown in fig. 7, fig. 7 includes not only the larger faults shown in fig. 3 to 5, but also the small faults shown in fig. 6, and further shows the large faults and the small faults in a fault distribution diagram.
The fault analysis is carried out by the principal component analysis technology of four attributes of variance, coherence, curvature and ants, and finally, a plurality of principal components which are obtained have better reflection on faults and a plurality of analysis results which are more obvious in response to fault change appear on the attribute of the depth domain along the layer.
And then, the key analysis is carried out by analyzing a plurality of principal components with larger contribution rate of the depth domain along-layer attributes, so that the attributes which are calculated based on the principal component analysis method and well reflect the abnormal change of the small fault in the depth domain along-layer attributes are used for the future seismic interpretation work.
In actual practice, an interval may be specified and single or multiple along-layer attributes may be entered to be classified.
Corresponding to embodiment 1 of the present invention, an embodiment of the present invention further provides a depth domain seismic data-based fine minor fault detection apparatus, including:
the collecting module is used for acquiring depth domain seismic data in the area to be identified by utilizing a prestack depth domain processing technology and collecting detection data in the area to be identified, wherein the detection data comprises: drilling well position, layering, logging data and histogram;
the acquisition module is used for acquiring seismic wavelets of a target interval in the area to be identified; acquiring depth domain seismic horizon interpretation of a target interval;
acquiring a corresponding seismic attribute body based on the seismic wavelets, wherein the seismic attribute body comprises: one or a combination of variance, coherence, curvature and ant;
an extraction module for extracting an attribute of an edge layer in a depth domain attribute body by using depth domain seismic horizon interpretation;
and the analysis module is used for analyzing the attribute of the bedding by utilizing a principal component analysis method to obtain a classified result and outlining the minor fault according to the classified result.
In a specific implementation manner of the embodiment of the present invention, the collection module is configured to:
and (3) establishing an accurate three-dimensional speed model by combining drilling, roadway exposure and working face extraction data as constraints, and performing high-precision depth domain imaging processing by adopting a matched prestack depth migration technology to obtain prestack depth migration data.
In a specific implementation manner of the embodiment of the present invention, the obtaining module is configured to:
selecting Hamon-Russell software, and loading a depth domain seismic data body, a logging curve and drilling hole layering data;
extracting Rake wavelets to perform first depth domain synthetic seismic record calibration, and firstly calibrating and identifying wave group characteristics of a target interval;
and then, finely calibrating the depth domain synthetic seismic record of the wave group characteristics of the target interval by using the well side channel wavelets, and matching with the well side channel according to the wave signal similarity principle to obtain the seismic wavelets of the wave group characteristics of the target interval.
In a specific implementation manner of the embodiment of the present invention, the extraction module is configured to: and (5) carrying out attribute sensitivity analysis and research on the development of the micro crack structure.
In a specific implementation manner of the embodiment of the present invention, the analysis module is configured to:
writing the extracted m seismic attribute data (each attribute has n tracks of data) into a data matrix X.
The data matrix X is normalized by a method that scales the data to fall within a small specific interval, thereby obtaining a normalized data array: r ═ R (R)ij)m×m；
And calculating a correlation coefficient matrix R of the data matrix, and calculating the eigenvalue and the corresponding eigenvector of the matrix by a Jacobi iteration method (only one multiplication of the matrix and the vector needs to be calculated once for each iteration, and the original matrix X is always unchanged in the calculation process).
Sorting the characteristic values in a descending order by a sorting algorithm to obtain lambda1≥λ2≥λ3≥...≥λmAnd corresponding feature vectors v1,v2,v3,...,vm。
Feature vector v is orthogonalized by using Schmidt1,v2,v3,...,vmPerforming unit orthogonalization to obtain u1,u2,u3,...,um。
The number of principal components is determined. Calculating the contribution rate and the accumulated contribution rate of the characteristic value, and extracting p main components, component F1,F2,F3,...,FpThe cumulative contribution rate g of the first p principal componentspNot less than a given extraction efficiency t (e.g. 85%), i.e. gp>t；
Calculating the projection F of the normalized data X on the extracted p eigenvectorsi＝ui1x1+ui2x2+...+uimxm1, 2.., n. The obtained F is the main component finally extracted, namely the comprehensive variable after dimensionality reduction.
And taking the contribution rate of the extracted principal component as a weighting coefficient, and performing weighting fusion processing on the principal component to obtain a principal component analysis fusion attribute.
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 (10)
1. A depth domain seismic data-based fine minor fault detection method is characterized by comprising the following steps:
acquiring depth domain seismic data in a region to be identified by utilizing a prestack depth domain processing technology, and collecting detection data in the region to be identified, wherein the detection data comprises: drilling well position, layering, logging data and histogram;
acquiring seismic wavelets of a target interval in an area to be identified; acquiring depth domain seismic horizon interpretation of a target interval;
acquiring a corresponding seismic attribute body based on the seismic wavelets, wherein the seismic attribute body comprises: variance, coherence, curvature, and ant;
extracting an attribute of an edge layer in a depth domain attribute body by using depth domain seismic horizon interpretation;
and analyzing the attribute of the bedding by using a principal component analysis method to obtain a classified result, and delineating the minor fault according to the classified result.
2. The method for fine fault detection based on depth domain seismic data as claimed in claim 1, wherein the obtaining of depth domain seismic data in the area to be identified by using the prestack depth domain processing technology comprises:
and (3) establishing an accurate three-dimensional speed model by combining drilling, roadway exposure and working face extraction data as constraints, and performing high-precision depth domain imaging processing by adopting a matched prestack depth migration technology to obtain prestack depth migration data.
3. The method for detecting the fine minor fault based on the depth domain seismic data as claimed in claim 1, wherein the step of obtaining the seismic wavelets of the target interval in the area to be identified comprises the following steps:
selecting Hamon-Russell software, and loading a depth domain seismic data body, a logging curve and drilling hole layering data;
extracting Rake wavelets to perform first depth domain synthetic seismic record calibration, and firstly calibrating and identifying wave group characteristics of a target interval;
and then, finely calibrating the depth domain synthetic seismic record of the wave group characteristics of the target interval by using the well side channel wavelets, and matching with the well side channel according to the wave signal similarity principle to obtain the seismic wavelets of the wave group characteristics of the target interval.
4. The method for fine minor fault detection based on depth domain seismic data of claim 1, wherein in extracting the attribute along the layer, the method further comprises: and (5) carrying out attribute sensitivity analysis and research on the development of the micro crack structure.
5. The method for detecting the fine minor fault based on the seismic data of the depth domain as claimed in claim 1, wherein the analyzing the attribute of the bedding by using the principal component analysis method to obtain the classified result comprises:
carrying out data standardization processing on the attribute data along the layer;
establishing a correlation coefficient array of variables: r ═ Rij)nxnWherein R is a correlation coefficient matrix; rijIs the correlation between the ith attribute and the jth attribute;
and determining the number of the main components.
6. A depth-domain seismic data-based fine minor fault detection apparatus, the apparatus comprising:
the collecting module is used for acquiring depth domain seismic data in the area to be identified by utilizing a prestack depth domain processing technology and collecting detection data in the area to be identified, wherein the detection data comprises: drilling well position, layering, logging data and histogram;
the acquisition module is used for acquiring seismic wavelets of a target interval in the area to be identified; acquiring depth domain seismic horizon interpretation of a target interval;
acquiring a corresponding seismic attribute body based on the seismic wavelets, wherein the seismic attribute body comprises: one or a combination of variance, coherence, curvature and ant;
an extraction module for extracting an attribute of an edge layer in a depth domain attribute body by using depth domain seismic horizon interpretation;
and the analysis module is used for analyzing the attribute of the bedding by utilizing a principal component analysis method to obtain a classified result and outlining the minor fault according to the classified result.
7. The fine fault detection device based on the depth domain seismic data as claimed in claim 6, wherein the collection module is configured to:
and (3) establishing an accurate three-dimensional speed model by combining drilling, roadway exposure and working face extraction data as constraints, and performing high-precision depth domain imaging processing by adopting a matched prestack depth migration technology to obtain prestack depth migration data.
8. The fine fault detection device based on the depth domain seismic data as claimed in claim 6, wherein the obtaining module is configured to:
selecting Hamon-Russell software, and loading a depth domain seismic data body, a logging curve and drilling hole layering data;
extracting Rake wavelets to perform first depth domain synthetic seismic record calibration, and firstly calibrating and identifying wave group characteristics of a target interval;
and then, finely calibrating the depth domain synthetic seismic record of the wave group characteristics of the target interval by using the well side channel wavelets, and matching with the well side channel according to the wave signal similarity principle to obtain the seismic wavelets of the wave group characteristics of the target interval.
9. The fine fault detection device based on the depth domain seismic data as claimed in claim 6, wherein the extraction module is configured to: and (5) carrying out attribute sensitivity analysis and research on the development of the micro crack structure.
10. The fine fault detection device based on the depth domain seismic data as claimed in claim 6, wherein the analysis module is configured to:
carrying out data standardization processing on the attribute data along the layer;
establishing a correlation coefficient array of variables: r ═ Rij)nxnWherein R is a correlation coefficient matrix; rijIs the correlation between the ith attribute and the jth attribute;
and determining the number of the main components.
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