CN112558145A - Micro-seismic effective event identification method and system based on image processing - Google Patents
Micro-seismic effective event identification method and system based on image processing Download PDFInfo
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Abstract
A microseism effective event identification method and a system based on image processing are disclosed, wherein the method comprises the following steps: acquiring a waveform image corresponding to a typical microseism effective event, wherein the waveform image corresponds to a first recording length; acquiring a field micro-seismic waveform image, wherein the field micro-seismic waveform image corresponds to a second recording length, and the second recording length is greater than the first recording length; and scanning the field micro-seismic waveform image by using the waveform image corresponding to the typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient. The method can quickly and effectively identify the effective microseism event by using the original data acquired on site.
Description
Technical Field
The invention relates to the field of oil and gas exploration, in particular to a microseism effective event identification method based on image processing.
Background
In shale gas exploration, the well fracturing technology is a conventional and mature technology. The fracturing can be monitored through the micro earthquake, effective events of the micro earthquake are collected and data processing is carried out on the effective events, and the size and the spatial distribution condition of the fractured underground rock after fracturing are known through the visual effect. And the fracturing technicians judge the fracturing effect or adjust the fracturing technical parameters through the visual effect or animation. Therefore, effective event identification in microseismic monitoring is the key to fracture microseismic monitoring. At present, the judgment and identification of the microseism effective event are all completed through vector diagram data of waveforms: fracture microseismic events are identified by a typical microseismic event waveform ratio versus waveform signals acquired by a multi-channel three-component geophone in a well (described herein as in-well monitoring), incorporating the time-depth relationship and amplitude (energy) of the first-arrival wave propagation. The method has the disadvantages that the method must firstly carry out multi-channel stacking processing on the original data and then carry out calculation according to a corresponding algorithm to identify the microseism effective event, and cannot meet the requirement of quick identification on the site.
Disclosure of Invention
The invention provides a microseism effective event identification method based on image processing, which comprises the following steps:
acquiring a waveform image corresponding to a typical microseism effective event, wherein the waveform image corresponds to a first recording length;
acquiring a field micro-seismic waveform image, wherein the field micro-seismic waveform image corresponds to a second recording length, and the second recording length is greater than the first recording length;
and scanning the field micro-seismic waveform image by using the waveform image corresponding to the typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient.
Preferably, the image processing-based microseism valid event identification method further comprises the following steps:
and adjusting the attributes of the field micro-seismic waveform image to be consistent with the attributes of the waveform image corresponding to the typical micro-seismic effective event, wherein the attributes comprise the resolution and the pixel coordinate system of the image.
Preferably, the image processing-based microseism valid event identification method further comprises the following steps:
carrying out gray continuity and raster image processing on the waveform image corresponding to the typical micro-seismic effective event to obtain gray discrete data of the waveform image corresponding to the typical micro-seismic effective event;
and carrying out gray continuity and raster image processing on the field micro-seismic waveform image to obtain gray discrete data of the field micro-seismic waveform image.
Preferably, the time range corresponding to the field microseism waveform image is T1-T2, the microseism valid event identification coefficient comprises a first identification coefficient, the scanning the field microseism waveform image by using the waveform image corresponding to the typical microseism valid event, and the calculating the microseism valid event identification coefficient in the scanning process comprises:
step 301: setting the start time T equal to T1;
step 302: selecting an image segment of start time T, end time T + L1 in the field microseismic waveform image, wherein L1 represents the first recording length;
step 303: calculating the first recognition coefficient λ 1 according to the following formula (1):
wherein, g (x)i,yj) Representing the coordinates in the image segment as (x)i,yj) Gray value of pixel point of f (x)i,yj) The coordinate in the waveform image corresponding to the typical microseism effective event is represented as (x)i,yj) M, N respectively represents the horizontal and vertical pixel numbers of the waveform image corresponding to the typical microseism effective event;
step 304: the start time T is increased by a time step Δ T and steps 302 to 303 are repeated until the end time T + L1 equals T2.
Preferably, the microseismic significant event identification coefficient further comprises a second identification coefficient, and the step 303 further comprises calculating the second identification coefficient λ 2 according to the following formula (2):
preferably, the identifying a microseismic significant event in the field microseismic waveform image according to the minimum value of the microseismic significant event identification coefficient comprises:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold, and if the minimum value of the first identification coefficient lambda 1 is smaller than the first preset effective event threshold, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value contains a micro-seismic effective event.
Preferably, the identifying a microseismic significant event in the field microseismic waveform image according to the minimum value of the microseismic significant event identification coefficient comprises:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold value, and comparing the minimum value of the second identification coefficient lambda 2 with a second preset effective event threshold value, and if the minimum value of the first identification coefficient lambda 1 is greater than the first preset effective event threshold value and the minimum value of the second identification coefficient lambda 2 is smaller than the second preset effective event threshold value, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value of the second identification coefficient lambda 2 contains a micro-seismic effective event.
In another aspect, the present invention provides an image processing-based microseism valid event recognition system, including:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
acquiring a waveform image corresponding to a typical microseism effective event, wherein the waveform image corresponds to a first recording length;
acquiring a field micro-seismic waveform image, wherein the field micro-seismic waveform image corresponds to a second recording length, and the second recording length is greater than the first recording length;
and scanning the field micro-seismic waveform image by using the waveform image corresponding to the typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient.
Preferably, the processor further performs the steps of:
and adjusting the attributes of the field micro-seismic waveform image to be consistent with the attributes of the waveform image corresponding to the typical micro-seismic effective event, wherein the attributes comprise the resolution and the pixel coordinate system of the image.
Preferably, the processor further performs the steps of:
carrying out gray continuity and raster image processing on the waveform image corresponding to the typical micro-seismic effective event to obtain gray discrete data of the waveform image corresponding to the typical micro-seismic effective event;
and carrying out gray continuity and raster image processing on the field micro-seismic waveform image to obtain gray discrete data of the field micro-seismic waveform image.
Preferably, the time range corresponding to the field microseism waveform image is T1-T2, the microseism valid event identification coefficient comprises a first identification coefficient, the scanning the field microseism waveform image by using the waveform image corresponding to the typical microseism valid event, and the calculating the microseism valid event identification coefficient in the scanning process comprises:
step 301: setting the start time T equal to T1;
step 302: selecting an image segment of start time T, end time T + L1 in the field microseismic waveform image, wherein L1 represents the first recording length;
step 303: calculating the first recognition coefficient λ 1 according to the following formula (1):
wherein, g (x)i,yj) Representing the coordinates in the image segment as (x)i,yj) Gray value of pixel point of f (x)i,yj) The coordinate in the waveform image corresponding to the typical microseism effective event is represented as (x)i,yj) M, N respectively represents the horizontal and vertical pixel numbers of the waveform image corresponding to the typical microseism effective event;
step 304: the start time T is increased by a time step Δ T and steps 302 to 303 are repeated until the end time T + L1 equals T2.
Preferably, the microseismic significant event identification coefficient further comprises a second identification coefficient, and the step 303 further comprises calculating the second identification coefficient λ 2 according to the following formula (2):
preferably, the identifying a microseismic significant event in the field microseismic waveform image according to the minimum value of the microseismic significant event identification coefficient comprises:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold, and if the minimum value of the first identification coefficient lambda 1 is smaller than the first preset effective event threshold, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value contains a micro-seismic effective event.
Preferably, the identifying a microseismic significant event in the field microseismic waveform image according to the minimum value of the microseismic significant event identification coefficient comprises:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold value, and comparing the minimum value of the second identification coefficient lambda 2 with a second preset effective event threshold value, and if the minimum value of the first identification coefficient lambda 1 is greater than the first preset effective event threshold value and the minimum value of the second identification coefficient lambda 2 is smaller than the second preset effective event threshold value, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value of the second identification coefficient lambda 2 contains a micro-seismic effective event.
The method has the advantages that the method can quickly and effectively identify the effective microseism event by using the original data acquired on site, and can provide a basis for evaluating the fracturing effect on site.
The present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts.
FIG. 1 shows a flow diagram of a method for image processing based microseismic active event identification in accordance with an embodiment of the present invention;
FIG. 2 shows grayscale dispersion data of a field microseismic waveform image in a pixel plane rectangular coordinate system according to an embodiment of the invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
FIG. 1 shows a flow chart of a microseismic active event identification method based on image processing according to an embodiment of the invention. Referring to fig. 1, the method comprises the steps of:
step 1: acquiring a waveform image corresponding to a typical microseism effective event, wherein the waveform image corresponds to a first recording length;
step 2: acquiring a field micro-seismic waveform image, wherein the field micro-seismic waveform image corresponds to a second recording length, and the second recording length is greater than the first recording length;
and step 3: and scanning the field micro-seismic waveform image by using the waveform image corresponding to the typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient.
The invention adopts an image processing method, and can automatically and rapidly identify the microseism effective event by using the original data acquired by the fracturing microseism monitoring construction site.
Specifically, in step 1, a waveform image corresponding to an existing typical microseismic active event is acquired. Due to the different energy of the underground rock fracture, the amplitudes reflected on the wave form are different, and the sizes are different. Different-scale microseism events cannot be represented by a typical image, so that a plurality of typical microseism effective events can be selected and respectively represented by waveform images of the microseism effective events. Typical microseismic events may be classified and numbered according to their size. For example, typical microseismic valid events may be classified into 6 levels, denoted as SJ1, SJ2, SJ3, SJ4, SJ5, SJ 6.
A typical microseismic event corresponds to a waveform image corresponding to a first record length, which may comprise a complete microseismic event.
Preferably, the waveform image corresponding to the typical microseism effective event can be adjusted to a proper resolution through PHOTSHOP software, and gray level continuity and raster image processing are performed to obtain gray level discrete data (the value is between 0 and 255) of the waveform image.
A pixel plane rectangular coordinate system can be established for a waveform image corresponding to a typical microseism effective event, the horizontal coordinate points to the time increasing direction, the vertical coordinate is the series of the detector and points to the underground, and the origin is arranged at the upper left corner. Each pixel point in the pixel plane rectangular coordinate system corresponds to a gray value.
In step 2, a field microseismic waveform image is acquired. The original image (sgy file) acquired by the GEOWAVES acquisition system is the field micro-seismic waveform image, and data processing is not needed. The field microseism waveform image corresponds to the second recording length, and the second recording length is larger than the first recording length, so that the identification of the microseism effective event is realized.
Preferably, the resolution of the field micro-seismic waveform image can be adjusted to be consistent with the resolution of the waveform image corresponding to the typical micro-seismic effective event through PHOTSHOP software, and the gray discrete data of the waveform image can be obtained through gray continuity and raster image processing. The gray level histogram can be adjusted to balance the occurrence frequency of each level of gray level in the image, so that the continuity of the gray level of the image is strongest.
In addition, a pixel coordinate system of the field micro-seismic waveform image is set to be consistent with a pixel plane rectangular coordinate system of the waveform image corresponding to the typical micro-seismic effective event.
In step 3, scanning the field micro-seismic waveform image by using the waveform image corresponding to the typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient.
Specifically, in g (x)i,yj) The coordinate in the image segment representing the field microseismic waveform image is (x)i,yj) The gray value of the pixel point is f (x)i,yj) The coordinate in the waveform image corresponding to the typical microseism effective event is represented as (x)i,yj) The gray value of the pixel point. FIG. 2 shows grayscale dispersion data for an image segment of a field microseismic waveform image in a pixel plane rectangular coordinate system. In fig. 2, each square represents a pixel having a gray value between 0 and 255. At the coordinate of (x)i,yj) The gray level difference between the field micro-seismic waveform image and the waveform image corresponding to the typical micro-seismic effective event at the pixel point of (2) is as follows:
△(i,j)＝g(xi,yj)-f(xi,yj)
defining a first identification coefficient λ 1:
accordingly, the corresponding time range of the field micro-seismic waveform image is represented as T1 to T2, the micro-seismic significant event identification coefficient may include a first identification coefficient, the field micro-seismic waveform image is scanned using the waveform image corresponding to a typical micro-seismic significant event, and calculating the micro-seismic significant event identification coefficient during the scanning process includes the steps of:
step 301: setting the start time T equal to T1;
step 302: selecting an image segment of a start time T, an end time T + L1 in the live microseismic waveform image, wherein L1 represents a first recording length;
step 303: the first recognition coefficient λ 1 is calculated according to the following formula (1):
wherein, g (x)i,yj) Representing the coordinates in the image segment as (x)i,yj) Gray value of pixel point of f (x)i,yj) The coordinate in the waveform image corresponding to the typical microseism effective event is represented as (x)i,yj) M, N respectively represents the horizontal and vertical pixel numbers of the waveform image corresponding to the typical microseism effective event;
step 304: the start time T is increased by a time step Δ T and steps 302 to 303 are repeated until the end time T + L1 equals T2.
Through the steps 301 to 304, a field microseism waveform image is scanned by using a waveform image corresponding to a typical microseism effective event, and a series of first identification coefficients lambda 1 are obtained. The smaller the lambda 1 value is, the closer the field micro-seismic waveform image is to the waveform image corresponding to the micro-seismic effective event. And comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold, and if the minimum value of the first identification coefficient lambda 1 is smaller than the first preset effective event threshold, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value contains the micro-seismic effective event.
When the waveform images corresponding to a plurality of typical micro-seismic effective events are acquired in step 1, the above determination may be performed for the waveform image corresponding to each typical micro-seismic effective event, so as to determine whether any typical micro-seismic effective event is included in the field micro-seismic waveform image.
In practical applications, due to the difference in image source conditions, in order to prevent systematic difference in gray values, the difference between the gray values of vertically and horizontally adjacent pixels can be used to offset the error.
Transverse effective incident gray-scale difference f (x)i+1,yj)-f(xi,yj)
Horizontal field image gray scale difference is g (x)i+1,yj)-g(xi,yj)
Vertical effective event gray-scale difference f (x)i,yj+1)-f(xi,yj)
Longitudinal field image gray scale difference is g (x)i,yj+1)-g(xi,yj)
When the typical microseism effective event image has gray level system difference with the microseism waveform image acquired in the field, a second identification coefficient lambda 2 is defined:
therefore, the above step 303 further includes calculating the second recognition coefficient λ 2 according to the following formula (2):
the smaller the lambda 2 value is, the closer the field micro-seismic waveform image is to the waveform image corresponding to the micro-seismic effective event. After scanning, a series of second identification coefficients lambda 2 are calculated, and the minimum value of the second identification coefficients lambda 2 is determined.
And comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold value, and comparing the minimum value of the second identification coefficient lambda 2 with a second preset effective event threshold value, and if the minimum value of the first identification coefficient lambda 1 is greater than the first preset effective event threshold value and the minimum value of the second identification coefficient lambda 2 is less than the second preset effective event threshold value, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value of the second identification coefficient lambda 2 contains the micro-seismic effective event.
Similarly, when the waveform images corresponding to a plurality of typical micro-seismic effective events are acquired in step 1, the above determination may be performed for the waveform image corresponding to each typical micro-seismic effective event, so as to determine whether any typical micro-seismic effective event is included in the field micro-seismic waveform image.
The identified field micro-seismic waveform image containing the micro-seismic effective event is an sgy file, and can be extracted through a file name and stored in a preset data directory. The GEOWAVES acquisition system has no playback function of the sgy file, and the problem that the file corresponding to the micro-seismic effective event is difficult to find can be avoided by automatically recording the field micro-seismic waveform image containing the micro-seismic effective event.
In another aspect, the present invention provides an image processing-based microseism valid event recognition system, including:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
acquiring a waveform image corresponding to a typical microseism effective event, wherein the waveform image corresponds to a first recording length;
acquiring a field micro-seismic waveform image, wherein the field micro-seismic waveform image corresponds to a second recording length, and the second recording length is greater than the first recording length;
and scanning the field micro-seismic waveform image by using a waveform image corresponding to a typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient.
In one example, the processor further performs the steps of:
and adjusting the attributes of the field micro-seismic waveform image to be consistent with the attributes of the waveform image corresponding to the typical micro-seismic effective event, wherein the attributes comprise the resolution and the pixel coordinate system of the image.
In one example, the processor further performs the steps of:
carrying out gray continuity and raster image processing on the waveform image corresponding to the typical micro-seismic effective event to obtain gray discrete data of the waveform image corresponding to the typical micro-seismic effective event;
and carrying out gray continuity and raster image processing on the field micro-seismic waveform image to obtain gray discrete data of the field micro-seismic waveform image.
In one example, the field microseismic waveform image corresponds to a time range from T1 to T2, the microseismic significant event identification coefficient comprises a first identification coefficient, the scanning the field microseismic waveform image using the waveform image corresponding to the typical microseismic significant event, and the calculating the microseismic significant event identification coefficient during the scanning comprises:
step 301: setting the start time T equal to T1;
step 302: selecting an image segment of start time T, end time T + L1 in the field microseismic waveform image, wherein L1 represents the first recording length;
step 303: calculating the first recognition coefficient λ 1 according to the following formula (1):
wherein, g (x)i,yj) Representing the coordinates in the image segment as (x)i,yj) Gray value of pixel point of f (x)i,yj) The coordinate in the waveform image corresponding to the typical microseism effective event is represented as (x)i,yj) M, N respectively represents the horizontal and vertical pixel numbers of the waveform image corresponding to the typical microseism effective event;
step 304: the start time T is increased by a time step Δ T and steps 302 to 303 are repeated until the end time T + L1 equals T2.
In one example, the microseismic significant event identification coefficient further includes a second identification coefficient, and the step 303 further includes calculating the second identification coefficient λ 2 according to the following equation (2):
in one example, the identifying a microseismic significant event in the field microseismic waveform image according to the minimum value of the microseismic significant event identification coefficient includes:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold, and if the minimum value of the first identification coefficient lambda 1 is smaller than the first preset effective event threshold, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value contains a micro-seismic effective event.
In one example, the identifying a microseismic significant event in the field microseismic waveform image according to the minimum value of the microseismic significant event identification coefficient includes:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold value, and comparing the minimum value of the second identification coefficient lambda 2 with a second preset effective event threshold value, and if the minimum value of the first identification coefficient lambda 1 is smaller than the first preset effective event threshold value and the minimum value of the second identification coefficient lambda 2 is smaller than the second preset effective event threshold value, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value of the second identification coefficient lambda 2 contains a micro-seismic effective event.
The microseism effective event identification method based on image processing is applied to microseism effective event monitoring of a four-way-Window 23 platform 4HF and 5HF fracturing well, the success rate of directly identifying microseism effective events reaches over 80% by using field original data acquired by a GEOWAVES system without data processing, and a basis can be provided for evaluating the fracturing effect on the field.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. A microseism effective event identification method based on image processing is characterized by comprising the following steps:
acquiring a waveform image corresponding to a typical microseism effective event, wherein the waveform image corresponds to a first recording length;
acquiring a field micro-seismic waveform image, wherein the field micro-seismic waveform image corresponds to a second recording length, and the second recording length is greater than the first recording length;
and scanning the field micro-seismic waveform image by using the waveform image corresponding to the typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient.
2. The image processing-based microseismic active event identification method of claim 1 further comprising:
and adjusting the attributes of the field micro-seismic waveform image to be consistent with the attributes of the waveform image corresponding to the typical micro-seismic effective event, wherein the attributes comprise the resolution and the pixel coordinate system of the image.
3. The image processing-based microseismic active event identification method of claim 2 further comprising:
carrying out gray continuity and raster image processing on the waveform image corresponding to the typical micro-seismic effective event to obtain gray discrete data of the waveform image corresponding to the typical micro-seismic effective event;
and carrying out gray continuity and raster image processing on the field micro-seismic waveform image to obtain gray discrete data of the field micro-seismic waveform image.
4. The image processing-based microseismic significant event recognition method of claim 1 wherein the time range corresponding to the field microseismic waveform image is T1-T2, the microseismic significant event recognition coefficient comprises a first recognition coefficient, the scanning the field microseismic waveform image with the waveform image corresponding to the typical microseismic significant event, and the calculating the microseismic significant event recognition coefficient during scanning comprises:
step 301: setting the start time T equal to T1;
step 302: selecting an image segment of start time T, end time T + L1 in the field microseismic waveform image, wherein L1 represents the first recording length;
step 303: calculating the first recognition coefficient λ 1 according to the following formula (1):
wherein, g (x)i,yj) Representing the coordinates in the image segment as (x)i,yj) Gray value of pixel point of f (x)i,yj) The coordinate in the waveform image corresponding to the typical microseism effective event is represented as (x)i,yj) M, N represent typical micro-pixels, respectivelyThe number of horizontal and vertical pixels of the waveform image corresponding to the effective earthquake event;
step 304: the start time T is increased by a time step Δ T and steps 302 to 303 are repeated until the end time T + L1 equals T2.
5. The image processing-based microseismic significant event identification method of claim 4 wherein the microseismic significant event identification coefficient further comprises a second identification coefficient, and wherein the step 303 further comprises calculating the second identification coefficient λ 2 according to the following equation (2):
6. the image processing-based microseismic valid event identification method of claim 4 wherein the identifying of the microseismic valid events in the field microseismic waveform image according to the minimum value of the microseismic valid event identification coefficient comprises:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold, and if the minimum value of the first identification coefficient lambda 1 is smaller than the first preset effective event threshold, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value contains a micro-seismic effective event.
7. The image processing-based microseismic valid event identification method of claim 5 wherein the identifying of the microseismic valid events in the field microseismic waveform image according to the minimum value of the microseismic valid event identification coefficient comprises:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold value, and comparing the minimum value of the second identification coefficient lambda 2 with a second preset effective event threshold value, and if the minimum value of the first identification coefficient lambda 1 is greater than the first preset effective event threshold value and the minimum value of the second identification coefficient lambda 2 is smaller than the second preset effective event threshold value, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value of the second identification coefficient lambda 2 contains a micro-seismic effective event.
8. An image processing based microseismic active event identification system, the system comprising:
a memory storing computer-executable instructions;
a processor executing computer executable instructions in the memory to perform the steps of:
acquiring a waveform image corresponding to a typical microseism effective event, wherein the waveform image corresponds to a first recording length;
acquiring a field micro-seismic waveform image, wherein the field micro-seismic waveform image corresponds to a second recording length, and the second recording length is greater than the first recording length;
and scanning the field micro-seismic waveform image by using the waveform image corresponding to the typical micro-seismic effective event, calculating a micro-seismic effective event identification coefficient in the scanning process, and identifying the micro-seismic effective event in the field micro-seismic waveform image according to the minimum value of the micro-seismic effective event identification coefficient.
9. The image processing-based microseismic significant event identification system of claim 8 wherein the field microseismic waveform image corresponds to a time range from T1 to T2, the microseismic significant event identification coefficients comprise a first identification coefficient and a second identification coefficient, and the scanning the field microseismic waveform image using the waveform image corresponding to the typical microseismic significant event comprises:
step 301: setting the start time T equal to T1;
step 302: selecting an image segment of start time T, end time T + L1 in the field microseismic waveform image, wherein L1 represents the first recording length;
step 303: calculating the first recognition coefficient λ 1 according to the following formula (1):
calculating the second recognition coefficient λ 2 according to the following formula (2):
wherein, g (x)i,yj) Representing the coordinates in the image segment as (x)i,yj) Gray value of pixel point of f (x)i,yj) The coordinate in the waveform image corresponding to the typical microseism effective event is represented as (x)i,yj) M, N respectively represents the horizontal and vertical pixel numbers of the waveform image corresponding to the typical microseism effective event;
step 304: the start time T is increased by a time step Δ T and steps 302 to 303 are repeated until the end time T + L1 equals T2.
10. The image processing-based microseismic valid event identification system of claim 9 wherein the identifying of a microseismic valid event in the field microseismic waveform image according to the minimum of the microseismic valid event identification coefficient comprises:
and comparing the minimum value of the first identification coefficient lambda 1 with a first preset effective event threshold value, and comparing the minimum value of the second identification coefficient lambda 2 with a second preset effective event threshold value, and if the minimum value of the first identification coefficient lambda 1 is greater than the first preset effective event threshold value and the minimum value of the second identification coefficient lambda 2 is smaller than the second preset effective event threshold value, judging that the image segment of the field micro-seismic waveform image corresponding to the minimum value of the second identification coefficient lambda 2 contains a micro-seismic effective event.
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