CN112991275B - Underground television image azimuth correction method - Google Patents
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- 238000003702 image correction Methods 0.000 claims abstract description 7
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
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Abstract
The invention discloses an underground television image azimuth correcting method, which is characterized in that a transparent organic glass sleeve with three equidistant fine lines of red, green and blue, length scales and interconnection and an image correcting algorithm are added on the basis of the original underground television. The specific method comprises the following steps: firstly, connecting the casings and lowering the casings to the bottom of a drilling hole until the total length of the casings exceeds the depth of the drilling hole, and then placing a camera of a downhole television in the casings and lowering the camera for shooting; after shooting is completed, three color thin lines and length scales in an image are used as correction references, preprocessing is carried out on an original image, image characteristic areas are identified, and a transformation model is constructed to carry out image correction. Thus obtaining an image very close to the real borehole surface.
Description
Technical Field
The invention belongs to the technical field of engineering geological investigation, and relates to an underground television image azimuth correction method.
Background
Currently, the whole national economy enters a high-speed development period, and the development of resources is greatly stimulated, wherein mineral resources occupy important positions. And is an important task for the investigation of engineering geological conditions in the resource development process.
The underground television imaging system utilizes high-precision, high-definition and high-resolution borehole imaging pictures to detect stratum structures and structural surface development conditions of borehole walls, and the detection results of the underground television imaging system are important supplements and perfects on engineering geological condition data. However, the existing downhole television imaging technology still has some problems, such as the problems of unclear logging image blurring, local distortion, dislocation or proportional distortion caused by swinging and autorotation of the downhole television probe in the process of lowering.
Disclosure of Invention
The invention aims to provide an underground television image azimuth correction method which can solve the problems of unclear logging image, distortion, dislocation or proportional distortion caused by swinging and autorotation of an underground television probe.
The invention provides an azimuth correcting method for an underground television image, which is characterized by comprising the following steps of:
step 1: placing a transparent organic glass sleeve with scales in the drilled hole, wherein three thin wires with different colors are coated on the outer wall of the transparent organic glass sleeve at equal intervals along the axial direction;
step 2: setting down the television probe into the borehole along the casing and logging;
step 3: after logging is completed, correcting the logging image by taking three fine wires and length scales on the logging image as references, and comprising the following steps: an image preprocessing step, an image feature region identification step and an image correction step.
In the method for correcting the azimuth of the underground television image, the image preprocessing step comprises the following steps:
s1: denoising the original image to obtain an image without noise points;
s2: closing and connecting the hole wall areas into a connected area by morphological treatment, removing surrounding background, and realizing the feature extraction of the hole wall areas;
s3: taking three thin lines in the image as reference lines to carry out reference line distribution, and arranging the reference lines as white seed points;
s4: seed points are arranged on the whole image;
s5: and (3) taking the position of the seed point as the center, carrying out gridding treatment on the image, and dividing the whole image into seed domains with numbers.
In the method for correcting the azimuth of the underground television image, the image characteristic region identification step comprises the following steps:
s6: determining the size and the position information of a seed field in a standard image;
s7: and identifying the coordinate positions of the seed points and the corner points.
In the method for correcting the azimuth of the underground television image, the image correction step comprises the following steps:
s8: constructing a transformation mode between the seed subfields of the original image and the seed subfields of the standard image, and correcting the original image;
s9: and rearranging the corrected seed domains according to the seed domain numbers to obtain a standard image, and exporting and labeling the standard image to obtain a final logging image.
In the method for correcting the azimuth of the underground television image, the reference line distribution point in the step S3 is specifically as follows: selecting seed points on the reference lines according to the three reference lines, and distributing the seed points according to the intervals of five unit scales of the length scales on the original image when the slope of the reference lines is 0; the base points are white in color, the horizontal spacing between the base points is arranged in a functional relationship of y=1/x when the slope of the base line changes, and the number of base points on the three base lines is consistent.
In the method for correcting the azimuth of the underground television image, the step S4 specifically comprises the following steps: four seed points are arranged between every two seed points of the datum lines in the vertical direction corresponding to the seed points of the datum lines, and when the slope of the datum line changes and the distance between the seed points of the datum line becomes smaller, the number of the seed points of the corresponding datum line correspondingly increases, so that the distribution of the whole image is completed.
In the method for correcting the azimuth of the underground television image, the step S5 is to divide the whole image into the following specific steps: the whole image is segmented by taking the seed points as the centers and the centers of the two seed points as the segmentation boundary.
In the method for correcting the azimuth of the underground television image of the invention, the step S6 specifically comprises the following steps: selecting seed points of 3 continuous columns meeting the conditions that the slopes of three reference lines are zero at the same time and the three reference lines are equidistant as a standard area, wherein seed points of a second column of the area are standard point columns, and performing average value processing on the sizes of squares where the seed points are positioned to obtain a square size which is a standard seed field; when the quantity of the base points with the larger slope of the datum line is larger than that of the base points of the standard column, the square grids are scaled in equal proportion according to the quantity ratio of the base points, and the standard seed domains of the column are set.
In the method for correcting the azimuth of the underground television image, the step S7 specifically comprises the following steps: and establishing an x rectangular coordinate system and a y rectangular coordinate system which take the seed point as a center point, identifying the vertex of the seed domain by taking the seed point as the center point, and recording the vertex coordinates.
In the method for correcting the azimuth of the underground television image, the step S8 specifically comprises the following steps: the transformation mode between the seed domains of the original image and the standard image is characterized in that the nonlinear mapping relation between the original image and the standard image is obtained by a least square method according to the seed points and the corner points corresponding to the original image and the standard image, so that a transformation model is constructed to realize the correction of the image.
The invention relates to an azimuth correction method of an underground television image, which is a correction method of a pixel level. The method can solve a series of problems of local logging image distortion, dislocation or proportion distortion and the like caused by rotation and horizontal swinging along an axis with uncertain speed and direction when a probe is lowered in the logging process of the underground television imaging system. Therefore, more real and accurate image information is obtained, and more reliable geological investigation data is provided for the design and construction of rock engineering. The method is simple in principle, convenient to operate and high in practicability, and for the two-dimensional image, different correction references are respectively provided in the transverse direction and the longitudinal direction, so that more real and accurate image information can be obtained.
Drawings
FIG. 1 is a flow chart of a method of downhole television image azimuth correction according to the present invention;
FIG. 2 is a schematic view of a transparent plexiglass sleeve;
FIG. 3a is a seed domain structure diagram of an original image;
FIG. 3b is a standard image seed field structure diagram;
FIG. 4a is a schematic illustration of a baseline seed point arrangement;
FIG. 4b is a schematic diagram of a global image seed point arrangement;
FIG. 5 is a meshing partition map;
FIG. 6 is a corrected gridding partition map;
fig. 7 is an original image;
fig. 8 is a corrected annotation structure surface image.
In the figure, 1-external thread, 2-red straight line, 3-length scale, 4-blue straight line, 5-internal thread, 6-yellow straight line, 15-seed point, 16-seed subdomain and 17-corner point are shown.
Detailed Description
The correction of the downhole television image mainly includes the following 3 aspects: 1. correcting the problem of local image distortion and dislocation caused by rotation along the axis with variable speed and direction in the probe descending process; 2. correcting the problem of partial image proportion transverse distortion caused by shaking in the probe lowering process; 3. and correcting the problem of partial image proportion longitudinal proportion distortion caused by uneven dropping speed and cable stretching deformation in the process of dropping the probe.
As shown in fig. 1, the present invention provides a method for correcting the azimuth of an underground television image, which comprises the following steps:
step 1: a transparent organic glass sleeve with length scales 3 is placed in the drilled hole, and three thin wires with different colors are coated on the outer wall of the transparent organic glass sleeve at equal intervals along the axial direction, as shown in fig. 2.
In specific implementation, according to the drilling depth, a plurality of sections of transparent organic glass sleeves can be arranged, threads are arranged at two ends of each transparent organic glass sleeve, and adjacent transparent organic glass sleeves are connected with each other through the internal threads 1 and the external threads 2. Equidistant lengthening of the sleeve can be realized to meet the requirements of different logging depths. And sequentially connecting and lowering the transparent organic glass sleeves to the bottom of the drilling hole, and enabling three color thin lines on the organic glass sleeves to be mutually aligned by adjusting the thread tightness among the sleeves in the putting process until the total length of the sleeves connected with each other exceeds the depth of the drilling hole. The three threads are in bright and easily identifiable colors. The outer wall of the sleeve can be coated with red straight lines 2, yellow 6 and blue 4.
Step 2: setting down the television probe into the borehole along the casing and logging;
step 3: after logging is completed, correcting the logging image by taking three fine wires and length scales on the logging image as references, and comprising the following steps: an image preprocessing step, an image feature region identification step and an image correction step.
After the borehole is subjected to well logging by the underground television, an image of the borehole wall can be generated, and three equidistant color thin lines and length scales coated on the transparent organic glass sleeve can be simultaneously displayed on the well logging image. Aiming at the problems in the 3 aspects, firstly, limiting the horizontal swing of the probe by using an organic glass sleeve with smaller inner diameter; next, the image is further corrected by an image processing program. By correcting the image by the method, an image with good continuity and high accuracy can be obtained, and the actual condition of the borehole wall can be truly reflected.
In particular, the image preprocessing step is to obtain a clear image which is easier to process without damaging the image information. The image preprocessing step comprises the following steps:
s1: removing noise points in the original image by using a mean filtering method, and denoising to obtain an image without the noise points;
s2: closing and connecting the hole wall areas into a connected area by morphological treatment, removing surrounding background, and realizing hole wall area extraction;
s3: taking three thin lines in the image as reference lines to carry out reference line distribution, and arranging the reference lines as white seed points;
the method comprises the following steps: as shown in fig. 4a, the reference line position is first identified based on RGB values of three reference lines, red, yellow, and blue. And extracting the actual length of the length scale in the image. After the datum line position and the seed point spacing are determined, selecting the seed points on the datum line, and when the datum line slope is 0 (the original image is horizontally placed and the datum line theoretical slope is 0), distributing the seed points according to the five unit scales of the length scales on the original image as the spacing. The seed point color is white, the horizontal spacing between the base points is arranged according to a function relation of y=1/x when the slope of the datum line changes, and the number of the base points on the three datum lines is consistent.
S4: seed points are arranged on the whole image;
as shown in fig. 4b, four seed points are arranged between each two seed points of the reference lines in the vertical direction corresponding to the seed points of the reference lines, and when the slope of the reference line changes and the distance between the seed points of the reference line becomes smaller, the number of the seed points of the corresponding reference line correspondingly increases, so that the distribution of the whole image is completed.
S5: and (3) taking the position of the seed point as the center, carrying out gridding treatment on the image, and dividing the whole image into seed domains with numbers.
The method comprises the following steps: as shown in fig. 5, the entire image is segmented with the seed points as the centers and the centers of the two seed points as the segmentation boundaries. And taking the middle points of the two seed points as segmentation base points in the horizontal direction, connecting the base points in sequence by using a smooth curve, completing the segmentation step of the original image, and numbering the seed domain where each seed point is positioned.
In specific implementation, the image feature region identification step includes:
s6: determining the size and the position information of a seed field in a standard image;
selecting seed points of 3 continuous columns meeting the conditions that the slopes of three reference lines are zero at the same time and the three reference lines are equidistant as a standard area, wherein seed points of a second column of the area are standard point columns, and performing average value processing on the sizes of squares where the seed points are positioned to obtain a square size which is a standard seed field; when the base line slope becomes larger by more than the standard column base point, the square is scaled equally according to the base point number ratio, and is set as the standard seed zone of the column, as shown in fig. 3b, which shows seed point 15, seed zone 16 and corner point 17.
The mean processing formula is as follows:
s＝(s1+s2+…+sn)/n；b＝(b1+b2+…+bn)/n
where s is the seed domain area and b is the aspect ratio of the seed domain.
The standard seed field position information is characterized in that an x, y rectangular coordinate system is established by taking a seed point as a center, the top point of the seed field is taken as a corner point, the coordinates of the corner point are (-L, H), (-L, -H) and (L, -H), and the coordinates of the seed point are (0, 0).
S7: and identifying the coordinate positions of the seed points and the corner points.
An x, y rectangular coordinate system with the seed point as the center is established, and the seed point is taken as the center point, so that the vertexes of the seed domain are identified and the vertex coordinates are recorded, as shown in fig. 3 a.
The first and last pixel points of the first and last rows are corner points, the coordinates of the corner points are (-X, Y), (-X, -Y), (X, -Y), and the coordinates of the seed points are (0, 0).
In specific implementation, the image correction step includes:
s8: constructing a transformation mode between the seed subfields of the original image and the seed subfields of the standard image, and correcting the original image; according to the seed points and the corner points of the original image and the standard image, the nonlinear mapping relation between the original image and the standard image is obtained by a least square method, so that a transformation model is constructed to realize the correction of the image.
The method comprises the following steps: and fitting a curve formed by the first row of pixel points in the original image into a straight line. And repeating the curve fitting step, fitting all pixel points into straight lines, and performing equal-proportion scaling mapping to the corresponding positions of the standard seed subfields.
S9: and rearranging the corrected seed domains according to the seed domain numbers to obtain a standard image, and deriving and labeling the standard image to obtain a final well logging image as shown in fig. 6.
Fig. 7 is an original image, and as shown in fig. 8, the corrected image is derived and converted into a three-dimensional histogram consistent with the borehole wall morphology, and new information such as structural surfaces therein is identified.
The foregoing description of the preferred embodiments of the invention is not intended to limit the scope of the invention, but rather to enable any modification, equivalent replacement, improvement or the like to be made without departing from the spirit and principles of the invention.
Claims (5)
1. A method for correcting the azimuth of a television image in a well, comprising:
step 1: placing a transparent organic glass sleeve with scales in the drilled hole, wherein three thin wires with different colors are coated on the outer wall of the transparent organic glass sleeve at equal intervals along the axial direction;
step 2: setting down the television probe into the borehole along the casing and logging;
step 3: after logging is completed, correcting the logging image by taking three fine wires and length scales on the logging image as references, and comprising the following steps: an image preprocessing step, an image characteristic region identification step and an image correction step;
the image preprocessing step comprises the following steps:
s1: denoising the original image to obtain an image without noise points;
s2: closing and connecting the hole wall areas into a connected area by morphological treatment, removing surrounding background, and realizing the feature extraction of the hole wall areas;
s3: taking three thin lines in the image as reference lines to carry out reference line distribution, and arranging the reference lines as white seed points;
s4: seed points are arranged on the whole image;
s5: taking the position of the seed point as the center, carrying out gridding treatment on the image, and dividing the whole image into seed domains with numbers;
the image characteristic region identification step comprises the following steps:
s6: determining the size and the position information of a seed field in a standard image;
s7: identifying the coordinate positions of the seed points and the corner points;
the image correction step includes:
s8: constructing a transformation mode between the seed subfields of the original image and the seed subfields of the standard image, and correcting the original image;
s9: rearranging the corrected seed domains according to the seed domain numbers to obtain a standard image, deriving the standard image, marking the standard image, and obtaining a final logging image;
the reference line distribution point in the step S3 specifically includes:
selecting seed points on the reference lines according to the three reference lines, and distributing the seed points according to the intervals of five unit scales of the length scales on the original image when the slope of the reference lines is 0; the base points are white in color, when the slope of the datum line changes, the horizontal intervals among the base points are arranged according to a function relation of y=1/x, and the quantity of the base points on the three datum lines is consistent;
the step S6 specifically includes:
selecting seed points of 3 continuous columns meeting the conditions that the slopes of three reference lines are zero at the same time and the three reference lines are equidistant as a standard area, wherein seed points of a second column of the area are standard point columns, and performing average value processing on the sizes of squares where the seed points are positioned to obtain a square size which is a standard seed field; when the quantity of the base points with the larger slope of the datum line is larger than that of the base points of the standard column, the square grids are scaled in equal proportion according to the quantity ratio of the base points, and the standard seed domains of the column are set.
2. The method for correcting the azimuth of a downhole television image according to claim 1, wherein the step S4 specifically comprises:
four seed points are arranged between every two seed points of the datum lines in the vertical direction corresponding to the seed points of the datum lines, and when the slope of the datum line changes and the distance between the seed points of the datum line becomes smaller, the number of the seed points of the corresponding datum line correspondingly increases, so that the distribution of the whole image is completed.
3. The method for correcting the azimuth of a video image in the pit according to claim 1, wherein the step S5 is to divide the whole image into:
the whole image is segmented by taking the seed points as the centers and the centers of the two seed points as the segmentation boundary.
4. The method for correcting the azimuth of a downhole television image according to claim 1, wherein the step S7 specifically comprises:
and establishing an x rectangular coordinate system and a y rectangular coordinate system which take the seed point as a center point, identifying the vertex of the seed domain by taking the seed point as the center point, and recording the vertex coordinates.
5. The method for correcting the azimuth of a downhole television image according to claim 1, wherein the step S8 specifically comprises:
the transformation mode between the seed domains of the original image and the standard image is characterized in that the nonlinear mapping relation between the original image and the standard image is obtained by a least square method according to the seed points and the corner points corresponding to the original image and the standard image, so that a transformation model is constructed to realize the correction of the image.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104792301A (en) * | 2015-04-22 | 2015-07-22 | 华中科技大学 | Method and device for correcting azimuth of borehole television probe under ferromagnetic interference |
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| WO2020081130A1 (en) * | 2018-10-16 | 2020-04-23 | Halliburton Energy Services, Inc. | Downhole ultrasound image correction in oil based mud |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104792301A (en) * | 2015-04-22 | 2015-07-22 | 华中科技大学 | Method and device for correcting azimuth of borehole television probe under ferromagnetic interference |
Non-Patent Citations (3)
| Title |
|---|
| Advances in borehole imaging technology and applications;Stephen E. Prensky;《Geological Society, London, Special Publications》;第159卷(第1期);1 - 43 * |
| 基于相关函数的超声图像方位跳变检测和校正;涂继辉;李长文;余厚全;谢凯;邹伟;;电视技术;第35卷(第03期);119-122 * |
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