Optical drilling imaging system for deep exploration engineering

Abstract

The optical borehole imaging system for deep exploration engineering involves the fields of geological survey and optical borehole imaging. The system can be divided into two parts: the ground system and the underground system. The ground system is a visual host computer interface; the underground system includes an image acquisition module, a borehole inner wall image transformation module, an image fusion and splicing module, and an image compression module. The image acquisition module is used to take a bird's-eye view of the underground borehole image; the image transformation module is used to remove the central black hole area in the borehole inner wall bird's-eye view image and perform transmission transformation to expand and correct the remaining effective area; the image fusion and splicing module is used to splice the corrected inner wall front view image into a complete borehole inner wall plane image; the image compression module is used to compress the finished image for easy upload to the host computer. The ground system and the underground system exchange information through the TCP / IP communication module. The present invention can effectively save bandwidth when uploading underground borehole images and increase the effective information content of the borehole inner wall image.

Description

Technical Field

[0001] The present invention relates to the fields of geological survey and optical borehole imaging, and in particular to an optical borehole imaging system for deep exploration engineering. Background Art

[0002] In recent years, the national economy has continued to develop rapidly, and sectors such as energy, water conservancy, and transportation have undergone tremendous changes. However, the increasing incidence of safety accidents such as coal mine flooding, tunnel collapses, and dam landslides has attracted widespread attention and is a common problem faced by my country and the world. Currently, geological exploration, which provides a preliminary understanding of the geological conditions in the operating area, can effectively reduce the incidence of safety accidents. Borehole surveying is the most commonly used technical means. Drilling coring, as a traditional borehole survey technique, allows for intuitive and clear geological structure detection, and is characterized by its intuitiveness, practicality, convenience, and simplicity. However, since the core loses its original situ and original state information after being removed from the borehole, it is impossible to determine the structure, fractures, and fissures within the borehole based on the core. Furthermore, the discontinuous nature of coring makes it impossible to determine changes in borehole diameter and borehole inclination. Furthermore, with increasing survey depths, the efficiency of uploading underground borehole images to the surface is an urgent issue.

[0003] With the rapid development of image acquisition and processing technologies, borehole imaging technology based on optical imaging principles has gradually been applied in the field of geological exploration. Borehole imaging technology can be used to observe changes in borehole wall lithology, structural fractures, fault karst, and rock mass information hidden outside the borehole wall. It is intuitive, visual, precise, and clear, and can be widely used in geophysical exploration and research of near-surface geological bodies, as well as in geological science research in engineering geology, hydrogeology, disaster geology, and environmental geology. Furthermore, in modern society, it can also be used for geological exploration, pipeline inspection, and geomorphological surveys during major engineering construction projects. With the continuous development of geological survey needs, single-use visual borehole imaging technology can no longer meet the actual application needs of geological survey projects. The large size of the borehole probe also limits the measurement of small-diameter wells. Summary of the Invention

[0004] The present invention aims to solve the problems in the background technology of low underground image upload rate and inability to measure due to a small wellhead, and provides an optical drilling imaging system for deep exploration engineering.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] The optical borehole imaging system for deep exploration engineering is characterized by including an upper computer display module, a lower computer image acquisition module, an image transformation module, an image fusion and splicing module, an image compression module, a communication module between the upper computer and the lower computer, and a power supply module, wherein the upper computer module is used to control the operation of the lower computer and display the image of the inner wall of the underground borehole; the image acquisition module is a Raspberry Pi Zero external camera used to shoot downward images of the inner wall of the borehole at 10 frames per second; the image transformation module is used to remove the black hole area in the overhead view taken by the Raspberry Pi and expand the remaining effective information area into a circular ring and convert the perspective into a front view rectangular image; the image fusion and splicing module is used to fuse and splice each frame of rectangular image into a complete long image of the inner wall of the borehole; the image compression module is used to compress the spliced ​​image; the communication module is used to connect the upper computer and the lower computer and perform two-way communication; the power supply module is used to ensure stable power supply for the entire system.

[0007] Click the "START" button in the upper computer display interface to start the Raspberry Pi to collect images; click the "STOP" button to pause the Raspberry Pi; click the "END" button to end the shooting; click the "View History" button to jump to the corresponding folder to view the image of the inner wall of the drill hole uploaded by the lower computer.

[0008] The Raspberry Pi camera is fixed at the center of the casing to ensure that its position will not shake, and the outer wall of the casing is tightly attached to the inner wall of the borehole with an elastic sheet and moves downward at a uniform speed, with a speed not exceeding 60 meters per minute.

[0009] In the image transformation module, the Kirchhoff circle algorithm is used to first circle out the central black hole area for removal, and then the polar coordinate pixel point copy and transmission transformation method are used to expand and stretch the remaining valid area. The specific steps are as follows:

[0010] Step 1: Use the Hough circle gradient algorithm to find the center of the circle, and determine the radius value by estimating the maximum probability of the radius, thereby circling and removing the black hole area in the top view; simplify the Cartesian coordinate circle equation: (xa) 2 +(yb) 2 =r 2 , simplify to obtain the polar coordinates of the circle center: a = xr·cosθ, b = yr·sinθ; then use the Hough circle gradient algorithm to determine the radius, calculate the distance from all non-zero points in the Canny edge binary image to the circle center; sort the distances from small to large, initialize the radius space N(r), and set all N(r) = 0; traverse the non-zero points in the Canny image and accumulate the radius space values; finally, take the maximum value of the radius space as the radius value of the circle;

[0011] Step 2: Take a circle of pixels outside the black hole circle on the image, then take another circle of pixels outward, and so on, until all the pixels on the ring are taken; convert the plane coordinates (x, y) of all the pixels in the ring into polar coordinates (r, θ); define the side length of each pixel as 1, start from the pixel point with a radius of r+1 outside the inner circle of the ring and traverse to the outer circle with a radius of R, and stack each row of pixels in order to form an isosceles trapezoid, with a total of n circles of pixels; the length of the upper base of the stacked trapezoid is the circumference of the inner circle 2πr, the length of the lower base is 2π(r+n), and the height is the number of circles of pixels taken n;

[0012] Step 3: Solve the transformation matrix using the transformation formula, transform the image projection in the plane rectangular coordinate system into the homogeneous coordinate system, obtain the coordinates of the four corner vertices of the isosceles trapezoid in step 2, stretch the short side of the trapezoid to be equal to the long side, and obtain the front-view rectangular image of the inner wall of the borehole; the general transformation formula for transmission transformation is:

[0013]

[0014] Where (u, v, w) is the homogeneous coordinate of the original image pixel, (x′, y′, w′) is the homogeneous coordinate of the image pixel after transformation; w is greater than or equal to 0, and when w>0, it is the scaling scale of the coordinates u and v, and w=0 is infinite; the corresponding transformed image pixel coordinates are (x, y), where x=x′ / w′ and y=y′ / w′; the coordinate formulas for the four vertices of the isosceles trapezoid are solved by the transformation formula as follows:

[0015]

[0016] The isosceles trapezoid is stretched and transformed into a rectangular image using the coordinates of the vertex pixels.

[0017] In the image transformation module, the boundaries of the effective circular area are not fixed; if a medium-high area is divided out from the inner wall of the borehole, the clarity of the image of all pixels around the black hole cannot be guaranteed. Therefore, if the area is too large, half of it must be taken to prevent out-of-bounds judgment.

[0018] In the image fusion and stitching module, a three-row matrix is ​​constructed from the pixels above one image to perform standard deviation calculations on each row of pixels in the other image. The row of pixels with the smallest standard deviation is the stitching line of the two images.

[0019] The design of a question-and-answer method for Socket communication solves the problem of packet sticking when the lower computer transmits images to the upper computer.

[0020] The system adopts a split design, consisting of an aboveground system and an underground system. The aboveground system consists of a host computer imaging system written in QT 5.15.0 on Windows; the underground system primarily consists of a Raspberry Pi Zero W connected to an external camera, forming the ground image acquisition system. The aboveground and underground systems are connected by a steel cable for communication.

[0021] The host computer system of the system mainly controls the downhole system by sending start, stop and end instructions through socket communication technology by clicking buttons on the interface, and successfully solves the problem of sticky packets in information transmission by adopting a question-and-answer communication method.

[0022] The system's lower computer uses a Raspberry Pi to capture an overhead image of the borehole's inner wall. This image is then transformed to remove the central black hole and stretched using a transmission transformation. Because the feature point information in each captured borehole image doesn't fully capture the desired clarity, existing image stitching algorithms struggle to accurately merge and stitch the images frame by frame. Therefore, a novel stitching method with a preset matrix was designed to stitch the borehole wall images. An image compression algorithm was then used to compress the stitched image for easier transmission. Finally, TCP / IP communication technology was used to transmit the vertical view of the borehole's inner wall via a cable to a surface system for presentation and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Shown is the overall system block diagram of the present invention;

[0024] Figure 2 Shown is a schematic diagram of the image conversion module of the lower computer of the present invention;

[0025] Figure 3 The figure shows the principle diagram of the image fusion and splicing module of the lower computer of the present invention;

[0026] Figure 4 Shown is the principle diagram of the lower computer image compression module of the present invention.

[0027] Figure 5 Shown is a schematic diagram of the host computer software system of the present invention.

[0028] Figure 6 Shown is a technical block diagram of the communication module between the above-ground system and the underground system of the present invention. DETAILED DESCRIPTION

[0029] The optical drilling imaging system for deep exploration engineering of the present invention will be further described below with reference to the accompanying drawings.

[0030] The overall block diagram of the optical drilling imaging system for deep exploration engineering is as follows: Figure 1As shown, it includes an above-ground system and an underground system, which are connected by cables and communicate with each other through TCP / IP communication technology.

[0031] The underground system mainly includes four major functional modules: image acquisition module, image transformation module, image fusion and splicing module, and image compression module.

[0032] The image acquisition module in the underground system uses a Raspberry Pi external camera to capture images at a speed of 10 pictures per second.

[0033] The image transformation module in the underground system mainly includes the following steps:

[0034] Step 1: Detect invalid areas and remove them, such as Figure 2 As shown in the figure, since this area is meaningless for the study of the well wall image, and this part of the area will occupy excessive bandwidth during transmission, resulting in a significant reduction in transmission efficiency and effectiveness, the black invalid area in the center of the top view of the borehole inner wall needs to be detected and removed using the Kirchhoff circle detection method.

[0035] Step 2: Expand the effective area, remove the black holes, and use the polar coordinate method to locate a circle of pixels outside the inner circle of the ring. Rotate and take the circle of pixels closest to the inner circle (radius is r+1), then expand outward to r+2, take all the pixels in that circle, and so on. Finally, take all the pixels with a radius of r+n. The pixels of each circle are spliced ​​in order to obtain a trapezoidal image.

[0036] Step 3: Stretch the orthographic image and use the transmission transformation algorithm to correct the expanded image into a rectangular image. Solve its transformation matrix based on the expanded trapezoid, and then use this matrix to stretch its four corners to turn it into a rectangular image of the inner wall of the drill hole.

[0037] The image fusion and splicing module in the underground system mainly includes the following steps (such as Figure 3 ):

[0038] Step 1: From the two images to be fused and stitched, take a row pixel matrix with a length equal to the image length and a width of 3 from the top of the second image.

[0039] Step 2: Traverse all the pixels of the first image and use the pixel matrix taken from the second image to approximately match it with the first image. The mean square error formula is:

[0040]

[0041] in is the sample mean, S is the sample mean square error, and N is the number of samples. The mean square error is used to measure the similarity between this matrix and each row of pixels on the first image. The smaller S is, the smaller the difference between the matrix pixels and the row pixels on the first image, and the higher the similarity. Therefore, after traversing all the pixels of the first image, the row of matrix pixels with the smallest mean square error S is the same row of matrix pixels as the second image matrix.

[0042] Step 3: When the matrix finds the same row as the first image, define this row as the stitching line, cut off the image below the stitching line, and stitch it up and down with the second image. Because the deep exploration project is shot by moving up and down, there is no left and right shaking problem, so the expanded image below the stitching line can be directly cut off and stitched with another picture.

[0043] Step 4: Image fusion, stitching the images into a complete image and then performing seam fusion, that is, eliminating the stitching lines.

[0044] The image compression module in the underground system mainly includes the following steps:

[0045] Step 1: Image segmentation, such as Figure 4 As shown in FIG, the fused and spliced ​​image is divided into small blocks of 8*8 in size, and these small blocks are processed separately during the entire compression process.

[0046] Step 2: Before compressing the color interior wall mosaic image, the color mode must be converted. The RGB image is converted to a YUV image, and then the image data is sampled using a sampling ratio of 4:1:1 or 4:2:2.

[0047] Step 3: Discrete cosine transform (DCT) transforms the image signal in the frequency domain, separating the high-frequency information (i.e., areas with dramatic changes in image quality, such as edges and details) and the low-frequency information (i.e., areas with slow changes in brightness or grayscale values, such as large flat areas). The high-frequency information is then compressed.

[0048] Step 4: The codebooks used in the subsequent encoding process are all integers. The transformed floating-point frequency coefficients are quantized and converted into integers.

[0049] Step 5: Huffman encoding is performed, encoding the one DC component and 63 AC components in each 8x8 block. Adjacent DC components are encoded using the simple and easy-to-use differential coding (DCPM). The DA components use a zigzag run-length encoding scheme to ensure that low-frequency components appear first, followed by high-frequency components.

[0050] The upper computer display module in the ground system is written by QT5.15.0 under the Windows operating system. Figure 5 As shown in the figure, click the "Start" button and the Raspberry Pi will start collecting images; click "Pause" to pause the work; click "End" to end the shooting; click "View History" to jump to the corresponding folder to view the image of the inner wall of the drill hole uploaded by the lower computer.

[0051] like Figure 6 As shown, the communication module between the above-ground and underground systems utilizes TCP / IP (Transmission Control Protocol / Internet Protocol) technology. This design employs a question-and-answer approach, effectively resolving packet sticking issues during data transmission. This interface enables bidirectional communication between the host and slave computers. The host computer sends commands to control the slave computer, and images captured by the slave computer are sent to the host computer interface.

[0052] Step 1: First, create a Socket object on both the host and slave computers to monitor the connection.

[0053] Step 2: Create an Endpoint object on the host and slave sides using the specified port number and IP address (the Endpoint is the final end of the communication between the two parties, visible to the other party).

[0054] Step 3: Bind this Endpoint object with a Socket object on the lower computer and start monitoring.

[0055] Step 4: Use the Socket object on the host computer to send a connection request signal to the slave computer.

[0056] Step 5: After the slave computer receives the signal and successfully connects, it uses the Accept method to create a new Socket object for communication with the host computer. The host computer uses the Send and Receive methods to create a new object to communicate with the slave computer.

[0057] Step 6: When the system acquisition is completed, close the socket to end the communication.

[0058] The above is an introduction to the optical drilling imaging system for deep exploration engineering proposed in the present invention, and the principles and implementation methods of the present invention are explained. The description of the above embodiments is only used to help understand the method and core ideas of the present invention; for technical personnel in this field, there may be changes in the specific implementation methods and applications without departing from the ideas of the present invention. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. The optical drilling imaging system for deep exploration engineering includes a host computer display module, a slave computer image acquisition module, an image conversion module, an image fusion and splicing module, an image compression module, a host computer and slave computer communication module, and a power supply module. The host computer module controls the operation of the slave computer and displays images of the inner wall of the underground borehole. The image acquisition module is a Raspberry Pi Zero external camera used to shoot downward images of the inner wall of the borehole at 10 frames per second. The image transformation module removes the black hole area in the overhead view taken by the Raspberry Pi and expands the remaining effective information area into a circular ring and converts the perspective into a front-view rectangular image. The image fusion and splicing module fuses and splices each frame of rectangular images into a complete long image of the inner wall of the borehole. The image compression module compresses the spliced ​​image. The communication module connects the host computer and the slave computer and performs two-way communication. Its characteristics are that, in the image transformation module, the Kirchhoff circle algorithm is used to first circle out and remove the central black hole area, and then the polar coordinate pixel point copy and transmission transformation method are used to expand and stretch the remaining effective area. The specific steps are as follows: Step 1: Use the Hough circle gradient algorithm to find the center of the circle, and determine the radius value by estimating the maximum probability of the radius, thereby circling and removing the black hole area in the top view; simplify the Cartesian coordinate circle equation: (xa) 2 +(yb) 2 =r 2 , simplify to obtain the polar coordinates of the circle center: a = xr·cosθ, b = yr·sinθ; then use the Hough circle gradient algorithm to determine the radius, calculate the distance from all non-zero points in the Canny edge binary image to the circle center; sort the distances from small to large, initialize the radius space N(r), and set all N(r) = 0; traverse the non-zero points in the Canny image and accumulate the radius space values; finally, take the maximum value of the radius space as the radius value of the circle; Step 2: Take a circle of pixels outside the black hole circle on the image, then take another circle of pixels outward, and so on, until all the pixels on the ring are taken; convert the plane coordinates (x, y) of all the pixels in the ring into polar coordinates (r, θ); define the side length of each pixel as 1, start from the pixel point with a radius of r+1 outside the inner circle of the ring and traverse to the outer circle with a radius of R, and stack each row of pixels in order to form an isosceles trapezoid, with a total of n circles of pixels; the length of the upper base of the stacked trapezoid is the circumference of the inner circle 2πr, the length of the lower base is 2π(r+n), and the height is the number of circles of pixels taken n; Step 3: Solve the transformation matrix using the transformation formula, transform the image projection in the plane rectangular coordinate system into the homogeneous coordinate system, obtain the coordinates of the four corner vertices of the isosceles trapezoid in step 2, stretch the short side of the trapezoid to be equal to the long side, and obtain the front-view rectangular image of the inner wall of the borehole; the general transformation formula for transmission transformation is: Among them, (u, v, w) is the homogeneous coordinates of the original image pixel, and (x′, y′, w′) is the image pixel after transformation. Homogeneous coordinates; w is greater than or equal to 0, and when w>0, it is the scaling scale of the coordinates u and v, and w=0 is infinite; the corresponding transformed image pixel coordinates (x, y), where x=x′ / w′, y=y′ / w′; the coordinate formulas for the four vertices of the isosceles trapezoid are solved by the transformation formula: Parameter a 11 to a 33 is the homogeneous coordinate transformation coefficient; the isosceles trapezoid is stretched and transformed into a rectangular image through the coordinates of the vertex pixels.

2. The deep exploration engineering optical drilling imaging system according to claim 1 is characterized in that: In the image transformation module, the boundaries of the effective circular area are not fixed; if a medium-high area is divided out from the inner wall of the borehole, the clarity of the image of all pixels around the black hole cannot be guaranteed. Therefore, if the area is too large, half of it must be taken to prevent out-of-bounds judgment.

3. The deep exploration engineering optical drilling imaging system according to claim 1 is characterized in that: In the image fusion and stitching module, a three-row matrix is ​​constructed from the pixels above one image to perform standard deviation calculations on each row of pixels in the other image. The row of pixels with the smallest standard deviation is the stitching line of the two images.

4. The optical drilling imaging system for deep exploration engineering according to claim 1 is characterized in that: The design of a question-and-answer method for Socket communication solves the problem of packet sticking when the lower computer transmits images to the upper computer.

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