Rock composition identification method, device, electronic device and storage medium

Abstract

The present disclosure proposes a rock component identification method, device, electronic device and storage medium, wherein the method includes: obtaining a first scanning image, wherein the first scanning image includes multiple rock layers of the rock to be identified; performing image processing on the first scanning image to generate a second scanning image, wherein the second scanning image includes first component distribution information of each mineral corresponding to the multiple rock layers; obtaining a third scanning image corresponding to any rock layer in the multiple rock layers, wherein the third scanning image includes second component distribution information of each mineral; determining the component segmentation threshold of each mineral in the multiple rock layers in the first scanning image based on the second component distribution information and the first component distribution information; determining the component information of each mineral in the multiple rock layers based on the component segmentation threshold of each mineral in the multiple rock layers, thereby accurately determining the component segmentation threshold of each mineral in the multiple rock layers of the rock to be identified and the component information of each mineral in the multiple rock layers.

Description

Technical Field

[0001] The present disclosure relates to the technical field of rock microstructure characterization, and in particular to a rock composition identification method, device, electronic device, and storage medium. Background Art

[0002] With the continuous advancement of observation techniques such as scanning electron microscopes and industrial computed tomography (CT), coupled with significant increases in computer computing power, it has become possible to quantitatively describe the heterogeneous distribution of minerals in rocks in three dimensions using digital image processing techniques. Unlike conventional radiometric imaging, CT scanning technology does not project a three-dimensional object onto a two-dimensional plane. Instead, it independently images the two-dimensional scanned image of the three-dimensional object, avoiding overlapping influences from different layers. This not only improves image quality and clarity, but also enables non-destructive perspective and three-dimensional reconstruction of the rock's internal spatial structure.

[0003] However, when the CT scanning system is working, the X-rays generated by the radiation source penetrate the rock being measured. Different mineral components in the rock have different absorption abilities for X-rays, resulting in slightly different grayscales on the CT image. How to accurately identify the mineral components corresponding to the grayscale on the CT image has become an urgent problem to be solved. Summary of the Invention

[0004] A first embodiment of the present disclosure provides a rock composition identification method.

[0005] A second embodiment of the present disclosure provides a rock composition identification device.

[0006] A third embodiment of the present disclosure provides an electronic device.

[0007] A fourth embodiment of the present disclosure provides a computer-readable storage medium.

[0008] A fifth aspect of the present disclosure provides a computer program product.

[0009] The first aspect embodiment of the present disclosure proposes a rock component identification method, including: obtaining a first scanning image, wherein the first scanning image contains multiple rock layers of the rock to be identified; performing image processing on the first scanning image to generate a second scanning image, wherein the second scanning image contains first component distribution information of each mineral corresponding to the multiple rock layers; obtaining a third scanning image corresponding to any rock layer in the multiple rock layers, wherein the third scanning image contains second component distribution information of each mineral; determining the component segmentation threshold of each mineral in the multiple rock layers in the first scanning image based on the second component distribution information and the first component distribution information; and determining the component information of each mineral in the multiple rock layers based on the component segmentation threshold of each mineral in the multiple rock layers.

[0010] In the technical solution disclosed herein, the composition distribution information of each mineral in the third scanning image corresponding to any rock layer in the multi-layer rock layer and the second composition distribution information of each mineral in the second scanning image are used to accurately determine the composition segmentation threshold of each mineral in the multi-layer rock layer of the rock to be identified and the composition information of each mineral in the multi-layer rock layer.

[0011] In addition, the rock composition identification method according to the above embodiment of the present disclosure may also have the following additional technical features:

[0012] In one embodiment of the present disclosure, determining the component segmentation threshold of the multiple rock formations in the first scanning image based on the second component distribution information and the first component distribution information includes: determining the first component distribution information of each mineral corresponding to any rock formation from the second scanning image; determining the component segmentation threshold of each mineral in the multiple rock formations in the first scanning image based on the second component distribution information of each mineral in any rock formation and the first component distribution information of each mineral corresponding to any rock formation.

[0013] In one embodiment of the present disclosure, the component segmentation thresholds of each mineral in the multiple rock layers of the first scanning image are determined based on the second component distribution information of each mineral in the any rock layer and the first component distribution information of each mineral corresponding to the any rock layer, including: for any mineral in the any rock layer, determining the component segmentation threshold of the any mineral based on the second component distribution information of the any mineral and the first component distribution information of the mineral corresponding to the any rock layer; determining the component segmentation thresholds of each mineral in the multiple rock layers in the first scanning image based on the component segmentation threshold of the any mineral.

[0014] In one embodiment of the present disclosure, for any mineral in any rock formation, the component segmentation threshold of any mineral is determined based on the second component distribution information and the first component distribution information of any mineral, including: determining the proportion information of any mineral in any rock formation based on the second component information of any mineral; performing statistics on the first component distribution information of each mineral corresponding to any rock formation to obtain the cumulative proportion information of minerals corresponding to multiple component segmentation thresholds; and querying the cumulative proportion information of minerals corresponding to the multiple component segmentation thresholds based on the proportion information to determine the component segmentation threshold corresponding to any mineral.

[0015] In one embodiment of the present disclosure, the image processing of the first scanned image to generate the second scanned image includes: performing image enhancement on the first scanned image to obtain an enhanced first scanned image; performing noise reduction processing on the enhanced first scanned image to obtain a noise-reduced first scanned image; performing artifact removal on the noise-reduced first scanned image, and using the artifact-removed first scanned image as the second scanned image.

[0016] In one embodiment of the present disclosure, the method further includes: creating a rock model based on a component segmentation threshold of each mineral in the multi-layer rock formation, wherein the rock model represents a correspondence between each mineral and each mineral color.

[0017] The second aspect of the present disclosure provides a rock component identification device, comprising: a first acquisition module for acquiring a first scanning image, wherein the first scanning image includes multiple rock layers of the rock to be identified; a processing module for performing image processing on the first scanning image to generate a second scanning image, wherein the second scanning image includes first component distribution information of each mineral corresponding to the multiple rock layers; a second acquisition module for acquiring a third scanning image corresponding to any one of the multiple rock layers, wherein the third scanning image includes second component distribution information of each mineral; a first determination module for determining a component segmentation threshold of each mineral in the multiple rock layers in the first scanning image based on the second component distribution information and the first component distribution information; and a second determination module for determining the component information of each mineral in the multiple rock layers based on the component segmentation threshold of each mineral in the multiple rock layers.

[0018] The third embodiment of the present disclosure proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the rock composition identification method as described in the first embodiment is implemented.

[0019] The fourth embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the rock composition identification method as described in the first embodiment is implemented.

[0020] The fifth embodiment of the present disclosure provides a computer program product. When the instructions in the computer program product are executed by a processor, the rock composition identification method described in the first embodiment of the present disclosure is executed.

[0021] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 1 is a flow chart of a rock composition identification method according to an embodiment of the present disclosure;

[0024] Figure 2 is a schematic diagram of a first scan image according to another embodiment of the present disclosure;

[0025] Figure 3 The relationship between the proportion of granite minerals and grayscale values ​​according to another embodiment of the present disclosure is shown;

[0026] Figure 4 Obtaining a schematic diagram for any rock layer of a rock to be identified according to one embodiment of the present disclosure;

[0027] Figure 5 Schematic diagram of TIMA test results for any rock formation according to one embodiment of the present disclosure;

[0028] Figure 6 1 is a flow chart of a rock composition identification method according to an embodiment of the present disclosure;

[0029] Figure 7 Schematic diagram showing the relationship between the proportion of mica minerals in granite and grayscale values ​​according to one embodiment of the present disclosure;

[0030] Figure 8 Schematic diagram of component segmentation thresholds corresponding to various minerals in rocks according to one embodiment of the present disclosure;

[0031] Figure 9 A schematic diagram of minerals and corresponding grayscale values ​​in a two-dimensional rock formation according to one embodiment of the present disclosure;

[0032] Figure 10is a schematic diagram of the mineral composition ratio of granite in a two-dimensional scanning image according to one embodiment of the present disclosure;

[0033] Figure 11 1 is a flow chart of a rock composition identification method according to an embodiment of the present disclosure;

[0034] Figure 12 Schematic diagram of the structure of a rock composition identification device according to one embodiment of the present disclosure;

[0035] Figure 13 A block diagram of an electronic device for the rock composition identification method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0037] The material and geometric characteristics of rocks are heterogeneous, and the microstructure of rocks, such as their mineral composition, directly affects their macroscopic physical and mechanical properties. At present, the relevant technologies commonly used to study the mineral composition of rocks include thin section analysis technology, X-ray diffraction technology, scanning electron microscopy analysis, etc. Although the above technologies have played an important role in the quantitative analysis of rock microstructures, they also have obvious limitations. For example, the test resolution of thin section analysis technology based on optical microscopy is limited by the diffraction limit, and the identification effect of minerals with low content is poor; the sampling position of X-ray diffraction experiments is uncertain, and the test results may have errors. In addition, the above technologies are limited to the detection of mineral composition in a two-dimensional plane, and cannot realize the spatial distribution characteristic analysis and quantitative characterization of the mineral composition in the three-dimensional space inside the entire rock.

[0038] In response to the above problems, the present disclosure proposes a rock composition identification method, device, electronic device and storage medium.

[0039] The rock composition identification method, device, electronic device, and storage medium according to the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0040] Figure 1 The figure is a flow chart of a rock composition identification method according to an embodiment of the present disclosure.

[0041] The embodiment of the present disclosure is described by taking the rock composition identification method configured in a rock composition identification device as an example. The rock composition identification device can be applied to any electronic device so that the electronic device can perform the rock composition identification function.

[0042] Among them, the electronic device can be any device with computing capabilities, such as a personal computer (PC), a mobile terminal, etc. The mobile terminal can be, for example, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and other hardware devices with various operating systems, touch screens and / or display screens.

[0043] like Figure 1 As shown, the rock composition identification method may include the following steps:

[0044] Step 101: Acquire a first scan image, wherein the first scan image contains multiple rock layers of a rock to be identified.

[0045] In an embodiment of the present disclosure, the rock to be identified may be placed in a CT scanning system, and the CT scanning system may scan the rock to be identified to obtain a first scanning image.

[0046] Among them, such as Figure 2 As shown, the first scanned image may contain multiple rock layers of the rock to be identified.

[0047] In addition, it should be noted that to improve the quality of the first scan image, an ultra-high-resolution (e.g., 2 μm resolution) in-situ loading imaging CT scanning and comprehensive analysis system can be used. When scanning the rocks to be identified, imaging modes such as rapid scanning, spiral scanning, or jitter scanning can be selected. Furthermore, for rocks to be identified with a diameter greater than or equal to 50 mm and a height greater than or equal to 100 mm, the spiral scanning imaging mode can be used.

[0048] Step 102 : performing image processing on the first scanned image to generate a second scanned image, wherein the second scanned image includes first component distribution information of each mineral corresponding to multiple rock layers.

[0049] It should be understood that since the scanned image is easily interfered with by various factors during the generation process, which affects the visual presentation quality of the image, in order to further improve the display quality of the first scanned image, in the embodiment of the present disclosure, the first scanned image can be processed to generate a second scanned image.

[0050] In the embodiment of the present disclosure, the grayscale values ​​corresponding to each mineral in the second scanned image are different. By statistically analyzing the mineral proportion data corresponding to different grayscale values ​​in the second scanned image, the first component distribution information of each mineral can be obtained, wherein the first component distribution information of each mineral can represent the relationship between the mineral proportion and the grayscale value.

[0051] Furthermore, the first component distribution information of the plurality of minerals can be statistically analyzed based on the change of the gray value in the second scanned image to determine the cumulative proportion of the plurality of minerals corresponding to the gray value, such as Figure 3As shown, taking the rock to be identified as granite as an example, as the grayscale value decreases from 65535 to 0, the proportion of the segmented mineral gradually increases from 0% to 100%. It should be noted that the second scanned image can be a 16-bit unsigned binary image with a grayscale range of 0 to 65535.

[0052] Step 103 : Acquire a third scanning image corresponding to any one of the multiple rock layers, wherein the third scanning image contains second component distribution information of each mineral.

[0053] As a possible implementation of the embodiment of the present disclosure, Figure 4 As shown in the figure, a thin rock section is taken perpendicular to the axial direction of the rock to be identified. After polishing and coating, it is placed under the TIMA scanning electron microscope. After setting the TIMA scanning resolution and acquisition mode, a backscattered image and energy spectrum data of the scanned area are generated. Based on TIMA's built-in mineral database, the backscattered image and energy spectrum data are matched and identified, effectively segmenting mineral boundaries and distinguishing them using pseudo-color to produce a third scan image.

[0054] For example, Figure 5 As shown in the figure, taking granite as an example, the backscattered image and energy spectrum data are matched and identified based on TIMA's own mineral database, effectively segmenting the mineral boundaries and determining the mineral composition and content.

[0055] It should be noted that TIMA's built-in offline data processing and analysis capabilities allow analysis and statistics of the various colors in the third scan image to determine the secondary component distribution information of each mineral. For example, the mineral component ratio, particle size distribution, porosity, and other results can be calculated. For example, the main mineral components and their ratios of a granite sample can be shown in Table 1:

[0056] Table 1 Main mineral components and volume fractions of granite samples

[0057]

[0058]

[0059] The main mineral components of the rock are quartz (32.75%), feldspar (including orthoclase, albite, and anorthite, totaling 59.24%), and mica (including biotite and muscovite, totaling 6.52%). The total volume fraction of other minerals is 1.49%.

[0060] Step 104 : Determine a component segmentation threshold for each mineral in the multiple rock layers in the first scanned image based on the second component distribution information and the first component distribution information.

[0061] In the embodiment of the present disclosure, the first component distribution information of the rock layer corresponding to any rock layer in the third scanned image can be determined from the second scanned image, and then, based on the second component distribution information of each mineral in the any rock layer and the first component distribution information corresponding to the any rock layer, the component segmentation threshold of each mineral in the multiple rock layers of the first scanned image can be determined, wherein the component segmentation threshold can be the grayscale value threshold corresponding to each mineral in the multiple rock layers in the first scanned image.

[0062] Step 105 : determining the composition information of each mineral in the multi-layer rock formation according to the composition segmentation threshold of each mineral in the multi-layer rock formation.

[0063] Furthermore, based on the component segmentation threshold of each mineral in the multi-layer rock formation, the minerals in the multi-layer rock formation in the first scan image can be distinguished to determine the component information of each mineral in the multi-layer rock formation, such as the type of minerals contained in each layer of the rock formation.

[0064] In summary, the composition distribution information of each mineral in the third scanning image corresponding to any rock layer in the multi-layer rock layer, and the second composition distribution information of each mineral in the second scanning image, can accurately determine the composition segmentation threshold of each mineral in the multi-layer rock layer of the rock to be identified. Furthermore, based on the composition segmentation threshold of each mineral in the multi-layer rock layer, the composition information of each mineral in the multi-layer rock layer can be determined.

[0065] In order to accurately determine the component segmentation threshold of the multi-layer rock layer in the first scan image, in the embodiment of the present disclosure, as shown in FIG. Figure 6 As shown, the first component distribution information of the minerals corresponding to any rock layer can be determined from the second scanned image, and the component segmentation threshold of each mineral in the multi-layer rock layer of the first scanned image can be determined based on the second component distribution information of each mineral in any rock layer and the first component distribution information of each mineral corresponding to any rock layer. Figure 6 The illustrated embodiment may include the following steps:

[0066] Step 601: Acquire a first scan image, wherein the first scan image contains multiple rock layers of a rock to be identified.

[0067] Step 602 : performing image processing on the first scanned image to generate a second scanned image, wherein the second scanned image includes first component distribution information of each mineral corresponding to multiple rock layers.

[0068] Step 603 : Acquire a third scanning image corresponding to any one of the multiple rock layers, wherein the third scanning image includes second component distribution information of each mineral.

[0069] Step 604: Determine first component distribution information of each mineral corresponding to any rock layer from the second scan image.

[0070] In the embodiment of the present disclosure, based on any rock layer in the third scanning image, the rock layer corresponding to any rock layer can be determined in the second scanning image, and the first component distribution information of each mineral corresponding to the rock layer corresponding to any rock layer can be obtained.

[0071] Step 605 : Determine a component segmentation threshold for each mineral in the multi-layer rock layer of the first scanned image based on the second component distribution information of each mineral in any rock layer and the first component distribution information of each mineral corresponding to any rock layer.

[0072] Optionally, for any mineral in any rock layer, the component segmentation threshold of any mineral is determined based on the second component distribution information of any mineral and the first component distribution information of each mineral corresponding to any rock layer; based on the component segmentation threshold of any mineral, the component segmentation threshold of each mineral in the multiple rock layers in the first scanning image is determined.

[0073] As an example, based on the second component information of any mineral, the proportion information of any mineral in any rock layer is determined; the first component distribution information of each mineral corresponding to any rock layer is statistically analyzed to obtain the cumulative proportion information of minerals corresponding to multiple component segmentation thresholds; based on the proportion information, the cumulative proportion information of minerals corresponding to the multiple component segmentation thresholds is queried to obtain the component segmentation threshold corresponding to any mineral; furthermore, since the component segmentation thresholds of minerals corresponding to each layer of rock in the same rock are the same, the component segmentation thresholds of each mineral in multiple rock layers in the first scanning image can be determined based on the component segmentation threshold of any mineral.

[0074] In the embodiment of the present disclosure, the first component distribution information of each mineral corresponding to any rock formation is statistically analyzed to obtain the cumulative proportion information of minerals corresponding to multiple component segmentation thresholds. That is, based on the change of grayscale values, the first component distribution information of multiple minerals is statistically analyzed to determine the cumulative proportion of multiple minerals corresponding to the grayscale values.

[0075] Since the density of the mineral is greater, the brightness in the second scan image is also greater (for example, the brightest part is mica, followed by quartz and feldspar), and the corresponding grayscale value is also greater. As an example, the proportion of each mineral can be determined in descending order according to the grayscale value range. For example, the proportion of mica obtained by TIMA detection is 6.59%. Based on this proportion, the cumulative proportion information of minerals corresponding to multiple component segmentation thresholds is queried, and the component segmentation threshold (grayscale value range) corresponding to mica can be determined to be 12851 to 65535. Similarly, the proportions of mica and feldspar are added. According to the result of the addition, the cumulative proportion information of minerals corresponding to the corresponding multiple component segmentation thresholds is queried, and the component segmentation thresholds corresponding to mica and feldspar can be determined to be 7197 to 65535. Since the component segmentation threshold corresponding to mica is 12851 to 65535, the component segmentation threshold corresponding to feldspar is 7197 to 12850.

[0076] In order to accurately determine the component segmentation threshold corresponding to each mineral, optionally, a relationship diagram between the proportion of each mineral and the change of grayscale can be generated based on the grayscale value corresponding to each mineral in the second scanning image and the proportion of each mineral. Based on this relationship diagram, the component segmentation threshold corresponding to each mineral can be verified.

[0077] For example, Figure 7 As shown in the figure, when the grayscale value is 12851, the mineral proportion shows a sudden step-like change. 12851 can be used as the component segmentation point. Since the brightness corresponding to mica in the second scanning image is the largest, the component segmentation threshold corresponding to mica can be determined as 12581 to 65535.

[0078] Then, based on the component segmentation threshold of any mineral, the component segmentation threshold of each mineral in the multiple layers of rock in the first scanned image is determined. For example, the component segmentation thresholds of the three minerals in the 288th layer of rock can be as follows: Figure 8 As shown, in Figure 8 In the figure, different grayscale areas can represent the marking results of mica, quartz and feldspar minerals respectively.

[0079] Step 606 : Determine the composition information of each mineral in the multi-layer rock formation according to the composition segmentation threshold of each mineral in the multi-layer rock formation.

[0080] In the embodiment of the present disclosure, after determining the component segmentation threshold of each mineral, the component information of each mineral in the multi-layer rock layer can be determined based on the component segmentation threshold of each mineral in the multi-layer rock layer. Then, the mineral composition of each pixel point in the two-dimensional plane can be assigned a value, and the same mineral is displayed in the same color on the image, for example, Figure 9 As shown, the black part may represent mica, the dark gray is quartz, and the light gray is feldspar.

[0081] In the embodiment of the present disclosure, after determining the composition information of each mineral in the multiple rock formations, the composition information of each mineral in the multiple rock formations may be displayed.

[0082] For example, Figure 10 As shown in the figure, taking granite as an example, the distribution of the three minerals mica, quartz and feldspar in different rock layers (slice layers) fluctuates to a certain extent, reflecting the heterogeneity of the granite samples.

[0083] It should be noted that the execution process of steps 601 to 603 can be implemented in any of the embodiments of the present disclosure, and the embodiments of the present disclosure do not limit this and will not be described in detail.

[0084] In summary, the first component distribution information of each mineral corresponding to any rock layer is determined from the second scanning image; the component segmentation threshold of each mineral in the multiple rock layers of the first scanning image is determined according to the second component distribution information of each mineral in any rock layer and the first component distribution information of each mineral corresponding to any rock layer. Thus, according to the second component distribution information of each mineral in any rock layer and the first component distribution information of each mineral corresponding to any rock layer, the component segmentation threshold of various minerals in any rock layer can be determined, thereby accurately determining the component segmentation threshold of each mineral in the multiple rock layers in the first scanning image.

[0085] In order to improve the display quality of the first scanned image, improve the contrast of each mineral component in the rock to be identified, and thus improve the accuracy of the component segmentation threshold of each mineral, such as Figure 11 As shown, in the embodiment of the present disclosure, operations such as image enhancement, noise reduction and artifact removal can be performed on the first scanned image to obtain a scanned image with higher display quality. Figure 11 The illustrated embodiment may include the following steps:

[0086] Step 1101: Acquire a first scan image, wherein the first scan image contains multiple rock layers of a rock to be identified.

[0087] Step 1102: Perform image enhancement on the first scanned image to obtain an enhanced first scanned image.

[0088] It should be understood that since the scanned image is easily affected by various factors during the generation process, which may affect the visual presentation quality of the image, low-quality scanned images are difficult to obtain the required digital information and feature parameters. Therefore, in the embodiment of the present disclosure, the display quality of the scanned image can be improved through image processing.

[0089] Alternatively, the rock scanning process is easily affected by external factors such as instrument parameter settings, resulting in low contrast in the scanned image. Visually acquired perceptual data cannot effectively reflect the implicit digital information in the grayscale image. Therefore, image enhancement is required to increase the grayscale differences between the various mineral components in the first scanned image and subjectively improve the visual presentation of the image.

[0090] Step 1103: Perform noise reduction processing on the enhanced first scanned image to obtain a noise-reduced first scanned image.

[0091] It is important to understand that digital image noise is useless information generated during image acquisition and transmission, often obscuring image details and degrading image quality. Therefore, a noise reduction method is employed to reduce the noise of the enhanced first scanned image to obtain a noise-reduced first scanned image. Such noise reduction methods may include, but are not limited to, linear filtering (e.g., box filtering, mean filtering, or Gaussian filtering) and nonlinear filtering (e.g., median filtering, bilateral filtering, etc.).

[0092] Step 1104 , performing artifact removal on the first scanned image after the noise reduction process, and using the first scanned image after the artifact removal as the second scanned image, wherein the second scanned image contains first component distribution information of each mineral corresponding to the multiple rock layers.

[0093] Because X-rays attenuate energy after penetrating the rock being tested, and the degree of attenuation is related to the path length of the light, a scanned image of a standard rock with a circular cross-section will appear brighter at the edges than within. Therefore, image processing is crucial to minimize the impact of artifacts on the scanned image. Optionally, a hardening correction is performed on the first scanned image after noise reduction to remove artifacts. This artifact-free first scanned image is then used as a second scanned image, where the second scanned image contains the first component distribution information for each mineral corresponding to the multiple rock layers.

[0094] Step 1105 : Acquire a third scanning image corresponding to any one of the multiple rock layers, wherein the third scanning image includes second component distribution information of each mineral.

[0095] Step 1106 : Determine a component segmentation threshold for each mineral in the multiple rock layers in the first scanned image based on the second component distribution information and the first component distribution information.

[0096] Step 1107 : determining the composition information of each mineral in the multi-layer rock formation according to the composition segmentation threshold of each mineral in the multi-layer rock formation.

[0097] In the embodiment of the present disclosure, in order to visualize the various mineral components in the rock, after obtaining the component segmentation thresholds of each mineral in the multi-layer rock layer, a rock model can be created based on the component segmentation thresholds of each mineral. As an example, the visualization software Avizo can be used to superimpose the component segmentation thresholds of all two-dimensional rock slices in three-dimensional space to obtain a rock model (three-dimensional visualization reconstruction model), such as Figure 12 As shown in the figure, the mineral composition of each voxel in three-dimensional space is assigned a value. In the image, the same mineral is displayed in the same color: black represents mica, dark gray represents quartz, and light gray represents feldspar. Based on the rock model, the spatial distribution characteristics of different mineral components can be obtained.

[0098] It should be noted that the execution process of step 1101 and steps 1105 to 1107 can be implemented in any way in the embodiments of the present disclosure, and the embodiments of the present disclosure do not limit this and will not be described in detail.

[0099] In summary, the first scanned image is enhanced to obtain an enhanced first scanned image; the enhanced first scanned image is denoised to obtain a noise-reduced first scanned image; the first scanned image after noise reduction is artifact removed, and the first scanned image after artifact removal is used as the second scanned image, wherein the second scanned image contains the first component distribution information of each mineral corresponding to the multiple rock layers. Therefore, by performing image enhancement, noise reduction and artifact removal on the first scanned image, a scanned image with higher display quality can be obtained.

[0100] The rock composition identification method of the disclosed embodiment is configured to obtain a first scanned image, wherein the first scanned image includes multiple rock layers of the rock to be identified; obtain a third scanned image corresponding to any one of the multiple rock layers, wherein the third scanned image includes second component distribution information of each mineral; determine a component segmentation threshold for each mineral in the multiple rock layers in the first scanned image based on the second component distribution information and the first component distribution information; and determine the component information of each mineral in the multiple rock layers based on the component segmentation threshold for each mineral in the multiple rock layers. Thus, by using the component distribution information of each mineral in the third scanned image corresponding to any one of the multiple rock layers and the second component distribution information of each mineral in the second scanned image, the component segmentation threshold for each mineral in the multiple rock layers of the rock to be identified and the component information of each mineral in the multiple rock layers can be accurately determined.

[0101] As an example to implement the above embodiment, the present disclosure also proposes a rock composition identification device.

[0102] Figure 12 Schematic diagram of the structure of a rock composition identification device according to an embodiment of the present disclosure.

[0103] like Figure 12 As shown, the rock component identification device 1200 includes: a first acquisition module 1210 , a processing module 1220 , a second acquisition module 1230 , a first determination module 1240 and a second determination module 1250 .

[0104] Among them, the first acquisition module 1210 is used to acquire a first scanning image, wherein the first scanning image contains multiple rock layers of the rock to be identified; the processing module 1220 is used to perform image processing on the first scanning image to generate a second scanning image, wherein the second scanning image contains first component distribution information of each mineral corresponding to the multiple rock layers; the second acquisition module 1230 is used to acquire a third scanning image corresponding to any rock layer in the multiple rock layers, wherein the third scanning image contains second component distribution information of each mineral; the first determination module 1240 is used to determine the component segmentation threshold of each mineral in the multiple rock layers in the first scanning image based on the second component distribution information and the first component distribution information; the second determination module 1250 is used to determine the component information of each mineral in the multiple rock layers based on the component segmentation threshold of each mineral in the multiple rock layers.

[0105] As a possible implementation method of the embodiment of the present disclosure, the first determination module 1240 is also used to: determine the first component distribution information of each mineral corresponding to any rock layer from the second scanning image; determine the component segmentation threshold of each mineral in the multi-layer rock layer of the first scanning image based on the second component distribution information of each mineral in any rock layer and the first component distribution information of each mineral corresponding to any rock layer.

[0106] As a possible implementation method of the embodiment of the present disclosure, the first determination module 1240 is also used to: determine, for any mineral in any rock formation, the component segmentation threshold of any mineral based on the second component distribution information of any mineral and the first component distribution information of each mineral corresponding to any rock formation; and determine the component segmentation threshold of each mineral in the multiple rock formations in the first scanning image based on the component segmentation threshold of any mineral.

[0107] As a possible implementation method of the embodiment of the present disclosure, for any mineral in any rock formation, the component segmentation threshold of any mineral is determined based on the second component distribution information and the first component distribution information of any mineral, including: determining the proportion information of any mineral in the any rock formation based on the second component information of any mineral; performing statistics on the first component distribution information of each mineral corresponding to any rock formation to obtain the cumulative proportion information of minerals corresponding to multiple component segmentation thresholds; based on the proportion information, querying the cumulative proportion information of minerals corresponding to multiple component segmentation thresholds to determine the component segmentation threshold corresponding to any mineral.

[0108] As a possible implementation method of the embodiment of the present disclosure, the processing module 1220 is also used to: perform image enhancement on the first scanned image to obtain an enhanced first scanned image; perform noise reduction processing on the enhanced first scanned image to obtain a first scanned image after noise reduction processing; perform artifact removal on the first scanned image after noise reduction processing, and use the first scanned image after artifact removal as the second scanned image.

[0109] As a possible implementation of the embodiment of the present disclosure, the rock component identification device 1200 further includes: a creation module.

[0110] The creation module is used to create a rock model based on the component segmentation threshold of each mineral in the multi-layer rock layer, wherein the rock model represents the corresponding relationship between each mineral and the color of each mineral.

[0111] The rock composition identification device of the disclosed embodiment obtains a first scanned image, wherein the first scanned image includes multiple rock layers of the rock to be identified; obtains a third scanned image corresponding to any one of the multiple rock layers, wherein the third scanned image includes second component distribution information of each mineral; determines a component segmentation threshold for each mineral in the multiple rock layers in the first scanned image based on the second component distribution information and the first component distribution information; and determines the component information of each mineral in the multiple rock layers based on the component segmentation threshold for each mineral in the multiple rock layers. Thus, by using the component distribution information of each mineral in the third scanned image corresponding to any one of the multiple rock layers and the second component distribution information of each mineral in the second scanned image, the component segmentation threshold for each mineral in the multiple rock layers of the rock to be identified and the component information of each mineral in the multiple rock layers can be accurately determined.

[0112] In order to implement the above embodiments, the embodiments of the present disclosure further provide an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the rock composition identification method described in the above embodiments of the present disclosure.

[0113] In order to implement the above embodiments, the present disclosure further proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the rock composition identification method described in the embodiments of the present disclosure.

[0114] In order to implement the above embodiments, the present disclosure also proposes a computer program product, including a computer program. When the computer program is executed by a processor of an electronic device, the electronic device can execute the rock composition identification method described in the embodiments of the present disclosure.

[0115] like Figure 13 As shown, Figure 13This is a block diagram of an electronic device for implementing a rock composition identification method according to an embodiment of the present disclosure. The term "electronic device" is intended to represent various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The term "electronic device" may also represent various forms of mobile devices, such as personal digital assistants (PDAs), cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided for example purposes only and are not intended to limit the implementation of the present disclosure as described and / or claimed herein.

[0116] like Figure 13 As shown, the electronic device includes: one or more processors 1301, a memory 1302, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 13 A processor 1301 is taken as an example.

[0117] Memory 1302 is a non-transitory computer-readable storage medium provided by the present disclosure. The memory stores instructions executable by at least one processor, causing the at least one processor to perform the rock composition identification method described in the above embodiments of the present disclosure. The non-transitory computer-readable storage medium of the present disclosure stores computer instructions for causing a computer to perform the rock composition identification method described in the above embodiments.

[0118] Memory 1302, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the transportation coordination control method in the above-described embodiments of the present disclosure (e.g., first acquisition module 1210, processing module 1220, second acquisition module 1230, first determination module 1240, and second determination module 1250). Processor 1301 executes the non-transitory software programs, instructions, and modules stored in memory 1302 to execute various server functional applications and data processing, thereby implementing the rock composition identification method described in the above-described embodiments of the present disclosure.

[0119] Memory 1302 may include a program storage area and a data storage area. The program storage area may store an operating system and applications required for at least one function; the data storage area may store data generated by the use of electronic devices generated by transport collaborative control. Furthermore, memory 1302 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, memory 1302 may optionally include memory remote from processor 1301. Such remote memory may be connected to the electronic device of the rock composition identification method via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0120] The electronic device of the rock composition identification method may further include: an input device 1303 and an output device 1304. The processor 1301, the memory 1302, the input device 1303 and the output device 1304 may be connected via a bus or other means. Figure 13 The bus connection is taken as an example.

[0121] Input device 1303 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device for rock composition identification. Input devices such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, a pointer, one or more mouse buttons, a trackball, a joystick, etc. Output device 1304 may include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0122] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0125] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0126] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0127] In addition, the acquisition, storage, and application of information involved in the technical solutions disclosed herein are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0128] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions proposed in this disclosure can be achieved. This is not limited herein.

[0129] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A rock composition identification method, characterized in that: include: Acquire a first scan image, wherein the first scan image includes multiple rock layers of a rock to be identified; performing image processing on the first scanned image to generate a second scanned image, wherein the second scanned image includes first component distribution information of each mineral corresponding to the multiple rock layers, wherein the first component distribution information of each mineral is obtained by statistically analyzing mineral ratio data corresponding to different grayscale values ​​in the second scanned image, and the first component distribution information of each mineral represents a relationship between the mineral ratio and the grayscale value; Obtaining a third scan image corresponding to any one of the multiple rock layers, wherein the third scan image contains second component distribution information of each mineral, including: matching and identifying the backscattered image with the energy spectrum data based on a mineral database provided by TIMA, segmenting mineral boundaries, and distinguishing them using pseudo-color to obtain the third scan image, and analyzing and statistically analyzing various colors in the third scan image to determine the second component distribution information of each mineral; determining a component segmentation threshold for each mineral in the multi-layer rock formation in the first scanned image based on the second component distribution information and the first component distribution information; determining the composition information of each mineral in the multi-layer rock formation according to the composition segmentation threshold of each mineral in the multi-layer rock formation; The determining, based on the second component distribution information and the first component distribution information, a component segmentation threshold of the multi-layer rock formations in the first scanned image includes: determining first component distribution information of each mineral corresponding to the any one rock formation from the second scanned image; Based on the second component distribution information of each mineral in any rock layer and the first component distribution information of each mineral corresponding to any rock layer, the component segmentation threshold of each mineral in the multiple rock layers in the first scanning image is determined, wherein the component segmentation threshold is the grayscale value threshold corresponding to each mineral in the multiple rock layers in the first scanning image.

2. The method according to claim 1, characterized in that The determining, based on the second component distribution information of each mineral in the any rock formation and the first component distribution information of each mineral corresponding to the any rock formation, a component segmentation threshold of each mineral in the multiple rock formations in the first scanned image includes: For any mineral in any rock formation, determining a component segmentation threshold of the mineral according to the second component distribution information of the mineral and the first component distribution information of each mineral corresponding to the rock formation; The component segmentation thresholds of the minerals in the multiple rock layers in the first scanned image are determined according to the component segmentation threshold of any one of the minerals.

3. The method according to claim 2, characterized in that The step of determining, for any mineral in any rock formation, a component segmentation threshold of the mineral based on the second component distribution information and the first component distribution information of the mineral, includes: determining, based on the second component information of the any mineral, the proportion information of the any mineral in the any rock formation; performing statistics on the first component distribution information of each mineral corresponding to any one of the rock formations to obtain cumulative proportion information of the minerals corresponding to multiple component segmentation thresholds; According to the proportion information, the cumulative proportion information of the minerals corresponding to the multiple component segmentation thresholds is queried to determine the component segmentation threshold corresponding to any one of the minerals.

4. The method according to claim 1, wherein The performing image processing on the first scanned image to generate a second scanned image includes: performing image enhancement on the first scanned image to obtain an enhanced first scanned image; performing noise reduction processing on the enhanced first scanned image to obtain a noise-reduced first scanned image; Artifact removal is performed on the first scanned image after the noise reduction processing, and the first scanned image after the artifact removal is used as the second scanned image.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: A rock model is created according to the component segmentation threshold of each mineral in the multi-layer rock formation, wherein the rock model represents the corresponding relationship between each mineral and the color of each mineral.

6. A rock composition identification device, characterized in that: include: A first acquisition module is configured to acquire a first scan image, wherein the first scan image includes multiple rock layers of a rock to be identified; a processing module, configured to perform image processing on the first scanned image to generate a second scanned image, wherein the second scanned image includes first component distribution information of each mineral corresponding to the multiple rock layers, wherein the first component distribution information of each mineral is obtained by statistically analyzing mineral ratio data corresponding to different grayscale values ​​in the second scanned image, and the first component distribution information of each mineral represents a relationship between the mineral ratio and the grayscale value; a second acquisition module, configured to acquire a third scanned image corresponding to any one of the multiple rock strata, wherein the third scanned image contains second component distribution information of each mineral, including: matching and identifying the backscattered image with the energy spectrum data according to the mineral database provided by TIMA, segmenting the mineral boundaries, and distinguishing them using pseudo-color to obtain the third scanned image, and analyzing and statistically analyzing various colors in the third scanned image to determine the second component distribution information of each mineral; a first determining module, configured to determine a component segmentation threshold of each mineral in the multi-layer rock formation in the first scanned image based on the second component distribution information and the first component distribution information; a second determining module, configured to determine the composition information of each mineral in the multi-layer rock formation according to a composition segmentation threshold of each mineral in the multi-layer rock formation; The determining, based on the second component distribution information and the first component distribution information, a component segmentation threshold of the multi-layer rock formations in the first scanned image includes: determining first component distribution information of each mineral corresponding to the any one rock formation from the second scanned image; Based on the second component distribution information of each mineral in any rock layer and the first component distribution information of each mineral corresponding to any rock layer, the component segmentation threshold of each mineral in the multiple rock layers in the first scanning image is determined, wherein the component segmentation threshold is the grayscale value threshold corresponding to each mineral in the multiple rock layers in the first scanning image.

7. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the rock component identification method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the rock component identification method according to any one of claims 1 to 5 is implemented.

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