Quantitative estimation method for bioturbation in shallow marine sediments

Abstract

The present invention discloses a method for quantitatively estimating the bioturbation effect in shallow submarine sediments. First, a grayscale image containing the bioturbation effect is obtained; then, the quality of the obtained grayscale image is evaluated to obtain a clear grayscale image; the clear image is binarized and the bioturbation effect is extracted; finally, the bioturbation effect in the shallow sediment core is quantitatively calculated. Through digital core image technology, X-ray scanning analysis is carried out on the sediment core taken from the seabed, and the optical image of the shallow seabed sediment can be quickly obtained. Through methods such as image preprocessing, local thresholding, inversion, and morphological processing, the quantitative characterization of the bioturbation effect in the submarine sediment is realized; this method greatly improves the recognition efficiency and accuracy of the bioturbation effect in submarine sediments, provides strong technical support for the research on early diagenesis and paleoclimate environment of marine sediments, and has significant application and popularization value.

Description

Technical Field

[0001] The present invention belongs to the field of marine sedimentology research in oceanography, and particularly relates to a method for quantitatively estimating the bioturbation effect in the early diagenesis of marine sediments and the paleoclimate environment record. Background Art

[0002] The bioturbation effect in shallow submarine sediments is the mixing effect of organisms on sediment particles, mainly referring to the mixing of sediment particles and the change of sediment structure caused by activities such as the crawling, feeding, burrowing and habitat construction of benthic organisms. It often appears in different ways, such as biogenic structures (wormhole structures and excavation structures, etc.), biogenic irrigation, and sediment redistribution caused by various kinetic processes such as biogenic diffusion mixing. The bioturbation effect can fully change the physical, chemical and biological properties of submarine sediments, and affect the early diagenesis and sedimentary record of submarine sediments. For example, the bioturbation effect will greatly affect the preservation of submarine biostratigraphy and primary sedimentary structure, as well as the recycling and burial rate of substances; change the dissolution rate of mineral particles such as calcite, opal, apatite, etc., and accelerate its reaction speed; at the same time, it will also interfere with the paleoclimate and paleocean information preserved in the sediments, resulting in the dislocation, fuzziness and loss of the marine environmental signal record. Therefore, the study of submarine bioturbation effect is extremely crucial for correctly understanding various biological activities, sedimentary processes and records on the seabed, and has attracted more and more attention from scholars.

[0003] Currently, the main method for identifying the bioturbation effect on the seabed is to obtain sediment cores from the seabed through gravity, box coring or in-situ sampling methods such as ROV and HOV on the seabed. Then, the sediment core is cut in half in the laboratory, and the bioturbation phenomenon of the entire sediment layer of the core is observed and systematically described. However, this method for identifying the bioturbation effect in sediment cores has certain defects: 1) The visual observation of the bioturbation phenomenon in the core requires high professionalism of technicians, and there will be differences in the description results of different technicians; 2) The method of description through visual observation is both time-consuming and laborious, and it is impossible to quickly and accurately identify the bioturbation phenomenon in the core; 3) The bioturbation phenomenon in the current core still remains at the stage of qualitative description, lacking a fast and accurate quantitative identification method.

[0004] Therefore, there is an urgent need to seek a method for accurately and quickly identifying the bioturbation effect in shallow submarine sediment cores and quantitatively characterizing the bioturbation effect, so as to provide strong technical support for the research on the early diagenesis of marine sediments and the paleoclimate environment record. Summary of the Invention

[0005] In view of the deficiencies in the prior art methods for identifying bioturbation in shallow marine sediments, such as low efficiency and lack of quantitative characterization, the present invention proposes a method for quantitatively estimating bioturbation in shallow marine sediments to quantitatively characterize bioturbation in sediments and improve the identification efficiency and accuracy.

[0006] The present invention is implemented by the following technical solutions: A method for quantitatively estimating bioturbation in shallow marine sediments, comprising the following steps:

[0007] Step A: Obtain a grayscale image of bioturbation in shallow marine sediments;

[0008] Step B: Evaluate the quality of the obtained grayscale image to obtain a clear grayscale image;

[0009] Step C: Perform binarization processing on the image evaluated in Step B and extract bioturbation;

[0010] Step D: Quantitative calculation of bioturbation: Proportion of bioturbation = Cumulative pixel value of the identified bioturbation / Total pixel matrix value of the sediment core grayscale image.

[0011] Further, Step A is specifically implemented in the following manner:

[0012] Step A1: Obtain a shallow marine sediment sample and perform scanning analysis on it to obtain an X-ray optical image of the shallow sediment core, where the optical image contains bioturbation;

[0013] Step A2: Preprocess the X-ray optical image to obtain a grayscale image containing bioturbation;

[0014] Step A3: Perform local threshold processing on the grayscale image to complete local threshold segmentation of the image;

[0015] Step A4: On the basis of Step A3, perform reverse processing on the grayscale image to achieve image inversion;

[0016] Step A5: After reverse processing, perform morphological processing on the grayscale image to obtain a high-definition grayscale image containing bioturbation.

[0017] Further, Step B is specifically implemented in the following manner:

[0018] Step B1: Define a(i,j) as the grayscale value at the point (i,j) in the grayscale image, and calculate the gradient change value F(i,j) at the point (i,j) in the image:

[0019]

[0020] Step B2: Evaluate the change characteristics of the image gray value gradient, and then evaluate the clarity of the obtained gray image:

[0021] (1) Accumulate the gradient change values of each point in the image to obtain the overall image clarity evaluation value M(z):

[0022]

[0023] (2) Define a curve discriminant factor G to perform image quality discrimination. The curve discriminant factor G is calculated in the following way:

[0024]

[0025] where σ is the standard deviation of the clarity evaluation values at n position points, is the arithmetic mean of the clarity evaluation values at n position points;

[0026] (3) Define a threshold S. The value range of S is 0.25 - 0.3. If G ≤ S, then retain the image. If G > S, it indicates that the image quality is poor and needs to be reprocessed.

[0027] Further, step C performs binarization processing on the image evaluated in step B and extracts the biological disturbance effect, which specifically includes:

[0028] 1) Perform binarization processing on the high - clarity gray image obtained in step B, set the gray value of the pixel points on the image to 0 or 255, and present the image containing the biological disturbance effect with only black and white.

[0029] 2) Mark each pixel point in the binary image: Mark the areas composed of pixels with the same pixel value and adjacent positions with the same label, and mark the areas composed of pixels with different pixel values and adjacent positions with different labels, so as to complete the recognition and extraction of the biological disturbance effect in the binary image. Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0030] The solution of the present invention uses digital core image technology to perform X - ray scanning analysis on the core taken from the shallow - sea bottom sediment, and can quickly obtain the optical image of the shallow - sea bottom sediment. Through methods such as image pre - processing, local thresholding, inversion, and morphological processing, it realizes the quantitative characterization of the biological disturbance effect in the shallow - sea bottom sediment; this method greatly improves the recognition efficiency and accuracy of the biological disturbance effect in the sea bottom sediment, provides strong technical support for carrying out research on the early diagenesis and paleoclimate environment records of marine sediments, and has significant application and promotion value. Description of the Drawings

[0031] Figure 1Flow chart of the method for quantitatively identifying bioturbation in shallow marine sediments in the embodiments of the present invention;

[0032] Figure 2 Quantitative calculation result diagram of the bioturbation and its influence degree independently identified according to the method of the present invention in Case 1 of the present invention;

[0033] Figure 3 Quantitative calculation result diagram of the bioturbation and its influence degree independently identified according to the method of the present invention in Case 2 of the present invention. Detailed implementation manners

[0034] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described below with reference to the accompanying drawings and embodiments. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] The embodiments of the present invention propose a method for quickly and independently identifying and quantitatively characterizing bioturbation in the seabed, as Figure 1 shown, including the following steps:

[0036] Step A: Obtain a grayscale image of the shallow marine sediment containing bioturbation;

[0037] Step B: Evaluate the effect of the obtained grayscale image to obtain a clear grayscale image;

[0038] Step C: Binarize the image evaluated in Step B and identify bioturbation;

[0039] Step D: Quantitative calculation of bioturbation: Proportion of bioturbation = Pixel accumulation value of the identified bioturbation / Total pixel matrix value of the sediment core grayscale image.

[0040] The following takes the seabed sediment as an example to introduce the solution of the present invention in detail, as follows:

[0041] In the above Step A, to obtain a grayscale image of the shallow marine sediment containing bioturbation, the following specific method is adopted:

[0042] Step A1: Obtain a sample of shallow marine sediment and perform scanning analysis on it to obtain an X-ray optical image of the sediment core, and the optical image contains bioturbation;

[0043] Step A2: Preprocess the obtained X-ray optical image to obtain a grayscale image containing bioturbation;

[0044] Step A3: Perform local threshold processing on the grayscale image to complete local threshold segmentation of the image;

[0045] Step A4: On the basis of Step A3, perform reverse processing on the grayscale image to achieve image inversion;

[0046] After reverse processing, perform morphological processing on the grayscale image to obtain a high-definition grayscale image with bioturbation effects;

[0047] Step A1: Sample acquisition and pretreatment:

[0048] Use a box sampler on the survey ship to obtain undisturbed shallow surface sediment samples from the seabed. After the box sampler reaches the ship's deck, insert a square glass tube into the box sampler to obtain the sediment samples to be analyzed. Then, use an X-ray analyzer in the laboratory to scan and analyze the square glass tube containing the sediment to obtain the X-ray optical image of the sediment core, and the optical image contains the bioturbation phenomenon in the present invention.

[0049] Generally, the obtained shallow surface sediment core samples from the seabed are square transparent special resins with a diameter of 6 cm × 3 cm, and the length is generally about several tens of centimeters. Among them, in this embodiment, there is no limitation on the X-ray scanner used, and both industrial or medical ones can be used. However, in order to be able to complete the one-time scanning of the obtained square sediment samples, the scanning imaging range of the generally selected X-ray imaging scanner is preferably greater than 100 cm.

[0050] Step A2: Obtain the grayscale image with bioturbation effects in the shallow seabed sediment

[0051] Perform preprocessing on the X-ray optical image of the shallow seabed sediment core obtained in Step A1, convert the optical image into a grayscale image, and then complete contrast enhancement through gamma transformation. The specific implementation method is as follows:

[0052] The image matrix is denoted as I, with height H and width W. The value of the r-th row and c-th column is denoted as I(r, c), and the output matrix is O. Then:

[0053] O (r,c) = I(r, c) γ , 0 ≤ r < H, 0 ≤ c < W

[0054] Set the value of γ in 0 < γ < 1 to enhance the contrast of the image;

[0055] Step A3: Local threshold processing of the grayscale image

[0056] Based on step A2, local threshold segmentation of the image is completed. In this embodiment, an adaptive threshold algorithm is adopted. The principle of the adaptive threshold is to calculate the threshold according to the neighborhood of each pixel and compare the value of each pixel with the average value of the neighborhood. If the value of a certain pixel is very different from its local average value, it will be regarded as an outlier and separated during the thresholding process.

[0057] The adaptive threshold algorithm selects a gray level t* with a relatively small occurrence probability as the threshold and maximizes the difference between the two classes. The calculation formula for the adaptive threshold is:

[0058]

[0059] Through this method, the local area of the grayscale image can be divided into two parts: the background and the foreground. Among them, p i represents the number of pixel points with a gray value of i in the background, μ1 represents the average gray value of the background pixels, μ2 represents the average gray value of the foreground pixels, and w2 represents the proportion of the foreground pixels.

[0060] Step A4: Inverse processing of the grayscale image

[0061] Based on step A3, inverse processing of the image is completed. The principle of inverse processing is that due to the large difference in gray values between the biological disturbance effect and the sediment, the biological disturbance effect usually shows a lower gray value, while the sediment shows a higher gray value. The present invention is implemented by the following method. Assuming that the gray value range of the obtained grayscale image is [0, L - 1], the image inversion is achieved through the formula s = L - 1 - r, where r and s represent the pixel values before and after processing, respectively.

[0062] Step A5: Morphological processing of the grayscale image

[0063] Based on step A4, morphological processing of the grayscale image is completed. The principle of morphological processing is to use a special structuring element to measure or extract the shape or features of the corresponding biological disturbance effect in the input grayscale image; in the present invention, the functions gray_erosion_shape in Matlab 2020a software are used to implement the erosion operation of the grayscale image, the function gray_dilation_shape is used to implement the dilation operation of the grayscale image, the function gray_opening_shape is used to implement the opening operation of the grayscale image, and the function gray closing shape is used to implement the closing operation of the grayscale image.

[0064] Opening operation can eliminate small objects, separate objects at slender parts, and smooth the boundaries of larger objects without significantly changing their areas. Opening operation is a process of erosion followed by dilation, which can eliminate small noises in the image; Closing operation is a process of dilation followed by erosion, which can eliminate holes existing in the image, connect adjacent objects, and smooth the boundaries without significantly changing the areas of the objects; After completing the opening and closing operations, the original information of the image is retained by the median filtering method, and the pixel points with large differences from the surrounding pixel points in the image are processed through smoothing to filter out the noises inside the image.

[0065] Dilation is a process of merging the background points of the target area into the target object and expanding the boundary of the target object outward. Selecting the structuring element B, each pixel point in the image X is expanded to B + x, and its mathematical model is as follows:

[0066]

[0067] Erosion is to shrink the boundary of the target area. Selecting the structuring element B, each subset B + x in the image X that is congruent to the structuring element B is shrunk to the point x, and its mathematical model is as follows:

[0068] X Θ Β = {x|B + x ∪ x ≠ φ}

[0069] After completing the opening and closing operations, in this embodiment, the median filtering is implemented by calling the medianBlur() function through the OpenCV software to complete the image smoothing, and the implementation method is: dst = cv2.medianBlur(src, ksize).

[0070] In step B, the following method is specifically adopted for the quality evaluation of the grayscale image:

[0071] After the contrast enhancement, adaptive thresholding, inversion, and morphological processing in step A, a grayscale image of the shallow marine sediments with bioturbation effects is obtained, and it is necessary to evaluate the obtained image effect. The specific implementation is as follows:

[0072] (1) Define a(i, j) as the grayscale value at the point (i, j) in the grayscale image. The following is to calculate the gradient change value F(i, j) at the point (i, j) in the image:

[0073]

[0074] (2) Since the clearer the obtained grayscale image with bioturbation effects, the greater the gradient change of the grayscale value, and the blurrier the image, the smaller the gradient change of the grayscale value. Therefore, it is necessary to evaluate the change characteristics of the gradient of the grayscale value of the image, and then overall evaluate the clarity of the obtained grayscale image:

[0075] Accumulate the gradient change values of each point in the image to obtain the overall image sharpness evaluation value M(z):

[0076]

[0077] Define a curve discrimination factor G to discriminate the image quality, and the curve discrimination factor G is calculated in the following way:

[0078]

[0079] where σ is the standard deviation of the sharpness evaluation values at n position points, is the arithmetic mean of the sharpness evaluation values at n position points.

[0080] Define a threshold value. The value of S is determined according to experience. Generally, the best value range of S is between 0.25 and 0.3. In this embodiment, S is preferably taken as 0.25. If G ≤ S, the image is retained. If G > S, it indicates that the image quality is poor and needs to be reprocessed.

[0081] Step C: Image binarization and extraction of biological disturbance effects

[0082] 1) Perform binarization processing on the high-definition grayscale image obtained in step B. Binarization means setting the grayscale value of the pixel points on the image to 0 or 255, and the processed image shows only black and white effects;

[0083] 2) Mark each pixel point in the binary image:

[0084] Regions composed of pixels with the same pixel value and adjacent positions in the image are marked the same (black or white), and regions composed of pixels with different pixel values and adjacent positions are marked black and white differently. On this basis, the recognition and extraction of biological disturbance effects in the binary image are completed.

[0085] Step D: Quantification of the proportion of biological disturbance effects

[0086] After the above processing is completed, the proportion of biological disturbance effects is achieved through the following formula:

[0087] Proportion of biological disturbance effects = Pixel accumulation value of the recognized biological disturbance effects / Total pixel matrix value of the sediment core grayscale image

[0088]

[0089] where: a ij represents the pixel value of this point; when the pixel value is greater than the set value SetValue, the pixel points are stored and accumulated, and the accumulated value divided by the total matrix value (list*row) of the image is the proportion of the biological disturbance effect.

[0090] Taking the following and actual application cases as examples, the advantages of this solution are described in detail:

[0091] Case 1: Select the shallow sediment core A from the shallow layer of the Yangtze River underwater delta in China.

[0092] Step 1: Obtain undisturbed shallow sediment samples from the seabed through a box sampler in this sea area. Then, use a special transparent resin square tube with a size of 6 cm × 3 cm to insert into the box sampler to obtain sediment samples. After that, seal and package them and store them frozen in a refrigerator at -80°C.

[0093] Step 2: Use an X-ray core scanner in the laboratory to scan the core obtained in Step 1 to obtain the optical image of Core A with bioturbation. Set the working conditions of the X-ray core scanner as: voltage is 45 kV, current is 30 mA, and exposure time is 1000 ms.

[0094] Step 3: Preprocess the obtained optical image with bioturbation. First, use the rgb2gray() function in Matlab 2020a software to convert the image into a grayscale image, and then complete contrast enhancement through gamma transformation. Secondly, perform local threshold processing, inversion processing, and morphological processing on the obtained grayscale image to obtain a high-definition grayscale image with bioturbation ( Figure 2 a); then, using the calculation method in Step 6, the curve discrimination factor G value of the image is obtained as 0.19, which is less than 0.25, indicating that the quality of the core image with bioturbation after processing meets the requirements.

[0095] Step 4: Binarize the high-definition grayscale image obtained in Step 3. Mark the pixel points with the same pixel value and adjacent positions in the image with the same (black or white) color, and mark the pixel points with different pixel values and adjacent positions with different black and white colors to identify the bioturbation in the image. The specific results are shown in Figure 2 b; According to the calculation formula in Step 8, the proportion of bioturbation in Core A is calculated to be 7.086%, indicating that the bioturbation in the shallow surface sediments of this area is relatively obvious.

[0096] The traditional sedimentological methods are mainly used to qualitatively describe the bioturbation in sediments, mainly qualitatively describing the types of bioturbation structures, the diameters and shapes of foraminifera in shallow sediments. However, this qualitative description depends more on the professional level of technicians in this field, and there will be human errors in the description of bioturbation in the same core, resulting in inaccurate identification of bioturbation. At the same time, it is also difficult to quantitatively estimate the impact of bioturbation on biogeochemical processes at the key seafloor interfaces. By using the technical solution of the present invention, the bioturbation in shallow sediments and its influence degree can be quickly and accurately quantified and identified, which further illustrates the high efficiency and accuracy of the method of the present invention.

[0097] Case 2: Select the shallow sediment core B from the inner shelf of the East China Sea in China.

[0098] Step 1: Use a box corer to obtain undisturbed shallow sediment samples on the seabed in this sea area, and then insert a special square transparent resin tube with a size of 6 cm × 3 cm into the box corer to obtain sediment samples with bioturbation. After that, seal and package them and store them frozen in a refrigerator at -80°C.

[0099] Step 2: Use an X-ray core scanner in the laboratory to scan and analyze the core samples obtained in Step 1 to obtain the optical image of core B with bioturbation. Set the working conditions of the X-ray core scanner as: voltage 45 kV, current 30 mA, and exposure time 1000 ms.

[0100] Step 3: Preprocess the obtained optical image with bioturbation. First, use the rgb2gray() function in Matlab 2020a software to convert the image into a grayscale image, and then complete the contrast enhancement through gamma transformation. Secondly, perform local threshold processing, inversion processing, and morphological processing on the obtained grayscale image to obtain a high-definition grayscale image with bioturbation ( Figure 3 a); then use the calculation method in Step 6 to obtain the curve discrimination factor G value of the image as 0.21, which is less than 0.25, indicating that the quality of the core image with bioturbation after processing meets the requirements;

[0101] Step 4: Binarize the high-definition grayscale image obtained in Step 3, mark the pixel points with the same pixel value and adjacent positions with the same (black or white), and mark the pixel points with different pixel values and adjacent positions with different black and white to identify the bioturbation in the image. The specific results are shown in Figure 3b; According to the calculation formula in Step 8, the proportion of bioturbation in Core A is calculated to be 2.461%, indicating that the influence degree of bioturbation in the shallow surface sediments of this area is medium, and it mainly affects the sediment layer at a depth of 0 - 3 cm.

[0102] Using traditional sedimentology methods to describe the bioturbation in this Core B, key element information such as the type of bioturbation structures, the diameter size and shape of foraminifera in the core can generally be obtained. However, this qualitative description relies more on the professional level of technicians in this field. Even for the description of bioturbation in the same core, there will be human errors, resulting in inaccurate identification of bioturbation. At the same time, it is difficult to quantitatively estimate the influence of bioturbation on biogeochemical processes at the key interfaces of the seabed, which will further lead to incorrect understandings in subsequent more in-depth scientific research.

[0103] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for quantitatively estimating the bioturbation effect in shallow submarine sediments, characterized in that, Including the following steps: Step A: Obtain a grayscale image of the shallow seabed sediment with bioturbation effects; Step A1: Obtain a shallow seabed sediment sample and conduct a scanning analysis on it to obtain an X-ray optical image of the shallow sediment core, where the optical image contains bioturbation effects; Step A2: Preprocess the X-ray optical image to obtain a grayscale image with bioturbation effects; Step A3: Perform local threshold processing on the grayscale image to complete local threshold segmentation of the image; Step A4: Based on Step A3, perform reverse processing on the grayscale image to achieve image inversion; Step A5: After reverse processing, perform morphological processing on the grayscale image to obtain a high-definition grayscale image with bioturbation effects; Step B: Evaluate the quality of the obtained grayscale image to obtain a clear grayscale image; Step B1: Define a(i,j) as the grayscale value at the (i,j) point in the grayscale image, and calculate the gradient change value F(i,j) at the (i,j) point in the image: Step B2: Evaluate the change characteristics of the gradient of the image grayscale value, and then evaluate the clarity of the obtained grayscale image: (1) Accumulate the gradient change values of each point in the image to obtain the overall clarity evaluation value M(z) of the image: (2) Define a curve discrimination factor G to perform image quality discrimination, and the curve discrimination factor G is calculated in the following way: where σ is the standard deviation of the clarity evaluation values of n position points, and is the arithmetic mean of the clarity evaluation values of n position points; (3) Define a threshold S, and the value range of S is 0.25 - 0.

3. If G ≤ S, then retain the image. If G > S, it indicates that the image quality is poor, and then reprocess it; Step C: Perform binarization processing on the image evaluated in Step B and identify the bioturbation effects; Step D: Quantitative calculation of bioturbation effects: Proportion of bioturbation effects = Cumulative pixel value of the identified bioturbation effects / Total pixel matrix value of the sediment core grayscale image.

2. The method for quantitatively estimating the bioturbation effect in shallow submarine sediments according to claim 1, characterized in that: The specific steps of Step C for binarizing the image evaluated in Step B and extracting the bioturbation effects include: 1) Binarize the high-definition grayscale image obtained in Step B: Set the grayscale value of the pixel points on the grayscale image to 0 or 255, so that the image containing bioturbation effects shows only black and white effects, and obtain a binary image; 2) Mark each pixel point in the binary image: Mark the areas composed of pixels with the same pixel value and adjacent positions in the binary image with the same label, and mark the areas composed of pixels with different pixel values and adjacent positions with different labels, so as to complete the identification and extraction of bioturbation effects in the binary image.

Images