A gravity measurement downsampling method, device, equipment and medium optimized by edge detection technology

Abstract

The present invention discloses a gravity measurement downsampling method, apparatus, device, and medium optimized using edge detection technology. The invention belongs to the field of gravity measurement technology and improves upon downsampling processing using a mean filter. A gravity measurement downsampling method optimized based on edge detection technology is proposed. Edge detection technology is used to assess the degree of gravity field variation. In areas where the gravity field varies dramatically, the length of the gravity measurement data sequence undergoing mean filtering is reduced, retaining more of the varying gravity measurement signals. In areas where the gravity field varies gently, the length of the gravity measurement data sequence undergoing mean filtering is increased, resulting in smoother filtering results, smaller amounts of downsampled data, and reduced subsequent computational effort.

Description

Technical Field

[0001] The present invention belongs to the field of gravity measurement technology, and more specifically, relates to a gravity measurement downsampling method, device, equipment and medium optimized by edge detection technology. Background Art

[0002] Marine and airborne gravimeters can measure gravity information over a large area and with high efficiency under dynamic base conditions. Since the 1980s, with the maturity of satellite navigation technology, inertial device technology, and inertial navigation system technology, marine and airborne gravimeters have made significant contributions to resource exploration and gravity mapping. However, due to the limitations of the measurement mechanism, the raw signal during gravity measurement contains significant high-frequency noise and external disturbance noise. Therefore, in engineering applications, low-pass filtering is generally used to reduce the impact of noise on gravity measurement results. Since the raw sensor signals typically have a high-frequency sampling frequency of 50-200Hz, directly filtering and resolving them with this high-frequency signal not only consumes significant processor computing resources, but also fails to accurately reflect low-frequency gravity information and is not conducive to attenuating high-frequency disturbances. Therefore, downsampling technology is needed to process the raw sensor data, reduce the sampling frequency, reduce the computational effort, and reduce high-frequency noise.

[0003] Downsampling is essentially a low-pass filter. While lowering the sampling frequency and reducing high-frequency noise, it also removes some changing gravity measurement signals. Therefore, in some areas where the gravity field changes rapidly, downsampling will distort the gravity measurement signal and reduce the accuracy of gravity measurement. Summary of the Invention

[0004] In response to the above-mentioned defects or improvement needs of the existing technology, in order to enable the raw gravity measurement data to retain the changing gravity values ​​after downsampling, the present invention uses edge detection technology to evaluate the degree of change in the gravity field, optimizes the downsampling sequence length according to the degree of change in the gravity field, and thus retains the changing gravity signal in areas where the gravity field changes drastically, and outputs a smoother and smaller data-volume downsampling result in areas where the gravity field changes gently.

[0005] To achieve the above object, according to one aspect of the present invention, a gravity measurement downsampling method optimized using edge detection technology is provided, comprising:

[0006] The gravity reference image is converted into a grayscale image by using edge detection, and the grayscale image is scanned to calculate the gradient sum of the grayscale image pixels;

[0007] Based on the sequence length of the gradient and optimized mean filter, the optimized mean filter is used to downsample the raw data of gravity measurement. The larger the gradient and the more drastic the gravity field change, the shorter the corresponding filter sequence.

[0008] In some optional embodiments, the converting of the gravity reference image into a grayscale image by edge detection, scanning the grayscale image and calculating the gradient sum of the grayscale image pixels includes:

[0009] Divide the gravity reference image into several evaluation areas and convert the gravity reference image into a grayscale image;

[0010] The grayscale image is scanned using the Sobel operator template to obtain the gradient sum of the grayscale image pixels in each evaluation area.

[0011] In some optional embodiments, after obtaining the gradient sum of the grayscale image pixels in each evaluation area, the method further includes:

[0012] The gradient sum of the grayscale image pixels in each evaluation area is normalized to obtain the normalized gradient sum in each evaluation area.

[0013] In some optional implementations, when the sum of the normalized gradients in the evaluation area corresponding to the current measurement area is greater than a set threshold, it is considered that the gravity field changes more drastically and the optimized filtering sequence is shorter;

[0014] When the sum of the normalized gradients in the corresponding evaluation area in the current measurement area is less than the set threshold, it is considered that the gravity field changes more slowly and the optimized filtering sequence is longer.

[0015] In some optional embodiments, According to the normalized gradient of the evaluation area corresponding to the current gravity measurement area and Get the filter sequence length N t ,in, represents the median of the sum of gradients in the gravity reference map, is the set threshold, and N0 is the set initial filtering sequence length.

[0016] In some optional implementation schemes, the gravity field comprehensive characteristic parameter screening threshold Set the median of the gradient sum as the upper limit of the increasing length of the filter sequence.

[0017] In some optional embodiments, Downsampling is performed by the mean filtering method, where is the downsampling result, f i is the i-th original gravity measurement value.

[0018] According to another aspect of the present invention, there is provided a gravity measurement downsampling device optimized using edge detection technology, comprising:

[0019] A gradient sum calculation module is used to convert the gravity reference image into a grayscale image by using edge detection, and scan the grayscale image to calculate the gradient sum of the grayscale image pixels;

[0020] The sequence length optimization module is used to downsample the raw gravity measurement data based on the sequence length of the gradient and optimized mean filter using the optimized mean filter. The more drastic the gravity field changes, the larger the gradient sum is, and the shorter the optimized filter sequence is. The more gradual the gravity field changes, the smaller the gradient sum is, and the longer the optimized filter sequence is.

[0021] According to another aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0023] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0024] (1) Aiming at the problem that the mean filter used for downsampling in areas with rapid changes in the gravity field tends to filter out valid gravity measurements as noise, a gravity measurement downsampling method optimized based on edge detection technology is proposed. The edge detection technology is used to evaluate the degree of change of the gravity field. In areas with drastic changes in the gravity field, the length of the downsampling sequence is reduced, and more changing gravity measurement signals are retained, which can more effectively preserve the details of the gravity measurement.

[0025] (2) In areas where the gravity field changes gently, the length of the downsampled sequence is increased to make the filtering results smoother, the amount of data after downsampling is smaller, and the subsequent computational effort is reduced. This improves the suppression of high-frequency noise and motion noise in areas where the gravity field changes gently, while also lowering the sampling frequency, reducing the computational effort of subsequent gravity measurement solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a gravity measurement downsampling method optimized using edge detection technology provided by an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of a Sobel operator template provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0029] In the examples of the present invention, “first”, “second”, etc. are used to distinguish different objects rather than to describe a specific order or sequence.

[0030] Aiming at the problem that the downsampling parameter setting of sea-air gravimeter affects the accuracy of gravity measurement, the present invention proposes a gravity measurement downsampling method based on edge detection technology optimization. Figure 1 As shown, during gravity measurements, the raw gravity measurement data is downsampled using a mean filter. Mean filtering is essentially a low-pass filter that, while downsampling, also filters out high-frequency noise and motion noise. In areas where the gravity field varies rapidly, some of the changing gravity measurement values ​​are treated as noise and removed by the filter, reducing gravity measurement accuracy. This invention proposes a gravity measurement downsampling method optimized based on edge detection technology. First, edge detection is used to convert a gravity reference image into a grayscale image. The grayscale image is scanned using a Sobel operator template and the gradient sum of the grayscale image pixels is calculated. Then, based on the normalized gradient sum, the length of the mean filter sequence is optimized, and the optimized mean filter is used to downsample the raw gravity measurement data. In this method, the gradient sum represents the degree of gravity field variation in a region. Regions with more drastic gravity field variations have larger gradient sums, resulting in shorter optimized filter sequences and more retained gravity measurement signals. Regions with more gradual gravity field variations have smaller gradient sums and longer optimized filter sequences, resulting in smoother filtering results and smaller downsampled data volumes.

[0031] Furthermore, the gravity reference map stores global gravity values ​​in matrix form at fixed latitude and longitude intervals. Assuming that the size of the gravity reference map is a p×q matrix grid, the gravity reference map is divided into several evaluation areas with a grid size of m×n. The current actual gravity measurement area corresponds to one of the m×n grid areas, and G represents the gravity value at the grid point in the gravity reference map.

[0032] The gravity reference image preprocessing based on edge detection technology specifically includes the following steps:

[0033] The gravity reference image is converted into an 8-bit grayscale image, and the image texture is analyzed by edge detection technology. By analyzing the relationship between the image texture features and pixels, the characteristic indicators of the gravity reference image are extracted. i,jis the gravity value at the grid point (i, j). The gravity reference image is converted into an 8-bit grayscale image with a grayscale range of 1 to 256 according to the following formula.

[0034]

[0035] Among them, H i,j is the grayscale value at the grid point (i, j), H min The minimum gray value is 1, H max The maximum grayscale value is 256, G max is the maximum gravity value in the gravity data, G min is the minimum gravity value in the gravity data, g i,j is the gravity value at the grid point (i, j).

[0036] Image edge is an important feature of the image, which is where the grayscale value of the pixel in the image changes dramatically. Using image edge detection, the grayscale value change characteristics of the gravity grayscale image can be reflected, and then the change characteristics of gravity can be reflected from the details. The Sobel operator is a commonly used method for image edge detection. Compared with other edge detection methods, the Sobel operator is easy to implement, has a small amount of calculation, and has good noise resistance. This method is used to extract the local change gradient of gravity anomaly in the gravity map. The Sobel operator template is as follows: Figure 2 shown.

[0037] Use the Sobel operator template to scan each pixel of the grayscale image and calculate the gradient value of the pixel in each direction using the following formula.

[0038]

[0039] Among them, h i is the grayscale value within the scan template range.

[0040] Therefore, the gradient of the center point of the scanning template and H gr (i,j) is:

[0041]

[0042] Among them, H gr (i, j) is the gradient sum of the center point of the scanning template, and the coordinates of the center point of the scanning template in the m×n grid of the evaluation area are (i, j); H gr is the total gradient in the evaluation area. gr If the value is small, it means that the grayscale change between the pixel in the grayscale image and the surrounding pixels is small, that is, the gravity amplitude change of the assessment area in the gravity reference map is small; on the contrary, H gr The value is large, and the gravity value of the assessment area varies greatly in the gravity reference map.

[0043] Furthermore, the gravity measurement downsampling method specifically includes the following steps:

[0044] During the gravity measurement process, the original signal contains a large amount of high-frequency noise and external disturbance noise. Therefore, in engineering applications, a low-pass filter is generally used to reduce the impact of noise on the gravity measurement results. Since the original sensor signal usually has a high-frequency sampling frequency of 50 to 200 Hz, using this high-frequency signal for direct filtering and solving will not only seriously consume processor computing resources, but also fail to correctly reflect the low-frequency gravity information, and will also be detrimental to the attenuation and suppression of high-frequency disturbances. Therefore, it is necessary to use downsampling technology to process the raw sensor data. This solution uses the mean filtering method to perform downsampling processing. The calculation formula is as follows:

[0045]

[0046] in, is the downsampling result; f i is the i-th original gravity measurement value; N is the length of the filtered sequence. Downsampling through mean filtering not only directly suppresses high-frequency noise but also facilitates subsequent gravity measurement filtering.

[0047] Furthermore, the gradient-based optimization-based downsampling parameter setting for gravity measurements can be achieved by the following steps:

[0048] (1) Data preprocessing

[0049] In order to facilitate the use of gradient and H gr To design the downsampling parameters, we need to convert H gr Perform normalization:

[0050]

[0051] Among them, H gr Indicates the gradient sum of the current gravity measurement area and the corresponding evaluation area; H gr min Indicates the minimum gradient sum of all assessment areas in the gravity reference map; H grmax Represents the maximum gradient sum of all assessment areas in the gravity reference map; the normalized gradient sum The value range is [0,1].

[0052] (2) Gradient-based and optimized mean filter sequence length

[0053] The filter sequence length selected in general engineering applications is the size of the original data collected within 1 second, assuming it is N0. Greater than the set threshold When , it can be considered that the gravity value of the current gravity field changes greatly and the detailed information of the gravity field is more. In order to retain more detailed features of the measured gravity values, the length of the filter sequence can be reduced so that the smaller changes in the original measured gravity values ​​can be retained. Less than the set threshold When , it can be considered that the gravity value of the current gravity field changes little, and the length of the filter sequence can be increased to reduce the sampling frequency and the amount of subsequent filtering calculations. At the same time, the high-frequency noise and external disturbance noise in the original measured gravity value can be removed more thoroughly. According to the current gravity measurement area gradient and The calculated filter sequence length is:

[0054]

[0055] in, Indicates the median of the gradient sum in the gravity reference map and the screening threshold of the comprehensive characteristic parameters of the gravity field In order to prevent the length of the filter sequence from being infinitely enlarged, the median of the gradient sum is set as the upper limit of the filter sequence length increase, and N0 is the set initial filter sequence length.

[0056] The formula for calculating the length of the gravity measurement downsampling filter sequence based on gradient and optimization is as follows:

[0057]

[0058] Through the above-mentioned technical solution, this paper proposes a gravity measurement downsampling method optimized based on edge detection technology. In response to changes in the gravity field, the grayscale image gradient corresponding to the gravity reference image is incorporated into the design of the gravity measurement downsampling sequence length. By utilizing the grayscale image gradient and adaptively adjusting the gravity measurement downsampling sequence length, more accurate downsampling results are provided in areas with rapid gravity field changes, while smoother, less data-intensive downsampling results are provided in areas with more gradual changes.

[0059] The present invention also provides a gravity measurement downsampling device optimized using edge detection technology, comprising:

[0060] A gradient sum calculation module is used to convert the gravity reference image into a grayscale image by using edge detection, and scan the grayscale image to calculate the gradient sum of the grayscale image pixels;

[0061] The sequence length optimization module is used to downsample the raw gravity measurement data based on the sequence length of the gradient and optimized mean filter using the optimized mean filter. The more drastic the gravity field changes, the larger the gradient sum is, and the shorter the optimized filter sequence is. The more gradual the gravity field changes, the smaller the gradient sum is, and the longer the optimized filter sequence is.

[0062] The specific implementation of each module can refer to the description of the above method embodiment, and the embodiment of the present invention will not be repeated.

[0063] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0064] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0065] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, or two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0066] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A gravity measurement downsampling method optimized by edge detection technology, characterized in that: include: The gravity reference image is converted into a grayscale image by using edge detection, and the grayscale image is scanned to calculate the gradient sum of the grayscale image pixels; Based on the gradient and the sequence length of the optimized mean filter, the raw gravity measurement data is downsampled using the optimized mean filter. The more drastic the gravity field changes, the larger the gradient sum is, and the shorter the optimized filter sequence is. The more gradual the gravity field changes, the smaller the gradient sum is, and the longer the optimized filter sequence is.

2. The method according to claim 1, characterized in that The method of converting the gravity reference image into a grayscale image by edge detection and scanning the grayscale image to calculate the gradient sum of the grayscale image pixels includes: Divide the gravity reference image into several evaluation areas and convert the gravity reference image into a grayscale image; The grayscale image is scanned using the Sobel operator template to obtain the gradient sum of the grayscale image pixels in each evaluation area.

3. The method according to claim 2, characterized in that After obtaining the gradient sum of the grayscale image pixels in each evaluation area, the method further includes: The gradient sum of the grayscale image pixels in each evaluation area is normalized to obtain the normalized gradient sum in each evaluation area.

4. The method according to claim 3, characterized in that When the sum of the normalized gradients in the corresponding evaluation area of ​​the current measurement area is greater than the set threshold, it is considered that the gravity field changes more drastically and the optimized filtering sequence is shorter; When the sum of the normalized gradients in the corresponding evaluation area in the current measurement area is less than the set threshold, it is considered that the gravity field changes more slowly and the optimized filtering sequence is longer.

5. The method according to claim 4, characterized in that Depend on According to the normalized gradient of the evaluation area corresponding to the current gravity measurement area and Get the filter sequence length ,in, represents the median of the sum of gradients in the gravity reference map, To set the threshold, is the set initial filter sequence length.

6. The method according to claim 5, characterized in that Screening threshold of comprehensive characteristic parameters of gravity field , setting the median of the gradient sum as the upper limit of the increasing length of the filter sequence.

7. The method according to claim 5 or 6, characterized in that Depend on Downsampling is performed by the mean filtering method, where is the downsampling result, For the Raw gravity measurements.

8. A gravity measurement downsampling device optimized using edge detection technology, characterized in that: include: A gradient sum calculation module is used to convert the gravity reference image into a grayscale image by using edge detection, and scan the grayscale image to calculate the gradient sum of the grayscale image pixels; The sequence length optimization module is used to downsample the raw gravity measurement data based on the sequence length of the gradient and optimized mean filter using the optimized mean filter. The more drastic the gravity field changes, the larger the gradient sum is, and the shorter the optimized filter sequence is. The more gradual the gravity field changes, the smaller the gradient sum is, and the longer the optimized filter sequence is.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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