A method for correcting the intensity jump of multi-beam backscattering

Abstract

This invention discloses a method for correcting backscatter intensity jumps in multibeam measurement strips. The method uses a Gaussian weighted function to automatically detect backscatter intensity jumps caused by factors such as equipment parameter settings, taking a moving window as the unit. Weighted average values ​​are then used for difference compensation. The corrected intensity data is iteratively detected and corrected for jump points until the jump points converge to 0. Example calculations show that this method can effectively detect and correct intensity jumps in multibeam strips, improving the efficiency and quality of multibeam strip backscatter intensity data processing and enhancing the accuracy of seabed geomorphological analysis based on multibeam intensity data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ocean exploration, and particularly relates to the field of a multi-beam backscattering intensity jump correction method. BACKGROUND

[0002] With the rapid development of ocean engineering construction, ocean resource development, and submarine pipeline exploration, it is crucial to accurately grasp the geological types of the seabed. As one of the core technologies of ocean exploration, the multi-beam sonar system can generate backscattering intensity data and three-dimensional topographic images of the seabed through the transmission and reception of sonar signals. These data are widely used in underwater measurement, ocean exploration, environmental monitoring, submarine cable laying, and other fields. However, due to the complexity of the marine environment and the limitations of sonar system hardware devices, the backscattering intensity data collected in actual applications often have deviations and jumps, which seriously affect the accuracy of the data and subsequent analysis and processing. Therefore, how to effectively correct these data has become a technical problem to be solved.

[0003] In actual applications, the deviations and jumps of backscattering intensity data are mainly caused by the following factors: terrain complexity: the propagation path of sound waves in complex terrain is subject to various disturbances from the underwater environment, such as seabed topography, geological type changes, etc., resulting in different intensity distributions of sound reflection signals at different angles and positions, thus causing data deviations. System parameter errors: the parameter settings of sonar devices themselves, such as sonar frequency, response of receiving array, transmission power, time gain compensation (TVG), etc., will affect the collected backscattering intensity data. For example, too high or too low gain will cause the echo signal to be excessively enhanced or weakened, affecting the authenticity of the measurement data. Environmental factors: environmental factors such as suspended solids, temperature, salinity, noise, etc. in the water body will cause energy loss and reflection intensity changes in the signal propagation process, thus affecting the quality of sonar data collection. These factors cannot be corrected by simple parameter adjustment. Data jumps: the strip jump phenomenon commonly seen in multi-beam sonar collected data, i.e. sudden changes in backscattering intensity data between consecutive Pings (one signal transmission and reception), forming discontinuity of the data. This jump may be caused by device switching, signal interference, etc., directly affecting the overall quality of the sonar image.

[0004] In view of the above problems, domestic and foreign scholars have conducted a lot of research, trying to correct the errors in the backscattering intensity data through different methods. However, the existing technology still has great limitations in solving the data jump problem.

[0005] Some solutions have studied various factors that affect the parameters of multi-beam sonar systems and their specific effects. Studies have shown that errors in system parameters, such as gain settings of sonar equipment, time gain compensation, etc., can cause deviations in backscatter intensity data. However, this research is limited to analyzing the causes of errors and does not provide effective correction methods. Some solutions aimed at correcting multi-beam system parameters have proposed methods to reduce system setup errors by measuring the response of the receiving and transmitting arrays, combining linear dynamic response correction and complex weighting correction. This method effectively reduces signal deviations caused by inaccurate device parameter settings by accurately measuring device response characteristics. However, this method is mainly based on correcting hardware parameters and cannot completely solve the problem of sudden changes in backscatter intensity data caused by environmental or other complex factors. Some solutions aimed at the angle response problem in backscatter intensity data have proposed an empirical correction method that can reduce angle-dependent artifacts and improve image uniformity. By calculating and removing angle artifacts in backscatter intensity, this method can improve the accuracy of sonar images to some extent. Some solutions have adopted an integrated processing and analysis technique, particularly for correcting the angle dependence of backscatter intensity. However, these methods cannot effectively address the data jump problem. Some methods reprocess Snippet echo intensity. These studies address different aspects of data discontinuity, but most focus on improving overall image gray scale uniformity and eliminating local anomalies, and cannot solve the core problem of backscatter intensity data jumps within the strip.

[0006] Currently, the correction of multi-beam backscatter intensity systems mainly focuses on the correction of hardware parameters and the processing of angle response. Although these methods have achieved certain results in reducing system errors and improving image uniformity, there are still many shortcomings in dealing with the strip intensity jump phenomenon:

[0007] Limitations of system parameter correction: Existing methods mostly rely on adjusting system hardware parameters such as gain, transmission power, etc. However, device hardware characteristics are not the only cause of jump phenomena, and complex environmental factors and device switching can also cause jumps. The current technical means for correcting these factors are limited.

[0008] Angle dependence: Although correction methods for angle-dependent artifacts effectively reduce the influence of geometric artifacts on images, these methods are mainly suitable for smoothing signal fluctuations caused by angle changes in images and do not significantly improve sudden jumps.

[0009] Lack of automatic processing mechanism for jump: Most existing methods rely on manual parameter setting or manual intervention to handle jump data, lacking an automatic and intelligent jump detection and repair mechanism. This makes the existing technology less adaptable in practical applications, especially in the complex marine environment, where manual adjustment is difficult to cope with complex situations. SUMMARY

[0010] To solve the problems existing in the prior art, the present patent proposes a multi-beam backscatter intensity jump correction method, which aims to solve the following technical problems: automatic detection of jump points: by introducing a Gaussian weighting function and a dynamic threshold detection algorithm, the jump points in the data strip can be automatically and accurately detected, avoiding the influence of manual intervention on precision and efficiency. Intelligent repair of jump data: based on the Gaussian weighted difference compensation technology, the data segments before and after the jump points are weighted, ensuring the smoothness and consistency of the repaired data, which can effectively adapt to the jump repair requirements in different environments. Enhance the adaptability and robustness of data: this method can adapt to different sonar working environments, whether it is complex terrain or harsh marine environment, it can effectively identify and repair the jump phenomenon in the data, improve the quality and consistency of the backscatter data. In order to achieve the above purposes, the present application discloses a multi-beam backscatter intensity jump correction method, which specifically comprises the following steps:

[0011] S1: data file import;

[0012] S2: intensity data extraction, pre-processing of single strip multi-beam backscatter intensity data, the pre-processing includes time gain compensation (TVG) and sonar parameter correction, obtaining the corrected backscatter intensity data;

[0013] Wherein, the backscatter intensity data is collected by a multi-beam depth sounding system, usually recorded in Ping, each Ping records hundreds or thousands of seabed point backscatter signals obtained by a complete signal transmission and reception cycle of multi-beam transducer at a certain time, each strip data contains multiple consecutive Pings, and the backscatter intensity data of each Ping is used to reflect the signal echo reflection strength of seabed within a certain range in the vertical track direction;

[0014] S3: intensity jump detection, based on Gaussian weighting function to detect jump, specifically including the following steps:

[0015] S3-1: calculate the Gaussian weighted average value, input the pre-processed backscatter intensity data in step S2, use the Gaussian weighted function method to detect jump, and the Gaussian weighted average value corresponding to each Ping is:

[0016]

[0017] wherein x i represents the intensity value of the i-th data, ω i is the Gaussian weight;

[0018] S3-2: Calculate the difference of Gaussian weighted average value, for a series of Pings whose Gaussian weighted average value has been calculated, denoted as Calculate the difference of Gaussian weighted average value between adjacent Pings, the specific calculation formula is as follows:

[0019]

[0020] wherein, represents the intensity value of the j-th data, ΔI j represents the difference of Gaussian weighted average value;

[0021] S3-3: Dynamic threshold detection of jump point, calculate the mean value of intensity difference between all Pings and the standard deviation σ ΔI of all intensity differences, according to which the dynamic threshold is set to detect the jump point, the specific calculation formula is as follows:

[0022]

[0023] Through the obtained and σ ΔI define the dynamic threshold as T:

[0024]

[0025] When ΔI > T, it means that there is a jump in the Ping, and the Ping is marked;

[0026] S4: Determine the jump non-compensation data segment, specifically including the following steps:

[0027] S4-1: Calculate the weighted average value within the window W before and after the jump point, specifically, for each jump point, take the intensity values within the window W forward and backward respectively with the jump point as the center, and calculate the weighted average value as the reference value for repair;

[0028] S4-2: Segment processing according to the order for all jump points, each two adjacent jump points determine a jump data segment, for each jump data segment, calculate the weighted average value of the start and end positions to determine the compensation difference value of the jump data segment;

[0029] S5: Intensity jump repair, based on the above compensation value, repair all Pings within the jump data segment, and adjust the data to keep consistent with the fluctuation range of the data at both ends, the weighted average value of each data in the first two jump segments after compensation is and The Ping weighted average value after compensation from the third jump segment is

[0030] The steps S3-S5 are performed again on the jump-repaired intensity data, and the iteration stops when there is no jump point.

[0031] S6: Derive the repaired intensity data, i.e. all the Pings repaired in step S5.

[0032] As a preferred technical solution of the present application, the preprocessing step in step S2 includes removing system parameters and human or operating system added influencing factors to ensure the authenticity of the backscattering intensity data, and specifically includes compensating for the time gain (TVG), and the calculation formula is:

[0033] TVG = Sp·log 10 (R) + 2·α·R (6)

[0034] wherein Sp is the propagation loss coefficient, R is the distance between the target and the sonar, and a is the attenuation coefficient in the propagation medium, and by applying the formula, the echo intensity value of each Ping is compensated to correct the energy loss caused by the distance and medium attenuation.

[0035] As a preferred technical solution of the present application, the sonar parameter correction in the preprocessing step in step S2 includes correction of system parameters, and specifically includes correcting the received signal by a system gain constant, and the calculation formula for sonar parameter correction is:

[0036] I corrected = I received + G sonar (7)

[0037] wherein I received is the received original signal intensity, and G sonar is the system gain constant for correcting the signal gain value added by human or system automatically.

[0038] As a preferred technical solution of the present application, in the process of setting the dynamic threshold in step S3-3, further includes noise smoothing processing and dynamic adjustment of the standard deviation weight factor to adapt to the needs of different sonar working environments, and the dynamic threshold calculation formula is:

[0039]

[0040] wherein k is the dynamically adjusted weight factor, which is specifically adjusted according to the actual environment and data variation characteristics of the sonar working; N fNoise smoothing processing factor, used for removing the influence of random noise in data, which is calculated by Kalman filtering algorithm.

[0041] As a preferred technical solution of the present application, the jump repair in step S5 comprises iterative detection and compensation repair of jump points, specifically comprising the following steps:

[0042] T1: After completing the jump point compensation repair, repeat steps S3-S5, and record the newly detected jump points;

[0043] T2: For the newly detected jump points, calculate the weighted average value and data compensation again, and gradually correct all jump points;

[0044] T3: Iteratively repeat the repair process of step S5, gradually reduce the repair error, until the number of jump points converges to zero.

[0045] As a preferred technical solution of the present application, the processing results of steps S1 to S2 also include preset conditions, which are specifically:

[0046] The multi-beam intensity is processed in units of a single strip, and the data is corrected with system parameters to remove human or operating system introduced factors, and a complete strip data is selected as the correction data source;

[0047] In the process of automatically detecting jumps by using Gaussian weighting, the statistical quantity of intensity difference is introduced to set a dynamic threshold, which changes with data and can maintain good adaptability in different situations.

[0048] All jump points are segmented in order, and each two adjacent jump points determine a jump data segment, and the intensity values in the window W before and after the jump points are taken respectively, and the weighted average value is calculated as the compensation difference.

[0049] Compared with the prior art, the present application has the following beneficial effects:

[0050] Automatic jump detection and repair: by introducing Gaussian weighting function and dynamic threshold detection algorithm, automatic jump point detection can be realized, which saves the tedious steps of manual setting and adjustment in the prior art, and improves the efficiency and accuracy.

[0051] Enhance data smoothness and consistency: based on the Gaussian weighted difference compensation technology, intelligent repair is performed before and after the jump points to ensure smooth transition of data, effectively eliminating the discontinuity caused by jumps and improving the quality of sonar image.

[0052] Adaptable to different environments and equipment: This method can dynamically adjust noise processing and threshold parameters to adapt to sonar data acquisition in different marine environments. It has strong applicability and can still operate stably under variable and complex marine conditions, ensuring data quality.

[0053] Improved data accuracy and system robustness: By correcting system parameters and compensating for time gain, the intensity deviation caused by equipment errors and environmental factors was corrected, thus improving the overall accuracy and robustness of the backscatter data.

[0054] Reduced human intervention: This invention reduces the need for human intervention, lowers operational complexity, and enhances the system's intelligence level through automated jump detection and repair. Attached Figure Description

[0055] Figure 1 This is a flowchart of a multi-beam backscattering intensity jump correction method according to the present invention;

[0056] Figure 2 This is a schematic diagram of the transition point ad in the original image of an embodiment of the present invention;

[0057] Figure 3 This is a cross-sectional view along the jump point ad direction of an embodiment of the present invention;

[0058] Figure 4 This is the jump marker result of an embodiment of the present invention;

[0059] Figure 5 These are the marked transition positions in this embodiment of the invention;

[0060] Figure 6 These are before-and-after image comparisons of the transition points in this embodiment of the invention.

[0061] Figure 7 This is a comparison image of the data segment at the jump point abcd in an embodiment of the present invention before and after repair;

[0062] Figure 8 This is a statistical chart showing the changes in the jump point before and after repair according to an embodiment of the present invention. Detailed Implementation

[0063] The present invention will be further described below with reference to the accompanying drawings and embodiments. However, the present invention can be implemented in many different ways and should not be construed as limited to the embodiments shown; rather, these embodiments provide those skilled in the art with implementation methods that meet applicable legal requirements.

[0064] Example 1: As Figure 1 As shown, the multi-beam backscattering intensity jump correction method of the present invention specifically includes the following steps:

[0065] S1: data file import;

[0066] S2: intensity data extraction, pre-processing the single strip multi-beam backscatter intensity data, the pre-processing including time gain compensation (TVG) and sonar parameter correction, obtaining the corrected backscatter intensity data;

[0067] The backscatter intensity data is collected by a multi-beam sounding system, usually recorded in Pings, each Ping record hundreds or thousands of seabed point backscatter signals obtained by a complete signal transmission and reception cycle of the multi-beam transducer at a certain time, each strip data contains multiple consecutive Pings, and the backscatter intensity data of each Ping is used to reflect the signal echo reflection strength of the seabed within a certain range in the vertical track direction;

[0068] S3: intensity jump detection, detecting the jump based on a Gaussian weighted function, specifically including the following steps:

[0069] S3-1: calculating the Gaussian weighted average value, inputting the pre-processed backscatter intensity data in step S2, detecting the jump by using the Gaussian weighted function method, and the Gaussian weighted average value corresponding to each Ping is:

[0070]

[0071] wherein x i represents the intensity value of the i-th data, ω i is the Gaussian weight;

[0072] S3-2: calculating the difference of the Gaussian weighted average value, for a series of Pings whose Gaussian weighted average values have been calculated, denoted as The difference of the Gaussian weighted average value between adjacent Pings is calculated, and the specific calculation formula is as follows:

[0073]

[0074] wherein, represents the intensity value of the j-th data, ΔI j represents the difference of the Gaussian weighted average value;

[0075] S3-3: detecting the jump point by a dynamic threshold, calculating the mean value of the intensity difference between all Pings and the standard deviation σ ΔI of all intensity differences, and setting a dynamic threshold to detect the jump point according to the calculation formula as follows:

[0076]

[0077] The jump point is detected by the obtained and σ ΔI The dynamic threshold is defined as T:

[0078]

[0079] When ΔI > T, it means that the Ping has a jump, and this Ping is marked;

[0080] S4: determining the jump compensation data segment, specifically comprising the following steps:

[0081] S4-1: calculating the weighted average value in the window W range before and after the jump point, specifically, for each jump point, taking the intensity values in the window W forward and backward respectively, and calculating the weighted average value as the reference value for repair;

[0082] S4-2: segmenting all jump points in order, each two adjacent jump points determining a jump data segment, and for each jump data segment, calculating the weighted average value of the start and end positions to determine the compensation difference value of the jump data segment;

[0083] S5: jump repair, based on the above compensation value, all Pings in the jump data segment are repaired, and the data is adjusted to be consistent with the fluctuation range of the two ends, and the weighted average value of each data in the first two jump segments after compensation is and and the weighted average value of the Pings starting from the third jump segment after compensation is

[0084] The intensity data after jump repair is subjected to steps S3-S5 again, and the iteration stops when there is no jump point.

[0085] S6: exporting the repaired intensity data, i.e. all Pings repaired in step S5.

[0086] The preprocessing step in step S2 includes removing system parameters and human or operating system added influencing factors to ensure the authenticity of the backscattering intensity data, specifically including compensating for the time gain (TVG), and the calculation formula is:

[0087] TVG = Sp·log 10 (R) + 2·α·R (6)

[0088] Where Sp is the propagation loss coefficient, R is the distance between the target and the sonar, and α is the attenuation coefficient in the propagation medium. By applying the formula, the echo intensity value of each Ping is compensated to correct the energy loss caused by distance and medium attenuation.

[0089] The single-strip multibeam backscatter intensity data underwent correction processing including TVG and sonar parameter adjustments. After processing, the resulting backscatter intensity data is considered to have largely eliminated or reduced the influence of system parameter factors. However, the original images show obvious grayscale value abrupt changes in the processed data strip images. Some of these abrupt changes and their cross-sectional views along the direction of the abrupt changes are shown below. Figure 2 , Figure 3 As shown.

[0090] Simple mean calculations are insufficient to reflect the changing trends of backscatter intensity data, making it difficult to effectively detect and correct jumps. To address this issue, a Gaussian weighted function method is proposed. The principle of detecting jumps based on the Gaussian weighted function is as follows:

[0091] Given a series of Pings containing intensity values, assuming each Ping contains n intensity values, let {x1, x2, ..., x...} n First, calculate the mean of the intensity differences among all Pings. and the standard deviation σ of all intensity differences ΔI Based on this, a dynamic threshold is set to detect transition points. The specific calculation formula is as follows:

[0092]

[0093] In the formula: x i Let μ represent the intensity value of the i-th data point. Based on the obtained μ and σ, calculate the Gaussian weighted average of each Ping, defining the Gaussian weight as ω. i Then the Gaussian weighted average value corresponding to each Ping is:

[0094] After obtaining the Gaussian-weighted average of each Ping in the data, jump detection and labeling are required. For a series of Pings whose Gaussian-weighted average has already been calculated: Calculate the difference ΔI between the Gaussian weighted averages of adjacent Pings. j The specific calculation formula is as follows:

[0095]

[0096] in, Let ΔI represent the intensity value of the j-th data point. j The difference represents the Gaussian weighted average;

[0097] The intensity difference between adjacent Pings reflects the amplitude of change between adjacent data points, but it is difficult to determine whether these changes are normal fluctuations or abnormal jumps by the degree of change alone. Therefore, a statistical calculation of the intensity difference is introduced, and a dynamic threshold is set accordingly. Since the dynamic threshold changes with the data, it can adapt well in different situations and remain sensitive to jumps at all times. In contrast, a fixed threshold may increase the probability of false detection or missed detection. In order to calculate the significance of the intensity change difference, the mean of the intensity difference between all Pings and the standard deviation σ of all intensity differences ΔI are calculated, and a dynamic threshold is set to detect jump points. The dynamic threshold T is defined as and σ ΔI

[0098]

[0099] When ΔI > T, it means that there is a jump in this Ping, and this Ping is marked.

[0100] Based on the above theory of jump detection, when the multi-beam strip intensity data is imported and jump detection is performed, the detected jump Pings are marked and displayed in the window (see Figure 4 ).

[0101] To verify the correctness of the algorithm, the saved jump Pings are displayed and compared with the original data to visually observe the detection effect. After comparison, it is found that the marked jump Pings are accurately positioned at the positions of the jumps in the original image (see Figure 5 ). This result proves the accuracy of the algorithm.

[0102] Based on the Gaussian weighted difference compensation to realize jump repair, the jump is defined as {y i} for the marked jump. In order to select appropriate data segments to compensate for the data segment starting from the jump point, a moving window W is defined, and the size of W determines the width of the selected window. In order to calculate the compensation value, μ p and μ n need to be calculated first, and the calculation formula is as follows:

[0103]

[0104]

[0105] In the formula, y i is the number of the i-th jump point; μ p is the weighted average value of the data segment of size W before the jump point y i ; μ n is the weighted average value of the data segment of size W after the jump point y​i the weighted average of the data segment of the latter window W size.

[0106] After the weighted average of the data segments before and after is calculated, the difference between the weighted averages of the two data segments is calculated as the compensation value. However, considering that the entire jump segment presents a convex or concave state in the entire strip, when selecting a moving window to compensate for the difference using the weighted average, the directionality problem needs to be considered. By comparison, it is found that no matter whether the jump segment is "protruding" or "concave", the former jump point is always compensated by the data of the previous window segment for the data of the latter window segment, and the latter jump point is the opposite, therefore, first, all the jump points are segmented according to the order, and each two adjacent jump points determine a jump data segment, and in each jump data segment, the compensation difference ΔV i and ΔV i+1 is calculated.

[0107] ΔV i = μ p - μ n (14)

[0108] ΔV i+1 = μ n - μ p (15)

[0109] In the formula, ΔV i is the compensation value of the data segment of the latter W size after the i-th jump point; and ΔV i+1 is the compensation value of the data segment of the former W size before the i+1-th jump point.

[0110] In order to ensure the continuity of compensation and the stability of intensity data, the accumulation of compensation value needs to be considered, therefore, from the third jump segment, the compensation value of the previous segment needs to be added to be the final compensation value, therefore, the compensation value of the third jump point is defined as ΔV k (k≥3):

[0111]

[0112] Based on the above compensation value, the jump part in the selected window is compensated, and the weighted average of each data in the first two jump segments after compensation is and and the weighted average of Ping after compensation from the third jump segment is

[0113]

[0114] The detection method based on the above Gaussian weighting setting dynamic threshold can accurately detect adjacent Pings exceeding the threshold limit difference between different data and mark these jumping Pings in real time. Through the compensation method of the Gaussian weighted moving window, the difference between the weighted average values of the data segments of the selected window size before and after the jumping point is calculated to compensate and repair, and the data of the jumping segment is adjusted to the same fluctuation range as the data at both ends of the jumping segment. After the first repair is completed, iterative detection and repair are started, the number of jumping points is gradually reduced, and finally the jumping points completely converge to 0, indicating that the repair of the strip has been completed.

[0115] Based on the jumping repair theory, the window size is set to 50 Pings. When the jumping points are completely repaired, it is found through comparison with the original image that the strip image has been repaired and balanced as shown in Figure 6 .

[0116] In order to verify the accuracy of the repair, the difference between the weighted average values of the jumping segments before and after the repair is analyzed, and the data of 25 Pings before and after the jumping points is selected to draw part of the profile for comparison and analysis before and after the repair, as shown in Figure 7 . By comparing the broken line change graph of the weighted average values before and after the repair of the three obvious jumping points, it can be seen that before the repair, there is a huge difference between the two segments before and after the jumping point, and the data changes abnormally and appears obvious jump. After the compensation and repair of the Gaussian weighted average value difference by moving window, the change difference between the front and rear segments is obviously reduced, and the internal and overall data segments of the jumping segment tend to be flat, indicating that the data has been effectively smoothed. Figure 7 It is shown that the detection of jumping points and the repair of jumping segments have achieved remarkable results.

[0117] Based on the standard deviation of a segment of data, the dispersion of the data can be better reflected, therefore, the change amplitude and trend of the data before and after the repair can be reflected by the standard deviation. Based on the comparison of the standard deviations before and after the jumping, it is shown that Figure 8 is the change amplitude statistical graph of the jumping points before and after the repair. For the data sequence after the repair , the overall mean before the repair is calculated and denoted as , and the overall mean before the repair is denoted as , then the standard deviation before the processing of the selected data segment is σ 前 , and the standard deviation after the processing is σ 后 .

[0118]

[0119] In the formula, is the weighted average value of the i th Ping before the repair; is the weighted average value of the i th Ping after the repair.

[0120] Table 1: Standard deviation statistics table of data segments before and after the jump point before and after repair

[0121] Pre-repair standard deviation σ 前 ]]> Standard deviation σ after repair 后 ]] Data segments before and after jump point a 1.724 0.4536 Data segments before and after jump point b 1.9203 0.3711 Data segments before and after jump point c 1.755 0.5235 Data segments before and after jump point d 1.8564 0.5902

[0122] As shown in Table 1, before repair, there is a large fluctuation before and after the jump point, resulting in a large standard deviation of the front and rear segments, indicating that the overall data tends to be discrete. After compensation and repair, the overall data tends to be smooth, and the standard deviation of the front and rear segments is small. The repair effectively reduces the fluctuation range of the jump point, and realizes the smooth repair of the overall jump.

[0123] The method based on Gaussian weighting detects and repairs the jump in the multi-beam backscattering intensity data. The dynamic threshold is set by weighted average, which can effectively detect the jump point. In the repair, the jump data segment is compensated by the difference between the weighted average values of the data segments before and after the jump point, and the part without jump is used to compensate the jump data segment. The automatic iterative detection and repair processing is realized by programming.

[0124] Compared with the existing methods for correcting the parameters of the multi-beam system at home and abroad, the method based on the weighted average of the statistical method of the backscattering intensity data can better smooth the intensity jump data and remove the jump part in the strip to the maximum extent. The image effect is more balanced and higher quality compared with the correction of the multi-beam system hardware parameters and other aspects. In summary, the method can effectively correct the intensity jump caused by the system parameters in the multi-beam backscattering intensity, and can be used as an important reference for such correction.

[0125] The above embodiments only express the embodiments of the present application, which are described in detail and specifically, but should not be understood as a limitation on the scope of the application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application.

Claims

1. A method for correcting multi-beam backscatter intensity jumps, the method comprising: The method comprises the following steps: ​ S1: data file import; S2: intensity data extraction, pre-processing of single strip multi-beam backscatter intensity data, the pre-processing comprising time gain compensation TVG and sonar parameter correction, to obtain corrected backscatter intensity data; wherein the backscatter intensity data is collected by a multi-beam sounding system, usually recorded in Pings, each Ping record hundreds or thousands of seabed point backscatter signals obtained by a multi-beam transducer in a complete signal transmission and reception cycle at a time, each strip data contains multiple consecutive Pings, and the backscatter intensity data of each Ping is used to reflect the signal echo reflection strength of the seabed within a certain range in the vertical track direction; S3: intensity jump detection, based on a Gaussian weighted function to detect jumps, specifically comprising the following steps: S3-1: calculating a Gaussian weighted average value, inputting the pre-processed backscatter intensity data in step S2, and detecting jumps by a Gaussian weighted function method, the Gaussian weighted average value corresponding to each Ping being: （1） wherein, represents the intensity value of the th data, is a Gaussian weight, is the total amount of Pings; S3-2: Calculate the difference of Gaussian weighted average value, for a series of Pings whose Gaussian weighted average values have been calculated, denoted as , the difference of Gaussian weighted average value between adjacent Pings is calculated, and the specific calculation formula is as follows: （2） wherein, represents the intensity value of the th data, represents the difference of the Gaussian weighted average value; S3-3: Dynamic threshold to detect the jump point, calculate the mean of all intensity differences between Pings and the standard deviation of all intensity differences , according to which the dynamic threshold is set to detect the jump point, the specific formula is as follows: （3） （4） By solving and defining the dynamic threshold as : （5） When indicates that the Ping has a hop, and marks this Ping, represents the total number of differences from the Gaussian weighted average. S4: determining a jump compensation data segment, specifically comprising the following steps: S4-1: calculating the weighted average value within a window W before and after the jump point, specifically, for each jump point, taking the intensity values within the window W forward and backward from the center of the jump point, and calculating the weighted average value as a repaired reference value; S4-2: segmenting all jump points in order, each two adjacent jump points determining a jump data segment, and for each jump data segment, calculating the weighted average values of the start and end positions to determine the compensation difference value of the jump data segment; S5: strength jump repair, based on the above compensation difference, all pings in the jump data segment are repaired, and the data is adjusted to be consistent with the fluctuation range of the data at both ends, and the weighted average value of each data in the first two jump segments after compensation is and , then the weighted average value of the pings starting from the third jump segment after compensation is ; The jump-repaired intensity data is subjected to steps S3-S5 again until the iteration stops when there is no jump point; S6: exporting the repaired intensity data, i.e. all Pings repaired in step S5.

2. The method of claim 1, wherein: The pre-processing step in step S2 comprises removing system parameters and human or operating system added influencing factors to ensure the authenticity of the backscatter intensity data, specifically comprising compensating for the time gain TVG, and the calculation formula is: （6） wherein, is a propagation loss coefficient, is the distance between the target and the sonar, is an attenuation coefficient in the propagation medium, by applying this formula, the echo intensity value of each Ping is compensated to correct the energy loss due to distance and medium attenuation.

3. The method of claim 1, wherein: The sonar parameter correction in the pre-processing step in step S2 comprises correcting system parameters, specifically correcting the received signal by a system gain constant, and the calculation formula of the sonar parameter correction is: （7） wherein, is the received raw signal strength, is a system gain constant used to correct for signal gain values that are artificially or automatically increased by the system.

4. The method of claim 1, wherein: In the process of setting a dynamic threshold in step S3-3, further comprising noise smoothing processing and dynamically adjusting a standard deviation weight factor to adapt to the needs of different sonar working environments, and the dynamic threshold calculation formula is: （8） wherein, is a dynamically adjusted weight factor, which is adjusted according to the actual environment and data variation characteristics of the sonar operation; is a noise smoothing processing factor, which is used to remove the influence of random noise in the data, and the factor is calculated by a Kalman filtering algorithm.

5. The method of claim 1, wherein: The jump repair in step S5 comprises iteratively detecting and compensating for jump points, specifically comprising the following steps: T1: after completing a jump point compensation repair, repeating steps S3-S5, and recording the newly detected jump points; T2: for the newly detected jump points, calculating the weighted average value again and compensating for the data to gradually correct all jump points; T3: iteratively repeating the repair process of step S5 to gradually reduce the repair error until the number of jump points converges to zero.

6. The method of claim 1, wherein: The processing results of the steps S1 to S2 further comprise preset conditions, which are specifically: The multi-beam intensity is processed in units of single strip, and the data is corrected by system parameters to remove the influence factors introduced by human or operating system, and a complete strip data is selected as the correction data source; In the process of using Gaussian weighting to automatically detect the jump, the statistical quantity of intensity difference is introduced to set a dynamic threshold, which changes with the data and can maintain good adaptability in different situations; All jump points are segmented in sequence, and each two adjacent jump points determine a jump data segment. The intensity values within the window W before and after the jump point are taken respectively, and the weighted average value is calculated as the compensation difference.

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