Method for inverting physical properties of water body surface sediments based on acoustic image information
By constructing a weighted grayscale model and using a single-peak trend constraint method, the physical properties of lake sediments are inverted based on acoustic image information. This solves the problem of obtaining continuous observations in traditional methods, achieves high-precision sediment property inversion, simplifies the data processing process, and improves detection efficiency and data acquisition.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANJING INST OF GEOGRAPHY & LIMNOLOGY
- Filing Date
- 2026-03-09
- Publication Date
- 2026-06-09
AI Technical Summary
Existing technologies cannot achieve continuous and in-situ observation of the physical properties of lake sediments, and traditional acoustic inversion methods are difficult to effectively eliminate environmental noise interference, leading to deviations in measurement results.
Based on acoustic image information, by constructing a weighted grayscale model and unimodal trend constraints, and using a shallow seismic profiler to collect water profile images and sediment column samples, a linear model of grayscale conversion and physical properties is established to achieve high-precision inversion of sediment physical properties.
It enables non-contact, low-cost, and efficient detection of sediment physical properties, obtaining continuous and abundant sediment data, and improving our understanding of water pollution processes and geochemical cycles.
Smart Images
Figure CN122174479A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aquatic environment sediment monitoring and geophysical exploration technology, specifically to a method for inverting the physical properties of surface sediments in water bodies based on acoustic image information. Background Technology
[0002] Sediments are an essential component of lacustrine and marine environments, playing a crucial role in nutrient and geochemical cycles. The physical properties of underwater sediments (such as density, water content, and rheology) are key parameters for dredging projects and research on marine / lake sediments and aquatic environments. Current methods for determining sediment physical properties primarily rely on traditional columnar sampling analysis. However, this method has significant limitations: firstly, the process of transporting samples from the field to the laboratory for analysis is time-consuming, labor-intensive, and inefficient; secondly, columnar samples are highly susceptible to vibration and temperature changes during slicing and long-distance transportation, leading to damage to the original sediment structure and thus inaccurate measurement results; and thirdly, laboratory analysis can only obtain data from discrete points, failing to reflect the continuous spatial variations of sediments. In contrast, while in-situ acoustic analysis technology offers significant advantages through non-contact measurement, it still faces considerable challenges in practical applications.
[0003] Under long-term complex hydrodynamic conditions, microbial activity, and sediment transport, lake sediments, especially the surface layer, may form a transport-oriented porous framework structure. Within a few tens of centimeters from the surface to the shallowest layer, the physical properties and acoustic effects of the sediments exhibit similar gradients. Lake sediments have always been an important research object in lake environmental engineering, lake water environment, and lake resource protection. Studying the physical properties of lake sediments is of great significance for both lake economic development and lake environmental protection. One of the key aspects of studying lake sediments is the difficulty in obtaining continuous and large-scale physical effects of lake sediments. Therefore, their acoustic characteristics can be studied, i.e., by observing the propagation of pulsed sound waves in the sedimentary medium to infer the physical properties of the sediments. The acoustic echo signals of sediments are highly susceptible to interference from multiple complex factors, including the ship's hull structure, hydrodynamic disturbances caused by sailing speed, and the signal gain settings of the acoustic instruments themselves. Traditional acoustic inversion methods often attempt to eliminate these disturbances by starting from physical principles, such as studying acoustic characteristic parameters like sound velocity and sound attenuation coefficient to establish complex sound field correction models. However, the calculation process is cumbersome and the disturbances and attenuation vary in different environments, making it difficult to completely eliminate the influence of environmental noise. Summary of the Invention
[0004] This invention provides a method for inverting the physical properties of surface sediments in water bodies based on acoustic image information, aiming to solve the technical problem that existing technologies cannot obtain continuous and in-situ observations of sediment physical properties, and to achieve high-precision continuous inversion of sediment physical properties.
[0005] The technical solution of the present invention is as follows:
[0006] A method for inverting the physical properties of surface sediments in water bodies based on acoustic image information, the method comprising:
[0007] RGB images of the water profile were acquired using a shallow seismic profiler in the target water area, and sediment column samples were collected simultaneously.
[0008] Measured sequences of physical properties of sediments at different depths were obtained based on sediment column samples;
[0009] A weighted model is constructed using the R, G, and B channel values of the image as independent variables, serving as the grayscale conversion model for the image.
[0010] The optimal grayscale conversion model is obtained by adjusting the weights of the weighted model under the constraints that the linear correlation between the converted grayscale sequence and the measured physical property sequence is optimal and the converted grayscale sequence satisfies a unimodal trend.
[0011] The image is converted to grayscale based on the optimal grayscale conversion model.
[0012] Based on the gray values of the gray-scale converted images and the measured sequences of various physical properties, estimation models for each physical property are constructed for the inversion of physical properties of surface sediments in water bodies.
[0013] In some embodiments of the present invention, a shallow seismic profiler is used to conduct a sea-based survey of the target water area, and points are selected along the survey line to collect sediment column samples using a column sampler; the sediment column samples contain sediment-water interfaces.
[0014] In some embodiments of the present invention, the weighted model takes the following form: in, This represents the grayscale value at depth z. , , These represent the R, G, and B channel values corresponding to depth z. , , The weighting coefficients to be optimized are: .
[0015] In some embodiments of the present invention, the linear correlation is evaluated based on the Pearson correlation coefficient.
[0016] In some embodiments of the present invention, the estimation model is a linear model.
[0017] In some embodiments of the present invention, the weights of the weighted model corresponding to the sediment physical property parameters to be inverted are adjusted using the parameters of the sediment physical property parameters to be inverted.
[0018] In some embodiments of the present invention, the physical properties of the sediment include wet density, water content, and rheological parameters.
[0019] The present invention further provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.
[0020] The present invention further provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.
[0021] The present invention further provides a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.
[0022] This invention proposes a processing method based on image terminal data, which avoids complex physical mechanism derivations and the need to eliminate interfering factors in intermediate steps. Instead, it takes a different approach, starting from the perspective of "finding patterns," directly utilizing the mapping relationship between acoustic image features and sediment physical properties for estimation. This data-driven "black box" processing effectively avoids the uncertainties introduced by hydrodynamic and equipment parameters, achieving accurate inversion of sediment physical properties in a convenient and efficient manner, greatly improving operational efficiency, and obtaining continuous and abundant sediment physical property data. This method deepens our understanding of water pollution processes and sediment geochemical cycles.
[0023] The method of the present invention has the following beneficial effects:
[0024] (1) This invention breaks the limitation of the traditional fixed grayscale conversion formula. By adaptively adjusting the RGB weight coefficient, it mines the information most sensitive to the physical properties of sediments in acoustic data, and thus obtains the physical properties of sediments at different locations and depths. Under the premise of ensuring accuracy, it greatly simplifies the data processing process and realizes non-contact, low-cost and high-efficiency detection of the physical properties of surface sediments in water bodies.
[0025] (2) The present invention introduces a “single-peak trend” constraint, which effectively eliminates clutter interference and ensures that the inversion results conform to the natural law of sediment compaction enhancement and acoustic energy attenuation with depth.
[0026] (3) The present invention can simultaneously estimate sediment physical properties such as density, water content and rheological parameters, providing comprehensive data support for engineering surveys and ecological environment research. Attached Figure Description
[0027] Figure 1 This is a flowchart of the method of the present invention.
[0028] Figure 2 This is a diagram illustrating data acquisition.
[0029] Figure 3 This is a cross-sectional view and a schematic diagram of the corresponding sediment sample. The units for the numbers in the figure are meters (m).
[0030] Figure 4 These are comparison images before and after the grayscale conversion coefficient optimization. (a) is a messy curve obtained using the standard grayscale formula, and (b) is a grayscale curve that exhibits a single-peak trend after optimization by this invention.
[0031] Figure 5 This is a linear fitting relationship between the optimized grayscale value and the measured wet density and water content of the sediment.
[0032] Figure 6 This is a continuous distribution profile of wet density of sediments in a lake obtained using the method of this invention. Detailed Implementation
[0033] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0034] Example 1
[0035] like Figure 1 As shown, the method of the present invention includes the following steps:
[0036] 1. Data Collection
[0037] Using a shallow seismic profiler to conduct a mobile survey of the lake area (e.g.) Figure 2 This process was used to obtain high-resolution acoustic reflection images. Simultaneously, typical points were selected along the survey line, and in-situ sediment samples (including the sediment-water interface) were collected using a columnar sampler. In the laboratory, the sediment columnar samples were layered, and the wet density (ρ), water content (W), and rheological parameters (τ) of each layer were tested.
[0038] 2. Construct a grayscale calculation model
[0039] The acoustic image output by the shallow seismic profilometer contains three channels: red (R), green (G), and blue (B). To extract the features most relevant to the physical properties, the grayscale value G(z) is defined as the weighted sum of the three channels:
[0040]
[0041] in, This represents the grayscale value at depth z. , , These represent the R, G, and B channel values corresponding to depth z. , , The weighting coefficients to be optimized are: Thus, we obtained The functional relationship between depth z and depth z.
[0042] 3. Parameter optimization (core step): Use computer algorithms to iteratively optimize the weight coefficients.
[0043] The criteria for optimization include two aspects:
[0044] Morphological constraints (single-peak trend): Sediments typically exhibit a loose upper layer and a compacted lower layer. Ideally, sediment density increases with depth, leading to stronger echo intensity; however, as depth increases further, the echo signal gradually weakens due to formation absorption and attenuation. Therefore, the true effective signal should exhibit a single-peak morphology of "increase first, then decrease."
[0045] Correlation constraint: Calculate grayscale sequence The Pearson correlation coefficient R between the physical properties at the same depth and the measured physical properties is adjusted by weighting to maximize R.
[0046] Taking the wet density parameter as an example, when it is necessary to construct an inversion model based on the wet density parameter, the correlation constraint is constructed using the wet density parameter, and the weight is optimized. The gray level calculated based on the optimized weight is used to construct the wet density inversion model.
[0047] 4. Model building and inversion
[0048] The grayscale value is finally calculated based on the optimal weights determined in step 3. And establish the following high-precision estimation formula:
[0049] (1) Empirical model for estimating wet density:
[0050] Formula for calculating wet density:
[0051] Empirical formula for calculating moisture content:
[0052] Empirical formulas for calculating rheological parameters:
[0053] in , is the fitting constant.
[0054] By applying the above formula to the acoustic data of the entire survey line, the density, water content, and rheological properties of sediments at any point on the survey line and at any depth can be obtained.
[0055] Example 2
[0056] This embodiment uses a bay of an algal-type lake as an example to illustrate the implementation effect of the present invention.
[0057] A commercial shallow seismic profiler, PLS300, was used for mobile surveying of the lake area. The installation depth was set to 0.6 meters, the measurement range to 10 meters, and the start and end points for data acquisition were 0 and 5 meters respectively. The acoustic emission frequency was set to 5 times, the difference frequency to 30 kHz, and the number of transmitted pulse cycles to 3. Image data was acquired using the accompanying PLS_Series software, such as... Figure 3 Simultaneously, in-situ sediment samples (including the sediment-water interface) were collected using a column sampler. In the laboratory, the sediment column samples were layered (3 cm each for the first three layers, and 5 cm each for the last two layers), and the wet density (ρ), water content (W), and rheological parameters (τ) of each layer were measured.
[0058] Weight coefficient optimization:
[0059] Set the initial weights to =0.299、 =0.587、 =0.114, using the constraints described in Example 1, the weights are iteratively optimized to select the optimal weights: when = 0.30, = 0.59, When the coefficient is 0.11, the Pearson correlation coefficient between image grayscale and density reaches above 0.85. Figure 4 And the final grayscale The relationship between sediment depth z and sediment depth z exhibits a clear unimodal pattern from the sediment interface to the bottom of the sediment. Finally, empirical formulas for sediment density, water content, and rheology were obtained, such as… Figure 5 :
[0060] Wet density: (R 2 = 0.76, p < 0.001)
[0061] Moisture content: (R 2 = 0.62, p < 0.001)
[0062] Yield stress: (R 2 = 0.48, p < 0.001).
[0063] The continuous distribution of lake wet density with depth is obtained using the constructed wet density model, such as... Figure 6 As shown.
[0064] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform with necessary customized functions. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0066] The embodiments of the present invention have been described above with reference to the accompanying drawings. The disclosed embodiments are merely preferred embodiments of the present invention. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many equivalent changes in form without departing from the spirit and scope of the claims of the present invention, and all such changes are within the protection scope of the present invention.
Claims
1. A method for inverting the physical properties of surface sediments in water bodies based on acoustic image information, characterized in that, The method includes: RGB images of the water profile were acquired using a shallow seismic profiler in the target water area, and sediment column samples were collected simultaneously. Measured sequences of physical properties of sediments at different depths were obtained based on sediment column samples; A weighted model is constructed using the R, G, and B channel values of the image as independent variables, serving as the grayscale conversion model for the image. The optimal grayscale conversion model is obtained by adjusting the weights of the weighted model under the constraints that the linear correlation between the converted grayscale sequence and the measured physical property sequence is optimal and the converted grayscale sequence satisfies a unimodal trend. The image is converted to grayscale based on the optimal grayscale conversion model. Based on the gray values of the gray-scale converted images and the measured sequences of various physical properties, estimation models for each physical property are constructed for the inversion of physical properties of surface sediments in water bodies.
2. The method according to claim 1, characterized in that, A shallow seismic profiler was used to conduct a sea survey of the target water area. At the same time, points were selected along the survey line, and sediment column samples were collected using a column sampler. The sediment column samples contained sediment-water interfaces.
3. The method according to claim 1, characterized in that, The weighted model takes the following form: in, This represents the grayscale value at depth z. , , These represent the R, G, and B channel values corresponding to depth z. , , The weighting coefficients to be optimized are: .
4. The method according to claim 1, characterized in that, The linear correlation was assessed based on the Pearson correlation coefficient.
5. The method according to claim 1, characterized in that, The estimation model is a linear model.
6. The method according to claim 1, characterized in that, The weights of the weighted model corresponding to the physical property parameters of the sediment to be inverted are adjusted.
7. The method according to claim 1, characterized in that, The physical properties of the sediments include wet density, water content, and rheological parameters.
8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.
9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.
10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.