An underwater geological exploration method for dredging across mud layers

Abstract

The present invention relates to the field of geological exploration technology, and in particular to an underwater geological exploration method for cross-mud layer dredging and excavation. The method comprises the following steps: acquiring water area hydrological data, and performing water flow simulation based on the water area hydrological data to obtain water area water flow data; selecting an exploration area based on the water area water flow data to obtain exploration area data; transmitting exploration waves on the exploration area data to obtain exploration wave data; performing target layer analysis based on the exploration wave data to obtain target layer data for cross-mud layer dredging operations. The present invention can accurately understand the depth and thickness of the target layer and the properties and thickness of the covering layer by combining water area analysis with precise target layer analysis, providing an important reference basis for dredging operations, improving operation efficiency, and reducing unnecessary costs and time waste.

Description

Technical Field

[0001] The invention relates to the technical field of geological exploration, and in particular to an underwater geological exploration method for dredging across mud layers. Background Art

[0002] In the past, geological exploration and dredging operations often faced challenges, such as the precise positioning of underground target layers in water bodies, insufficient understanding of the geological structure under the riverbed, and the impact of dredging operations on the ecological environment of water bodies. Traditional exploration methods often rely on ground equipment or manual sampling, which is limited by the inability to obtain underwater geological information in real time and the large interference with the ecological environment of water bodies. At the same time, existing dredging methods often cannot effectively reduce damage to the ecological environment of water bodies, nor can they accurately locate and analyze the target layers. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes an underwater geological exploration method for dredging and excavation across mud layers to solve at least one of the above technical problems.

[0004] The present application provides an underwater geological exploration method for dredging across mud layers, comprising the following steps:

[0005] Step S1: Acquire hydrological data of the water area, and perform water flow simulation based on the hydrological data of the water area to obtain water flow data of the water area;

[0006] Step S2: selecting an exploration area according to water flow data to obtain exploration area data;

[0007] Step S3: transmitting exploration waves to the exploration area data, thereby obtaining exploration wave data;

[0008] Step S4: Use geophysical principles and methods to extract target layer electrical difference characteristics from exploration wave data to obtain target layer electrical difference characteristic data, and perform target layer analysis based on the target layer electrical difference characteristic data to obtain target layer data for cross-mud layer dredging operations, wherein the target layer data includes underground target layer position data, underground target layer depth data, and underground target layer thickness data.

[0009] In the present invention, water flow simulation is performed through water area hydrological data, so that the water flow conditions of the water area can be accurately understood, which is helpful to accurately select an exploration area suitable for dredging in the water area, and improves the efficiency and accuracy of dredging. After the exploration area is selected based on the water area water flow data, the emission of exploration waves is more targeted, avoiding unnecessary energy waste and improving the quality and reliability of exploration wave data. Since the selected exploration area is more accurate, the obtained exploration wave data is also more accurate, which is helpful to perform a more detailed analysis of the target layer, accurately analyze the structure, properties and distribution of the target layer, and provide a more powerful basis for cross-mud layer dredging operations. By obtaining hydrological data in advance and performing water flow simulation, it is possible to avoid selecting an unsuitable dredging area or the occurrence of an incorrect dredging position, thereby reducing unnecessary adjustments and corrections during the dredging process and reducing the dredging cost. Selecting a suitable exploration area and an optimized dredging plan reduces the risk of accidents during the dredging process and improves the safety and stability of the work.

[0010] The purpose of the present invention is to provide an accurate hydrological environment basis for exploration work through hydrological data acquisition and water flow simulation, which helps to understand the water flow dynamics and hydrological characteristics in the water area and provides an important reference basis for subsequent exploration operations. By selecting the exploration area according to the water flow data of the water area, the area that needs to be explored can be accurately determined, avoiding blind exploration of the entire water area, saving time and resource costs. The exploration wave data is obtained by transmitting the exploration wave. This step adopts advanced exploration technology and can quickly obtain geological information at the bottom of the water area, providing data support for subsequent target layer analysis. The target layer analysis uses advanced electromagnetic exploration technology, combined with complex steps such as electromagnetic data processing, filtering, and superposition, to accurately identify the target layer structure and characteristics of the bottom of the water, providing important geological information and guidance for cross-mud layer dredging operations. By integrating hydrological data and water flow simulation, as well as accurately selecting the exploration area, unnecessary exploration can be avoided, interference with the ecological environment of the water area and waste of resources can be reduced, which is conducive to protecting the integrity and stability of the water ecosystem. Through precise target layer analysis, the depth and thickness of the target layer as well as the nature and thickness of the covering layer can be accurately understood, which provides an important reference for dredging operations, improves operational efficiency, and reduces unnecessary costs and time waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0012] Figure 1 A flowchart showing the steps of an underwater geological exploration method for dredging across mud layers according to an embodiment of the present invention is shown;

[0013] Figure 2A flow chart showing the steps of a method for simulating hydrological and water flow in a water area according to an embodiment is shown;

[0014] Figure 3 A flowchart showing the steps of a method for selecting an exploration area according to an embodiment is shown;

[0015] Figure 4 A flowchart showing a method for analyzing target layer exploration waves according to an embodiment of the present invention is provided;

[0016] Figure 5 A diagram showing the effect of an underwater geological exploration method for dredging across mud layers according to an embodiment is shown;

[0017] Figure 6 A flowchart of the steps of an acoustic parameter array analysis method according to an embodiment is shown;

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0021] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0022] See also Figures 1 to 6 The present application provides an underwater geological exploration method for dredging across mud layers, comprising the following steps:

[0023] Step S1: Acquire hydrological data of the water area, and perform water flow simulation based on the hydrological data of the water area to obtain water flow data of the water area;

[0024] Specifically, hydrological measurement equipment, such as water velocity measuring instruments and water level meters, are used to conduct field measurements in the target water area, and hydrological parameters such as water velocity, water level, and flow direction are recorded. For example, a velocity meter is used to measure water velocity, and a level meter is used to measure water level. Numerical simulation software, such as fluid dynamics simulation software (such as FLUENT, OpenFOAM, etc.) or hydrodynamic models (such as MIKE21, Delft3D, etc.), is used to simulate water flow based on the collected hydrological data. These simulation software are based on physical principles such as the Navier-Stokes equation and the continuity equation, and use computers to simulate the flow process of water bodies. For example, the MIKE21 model is used to simulate the water flow, and the collected hydrological data is input to perform numerical calculations to obtain the water flow velocity field and flow direction field. After the simulation is completed, the water flow velocity, flow direction and other data at each location in the water area are obtained. These data can be stored in numerical form, such as velocity fields and flow direction fields on two-dimensional or three-dimensional grids. These data can be used as the basis for the selection of subsequent exploration areas.

[0025] Step S2: selecting an exploration area according to water flow data to obtain exploration area data;

[0026] Specifically, the collected water flow data of the water area is processed and analyzed to facilitate the selection of the exploration area. The water flow data is visualized and analyzed using Geographic Information System (GIS) software. According to the analysis results of the water flow data of the water area, areas with relatively gentle water flow and moderate water flow speed are selected as exploration areas. These areas have good conditions for dredging the target layer, such as stable water flow and moderate water depth. The location coordinates, water flow conditions, terrain characteristics and other data of the selected exploration area are recorded as the basis for the subsequent exploration wave emission.

[0027] Step S3: transmitting exploration waves to the exploration area data, thereby obtaining exploration wave data;

[0028] Specifically, equipment for geological exploration of water areas is selected, such as sonar, electromagnetic detection equipment, etc. For example, an electromagnetic prospector is selected as the exploration equipment. According to the hydrological data and geological characteristics of the exploration area, the working parameters of the prospector are set, including frequency, power, detection depth, etc. For example, the transmitting frequency and receiving parameters of the electromagnetic prospector are set. The exploration equipment is navigated or fixedly installed along the selected exploration area, and then the exploration wave is emitted. For example, an electromagnetic prospector is used to emit electromagnetic waves in the water area. During the exploration process, the reflected signal of the exploration wave is recorded, and data processing is performed, including filtering, correction and other operations, to obtain clear exploration wave data.

[0029] Step S4: Use geophysical principles and methods to extract target layer electrical difference characteristics from exploration wave data to obtain target layer electrical difference characteristic data, and perform target layer analysis based on the target layer electrical difference characteristic data to obtain target layer data for cross-mud layer dredging operations, wherein the target layer data includes underground target layer position data, underground target layer depth data, and underground target layer thickness data.

[0030] Specifically, the collected exploration wave data is preprocessed, including denoising, filtering, correction, etc., to improve the quality and clarity of the data. The exploration wave data is interpreted and analyzed using geophysical principles and methods to identify the electrical difference characteristics of the underground target layer and the overburden layer. According to the electrical difference characteristics in the exploration wave data, the location, depth and thickness of the underground target layer are determined, and technical means such as data processing, image interpretation, and inversion are used. The determined target layer is verified and confirmed, and ground verification methods or comparisons with field measured data can be used to ensure the accuracy and reliability of the target layer. The analyzed target layer data is output as a digital stratigraphic model or stratigraphic profile for subsequent engineering planning and design. According to the target layer data, cross-mud layer dredging operations are carried out, the dredging plan and construction parameters are determined, and the excavation and construction of underground projects are carried out. In this process, it is necessary to accurately control the underground structure according to the target layer data to ensure the safety and effectiveness of the dredging operation.

[0031] Specifically, the present invention can be applied to dredging devices that can be used for cross-mud layer dredging operations (CN218437267U) and other dredging devices with the function of cross-mud layer dredging operations. The function of the dredging device can be used for sand and gravel mining. Since the dredging is directly carried out in the target layer, the covering layer containing biological communities and bottom sediments is almost not disturbed during the entire dredging process, thereby reducing water turbidity and mud diffusion caused by soil disturbance; the present invention selects the depth of the exploration area, on the one hand, provides a relatively flat and safe excavation area, and on the other hand, the relative stability of water activities is relatively conducive to the stability of the underground structure of the water area, and can provide a good data support effect for the stability of cross-mud layer dredging operations, thereby reducing the extraction of sand and gravel with high impurity content due to the overly complex and scattered underground hierarchical structure of the water area.

[0032] Specifically, in one embodiment, the shallow formation profile detection converts the control signal into an acoustic pulse of different frequencies (100 Hz to 10 kHz) through a transducer and transmits it to the seabed. The sound wave encounters an acoustic impedance interface during the propagation of seawater and sedimentary layers, and is recorded after being reflected back to the transducer and converted into an analog or digital signal, and output as a shallow formation acoustic recording profile that can reflect the acoustic characteristics of the formation. The acoustic formation detection system uses the principle of sound wave reflection to detect the formation. When the sound wave propagates on the seabed and encounters a reflection interface (there is a difference in the properties of the medium on both sides of the interface), it is reflected. The condition for generating a reflected wave is that the wave impedance of the medium on both sides of the interface is unequal, and the factor that determines the condition of sound wave reflection is the wave impedance difference (reflection coefficient Rpp). Wave impedance is the product of the speed V of sound waves propagating in the medium and the medium density ρ. In shallow strata profile surveys, it is approximately assumed that the sound waves are incident vertically. At this time, it can be seen from the formula that in order to obtain strong reflection, there must be a large density difference and a large sound velocity difference. If two adjacent layers have a certain density and sound velocity difference, the adjacent interface between the two layers will have a stronger sound intensity, and a profile interface line with a stronger grayscale will be reflected on the profiler terminal display.

[0033] ;

[0034] is the density of the second medium, is the first medium density, is the second propagation speed, is the first propagation velocity, where the density of the first medium and the density of the second medium are used to describe the physical properties of two different adjacent substances on the sound wave propagation path. They represent the physical conditions encountered by sound waves when propagating in different media. The density of the first medium refers to the density of the medium in which the sound wave is located before reflection occurs during the sound wave propagation process. It is usually the medium close to the source of the sound wave, that is, the density of the first layer of material encountered before the sound wave is incident, such as the density of the water layer or the surface soft silt layer. The density of the second medium refers to the density of another medium that the sound wave reflects or penetrates into after reaching the interface. It is the density of the second layer of material adjacent to the first medium and in contact with the sound wave, such as the density of the sediment layer or deeper rock layer.

[0035] The acoustic parametric array (Parametric Array) is an acoustic emission device that utilizes the nonlinear effect of sound waves propagating in a medium, and uses a transducer (array) to propagate two high-frequency initial waves in the same direction to obtain sound waves such as difference frequency and sum frequency. When the transducer emits sound waves that propagate in the medium, a series of secondary frequencies will be generated in the emission direction of the transducer, such as f1, f2, (f1+f2), (f1-f2), 2f1, and 2f2 sound wave signals. Because the frequencies of f1 and f2 are very close, the frequency of the difference frequency (f1-f2) is very low, achieving the purpose of the piezoelectric transducer to generate low frequencies. The bottom layer that is free on the surface of the riverbed and within a certain density is usually called the surface soft silt layer, which is composed of sticky fine-grained silt. The bottom surface silt has the characteristics of small particle size, low density and low consolidation degree. If it is used as a natural foundation or fill material, it will be an extremely weak bearing layer, and serious foundation settlement and other safety accidents are very likely to occur during the construction and operation of the project. In addition, in shipping projects, it is often necessary to measure the sedimentation of waterway silt and calculate the amount of earthwork for dredging. The source type of the profiler determines the frequency of the emitted sound waves. The frequency of the emitted sound waves determines the detection depth and resolution. The classification of profilers is based on the frequency of the emitted sound waves, that is, the source type (power classification of the same type of source).

[0036] In the present invention, water flow simulation is performed through water area hydrological data, so that the water flow conditions of the water area can be accurately understood, which is helpful to accurately select an exploration area suitable for dredging in the water area, and improves the efficiency and accuracy of dredging. After the exploration area is selected based on the water area water flow data, the emission of exploration waves is more targeted, avoiding unnecessary energy waste and improving the quality and reliability of exploration wave data. Since the selected exploration area is more accurate, the obtained exploration wave data is also more accurate, which is helpful to perform a more detailed analysis of the target layer, accurately analyze the structure, properties and distribution of the target layer, and provide a more powerful basis for cross-mud layer dredging operations. By obtaining hydrological data in advance and performing water flow simulation, it is possible to avoid selecting an unsuitable dredging area or the occurrence of an incorrect dredging position, thereby reducing unnecessary adjustments and corrections during the dredging process and reducing the dredging cost. Selecting a suitable exploration area and an optimized dredging plan reduces the risk of accidents during the dredging process and improves the safety and stability of the work.

[0037] Optionally, step S1 specifically includes:

[0038] Step S11: Acquire water area hydrological data;

[0039] Specifically, hydrological stations are set up in the waters to cover the entire exploration area to ensure the comprehensiveness and representativeness of the data. Hydrological observation equipment, such as water level meters and current meters, are equipped to collect data from each hydrological station regularly or in real time. Hydrological data includes parameters such as water level, flow velocity, and flow, and the collection frequency is usually once an hour or a day. The collected hydrological data is quality controlled and processed, including data cleaning, outlier processing, data interpolation, etc., to ensure the accuracy and reliability of the data.

[0040] Step S12: constructing a hydrodynamic model according to the hydrological data of the water area to obtain a hydrodynamic model of the water area;

[0041] Specifically, a hydrodynamic model is selected according to the characteristics of the water area and the purpose of the study. Models include two-dimensional or three-dimensional numerical simulation models, statistical models, etc. Determine the parameters of the hydrodynamic model, such as bottom friction coefficient, turbulence parameters, etc. These parameters need to be calibrated and adjusted based on field observation data, or obtained through inquiries of relevant hydrological historical data. A hydrodynamic model is established based on hydrological data and topographic information. The two-dimensional model is based on data such as the water depth and flow velocity of the water area, and the three-dimensional model also needs to consider factors such as the water depth change and flow state of the water area. Verify the established hydrodynamic model, compare it with the measured data, and evaluate the accuracy and applicability of the model.

[0042] Step S13: Utilize the water area hydrodynamic model to simulate water flow and obtain water flow data of the water area.

[0043] Specifically, the boundary conditions of the hydrodynamic model are set, including water level boundary conditions, flow boundary conditions, etc. These boundary conditions are based on measured data or simulation results. The hydrodynamic model is used to simulate the water flow, and the motion equation of the water flow is solved by numerical methods to obtain water flow information such as flow velocity and flow direction at each point in the water area. The simulated water flow data is output in numerical form, usually including flow velocity field, flow direction field, etc. These data can be used for the selection and analysis of subsequent exploration areas.

[0044] In the present invention, step S11 obtains hydrological data of the water area, including information such as water level, flow velocity, and flow direction, which can accurately understand the hydrological characteristics of the water area and provide accurate basic data for the subsequent construction of the hydrodynamic model. Step S12 constructs a hydrodynamic model based on the hydrological data of the water area, taking into account factors such as the flow direction, speed, and flow rate of the water flow, and can more accurately simulate the movement law of the water flow in the water area, providing a reliable model basis for the subsequent water flow simulation. Step S13 performs water flow simulation on the water area hydrodynamic model, which can perform virtual simulation of the water flow in the water area, simulate the flow velocity, flow direction, etc. of the water flow at different positions, thereby obtaining water flow data of the water area, analyzing the dynamic changes of the water flow, and providing an accurate basis for the selection of subsequent exploration areas. By accurately simulating the water flow conditions of the water area, the area suitable for exploration can be more accurately determined, avoiding the selection of unsuitable areas or ignoring potential exploration targets, and improving the efficiency and success rate of exploration. By accurately simulating the water flow conditions, it is possible to avoid selecting risky areas for dredging, reducing the accidental risks during the dredging process, and improving the safety and stability of the work.

[0045] Optionally, step S13 is specifically:

[0046] Performing edge processing on water area image data in water area hydrological data to obtain water area image edge processing data;

[0047] Specifically, edge detection and enhancement are performed on the water area image data in the water area hydrological data to highlight the edge features of the water area and remove unnecessary background information. Edge detection algorithms such as Sobel operator and Canny edge detection are used.

[0048] Grid parameter generation is performed according to the edge processing data of the water area image to obtain grid parameter data;

[0049] Specifically, based on the edge-processed water area image data, mathematical modeling methods are used to generate the grid parameters required for the water area hydrodynamic model. The parameters include grid size, grid shape, grid density, etc., which need to be reasonably set according to the specific water area characteristics and water flow simulation requirements.

[0050] The hydrodynamic model of the water area is grid-divided by grid parameter data to obtain a hydrodynamic grid model of the water area;

[0051] Specifically, the hydrodynamic model of the water area is meshed through the generated mesh parameters, the water area is divided into multiple small mesh units, and numerical methods such as the finite element method and the finite volume method are used for meshing to achieve a refined description of the flow field in the water area.

[0052] The water area hydrodynamic grid model is used to perform numerical simulation calculations to obtain water flow data in the water area.

[0053] Specifically, after the grid division is completed, the numerical simulation method is used to calculate and simulate the water flow in the water area. The fluid dynamics equations such as the Navier-Stokes equations are used, combined with appropriate boundary conditions and initial conditions, and the numerical solution is iteratively solved to obtain the velocity field and pressure field data of the water flow in each grid unit. Computer simulation software such as FLUENT and OpenFOAM are needed for numerical simulation calculations.

[0054] Analyze and verify the water flow data obtained to evaluate the accuracy and reliability of the simulation results. The results can be verified by comparing with measured data and existing hydrological model results to ensure the scientificity and credibility of the water flow simulation results.

[0055] In the present invention, by edge processing and grid parameter generation of water area hydrological data, a water area hydrodynamic grid model is obtained, which can describe the morphology and structure of the water area more finely, thereby simulating the movement law of water flow more accurately, and improving the accuracy and reliability of water flow data in the water area. When the water area hydrodynamic grid model is used for numerical simulation calculation, since the grid division has been performed, the calculation is performed more efficiently, the computing resources and time cost are saved, and the computing efficiency is improved. By accurately simulating the water flow situation, the distribution and change of the water flow in the water area can be better understood, which helps to optimize the selection of the exploration area, select the appropriate area for exploration, and improve the efficiency and success rate of the exploration. By accurately simulating the water flow data of the water area, an accurate basis can be provided for the formulation of the dredging plan, avoiding the selection of unsuitable areas or ignoring potential risks, and improving the accuracy and feasibility of the dredging plan. By accurately simulating the water flow situation and optimizing the dredging plan, the accidental risks in the dredging process are reduced, and the safety and stability of the work are improved.

[0056] Optionally, step S2 specifically includes:

[0057] Step S21: Acquire data of the area to be explored;

[0058] Specifically, relevant data of the waters to be explored are obtained and selected through water maps or satellite remote sensing images, including water depth, water flow velocity, hydrological characteristics, etc.

[0059] Step S22: dividing the data of the area to be explored into exploration areas according to the water area flow data to obtain exploration area division data;

[0060] Specifically, the water flow data of the water area is used to reasonably divide the exploration area according to factors such as water flow velocity, water depth, water area morphology, etc. For example, the water area is divided into different sub-areas such as high flow velocity area, low flow velocity area, deep water area, shallow water area, etc.

[0061] Step S23: extracting exploration area features from the exploration area division data to obtain exploration area feature data;

[0062] Specifically, feature extraction is performed on the divided water areas, including water flow velocity, water depth, water quality and other characteristics. Relevant data is acquired through remote sensing technology, hydrological measurement and other means, and then processed and analyzed.

[0063] Step S24: Prioritizing the exploration areas according to the exploration area characteristic data and the exploration area division data to obtain first exploration area data;

[0064] Specifically, the exploration area characteristic data and division data are combined to prioritize each area. Different priority evaluation indicators can be set according to different exploration purposes, such as water depth, water flow speed, water quality, etc., and then the exploration areas are ranked according to these indicators.

[0065] Step S25: Identify the dredging difficulty according to the characteristic data of the exploration area to obtain dredging difficulty data;

[0066] Specifically, based on the characteristic data of the exploration area, areas where dredging is more difficult are identified. For example, if the water flow in a certain area is turbulent and the water depth is large, dredging may be increased using hydrological data or water flow simulation data to obtain the flow velocity and direction information of the water flow in the exploration area.

[0067] Use fluid dynamics models, such as the Reynolds-averaged Navier-Stokes equations, to simulate water flow conditions. Analyze water flow velocity to detect whether there are areas with turbulent water flow. Difficulty of operation. Obtain the distribution of water depth in the exploration area based on hydrological data or topographic data. Areas with greater water depth require greater excavation depths, which increases the difficulty of dredging. Use topographic data in water hydrological data, such as digital elevation models (DEMs) or topographic maps, to analyze the topographic features of the exploration area. For example, whether there are steep slopes or cliffs, these topographic features increase the danger and difficulty of dredging operations. Comprehensively evaluate factors such as water flow conditions, water depth, and topography, and assign them corresponding weights. Use expert experience or data-driven methods to determine weights. Then, substitute the values ​​of each factor into the evaluation model to calculate the dredging difficulty score for each area. Compare the calculated dredging difficulty score with the preset threshold to identify areas with greater dredging difficulty, which require additional safety measures or adjustments to the operation plan.

[0068] Step S26: Prioritize the exploration areas according to the dredging difficulty data and the exploration area division data to obtain second exploration area data;

[0069] Specifically, the exploration areas are prioritized again by combining the dredging difficulty data and the division data. This time, the dredging difficulty factor will be taken into account, and areas with lower dredging difficulty will be selected as priority exploration areas.

[0070] Step S27: performing similarity weighted combination on the first exploration area data and the second exploration area data to obtain exploration area data.

[0071] Specifically, the results obtained from the first and second priority sorting are weighted and combined, and the characteristics of each area and the difficulty of dredging are considered to obtain the final exploration area data. The weighting method can be set according to the actual situation, and linear weighting, exponential weighting and other methods can be used.

[0072] In the present invention, through step S22, the data of the area to be explored is divided using the water flow data of the water area, and the area to be explored can be divided into different areas to better carry out subsequent exploration work. Step S23 extracts features from the exploration area division data, which can help identify the characteristics of different areas, such as geological structure, water flow conditions, etc., and provide a reference basis for subsequent exploration work. Step S24 prioritizes the exploration area feature data and the division data, and can screen out areas with higher exploration priorities, optimize the selection of exploration areas, and improve exploration efficiency. Step S25 identifies the difficulty of dredging, and can take the difficulty of dredging into account in the process of prioritizing the exploration area, so that the selected area is more in line with the actual operation requirements and reduces the possible risks in the dredging process. Steps S26 and S27 perform comprehensive weighted sorting of the exploration area according to different factors, so that the final selected exploration area is more comprehensive and reliable, which helps to improve the success rate and efficiency of exploration.

[0073] Optionally, step S22 is specifically:

[0074] Extract water flow velocity characteristics and water flow direction angle characteristics of the water area according to the water flow data of the water area, and obtain water flow velocity characteristic data and water flow direction angle characteristic data of the water area;

[0075] Specifically, hydrological measuring instruments or hydrological sensors are used to measure the water flow velocity at different locations in the water area, and the instruments include current meters, laser Doppler current meters, etc. The collected water flow velocity data is processed and analyzed to extract the water flow velocity characteristics at each location. Similarly, hydrological measuring instruments or sensors are used to collect water flow direction angle data at different locations in the water area, and the data is measured by current meters and other equipment, or the water area images are analyzed using computer vision technology to extract the water flow direction angle characteristics at each location.

[0076] Specifically, the image data of the water area can be obtained by a camera, satellite remote sensing or other image acquisition equipment. Edge detection algorithms, such as Canny edge detection, Sobel operator, etc., are used to detect edge information in the water area image. The edge represents the boundary of different areas in the image. For the detected edge information, the normal angle of the edge is calculated, the gradient information of the edge is detected using the Sobel operator or other gradient operators, and then the normal angle of each pixel is calculated. The calculated angle information is Gaussian filtered to eliminate noise or smooth angle changes. According to the calculated angle information, the water flow direction angle feature of each position is extracted, and the angle information of the surrounding pixels is comprehensively considered by using local area statistics or other feature extraction methods to obtain the water flow direction angle feature of each position. The extracted water flow direction angle feature is associated with the corresponding position information to form the water flow direction angle feature data of the water area.

[0077] Annotating the image data of the area to be explored in the data of the area to be explored according to the characteristic data of the water flow velocity in the water area and the characteristic data of the water flow direction angle in the water area to obtain the image annotation data of the area to be explored;

[0078] Specifically, the collected water area water flow velocity characteristic data and water flow direction angle characteristic data are annotated with the image data of the area to be explored through geographic information system (GIS) software, or the corresponding positions of the image data are directly annotated with the two extracted characteristic data to associate the water flow characteristic data with the map layer.

[0079] Performing color feature extraction on the image annotation data of the area to be explored to obtain color feature data of the image of the area to be explored;

[0080] Specifically, color features are extracted from the image data of the area to be explored, and the color analysis algorithm in the image processing technology is used, or the division based on the change threshold is used to identify different color areas in the image and extract their color features.

[0081] Specifically, color change threshold data is generated according to pixel data in the image data of the area to be explored, and color features are extracted from the image annotation data of the area to be explored according to the color change threshold data.

[0082] Preliminarily dividing the image annotation data of the area to be explored according to the color feature data of the image of the area to be explored, and obtaining preliminary division data of the area to be explored;

[0083] Specifically, the area to be explored is preliminarily divided according to the characteristic data of water flow velocity, water flow direction angle and image color, and the area to be explored is divided into different sub-areas according to the changes of these characteristic data.

[0084] Calculate the degree of change of the water flow velocity characteristic data and the water flow direction angle characteristic data in the preliminary division data of the exploration area to obtain the water flow change degree data and the water flow direction angle change degree data;

[0085] Specifically, the degree of change of the characteristic data of water flow velocity and the characteristic data of water flow direction angle in the divided exploration area sub-data is calculated by statistical methods or mathematical models, such as calculating the standard deviation and variance of the velocity and angle.

[0086] When it is determined that the water area water flow change degree data is less than or equal to the preset water area water flow change threshold data and the water area water flow direction angle change degree data is less than or equal to the preset water area water flow direction angle change threshold data, the exploration area sub-data in the preliminary division data of the area to be explored corresponding to the corresponding water area water flow change degree data and the corresponding water area water flow direction angle change degree data are determined as the exploration area division data.

[0087] Specifically, based on the preset water area water flow change threshold data and water flow direction angle change threshold data, it is determined which sub-areas meet the conditions and use them as exploration area division data.

[0088] In the present invention, by extracting the water flow velocity feature and the water flow direction angle feature in step S22, the velocity and direction features of the water flow in the water area can be accurately identified, and accurate hydrological information can be provided for subsequent exploration work. According to the water flow feature data, the image data of the area to be explored is annotated (step S22), and the area with similar water flow features is marked, which is helpful for more detailed division and identification of the exploration area. By calculating the water flow change degree data and the water flow direction angle change degree data (step S22), the area with similar water flow change degree and flow direction angle in the water area is divided into exploration areas, which improves the accuracy and reliability of the division. According to the preset water flow change threshold and water flow direction angle change threshold, the exploration area with relatively stable water flow characteristics is determined, which is conducive to optimizing the selection of the exploration area and improving the efficiency and success rate of the exploration. By accurately dividing the exploration area with stable water flow characteristics, it is possible to avoid selecting areas with unstable water flow or large changes in water flow direction for exploration, thereby reducing the risk in the exploration process.

[0089] Optionally, the exploration wave data includes first exploration wave data and second exploration wave data, and step S3 is specifically:

[0090] Controlling the underwater seismic exploration device to transmit exploration waves to the exploration area data, thereby obtaining first exploration wave data;

[0091] Specifically, underwater seismic exploration devices, such as sonar or pressure sensors, are used to transmit exploration waves to the exploration area. These devices are installed on underwater equipment such as ships, buoys or submersibles, and emit sound waves or pressure waves through control devices to detect geological structures in the water area. The emitted waves can be single-frequency or multi-frequency waveforms.

[0092] The underwater electromagnetic exploration device is controlled to transmit exploration waves to the exploration area data, thereby obtaining second exploration wave data.

[0093] Specifically, underwater electromagnetic exploration devices, such as electromagnetic sensors, magnetometers, etc., are used to transmit exploration waves to the exploration area. These devices can be installed on the same underwater equipment, and electromagnetic waves are emitted by the control device to detect the geological and electromagnetic characteristics in the water area. The emitted waves can be single-frequency or multi-frequency waveforms.

[0094] Specifically, in one embodiment, the underwater electromagnetic exploration device is installed on a probe that can be used for drilling underwater underground holes to transmit exploration waves at different depths, thereby realizing electromagnetic wave signal collection at different depths.

[0095] Specifically, after each exploration wave is emitted, the returned waveform is received and recorded using corresponding data acquisition equipment, such as sonar receivers, electromagnetic sensors, etc. The data includes the reflection of sound waves or the response of electromagnetic signals, and its characteristics will reflect the geological structure and electrical characteristics of the bottom of the water area.

[0096] In the present invention, by using an underwater seismic exploration device and an underwater electromagnetic exploration device, exploration wave data of different physical properties are obtained, thereby obtaining geological information of more dimensions. Seismic exploration can provide acoustic wave velocity information of underground media, while electromagnetic exploration can provide electrical information of underground media. The combination of the two can provide a more comprehensive understanding of the underground structure. The first exploration wave data and the second exploration wave data can verify and complement each other, thereby improving the credibility and reliability of the data. For example, seismic exploration is disturbed by water bodies, and electromagnetic exploration can make up for this deficiency, and mutual verification can reduce the error of the exploration results. Combining the data of seismic exploration and electromagnetic exploration, the underground geological structure can be interpreted more comprehensively. Seismic data can provide velocity information of the stratum, and electromagnetic data can provide electrical information of the stratum. Comprehensive analysis can more accurately identify the characteristics of the target layer and the overburden. By comprehensively utilizing different types of exploration data, the location, thickness and properties of the target layer can be more accurately determined, thereby optimizing the dredging plan. By comprehensively analyzing seismic data and electromagnetic data, the underground structure can be better understood and the risks and uncertainties in the dredging process can be reduced. Conducting seismic and electromagnetic surveys simultaneously can obtain more geological information in the same period of time, improving the efficiency and cost-effectiveness of exploration.

[0097] Optionally, the target layer data includes first target layer data and second target layer data, and step S4 is specifically as follows:

[0098] Step S41: Obtain target layer parameter data;

[0099] Specifically, parameter data related to the target layer are collected, including information such as formation thickness, density, conductivity, dielectric constant, etc. These parameter data are obtained through geological exploration, geophysical exploration, groundwater exploration, etc., pre-stored in the database, and then obtained through database import or input.

[0100] Step S42: performing a first target layer analysis according to the first exploration wave data and the target layer parameter data to obtain first target layer data;

[0101] Specifically, the first exploration wave data and the target layer parameter data are used to perform the first target layer analysis, such as sampling seismic waveform analysis, inversion algorithm, model matching and other techniques, to determine the geological characteristics, thickness and distribution of the first target layer. For example, the seismic reflection method or electromagnetic method can be used to identify underground rock layers, mineral deposits or groundwater layers.

[0102] Step S43: performing a second target layer analysis according to the second exploration wave data and the target layer parameter data to obtain second target layer data, wherein the first target layer analysis and the second target layer analysis are different target layer analysis methods;

[0103] Specifically, the second target layer analysis is performed using the second exploration wave data and the target layer parameter data. Similar to the analysis of the first target layer, an analysis method is used to determine the geological characteristics, thickness and distribution of the second target layer. Different exploration waves and analysis techniques will reveal strata of different depths or properties. For example, the second exploration wave data is filtered to eliminate noise and highlight the signal of the target layer, including low-pass filtering, band-pass filtering, etc. After filtering, the data is pre-processed, including removing ground reflections, correcting data offsets, etc. (identifying ground reflection signals through data feature analysis and algorithms; removing detected ground reflection signals and retaining valid signals; determining the parameters required for data offset correction through data analysis and water environment conditions; and performing offset correction operations on the data according to the determined parameters). Multiple sampling points or time series data in the filtered electromagnetic wave data are superimposed to enhance the strength and stability of the signal, so as to better identify the target layer. Correction processing is performed on the superimposed data to eliminate existing interference factors. Conduct electrical analysis on the corrected electromagnetic wave data, including calculation and analysis of parameters such as conductivity and dielectric constant, to understand the physical properties of the target layer. Based on the data obtained from the electrical analysis, combined with the geological model and the target layer parameter data, generate the underground parameter data of the target layer, such as underground structure, conductivity distribution, etc. According to the generated underground parameter data, the target layer parameter data is used to determine the key information such as the position, thickness, and properties of the second target layer by numerical matching or mapping within a preset error range.

[0104] Removing ground reflections involves detecting the presence of ground reflections by analyzing the characteristics of the data, which can be detected by observing the time domain and frequency domain characteristics of the data. Once the ground reflection is detected, it is removed from the data using methods such as filtering and threshold processing.

[0105] Data offset refers to the phenomenon that the data position is offset due to various factors (such as equipment errors, water flow effects, etc.) during the data collection process. In order to ensure the accuracy and reliability of the data, the data needs to be offset corrected. By analyzing the data and the factors that cause the offset, the offset correction parameters, such as water flow speed, water flow direction, etc., are determined. Using the determined correction parameters, the corresponding offset correction operation is performed on the data to readjust the data position to the correct position.

[0106] Step S44: performing cross-mud layer dredging operations according to the first target layer data and the second target layer data.

[0107] Specifically, according to the analysis results of the first and second target layers, a cross-mud layer dredging operation plan is formulated, such as drilling, blasting, mining or underwater excavation, so as to effectively dredge the target layer. For example, after determining the location and properties of the mineral deposit or groundwater layer, a mining or pumping plan can be formulated to achieve resource utilization or groundwater level regulation.

[0108] In the present invention, by analyzing the first exploration wave data and the second exploration wave data, target layers of different depths or different properties underground can be identified, and multi-level geological information extraction can be achieved. This helps to fully understand the underground structure and provide more accurate target positioning for subsequent dredging operations. By adopting different target layer analysis methods, suitable analysis methods can be adopted for different types of geological structures, thereby improving the accuracy and reliability of the analysis. According to different geological characteristics, a suitable analysis method can be selected to better identify the target layer. By obtaining the first target layer data and the second target layer data, the characteristics and differences of the underground structure can be fully understood, providing a reference basis for the planning and design of dredging operations. According to the different characteristics of the target layer, the dredging plan can be optimized to improve construction efficiency and quality. Comprehensive analysis of multi-layer target layer data can reduce the risks and uncertainties in dredging operations. By fully understanding the characteristics of the underground structure, potential geological disasters and engineering risks can be effectively avoided, and the smooth progress of construction can be guaranteed. By accurately identifying the target layer and optimizing the dredging plan, sand and gravel resources can be more effectively utilized, mining costs can be reduced, resource utilization can be improved, and economic benefits can be maximized.

[0109] Optionally, step S42 is specifically:

[0110] Superimposing each seismic exploration wave data in the first exploration wave data to obtain underwater seismic profile data;

[0111] Specifically, each seismic exploration wave data in the first exploration wave data is superimposed to enhance the strength and clarity of the signal and obtain the underwater seismic profile data, which is achieved by superimposing and averaging the multiple seismic exploration wave data or superimposing and stacking them.

[0112] Performing filtering processing on the underwater seismic profile data to obtain underwater seismic profile filtered data;

[0113] Specifically, filtering is performed on the bottom seismic profile data to remove noise and extract target signals. The filtering process includes low-pass filtering, band-pass filtering or high-pass filtering.

[0114] Perform reverse time migration processing on the filtered data of the underwater seismic profile to obtain the deviation-corrected data of the underwater seismic profile;

[0115] Specifically, the filtered underwater seismic profile data is reverse-time migrated to correct the propagation path of the seismic waves, thereby accurately locating the underground target. Reverse-time migration is a seismic data processing method that uses the velocity model of the underground medium to invert the seismic record back to the reflection position of the underground stratum.

[0116] Extract reflection wave characteristics from the bottom seismic profile correction data to obtain profile reflection wave characteristic data;

[0117] Specifically, the characteristics of the reflected waves, such as amplitude, frequency, waveform, etc., are extracted from the underwater seismic profile correction data. The characteristics are used to identify the location, properties and thickness of the underground target layer.

[0118] The water bottom formation data is generated according to the profile reflection wave characteristic data to obtain the water bottom formation data;

[0119] Specifically, based on the extracted reflection wave characteristic data, the underwater stratum data is generated, including the position, thickness, density, velocity and other information of the stratum. The data reflects the structure and properties of the underground stratum.

[0120] Parameter mapping is performed according to the target layer parameter data and the water bottom formation data to obtain the first target layer data.

[0121] Specifically, parameter mapping is performed according to the target layer parameter data and the underwater stratum data, and the characteristic parameters of the underground target layer are mapped to the underwater stratum data to obtain the first target layer data.

[0122] In the present invention, by filtering, superimposing and correcting the second exploration wave data, the data quality and clarity can be improved, the influence of noise and interference can be reduced, and the characteristics of the underground structure can be more accurately reflected. The electromagnetic exploration wave data after correction can be used for more accurate electrical analysis to obtain more reliable underwater underground parameter data. This helps to accurately identify the target layer and more finely divide the boundaries and characteristics of the target layer. By constructing an underwater geological model based on the underwater underground parameter data, the geological structure and characteristics of the bottom of the water area can be more intuitively understood, providing a reliable basis for determining the target layer. Combining the target layer parameter data and the underwater geological model, the position, thickness and properties of the second target layer can be more accurately determined. This helps to improve the accuracy and efficiency of dredging operations and reduce resource waste and environmental impact. Combining the second exploration wave data with the target layer parameter data for analysis can comprehensively consider the various characteristics of the underground structure, thereby more comprehensively understanding the geological conditions and providing a more scientific basis for the planning and design of dredging operations.

[0123] Optionally, step S43 is specifically:

[0124] Step S431: filtering the second exploration wave data to obtain electromagnetic exploration wave filtered data;

[0125] Specifically, for the second exploration wave data, filtering is first performed to remove noise and interference to obtain electromagnetic exploration wave filtered data. The filtering operation adopts methods such as digital filter or wavelet transform.

[0126] Step S432: performing superposition processing on the electromagnetic acquisition point data in the electromagnetic exploration wave filtering data to obtain electromagnetic exploration wave superposition data;

[0127] Specifically, the electromagnetic acquisition point data in the electromagnetic exploration wave filtering data is superimposed to enhance the signal strength and clarity to obtain electromagnetic exploration wave superposition data, which is achieved by averaging or weighted superposition of data from multiple acquisition points.

[0128] Step S433: performing correction processing on the electromagnetic exploration wave superposition data to obtain electromagnetic exploration wave correction data;

[0129] Specifically, the electromagnetic exploration wave stacking data is corrected to eliminate the influence of factors such as instrument drift and terrain effect on the data, and electromagnetic exploration wave correction data is obtained.

[0130] Step S434: performing electrical analysis on the electromagnetic exploration wave correction data to obtain underwater underground parameter data;

[0131] Specifically, electrical analysis is performed on the electromagnetic exploration wave correction data, and the underwater underground parameter data, including conductivity, dielectric constant, etc., are obtained by analyzing the propagation and reflection characteristics of electromagnetic signals at the bottom of the water.

[0132] Step S435: constructing an underwater geological model according to underwater underground parameter data;

[0133] Specifically, based on the underwater underground parameter data, an underwater geological model is constructed to reflect the structure, composition and properties of the underwater strata. The geological model is in two-dimensional or three-dimensional form.

[0134] Step S436: determining the target layer according to the target layer parameter data and the underwater geological model to obtain second target layer data;

[0135] Specifically, the target layer is determined by combining the target layer parameter data and the underwater geological model. By comparing the target layer parameter data and the geological model, the location, thickness and properties of the target layer are determined to obtain the second target layer data.

[0136] The correction process includes the following steps:

[0137] Performing stability processing on the exploration area according to the exploration area data to obtain stability data of the exploration area;

[0138] Specifically, the stability of the survey area is evaluated by calculating factors such as the topography and water flow conditions. For example, numerical simulation or field measurements are used to obtain information such as water flow speed and direction.

[0139] Generate environmental correction parameters based on the stability data of the exploration area to obtain environmental correction parameter data;

[0140] Specifically, environmental correction parameters are generated by using environmental data of the exploration area, such as water temperature, water depth, etc., combined with the characteristics of underwater equipment. They are estimated through field measurements or previous research data.

[0141] Correcting the electromagnetic exploration wave superposition data according to the environmental correction parameter data to obtain first electromagnetic exploration wave correction data;

[0142] Specifically, the electromagnetic exploration wave superposition data is corrected according to the environmental correction parameters to eliminate the influence of environmental factors on the data, for example, the position offset of underwater equipment and the influence of water temperature on signal transmission speed are corrected.

[0143] Specifically, based on data such as water flow velocity and water depth, an environmental correction coefficient is calculated for correcting exploration data. The correction coefficient is derived based on experiments or models, and reflects the attenuation of electromagnetic signal propagation by water flow. Based on water flow velocity and water depth data, a correction algorithm is used to correct the electromagnetic exploration wave superposition data. For example, an acoustic formula or a hydrodynamic model is used to calculate the propagation speed and path of electromagnetic waves in water. The environmental correction coefficient is applied to the exploration data to correct the data. The correction coefficient can be used to adjust the signal strength or phase to compensate for the influence of water flow on the exploration wave. The data is adjusted and compensated according to the correction coefficient to eliminate the influence of environmental factors such as water flow on the exploration data, such as weighting, filtering or interpolation of the data.

[0144] Performing frequency domain conversion on the electromagnetic exploration wave superposition data to obtain electromagnetic exploration wave superposition frequency domain data;

[0145] Specifically, the electromagnetic survey wave stack data is converted to the frequency domain using Fourier transform or other frequency domain conversion techniques.

[0146] Performing frequency response processing on the electromagnetic exploration wave superposition frequency domain data to obtain the electromagnetic exploration wave frequency response data;

[0147] Specifically, the frequency response processing of the electromagnetic exploration wave superposition data is performed according to the frequency domain data to adjust the frequency characteristics of the signal, using digital filtering and other technologies. The frequency characteristics of the signal are adjusted as needed, and a low-pass filter is used to remove high-frequency noise, or a band-pass filter is used to highlight specific frequency components.

[0148] Performing frequency deviation correction on electromagnetic exploration wave superposition frequency domain data according to electromagnetic exploration wave frequency response data to obtain electromagnetic exploration wave travel frequency correction data;

[0149] Specifically, the frequency deviation is corrected according to the frequency response data to ensure that the frequency of the signal is consistent with the expected. The frequency deviation of the signal is analyzed using the frequency domain data after the frequency response processing. According to the frequency deviation information, the frequency domain data is corrected for frequency offset to correct the frequency deviation to the expected range.

[0150] Specifically, the frequency deviation is analyzed to understand how the frequency characteristics of the signal deviate from expectations, such as checking the distribution of frequency deviation and checking whether there are abnormal values ​​or frequency drift. Based on the analysis results of the frequency deviation, a correction strategy is formulated, including determining the correction method (such as simple frequency offset correction or complex filtering processing), adjusting the correction parameters, etc. Based on the determined correction strategy, the frequency domain data is corrected for frequency offset, such as applying mathematical algorithms or digital signal processing technology to correct the frequency deviation to the expected range.

[0151] Performing frequency domain inverse conversion on the electromagnetic exploration wave frequency correction data to obtain second electromagnetic exploration wave correction data;

[0152] Specifically, the frequency domain data after frequency response processing is converted back to the time domain to obtain the adjusted electromagnetic exploration wave frequency response data, which is achieved through algorithms such as inverse Fourier transform (IFFT).

[0153] Comparing adjacent data points on electromagnetic exploration wave stacking data to obtain adjacent data point comparison data;

[0154] Specifically, the values ​​of adjacent data points are compared to detect anomalies or discontinuities.

[0155] Smoothing the electromagnetic exploration wave stacking data according to the comparison data of the adjacent data points to obtain the third electromagnetic exploration wave correction data;

[0156] Specifically, the comparison results are smoothed to reduce the influence of noise or abnormal points, using methods such as moving average and median filtering.

[0157] Performing modification amplitude similarity calculation on the first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data to obtain modification amplitude similarity data;

[0158] Specifically, the modification amplitude similarity between different correction data is calculated to determine whether weighted calculation is required.

[0159] Determine whether the modification amplitude similarity data is greater than or equal to a preset modification amplitude similarity threshold data;

[0160] Specifically, the difference between the modification amplitude similarity data and the preset threshold is calculated, and the difference is compared with zero (or other specific preset threshold data). If the difference is greater than or equal to zero (or other specific preset threshold data), it means that the modification amplitude similarity data is greater than or equal to the preset modification amplitude similarity threshold data.

[0161] When it is determined that the modification amplitude similarity data is greater than or equal to the preset modification amplitude similarity threshold data, weighted calculation is performed on the first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data to obtain the electromagnetic exploration wave correction data;

[0162] Specifically, if the condition is met, weighted calculation is performed to perform weighted operations on the first electromagnetic survey wave correction data, the second electromagnetic survey wave correction data, and the third electromagnetic survey wave correction data to obtain electromagnetic survey wave correction data.

[0163] When it is determined that the modification amplitude similarity data is less than the preset modification amplitude similarity threshold data, the correction amplitude is calculated for the first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data to obtain the first correction amplitude data, the second correction amplitude data and the third correction amplitude data;

[0164] Specifically, whether the correction amplitude needs to be calculated is determined based on the similarity data and a preset threshold.

[0165] Extracting the minimum correction amplitude from the first correction amplitude data, the second correction amplitude data, and the third correction amplitude data to obtain the minimum correction amplitude data;

[0166] Specifically, the minimum value is extracted from the correction amplitude data for subsequent data selection.

[0167] The first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data are selected according to the minimum correction amplitude data to obtain the electromagnetic exploration wave correction data.

[0168] Specifically, different correction data are selected according to the minimum correction amplitude data, and the data with the least influence is selected as the final electromagnetic exploration wave correction data.

[0169] In the present invention, by extracting conductivity characteristics and dielectric characteristics of electromagnetic exploration wave correction data, the conductivity characteristic data and dielectric characteristic data of the bottom of the water can be obtained, which is helpful to understand the electrical distribution characteristics of the bottom geology. The electrical difference calculation based on the conductivity characteristic data and the dielectric characteristic data can effectively identify the electrical differences between different geological layers under the water, and then determine the location and nature of the underground stratification. According to the electrical difference data and the preset electrical difference threshold, the location and range of the underground stratification can be accurately determined, different geological layers can be identified, and target positioning can be provided for subsequent dredging operations. According to the bottom conductivity characteristic data and the bottom dielectric characteristic data, the bottom underground cover layer can be identified, and the location, thickness and nature of the bottom cover layer can be determined, providing more comprehensive geological information for dredging operations.

[0170] Optionally, step S434 is specifically:

[0171] Conductivity feature extraction and dielectric feature extraction are performed on the electromagnetic exploration wave correction data to obtain bottom conductivity feature data and bottom dielectric feature data;

[0172] Specifically, the electromagnetic exploration wave correction data is processed, an algorithm or method is used to extract the conductivity characteristics of the water bottom, and the propagation and reflection characteristics of the electromagnetic signal at the water bottom are analyzed, including spectrum analysis, time-frequency analysis, etc.

[0173] The electromagnetic exploration wave correction data is processed to extract the dielectric characteristics of the water bottom and analyze the propagation and reflection of electromagnetic signals in the water bottom medium through waveform analysis, spectrum analysis and other methods.

[0174] Specifically, there is a set of electromagnetic exploration wave correction data, each data point includes the amplitude and phase information of the electromagnetic wave. Use Fourier transform to convert time domain data into frequency domain data. Analyze the power spectrum density in the frequency domain data to find the main frequency components. According to the spectrum density distribution, extract the conductivity characteristics, such as the distribution of the main frequency components. Use short-time Fourier transform (STFT) to decompose the time domain signal into spectrum information of different time periods. Analyze the changes in spectrum characteristics in different time periods to obtain the changes in conductivity over time. Analyze the amplitude and phase information of the electromagnetic wave and observe its changes with depth. By comparing the waveform characteristics in different media, extract the dielectric characteristic information.

[0175] Based on the analysis results of the spectrum changes, the trend of the change of the bottom conductivity is inferred. If the attenuation rate of the high-frequency components in the spectrum increases, it is preliminarily inferred that the bottom conductivity increases; otherwise, the conductivity decreases. By combining Maxwell's equations and Ohm's law, the propagation of the electromagnetic field in the medium and the relationship between the current and the electric field in the medium are calculated, thus obtaining the bottom conductivity data.

[0176] Specifically, the extraction of dielectric characteristic data relies on mapping of electrical characteristic data in electromagnetic exploration wave correction data, and the mapping method adopts a model mapping method.

[0177] Steps for model construction: Use electromagnetic exploration equipment to collect a series of electromagnetic wave data in the water area, covering areas of different media, including information such as reflection coefficient, propagation time and waveform. Perform waveform analysis on the collected data and compare the waveform characteristics in different media. Use signal processing techniques such as Fourier transform and wavelet transform to convert time domain signals into frequency domain signals in order to better analyze the spectral characteristics of the waveform. In the frequency domain, the waveform spectrum characteristics in different media can be extracted, including the main components of the spectrum, the energy distribution of the spectrum, the peak of the spectrum and other information. Compare the spectral characteristics in different media, analyze the differences between them, and use statistical methods or machine learning algorithms to cluster or classify the spectral characteristics to identify patterns and differences between different media. Based on the results of the comparative analysis, extract characteristic information reflecting the characteristics of the medium, including the peak frequency of the spectrum, the energy distribution ratio of the spectrum, the shape parameters of the spectrum, etc. Based on the extracted characteristic information, establish a mathematical model of the medium characteristics (based on the electromagnetic exploration wave correction data and the mathematical mapping formula of the dielectric characteristic data, , is the estimated data of the bottom dielectric characteristic data, is the initial dielectric characteristic data, is the first weight term, Correction data for electromagnetic exploration waves, is the second weight term, is the dielectric characteristic data, as correction term) or machine learning models (such as multivariate regression models, support vector machine models, and integrated learning models). The model is used to predict or classify the medium and provide information about the dielectric properties of the medium. The electromagnetic exploration wave correction data collected by the present invention is identified by the model to obtain the corresponding bottom dielectric characteristic data.

[0178] The electrical difference is calculated according to the bottom conductivity characteristic data and the bottom dielectric characteristic data to obtain the electrical difference data;

[0179] Specifically, the electrical property differences between different areas under the water are calculated based on the extracted conductivity characteristic data and dielectric characteristic data, and this is achieved by comparing the conductivity and dielectric properties of different areas. For example, the conductivity difference and dielectric property difference of different areas are calculated.

[0180] When it is determined that the electrical difference data is greater than or equal to the preset electrical difference threshold data, the electromagnetic exploration parameter data corresponding to the electromagnetic exploration wave correction data corresponding to the electrical difference data is determined as underwater underground stratification data;

[0181] Specifically, based on the calculation of electrical difference, a preset electrical difference threshold is set. The threshold can be set according to actual conditions and needs, and is used to determine whether the electrical difference between different underground areas is significant. According to the electrical difference data and the electrical difference threshold, the part with significant electrical difference in the electromagnetic exploration wave correction data is determined, and the corresponding electromagnetic exploration parameter data is determined as the underwater underground stratification data.

[0182] Identify the underwater underground covering layer according to the underwater conductivity characteristic data and the underwater dielectric characteristic data to obtain the underwater underground covering layer data;

[0183] Specifically, the underwater underground cover layer is identified by combining the underwater conductivity characteristic data and the underwater dielectric characteristic data, and the underground cover layer in different areas is identified by using machine learning, pattern recognition and other methods. Among them, the machine learning method, such as convolutional neural network (CNN), is used to learn the characteristics of the underground cover layer from the underwater conductivity characteristic data and the underwater dielectric characteristic data, and classify and identify them. The steps of CNN are as follows:

[0184] Input data: water bottom conductivity characteristic data and water bottom dielectric characteristic data, which are raw data obtained from underwater exploration equipment, obtained after preprocessing and feature extraction. Label data: used to indicate the underground cover layer category corresponding to each data sample, such as sand, mud, rock, etc.

[0185] Model training: Input data: underwater conductivity characteristic data and underwater dielectric characteristic data (divided into training set and validation set before input, the training set is used for model training, and the validation set is used for model validation). Intermediate structure: Feature representation of each layer of CNN (convolutional layer, pooling layer, fully connected layer and other basic layer types, and time gates or recurrent gates can also be added on this basis to enhance the prediction function). Training process: Use labeled data to train CNN, and optimize network parameters by minimizing the loss function (such as the cross entropy loss function) so that the prediction results output by the network are as close to the true label as possible. Output results: The trained CNN model can predict new underwater characteristic data and output the probability distribution of each sample belonging to each underground cover layer category. Generate underwater underground parameter data based on underwater underground stratification data and underwater underground cover layer data.

[0186] The cross entropy loss function is used to measure the gap between the prediction results output by the model and the true label, which is defined as follows: , is the model loss data, is the training sample data, is the training sample sequence data, is the categorical ordinal data, For category quantity data, For sample Belongs to category The real label, For sample Belongs to category The trained model is evaluated using the validation set, and the model is tuned based on the evaluation results to improve the generalization ability and accuracy of the model.

[0187] Specifically, the underwater underground parameter data is generated based on the underwater underground layer data and the underwater underground cover layer data. The data includes parameters such as conductivity and dielectric constant of different strata, as well as information such as thickness and type of the underground cover layer.

[0188] The present invention can help accurately locate the target layer of the underwater ground, including sand, gravel, minerals and other resources, by extracting and analyzing the electrical characteristics of the electromagnetic exploration wave correction data, and provide accurate target location information for subsequent dredging operations. By using electrical difference data, the stratification and cover characteristics of the underwater ground can be more effectively identified, avoiding the blind detection of a large number of invalid areas in traditional exploration methods, thereby saving exploration costs. By generating underwater stratification data and cover data, detailed underground structure information can be provided for dredging operations, guiding operations during dredging, and improving the accuracy and efficiency of dredging. When conducting underwater geological exploration and dredging, the impact on the ecological environment of the water area is fully considered, and through fine underground structure analysis and positioning, interference and damage to the ecological environment of the water area are reduced, which helps to protect the integrity of the ecological environment of the water area.

[0189] The purpose of the present invention is to provide an accurate hydrological environment basis for exploration work through hydrological data acquisition and water flow simulation, which helps to understand the water flow dynamics and hydrological characteristics in the water area and provides an important reference basis for subsequent exploration operations. By selecting the exploration area according to the water flow data of the water area, the area that needs to be explored can be accurately determined, avoiding blind exploration of the entire water area, saving time and resource costs. The exploration wave data is obtained by transmitting the exploration wave. This step adopts advanced exploration technology and can quickly obtain geological information at the bottom of the water area, providing data support for subsequent target layer analysis. The target layer analysis uses advanced electromagnetic exploration technology, combined with complex steps such as electromagnetic data processing, filtering, and superposition, to accurately identify the target layer structure and characteristics of the bottom of the water, providing important geological information and guidance for cross-mud layer dredging operations. By integrating hydrological data and water flow simulation, as well as accurately selecting the exploration area, unnecessary exploration can be avoided, interference with the ecological environment of the water area and waste of resources can be reduced, which is conducive to protecting the integrity and stability of the water ecosystem. Through precise target layer analysis, the depth and thickness of the target layer as well as the nature and thickness of the covering layer can be accurately understood, which provides an important reference for dredging operations, improves operational efficiency, and reduces unnecessary costs and time waste.

[0190] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0191] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An underwater geological exploration method for dredging across mud layers, characterized in that: The following steps are involved: Step S1: Acquire hydrological data of the water area, and perform water flow simulation based on the hydrological data of the water area to obtain water flow data of the water area; Step S2: selecting an exploration area according to water flow data to obtain exploration area data; Step S3: transmitting exploration waves to the exploration area data, thereby obtaining exploration wave data; Step S4: extracting electrical difference characteristics of the target layer from the exploration wave data using geophysical principles and methods to obtain electrical difference characteristic data of the target layer, and performing target layer analysis based on the electrical difference characteristic data of the target layer to obtain target layer data for cross-mud layer dredging operations, wherein the target layer data includes underground target layer position data, underground target layer depth data, and underground target layer thickness data; Step S2 is specifically as follows: Step S21: Acquire data of the area to be explored; Step S22: dividing the data of the area to be explored into exploration areas according to the water area flow data to obtain exploration area division data; Step S23: extracting exploration area features from the exploration area division data to obtain exploration area feature data; Step S24: Prioritizing the exploration areas according to the exploration area characteristic data and the exploration area division data to obtain first exploration area data; Step S25: Identify the dredging difficulty according to the characteristic data of the exploration area to obtain dredging difficulty data; Step S26: Prioritize the exploration areas according to the dredging difficulty data and the exploration area division data to obtain second exploration area data; Step S27: performing similarity weighted combination based on the first exploration area data and the second exploration area data to obtain exploration area data; Step S22 is specifically as follows: Extract water flow velocity characteristics and water flow direction angle characteristics of the water area according to the water flow data of the water area, and obtain water flow velocity characteristic data and water flow direction angle characteristic data of the water area; Annotating the image data of the area to be explored in the data of the area to be explored according to the characteristic data of the water flow velocity in the water area and the characteristic data of the water flow direction angle in the water area to obtain the image annotation data of the area to be explored; Performing color feature extraction on the image annotation data of the area to be explored to obtain color feature data of the image of the area to be explored; Preliminarily dividing the image annotation data of the area to be explored according to the color feature data of the image of the area to be explored, and obtaining preliminary division data of the area to be explored; Calculate the degree of change of the water flow velocity characteristic data and the water flow direction angle characteristic data in the preliminary division data of the exploration area to obtain the water flow change degree data and the water flow direction angle change degree data; When it is determined that the water area water flow change degree data is less than or equal to the preset water area water flow change threshold data and the water area water flow direction angle change degree data is less than or equal to the preset water area water flow direction angle change threshold data, the exploration area sub-data in the preliminary division data of the area to be explored corresponding to the corresponding water area water flow change degree data and the corresponding water area water flow direction angle change degree data are determined as the exploration area division data.

2. The method according to claim 1, characterized in that Step S1 is specifically as follows: Step S11: Acquire water area hydrological data; Step S12: constructing a hydrodynamic model according to the hydrological data of the water area to obtain a hydrodynamic model of the water area; Step S13: Utilize the water area hydrodynamic model to simulate water flow and obtain water flow data of the water area.

3. The method according to claim 2, characterized in that Step S13 is specifically as follows: Performing edge processing on water area image data in water area hydrological data to obtain water area image edge processing data; Grid parameter generation is performed according to the edge processing data of the water area image to obtain grid parameter data; The hydrodynamic model of the water area is grid-divided by grid parameter data to obtain a hydrodynamic grid model of the water area; The water area hydrodynamic grid model is used to perform numerical simulation calculations to obtain water flow data in the water area.

4. The method according to claim 1, characterized in that: The exploration wave data includes first exploration wave data and second exploration wave data, and step S3 is specifically as follows: Controlling the underwater seismic exploration device to transmit exploration waves to the exploration area data, thereby obtaining first exploration wave data; The underwater electromagnetic exploration device is controlled to transmit exploration waves to the exploration area data, thereby obtaining second exploration wave data.

5. The method according to claim 4, characterized in that The target layer data includes first target layer data and second target layer data, and step S4 is specifically as follows: Step S41: Obtain target layer parameter data; Step S42: performing a first target layer analysis according to the first exploration wave data and the target layer parameter data to obtain first target layer data; Step S43: performing a second target layer analysis according to the second exploration wave data and the target layer parameter data to obtain second target layer data, wherein the first target layer analysis and the second target layer analysis are different target layer analysis methods; Step S44: performing cross-mud layer dredging operations according to the first target layer data and the second target layer data.

6. The method according to claim 5, characterized in that Step S42 is specifically as follows: Superimposing each seismic exploration wave data in the first exploration wave data to obtain underwater seismic profile data; Performing filtering processing on the underwater seismic profile data to obtain underwater seismic profile filtered data; Perform reverse time migration processing on the filtered data of the underwater seismic profile to obtain the deviation-corrected data of the underwater seismic profile; Extract reflection wave characteristics from the bottom seismic profile correction data to obtain profile reflection wave characteristic data; The water bottom formation data is generated according to the profile reflection wave characteristic data to obtain the water bottom formation data; Parameter mapping is performed according to the target layer parameter data and the water bottom stratum data to obtain the first target layer data.

7. The method according to claim 5, characterized in that Step S43 is specifically as follows: Step S431: filtering the second exploration wave data to obtain electromagnetic exploration wave filtered data; Step S432: performing superposition processing on the electromagnetic acquisition point data in the electromagnetic exploration wave filtering data to obtain electromagnetic exploration wave superposition data; Step S433: performing correction processing on the electromagnetic exploration wave superposition data to obtain electromagnetic exploration wave correction data; Step S434: performing electrical analysis on the electromagnetic exploration wave correction data to obtain underwater underground parameter data; Step S435: constructing an underwater geological model according to underwater underground parameter data; Step S436: determining the target layer according to the target layer parameter data and the underwater geological model to obtain second target layer data; The correction process includes the following steps: Performing stability processing on the exploration area according to the exploration area data to obtain stability data of the exploration area; Generate environmental correction parameters based on the stability data of the exploration area to obtain environmental correction parameter data; Correcting the electromagnetic exploration wave superposition data according to the environmental correction parameter data to obtain first electromagnetic exploration wave correction data; Performing frequency domain conversion on the electromagnetic exploration wave superposition data to obtain electromagnetic exploration wave superposition frequency domain data; Performing frequency response processing on the electromagnetic exploration wave superposition frequency domain data to obtain the electromagnetic exploration wave frequency response data; Performing frequency deviation correction on electromagnetic exploration wave superposition frequency domain data according to electromagnetic exploration wave frequency response data to obtain electromagnetic exploration wave travel frequency correction data; Performing frequency domain inverse conversion on the electromagnetic exploration wave frequency correction data to obtain second electromagnetic exploration wave correction data; Comparing adjacent data points on electromagnetic exploration wave stacking data to obtain adjacent data point comparison data; The electromagnetic exploration wave stacking data is smoothed according to the comparison data of the adjacent data points to obtain the third electromagnetic exploration wave correction data; Performing modification amplitude similarity calculation on the first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data to obtain modification amplitude similarity data; Determine whether the modification amplitude similarity data is greater than or equal to a preset modification amplitude similarity threshold data; When it is determined that the modification amplitude similarity data is greater than or equal to the preset modification amplitude similarity threshold data, weighted calculation is performed on the first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data to obtain the electromagnetic exploration wave correction data; When it is determined that the modification amplitude similarity data is less than the preset modification amplitude similarity threshold data, the correction amplitude is calculated for the first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data to obtain the first correction amplitude data, the second correction amplitude data and the third correction amplitude data; Extracting the minimum correction amplitude from the first correction amplitude data, the second correction amplitude data, and the third correction amplitude data to obtain the minimum correction amplitude data; The first electromagnetic exploration wave correction data, the second electromagnetic exploration wave correction data and the third electromagnetic exploration wave correction data are selected according to the minimum correction amplitude data to obtain the electromagnetic exploration wave correction data.

8. The method according to claim 7, characterized in that Step S434 is specifically as follows: Conductivity feature extraction and dielectric feature extraction are performed on the electromagnetic exploration wave correction data to obtain bottom conductivity feature data and bottom dielectric feature data; The electrical difference is calculated according to the bottom water conductivity characteristic data and the bottom water dielectric characteristic data to obtain the electrical difference data; When it is determined that the electrical difference data is greater than or equal to the preset electrical difference threshold data, the electromagnetic exploration parameter data corresponding to the electromagnetic exploration wave correction data corresponding to the electrical difference data is determined as underwater underground stratification data; Identify the underwater underground covering layer according to the underwater conductivity characteristic data and the underwater dielectric characteristic data to obtain the underwater underground covering layer data; The underwater underground parameter data is generated based on the underwater underground layer data and the underwater underground cover layer data.

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