Method and system for detecting internal structure of dredged soil accumulation body
By combining cross-hole radar with improved full-waveform inversion and reverse time migration imaging methods, the problems of low efficiency and unsatisfactory imaging in the detection of internal structures of dredged soil deposits have been solved, achieving high-precision and visualized defect identification and imaging, thus improving engineering quality and resource utilization.
Patent Information
- Application Number
- CN202610548116.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-14
AI Technical Summary
Traditional ground-penetrating radar has limited ability to detect the deep structure inside dredged soil deposits. Existing full-waveform inversion methods have shortcomings in initial model selection, inversion stability, and handling of multiple solutions. The limited electromagnetic wave reflection signals in cross-hole radar detection result in unsatisfactory imaging effects. Existing detection methods are inefficient, destructive, and have low spatial coverage, failing to meet the requirements of high-quality engineering construction.
By combining cross-aperture radar with an improved full-waveform inversion method and a reverse-time migration imaging method, the dielectric constant and conductivity are inverted through alternating iterations. Inequality constraints on the parameter vector are applied, and single-aperture reflection data are synthesized using a parabolic step size and wave interference virtual source algorithm to achieve high-precision imaging.
It enables efficient and visual detection of internal defects in dredged soil deposits, accurately identifies cavities and weak interlayers, improves the level of precision in engineering construction, reduces operation and maintenance risks and costs, and promotes resource utilization and ecological protection.
Smart Images
Figure CN122386407A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of dredged soil technology, specifically relating to a method and system for detecting the internal structure of dredged soil deposits. Background Technology
[0002] The statements herein provide only background information in relation to this invention and do not necessarily constitute prior art.
[0003] With the rapid development of global urbanization and the shipping industry, the scale of water-related projects continues to expand, generating a large amount of dredged soil. Existing methods for disposing of dredged soil suffer from technical drawbacks such as environmental pollution and land resource occupation, making its resource utilization a research hotspot in this field. The internal quality of dredged soil deposits directly affects the safety, stability, and durability of projects; therefore, accurate detection of the internal structure of dredged soil deposits is of great significance.
[0004] In recent years, ground-penetrating radar (GPR) technology has been increasingly applied to the field of civil engineering inspection. However, the inventors have found that traditional GPR methods, which primarily rely on ground-based detection, have limited ability to detect the deep structures within dredged soil deposits. Furthermore, existing full-waveform inversion methods have shortcomings in initial model selection, inversion stability, and handling of multiple solutions. Additionally, cross-hole radar detection results in less electromagnetic wave reflection, leading to suboptimal imaging. Therefore, traditional methods for inspecting the quality of dredged soil deposits still suffer from low efficiency, destructive nature, and low spatial coverage, failing to meet the actual needs of high-quality engineering construction. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for detecting the internal structure of dredged soil deposits. By combining cross-hole radar with an improved full-waveform inversion method and a reverse-time offset imaging method, high-precision, high-efficiency, and visual detection of internal defects in dredged soil deposits can be achieved.
[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: In a first aspect, the technical solution of the present invention provides a method for detecting the internal structure of a dredged soil deposit, comprising: Based on the existing core sampling holes on the dredged soil deposit, cross-hole radar was used to acquire cross-hole radar detection data. The full waveform inversion processing of the cross-hole radar detection data is used to obtain the dielectric constant and conductivity distribution inside the dredged soil accumulation. The full waveform inversion processing adopts an alternating iterative method to simultaneously invert the dielectric constant and conductivity. The initial model of the inversion uses the full waveform inversion results in the complex frequency domain. Inequality constraints based on parameter vectors are applied during the inversion process, and the iteration step size adopts a parabolic step size. The results of the full waveform inversion processing are subjected to reverse time migration imaging to obtain the contour boundary information of the dredged soil accumulation body. Among them, the reverse time migration imaging processing synthesizes the cross-hole detection data into single-hole reflection data through the wave interferometry virtual source algorithm, and uses the dielectric constant distribution obtained from the full waveform inversion processing as the initial model.
[0007] In at least one embodiment, using existing core holes on the dredged soil deposit, the cross-hole radar transmitting and receiving antennas are placed in two boreholes for detection to obtain cross-hole radar detection data.
[0008] In at least one embodiment, during the complex frequency domain full waveform inversion process, the forward modeling of the finite-difference time-domain method is performed in the time domain, while the residual, gradient, and step size are calculated in the complex frequency domain. During the finite-difference time-domain calculation process, the electric field values of each time step are superimposed, without the need to save the field values of all time steps.
[0009] In at least one embodiment, the iterative update of the model parameters in the complex frequency domain is specifically as follows: the updated model parameters are obtained by subtracting the product of the iteration step size and the iteration gradient from the current model parameters; wherein, the model parameters include dielectric constant and conductivity.
[0010] In at least one embodiment, each inversion iteration of the full waveform inversion process requires six time-domain finite-difference method calculations.
[0011] In at least one embodiment, the inequality constraint based on the parameter vector specifically involves applying the range of variation of the model parameters as prior information to the inversion equation through inequality constraints; wherein the model parameters include dielectric constant and conductivity.
[0012] In at least one embodiment, the parabolic step size is specifically calculated as follows: the iteration step size is calculated by parabolic fitting method, the objective function value corresponding to the three step sizes is calculated and then the parabolic function is constructed. The minimum objective function value of the parabolic function segment is obtained by interpolation, and the corresponding step size is the optimal step size.
[0013] In at least one embodiment, the reverse time-shift imaging process specifically includes: The cross-aperture detection data is synthesized into single-aperture reflection data using the wave interferometry virtual source algorithm; Full waveform inversion was performed on the cross-hole detection data, and the obtained dielectric constant distribution was used as the initial model to calculate the wave field of the electromagnetic wave propagating in the forward direction emitted by the excitation source. Calculate the wavefield propagating in reverse time from synthetic single-aperture reflection data as the source; Imaging conditions are applied to the forward propagation wavefield and the reverse propagation wavefield at the same time, and the migration results from different source locations are superimposed to obtain a complete reverse migration imaging result.
[0014] In at least one embodiment, cross-aperture detection data is synthesized into single-aperture reflection data using a wave interferometric virtual source algorithm. Specifically, by performing wave interferometric cross-correlation on any two radar records, a virtual empirical Green's function between the two receiving positions is synthesized, thereby synthesizing cross-aperture detection data into single-aperture reflection data with a fixed transmit / receive distance or a variable transmit / receive distance on the receiving side.
[0015] Secondly, the technical solution of the present invention also provides a system for detecting the internal structure of dredged soil deposits, comprising: The data acquisition module is configured to acquire cross-hole radar detection data based on existing core holes on the dredged soil deposit. The full waveform inversion module is configured to perform full waveform inversion processing on the cross-hole radar detection data to obtain the dielectric constant and conductivity distribution inside the dredged soil accumulation body. The full waveform inversion processing adopts an alternating iterative method to simultaneously invert the dielectric constant and conductivity. The initial model of the inversion adopts the full waveform inversion result in the complex frequency domain. Inequality constraints based on parameter vectors are applied during the inversion process, and the iteration step size adopts a parabolic step size. The reverse time migration imaging module is configured to perform reverse time migration imaging processing on the results of the full waveform inversion processing to obtain the contour boundary information of the dredged soil accumulation body; wherein, the reverse time migration imaging processing synthesizes single-hole reflection data from the cross-hole detection data through the wave interferometry virtual source algorithm, and uses the dielectric constant distribution obtained from the full waveform inversion processing as the initial model.
[0016] The beneficial effects of the above-described technical solution of the present invention are as follows: 1) The method for detecting the internal structure of dredged soil accumulation in this invention has the technical advantages of high detection efficiency, high spatial resolution, and intuitive imaging. It can accurately identify hidden defects such as cavities and weak interlayers inside dredged soil accumulation and realize the precise quantification and visualization of the location, range, and depth of defects. It provides reliable data support for engineering construction optimization and defect reinforcement, effectively improves the level of precision in dredged soil filling engineering construction, reduces the risks and costs of later operation and maintenance of the project, and promotes the coordinated development of dredged soil resource utilization and ecological protection.
[0017] 2) This invention adopts the full waveform inversion method. Its initial model uses the full waveform inversion results in the complex frequency domain. In the inversion process, inequality constraints based on the parameter vector method are applied to the relative permittivity and conductivity, which improves the initial model problem and the non-uniqueness problem and improves the effect of full waveform inversion.
[0018] 3) This invention uses the full waveform inversion result as the initial model for reverse time migration imaging, and uses the wave interferometry virtual source algorithm to synthesize single aperture reflection data from cross-aperture detection data. This improves the problem of unsatisfactory imaging results caused by the lack of electromagnetic wave reflection signals in cross-aperture radar detection, and ultimately improves the effect of applying reverse time migration imaging to cross-aperture radar detection. Attached Figure Description
[0019] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.
[0020] Figure 1 This is a schematic diagram of a method for detecting the internal structure of a dredged soil deposit disclosed in Embodiment 1 of the present invention; Figure 2 This is a flowchart of the full waveform inversion disclosed in Embodiment 1 of the present invention; Figure 3 This is the complex frequency domain full waveform inversion process disclosed in Embodiment 1 of the present invention; Figure 4 This is a flowchart of the reverse time-shift imaging process disclosed in Embodiment 1 of the present invention. Detailed Implementation
[0021] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0022] As described in the background section, the purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for detecting the internal structure of dredged soil deposits. By combining cross-hole radar with an improved full-waveform inversion method and a reverse-time offset imaging method, high-precision, high-efficiency, and visual detection of internal defects in dredged soil deposits can be achieved.
[0023] Example 1 In a typical embodiment of the present invention, such as Figures 1 to 4 As shown in the figure, this embodiment discloses a method for detecting the internal structure of a dredged soil deposit, which specifically includes the following steps: S1. Based on the existing core holes on the dredged soil deposit, cross-hole radar is used to acquire cross-hole radar detection data; S2. Perform full waveform inversion processing on the cross-hole radar detection data to obtain the dielectric constant and conductivity distribution inside the dredged soil accumulation body; wherein, the full waveform inversion processing adopts an alternating iterative method to simultaneously invert the dielectric constant and conductivity, the initial model of the inversion adopts the full waveform inversion result in the complex frequency domain, and applies inequality constraints based on parameter vectors during the inversion process, and the iteration step size adopts a parabolic step size; S3. Perform reverse time migration imaging on the results of the full waveform inversion to obtain the contour boundary information of the dredged soil accumulation body; wherein, the reverse time migration imaging process synthesizes the cross-hole detection data into single-hole reflection data through the wave interferometry virtual source algorithm, and uses the dielectric constant distribution obtained from the full waveform inversion as the initial model.
[0024] The method for detecting the internal structure of the dredged soil deposit is described in detail below with reference to specific implementation methods.
[0025] S1. Based on the existing core holes on the dredged soil deposit, cross-hole radar is used to acquire cross-hole radar detection data.
[0026] In this step, existing core holes on the dredged soil deposit are used to place the cross-hole radar transmitting and receiving antennas in two boreholes for detection, thereby acquiring cross-hole radar detection data. Specifically, the cross-hole radar transmitting antenna is placed in the first borehole, and the cross-hole radar receiving antenna is placed in the second borehole. The antennas are moved along the borehole depth direction to collect cross-hole radar waveform data at different depth positions.
[0027] S2. Perform full waveform inversion processing on the cross-hole radar detection data to obtain the dielectric constant and conductivity distribution inside the dredged soil accumulation body.
[0028] In this step, the cross-hole radar detection data undergoes full waveform inversion processing to obtain the dielectric constant and conductivity distribution inside the dredged soil deposit. The full waveform inversion processing employs an alternating iterative method to simultaneously invert the dielectric constant and conductivity. The iteration step size is parabolic, and the initial model uses the full waveform inversion results in the complex frequency domain (Laplace domain). Inequality constraints based on parameter vectors are applied during the inversion process. Each inversion iteration requires six finite-difference time-domain (FDTD) calculations to obtain the dielectric constant and conductivity distribution inside the dredged soil deposit, facilitating the assessment of its internal structure.
[0029] In the full waveform inversion process in the complex frequency domain (Laplace domain), the FDTD forward modeling is performed in the time domain, while the residuals, gradients, and step sizes are calculated in the Laplace domain. After each forward modeling is completed, the electric field values in the time domain need to be converted to the frequency domain through discrete Laplace transform. During the FDTD calculation, the electric field values of each time step are superimposed, eliminating the need to save the field values of all time steps, thus reducing memory consumption. Moreover, it is not necessary to calculate the backpropagation residual; the Laplace domain reverse propagation residual can be used as the source for reverse propagation. After calculating the gradient and iteration step size, the model is updated until the objective function value is less than 1% of the initial objective function value or the specified number of inversion iterations is reached.
[0030] Specifically, the full waveform inversion result in the complex frequency domain (Laplace domain) is used as the initial model for the inversion, expressed as:
[0031] In the formula, It is the dielectric constant; is the electrical conductivity.
[0032] Using the initial dielectric constant and initial conductivity The first FDTD forward modeling simulation was performed to obtain the first forward propagation wavefield. The difference between the first forward propagation wavefield and the actual observed wavefield data was calculated to obtain the first forward propagation residual, which is specifically expressed as follows:
[0033] In the formula, The first main story residual; This is the first forward propagation wave field; This is actual wavefield observation data.
[0034] FDTD forward modeling refers to the process of modeling forward from any point in three-dimensional space. A single FDTD forward simulation can be expressed as:
[0035] In the formula, These are the components of the electric field in each direction; Let the elements of the Green's tensor be in each direction; The components of the source in each direction; subscript This indicates that the electric field value is the sum of the source's values in all directions.
[0036] With the first main pass residual The first inverse-time wave field was obtained by performing a second FDTD forward modeling simulation as the source. And according to the first forward propagation wave field and the first reverse time wave field Iterative gradient for calculating dielectric constant update And the iterative gradient updated based on the dielectric constant. The dielectric constant parameter of the updated inversion model is: ,in, The small stability factor represents the dielectric constant.
[0037] Based on the updated dielectric constant parameters With initial conductivity parameters The third FDTD forward simulation was performed, and the iteration step size for updating the dielectric constant was calculated using the parabolic fitting method. Specifically, calculating the iteration step size using the parabolic fitting method requires calculating three step sizes: the first step size is set to 0, the second step size is set to a small stability factor, and so on. The third step size is taken as the calculated linear iteration step size. After calculating the objective function values corresponding to the three step sizes, a parabolic function can be constructed. Then, interpolation is used to obtain the minimum objective function value of this parabolic function segment; the corresponding step size is the optimal step size. The iterative step size for updating the dielectric constant using the parabolic fitting method is specifically expressed as:
[0038] In the formula, The iteration step size for the dielectric constant in the Laplace domain; Source location; For receiving location; For observation time; Forward simulation data in the Laplace domain; This represents the gradient direction for updating model parameters in the Laplace domain. The data used are actual observations in the Laplace domain. A parabolic step size is adopted for the iteration, which can further improve the fitting accuracy of the full waveform inversion and improve the inversion effect.
[0039] To address the multiple solutions problem in cross-aperture radar inversion, the range of model parameters is used as prior information and applied to the inversion equations through inequality constraints. The inequality constraint on the dielectric constant is specifically expressed as:
[0040] In the formula, For the inversion calculation The dielectric constant of each grid cell; For the inversion calculation Lower limit of dielectric constant for each grid cell; For the inversion calculation The upper limit of the dielectric constant of each grid. The range of the dielectric constant can be a relatively broad range obtained from common sense physics, or a more precise range obtained from methods such as core drilling.
[0041] For the In the nth iteration The dielectric constant of each grid Its update formula is expressed as:
[0042] In the formula, For the inversion calculation The perturbation of the dielectric constant parameter vector of each grid.
[0043] By applying the inequality constraint on the dielectric constant to the inversion equation and updating it, the updated dielectric constant is obtained, specifically expressed as:
[0044] In the formula, This is the updated dielectric constant.
[0045] The conductivity parameter is iteratively updated based on the updated dielectric constant. With initial conductivity The fourth FDTD forward modeling simulation was performed to obtain the second forward propagation wavefield. The difference between the second forward propagation wavefield and the actual wavefield observation data was calculated to obtain the second forward propagation residual, which is specifically expressed as follows:
[0046] In the formula, The second main pass residual; This is the second forward propagation wave field; This is actual wavefield observation data.
[0047] With the second main pass residual The fifth FDTD forward modeling was performed as the source to obtain the second reverse-time wave field. And according to the second forward propagation wave field Second Reverse Time Wave Field Iterative gradient for calculating conductivity update And based on the iterative gradient updated by conductivity The conductivity parameters of the updated inversion model are as follows: ,in, The small stability factor representing electrical conductivity.
[0048] Based on the updated dielectric constant parameters With updated conductivity parameters The sixth FDTD forward simulation was performed, and the iteration step size for conductivity updates was calculated using the parabolic fitting method. Specifically, it is expressed as:
[0049] In the formula, The iteration step size for the dielectric constant in the Laplace domain; Let be the iteration step size for conductivity in the Laplace domain.
[0050] The inequality constraint on conductivity is specifically expressed as follows:
[0051] In the formula, For the inversion calculation The conductivity of each grid; For the inversion calculation Lower limit of conductivity for each grid cell; For the inversion calculation The upper limit of conductivity for each grid. The range of conductivity variation can be a relatively broad range obtained from common sense physics, or a more precise range obtained from methods such as core drilling.
[0052] For the In the nth iteration Conductivity of each grid Its update formula is expressed as:
[0053] In the formula, For the inversion calculation The perturbation of the conductivity parameter vector of each grid.
[0054] The conductivity inequality constraint is applied to the inversion equation and updated to obtain the updated conductivity, specifically expressed as:
[0055] In the formula, This is the updated conductivity.
[0056] This completes a simultaneous inversion of dielectric constant and conductivity, based on the updated dielectric constant. and updated conductivity Repeat the iterative update until the objective function value is less than 1% of the initial objective function value or the specified number of inversion iterations is reached.
[0057] S3. Perform reverse time migration imaging on the results of the full waveform inversion to obtain the contour boundary information of the dredged soil accumulation body.
[0058] For cross-aperture radar detection, pre-stack reverse-time migration imaging should be used, which can be divided into four steps: forward propagation, reverse-time propagation, applied imaging, and stacking migration. The forward propagation process calculates the wavefield of the electromagnetic wave emitted by the excitation source propagating forward along the time axis, and stores the wavefield information at each time step. When performing reverse-time migration imaging on forward-modeled composite data, the excitation source is known (usually using the Ricker wavelet); when performing reverse-time migration imaging on actual detection data, the excitation source is unknown, requiring estimation of the source wavelet.
[0059] The reverse-time propagation process calculates the wavefield propagating backward along the time axis from the observed data as the source, and stores the wavefield information at each time step. Since there is a source at each receiving location, a multi-point source is used in reverse-time propagation.
[0060] By reading the forward and reverse wave fields stored at the same time during the imaging process, and performing imaging calculations based on the imaging conditions, the migration results of the corresponding excitation source can be obtained.
[0061] Offset stacking combines the offset results from different excitation source locations to obtain a complete reverse time migration imaging result.
[0062] In reverse time migration (RTM) imaging algorithms, the most crucial factor affecting imaging performance is the initial model. Experience shows that if the initial model deviates significantly from the actual situation, the RTM imaging results are inferior to those using a uniform initial model. While the initial model can typically be obtained using ray casting, this embodiment employs a full-waveform inversion method to retrieve the relative permittivity of the subsurface medium. This method provides more reliable parameter information than conventional ray casting, resulting in better RTM imaging performance.
[0063] Based on this, this step performs reverse time migration imaging on the results of the full waveform inversion processing. First, the cross-hole detection data is synthesized into single-hole reflection data using the wave interferometry virtual source algorithm. Then, the full waveform inversion is performed on the cross-hole detection data, and the obtained dielectric constant distribution is used as the initial model to calculate the wave field of the electromagnetic wave propagating forward from the excitation source. Subsequently, the wave field propagating backward from the synthesized single-hole reflection data is calculated. Next, imaging conditions are applied to the forward and backward wave fields at the same time to perform imaging, and the migration results from different source locations are superimposed to obtain the complete reverse time migration imaging result, thereby obtaining the contour boundary information of the dredged soil accumulation body.
[0064] In order to obtain borehole 2 Point as virtual source, The reflected response received by the point can be obtained from the source point in borehole 1. and two receiving points in borehole 2 , The propagation response between them is obtained as follows:
[0065] In the formula, For the transmission field.
[0066] The above formula shows that in the same borehole , Virtual reflected wave field between two points It can be propagated by two fields. and Perform cross-correlation operations and sum the results across all source points, recording the time. The longer, , The more information that can be obtained from the space between them.
[0067] As shown above, by performing wave interferometric cross-correlation on any two radar records, a virtual empirical Green's function between the two receiving positions can be synthesized. This allows the cross-aperture detection data to be synthesized into single-aperture reflection data with a fixed transmit / receive distance or a variable transmit / receive distance on the receiving side. When synthesizing cross-aperture detection data into variable transmit / receive distance single-aperture reflection data, one receiving position is used as a virtual source to transmit electromagnetic waves, while the other receiving positions receive the reflected electromagnetic wave signals.
[0068] Example 2 In a typical embodiment of the present invention, this embodiment discloses a system for detecting the internal structure of dredged soil deposits, comprising: The data acquisition module is configured to acquire cross-hole radar detection data based on existing core holes on the dredged soil deposit. The full waveform inversion module is configured to perform full waveform inversion processing on the cross-hole radar detection data to obtain the dielectric constant and conductivity distribution inside the dredged soil accumulation body. The full waveform inversion processing adopts an alternating iterative method to simultaneously invert the dielectric constant and conductivity. The initial model of the inversion adopts the full waveform inversion result in the complex frequency domain. Inequality constraints based on parameter vectors are applied during the inversion process, and the iteration step size adopts a parabolic step size. The reverse time migration imaging module is configured to perform reverse time migration imaging processing on the results of the full waveform inversion processing to obtain the contour boundary information of the dredged soil accumulation body; wherein, the reverse time migration imaging processing synthesizes single-hole reflection data from the cross-hole detection data through the wave interferometry virtual source algorithm, and uses the dielectric constant distribution obtained from the full waveform inversion processing as the initial model.
[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for detecting the internal structure of a dredged soil deposit, characterized in that, include: Based on the existing core holes on the dredged soil deposit, cross-hole radar was used to acquire cross-hole radar detection data. The full waveform inversion processing of the cross-hole radar detection data is used to obtain the dielectric constant and conductivity distribution inside the dredged soil accumulation. The full waveform inversion processing adopts an alternating iterative method to simultaneously invert the dielectric constant and conductivity. The initial model of the inversion uses the full waveform inversion results in the complex frequency domain. Inequality constraints based on parameter vectors are applied during the inversion process, and the iteration step size adopts a parabolic step size. The results of the full waveform inversion processing are subjected to reverse time migration imaging to obtain the contour boundary information of the dredged soil accumulation body. Among them, the reverse time migration imaging processing synthesizes the cross-hole detection data into single-hole reflection data through the wave interferometry virtual source algorithm, and uses the dielectric constant distribution obtained from the full waveform inversion processing as the initial model.
2. The method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, Using the existing core holes on the dredged soil deposit, the cross-hole radar transmitting and receiving antennas were placed in two boreholes for detection, and cross-hole radar detection data was obtained.
3. The method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, In the complex frequency domain full waveform inversion process, the forward modeling of the finite-difference time-domain method is performed in the time domain, while the residual, gradient, and step size are calculated in the complex frequency domain. During the finite-difference time-domain calculation, the electric field values of each time step are superimposed, so there is no need to save the field values of all time steps.
4. The method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, The iterative update of model parameters in the complex frequency domain is as follows: the updated model parameters are obtained by subtracting the product of the iteration step size and the iteration gradient from the current model parameters; the model parameters include dielectric constant and conductivity.
5. The method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, Each inversion iteration of the full waveform inversion process requires 6 calculations using the finite-difference time-domain method.
6. The method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, Specifically, the inequality constraints based on parameter vectors are as follows: the range of variation of model parameters is used as prior information and applied to the inversion equation through inequality constraints; where the model parameters include dielectric constant and conductivity.
7. The method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, The parabolic step size is specifically calculated as follows: the iteration step size is calculated by parabolic fitting method, the objective function value corresponding to the three step sizes is calculated, the parabolic function is constructed, and the minimum objective function value of the parabolic function segment is obtained by interpolation. The corresponding step size is the optimal step size.
8. The method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, The reverse time migration imaging process specifically includes: The cross-aperture detection data is synthesized into single-aperture reflection data using the wave interferometry virtual source algorithm; Full waveform inversion was performed on the cross-hole detection data, and the obtained dielectric constant distribution was used as the initial model to calculate the wave field of the electromagnetic wave propagating in the forward direction emitted by the excitation source. Calculate the wavefield propagating in reverse time from synthetic single-aperture reflection data as the source; Imaging conditions are applied to the forward propagation wavefield and the reverse-time wavefield at the same moment, and the migration results from different source locations are superimposed to obtain a complete reverse-time migration imaging result.
9. A method for detecting the internal structure of a dredged soil deposit as described in claim 1, characterized in that, The cross-aperture detection data is synthesized into single-aperture reflection data by using the wave interferometry virtual source algorithm. Specifically, by performing wave interferometry cross-correlation operation on any two radar records, a virtual empirical Green's function between the two receiving positions is synthesized, thereby synthesizing the cross-aperture detection data into single-aperture reflection data with fixed transmit / receive distance or variable transmit / receive distance on the receiving side.
10. A system for detecting the internal structure of a dredged soil deposit, characterized in that, include: The data acquisition module is configured to acquire cross-hole radar detection data based on existing core holes on the dredged soil deposit. The full waveform inversion module is configured to perform full waveform inversion processing on the cross-hole radar detection data to obtain the dielectric constant and conductivity distribution inside the dredged soil accumulation body. The full waveform inversion processing adopts an alternating iterative method to simultaneously invert the dielectric constant and conductivity. The initial model of the inversion adopts the full waveform inversion result in the complex frequency domain. Inequality constraints based on parameter vectors are applied during the inversion process, and the iteration step size adopts a parabolic step size. The reverse time migration imaging module is configured to perform reverse time migration imaging processing on the results of the full waveform inversion processing to obtain the contour boundary information of the dredged soil accumulation body; wherein, the reverse time migration imaging processing synthesizes single-hole reflection data from the cross-hole detection data through the wave interferometry virtual source algorithm, and uses the dielectric constant distribution obtained from the full waveform inversion processing as the initial model.