Frequency response signal analysis and target medium spectrum energy value calculation method
By using technical means such as Gaussian filtering and three-dimensional elastic wave equations in ground detection radar, the problem of analysis of reflected wave signals in complex geological environments is solved, and geological feature recognition and analysis with higher accuracy is achieved, reducing the uncertainty of understanding the translation results.
Patent Information
- Application Number
- CN202411899553.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-23
- Publication Date
- 2025-04-15
AI Technical Summary
The prior art is difficult to effectively analyze reflected wave signals in complex geological environments, resulting in multiple solutions and uncertainties in the interpretation of geological characteristics, and traditional time-domain analysis is difficult to capture the nonlinear characteristics and complex changes of the formation.
Through ground detection radar transmits short-pulse electromagnetic waves and receives reflected signals, Gaussian filtering optimizes the signal, defines the spatial particles and their physical characteristics of the target medium, establishes a three-dimensional elastic wave equation and spring model, and uses finite difference method and frequency response characteristic analysis to calculate the spectral energy value and frequency response characteristics of the target medium, generates a feature matrix for Fourier transformation, identify the main frequency components and visualize it.
It improves the quality and analysis accuracy of reflected signals, reduces noise interference, can more accurately identify and analyze geological characteristics, reduces uncertainty in understanding translation results, and enhances the understanding of complex geological environments.
Smart Images
Figure CN119828242A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of geological exploration, and in particular to a method for analyzing frequency response signals and calculating a target medium spectral energy value. Background Art
[0002] In the field of geological exploration, complex geological environments pose a major challenge to the analysis of geophysical reflection wave signals. The stratigraphic structure in nature is usually diverse and changeable, involving different types of rocks, soils and water bodies, which together affect the propagation and reflection characteristics of the reflection wave signal. The physical properties of different strata have a significant impact on the propagation speed and reflection intensity of the reflection wave. The interface between soft rock layers and hard rock layers often produces strong reflections, and the contrast between water-saturated layers and dry layers can also be identified by reflection waves. However, when the differences in these physical media are complex and intertwined, the characteristics of the reflection wave signal are more blurred, which brings difficulties to signal analysis. In areas with complex hydrogeological conditions, the distribution and flow of groundwater will directly affect the stress state and elastic wave velocity of the rock mass, and then affect the reflection wave signal. Faults are important geological structures, and their existence will cause signal attenuation and distortion, resulting in errors or omissions in the interpretation process. The complex fault structure makes the propagation path of the reflection wave diverse, increasing the uncertainty of the interpretation.
[0003] The existing technology has the following deficiencies: the existing three-wave phase analysis based on time domain often relies on simplified assumptions and models when facing complex geological environments, and cannot fully consider the diversity and complexity of geological bodies. Due to the significant differences in the physical properties of strata in practical applications, these differences cannot be effectively distinguished in the reflection wave signal. This leads to multiple solutions for the interpretation of geological body characteristics, that is, the same signal may correspond to multiple different geological models, increasing the uncertainty of the interpretation. In a complex geological environment, the propagation of reflection waves is affected by many factors, including the thickness and shape of the strata and the interaction between the layers. When processing signals, the nonlinear characteristics of the signals and the limitations of time domain analysis are often ignored. It is difficult for the three-wave phase analysis based on time domain to effectively capture these changes, which in turn affects the accurate identification of geological bodies. With the development of geological exploration technology, the rapid increase in the amount of data has made the traditional manual analysis and experience judgment methods increasingly insufficient and unable to meet the needs of efficiency and accuracy. The deviation of the interpretation results not only affects the evaluation of resources, but also may lead to major safety hazards and economic losses in engineering construction.
[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention
[0005] The purpose of the present invention is to provide a method for analyzing frequency response signals and calculating the spectral energy value of a target medium, so as to solve the problems in the above-mentioned background technology.
[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for frequency response signal analysis and target medium spectrum energy value calculation, which specifically includes the following steps:
[0007] Step 1: Use the ground detection radar to emit short pulse electromagnetic waves and receive the electromagnetic wave reflection signal, define the threshold and processing strategy, and use Gaussian filtering to optimize the electromagnetic wave reflection signal;
[0008] Step 2: Define the spatial particles and their physical properties of the target medium, establish a three-dimensional elastic wave equation and spring model, and use the finite difference method and frequency response characteristic analysis to calculate the spectral energy value of the target medium and extract the quantitative wave behavior and frequency response characteristics;
[0009] Step 3: Generate a characteristic matrix by selecting a Hamming window function and perform Fourier transform, identify the main frequency components of the target medium, perform coordinate transformation and three-dimensional coordinate plotting, calculate the frequency response function and its amplitude and phase spectra, and analyze the dynamic response characteristics of the target medium at different frequencies;
[0010] Step 4: By combining the coordinates of the spatial particles with their corresponding frequency response spectrum energy values, a point cloud data set is formed and reconstructed into a three-dimensional view, which is visualized using color gradient and texture mapping to provide interactive operations to dynamically display the spatial position and scale of the target medium.
[0011] Preferably, short pulse electromagnetic waves are emitted by a ground detection radar and electromagnetic wave reflection signals are received, and a lattice radar array is set according to electromagnetic wave propagation characteristics to match the working frequency range of the ground detection radar to twice the target medium response frequency.
[0012] Preferably, the missing values of the electromagnetic wave reflection signal and the reasonable upper and lower limit thresholds of the ground detection radar are detected. For the missing values and the electromagnetic wave reflection signals exceeding the reasonable upper and lower limit thresholds, the corresponding records are selected to be deleted, and the corresponding filling is performed using the average value of the electromagnetic wave reflection signal. The outliers of the electromagnetic wave reflection signal are detected, and the degree of deviation of the electromagnetic wave reflection signal from its own mean is detected according to the standard deviation of the electromagnetic wave reflection signal. The electromagnetic wave reflection signal that exceeds its own mean plus or minus two times the standard deviation is regarded as an abnormal value. The duplicate records of the electromagnetic wave reflection signal are detected, and the duplicate records are selected to be deleted.
[0013] Preferably, the standard deviation of the electromagnetic wave reflection signal is selected as the Gaussian filter parameter, and the Gaussian filter kernel is constructed by using the weight through the Gaussian function. The specific formula is:
[0014]
[0015] Among them, G(x,y) represents the value of the two-dimensional Gaussian kernel, σ represents the Gaussian filter parameter, x and y represent the distance between the Gaussian filter kernel and the filter center in the horizontal direction and the vertical direction respectively, and the filter window is generated according to the Gaussian filter parameter for convolution. The specific formula is:
[0016]
[0017] Among them, f filtered [n] represents the filtered electromagnetic wave reflection signal at a distance n from the filter center, f[nm] represents the electromagnetic wave reflection signal at a distance nm from the filter center, G[m] represents the weight at a distance m from the filter center, and k represents half of the filter window.
[0018] Preferably, the spatial particles of the target medium are defined, the mass of each particle in the medium is determined according to the geophysical properties, the elastic modulus of the spatial particle structure is selected by the uniformity of the electromagnetic wave reflection signal, the density of the target medium is adaptively defined according to the electromagnetic wave reflection signal, and a three-dimensional elastic wave equation is established, the specific formula of which is:
[0019]
[0020] Among them, β represents the density of the target medium, u represents the displacement of the particle in space, t represents the time of the displacement of the particle in space, and T represents the internal stress of the target medium. The coupling relationship of the particles is defined according to the interaction between the particles. The spring model is established by applying Newton's second law, the initial displacement and velocity field are defined, and the boundary conditions are set. The spring model is discretized by the finite difference method, and the geometric characteristics of the target medium are defined. The wave behavior of the target medium is described by the spring model, the spectral energy value of the target medium in the spatial particle structure is calculated, and the frequency response characteristics of the target medium are extracted. The specific formula is:
[0021]
[0022] Among them, A n represents the spectral energy value of the target medium, ω1 and ω2 represent the resonance frequency response bandwidth of the target medium, and f n (ω) represents the frequency response spectrum of the target medium, F(ω) represents the global spectrum of the reflected wave signal, and ω represents the angular frequency. According to the spectral energy value of the target medium, the spectral energy proportion intensity value of different target media on the spectrum diagram is accurately calculated to deconstruct the frequency response characteristics of different target media.
[0023] Preferably, a Hamming window function is selected to determine the window length and overlap ratio as 50%, and Fourier transform is performed on each Hamming window to generate a characteristic matrix represented by time-frequency response, significant peaks are identified through short-time spectrum, the main frequency components of the target medium are determined, and the corresponding time positions are recorded, coordinate transformation is performed according to the geometric characteristics of the target medium and the electromagnetic wave reflection signal, and a three-dimensional coordinate diagram of the particle is drawn and marked using the center moment and frequency response of the Hamming window, and an excitation signal corresponding to the electromagnetic wave reflection signal is added to the spatial particle of the target medium, and a frequency response function is calculated according to the excitation in the frequency response domain and the response signal corresponding to the electromagnetic wave reflection signal, and the resonant frequency of the target medium is identified according to the amplitude spectrum of the frequency response function, and the phase delay characteristics of the target medium under different frequency responses are analyzed by observing the phase response of the frequency response function, and the amplitude and phase curves of the frequency response function are drawn to observe the dynamic response characteristics of the target medium under different frequencies.
[0024] Preferably, the coordinates of each spatial particle are combined with its corresponding frequency response spectrum energy value to form a point cloud data set containing three-dimensional coordinates and corresponding energy values and reconstructed to extract the three-dimensional morphological features of the target medium, and generate three-dimensional views from different perspectives to display the geological structure and physical properties. The color gradient and texture mapping are used to enhance the visual sense of the geological structure and physical properties, and interactive visualization is provided. Users can dynamically interact with the three-dimensional view by rotating, zooming and translating, and determine the accurate spatial position and scale of the target medium.
[0025] In the above technical solution, the technical effects and advantages provided by the present invention are:
[0026] The application of Gaussian filtering can reduce the noise in the electromagnetic wave reflection signal and improve the quality of the electromagnetic wave reflection signal. Effective processing of missing values and outliers can ensure the integrity and consistency of the electromagnetic wave reflection signal and avoid the influence of erroneous or inaccurate electromagnetic wave reflection signals on subsequent analysis results. The standard deviation method is used to identify outliers, which can timely discover and process abnormal electromagnetic wave reflection signals, improve the credibility of electromagnetic wave reflection signal analysis, clear duplicate records, avoid interference from redundant information, and make signal analysis more concise and clear.
[0027] By defining the spatial particles of the target medium and their physical properties to establish a three-dimensional elastic wave equation, it is helpful to more accurately reflect the wave behavior of the actual medium. By using the uniformity of the electromagnetic wave reflection signal and adaptively defining the density of the target medium, it is possible to more effectively reflect the real physical properties of the medium. By calculating the spectral energy value of the target medium, the wave behavior and frequency response characteristics can be quantified, making the analysis of the medium more objective and quantitative. Defining the coupling relationship between the particles and applying Newton's second law to establish a spring model can help to deeply understand the interaction within the medium. By calculating the spectral energy ratio intensity value on the spectrum diagram of different target media, the frequency response characteristics of different media can be effectively deconstructed, thereby more effectively identifying and analyzing the characteristics of the geological structure, setting the initial displacement and velocity field and boundary conditions, and simulating the wave behavior of the target medium under actual conditions.
[0028] By selecting the Hamming window function and performing Fourier transform, the main frequency components of the target medium can be effectively extracted. The generated characteristic matrix provides the relationship between time and frequency, which helps to identify significant peaks and dynamic changes. Through coordinate transformation and three-dimensional coordinate plotting, the spatial distribution of the target medium and its dynamic characteristics can be intuitively displayed, enhancing comprehensibility and visualization. The frequency response function and its amplitude and phase spectra are calculated, which enables a deeper understanding of the dynamic response characteristics of the target medium and can reveal its behavior patterns at different frequencies. The resonant frequency of the target medium can be accurately identified by observing the amplitude spectrum of the frequency response function. The phase response of the frequency response function is analyzed to reveal the phase delay characteristics of the target medium, helping to understand the relationship between its dynamic behavior and external excitation. The dynamic response characteristics of the target medium at different frequencies are comprehensively evaluated in combination with the plotting of amplitude and phase curves.
[0029] Combining the coordinates of the spatial particles with the corresponding frequency response spectrum energy values to form a point cloud data set can intuitively display the spatial distribution of the medium and its energy characteristics. By reconstructing the point cloud data set, the three-dimensional morphological characteristics of the target medium can be effectively extracted, which helps to understand its geometric structure and physical properties. The use of color gradients and texture mapping to highlight the geological structure and physical properties makes the visual effect more vivid. The ability to provide users with interactive operations facilitates the observation of three-dimensional views from different angles and a deeper understanding of the characteristics of the target medium. Through dynamic display, users can more accurately determine the spatial position and scale of the target medium. Three-dimensional displays from different perspectives can reveal potential geological characteristics and heterogeneity, which is conducive to more comprehensive geological analysis and decision support. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.
[0031] Figure 1 The present invention is a method flow chart of the method for frequency response signal analysis and target medium spectral energy value calculation. DETAILED DESCRIPTION
[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.
[0033] The present invention provides Figure 1 The frequency response signal analysis and target medium spectrum energy value calculation method shown in the figure specifically includes the following steps:
[0034] Step 1: Use the ground detection radar to emit short pulse electromagnetic waves and receive the electromagnetic wave reflection signal, define the threshold and processing strategy, and use Gaussian filtering to optimize the electromagnetic wave reflection signal;
[0035] The ground detection radar emits short pulse electromagnetic waves and receives the reflected electromagnetic wave signals, and sets a grid radar array according to the propagation characteristics of the electromagnetic waves to match the working frequency range of the ground detection radar to twice the response frequency of the target medium.
[0036] Detect missing values of electromagnetic wave reflection signals and reasonable upper and lower limit thresholds of ground detection radar. For missing values and electromagnetic wave reflection signals exceeding reasonable upper and lower limit thresholds, choose to delete the corresponding records and use the average value of the electromagnetic wave reflection signal to fill in the corresponding values. Detect outliers of electromagnetic wave reflection signals. Detect the degree of deviation of electromagnetic wave reflection signals from their own mean according to the standard deviation of the electromagnetic wave reflection signals. The electromagnetic wave reflection signals that exceed their own mean plus or minus two times the standard deviation are regarded as outliers. Detect duplicate records of electromagnetic wave reflection signals and choose to delete duplicate records.
[0037] The standard deviation of the electromagnetic wave reflection signal is selected as the Gaussian filter parameter, and the Gaussian filter kernel is constructed by using the weight through the Gaussian function. The specific formula is:
[0038]
[0039] Among them, G(x,y) represents the value of the two-dimensional Gaussian kernel, σ represents the Gaussian filter parameter, x and y represent the distance between the Gaussian filter kernel and the filter center in the horizontal direction and the vertical direction respectively, and the filter window is generated according to the Gaussian filter parameter for convolution. The specific formula is:
[0040]
[0041] Among them, ffiltered [n] represents the filtered electromagnetic wave reflection signal at a distance n from the filter center, f[nm] represents the electromagnetic wave reflection signal at a distance nm from the filter center, G[m] represents the weight at a distance m from the filter center, and k represents half of the filter window.
[0042] Step 2: Define the spatial particles and their physical properties of the target medium, establish a three-dimensional elastic wave equation and spring model, and use the finite difference method and frequency response characteristic analysis to calculate the spectral energy value of the target medium and extract the quantitative wave behavior and frequency response characteristics;
[0043] Define the spatial particles of the target medium, determine the mass of each particle in the medium according to the geophysical properties, select the elastic modulus of the spatial particle structure by the uniformity of the electromagnetic wave reflection signal, adaptively define the density of the target medium according to the electromagnetic wave reflection signal, and establish a three-dimensional elastic wave equation. The specific formula is:
[0044]
[0045] Among them, β represents the density of the target medium, u represents the displacement of the particle in space, t represents the time of the displacement of the particle in space, and T represents the internal stress of the target medium. The coupling relationship of the particles is defined according to the interaction between the particles. The spring model is established by applying Newton's second law, the initial displacement and velocity field are defined, and the boundary conditions are set. The spring model is discretized by the finite difference method, and the geometric characteristics of the target medium are defined. The wave behavior of the target medium is described by the spring model, the spectral energy value of the target medium in the spatial particle structure is calculated, and the frequency response characteristics of the target medium are extracted. The specific formula is:
[0046]
[0047] Among them, A n represents the spectral energy value of the target medium, ω1 and ω2 represent the resonance frequency response bandwidth of the target medium, and f n (ω) represents the frequency response spectrum of the target medium, F(ω) represents the global spectrum of the reflected wave signal, and ω represents the angular frequency. According to the spectral energy value of the target medium, the spectral energy proportion intensity value of different target media on the spectrum diagram is accurately calculated to deconstruct the frequency response characteristics of different target media.
[0048] Step 3: Generate a characteristic matrix by selecting a Hamming window function and perform Fourier transform, identify the main frequency components of the target medium, perform coordinate transformation and three-dimensional coordinate plotting, calculate the frequency response function and its amplitude and phase spectra, and analyze the dynamic response characteristics of the target medium at different frequencies;
[0049] The Hamming window function is selected to determine the window length and overlap ratio of 50%, and Fourier transform is performed on each Hamming window to generate a characteristic matrix represented by time-frequency response. Significant peaks are identified through short-time spectrum, the main frequency components of the target medium are determined, and their corresponding time positions are recorded. Coordinate transformation is performed according to the geometric characteristics of the target medium and the electromagnetic wave reflection signal. The three-dimensional coordinate diagram of the particle is drawn and marked using the center moment and frequency response of the Hamming window. The excitation signal corresponding to the electromagnetic wave reflection signal is added to the spatial particle of the target medium. The frequency response function is calculated according to the excitation in the frequency response domain and the response signal corresponding to the electromagnetic wave reflection signal. The resonant frequency of the target medium is identified according to the amplitude spectrum of the frequency response function. The phase delay characteristics of the target medium under different frequency responses are analyzed by observing the phase response of the frequency response function. The amplitude and phase curves of the frequency response function are drawn to observe the dynamic response characteristics of the target medium under different frequencies.
[0050] Step 4: By combining the coordinates of the spatial particles with their corresponding frequency response spectrum energy values, a point cloud data set is formed and reconstructed into a three-dimensional view, which is visualized using color gradient and texture mapping to provide interactive operations to dynamically display the spatial position and scale of the target medium;
[0051] The coordinates of each spatial particle are combined with its corresponding frequency response spectrum energy value to form a point cloud data set containing three-dimensional coordinates and corresponding energy values and reconstruct them. The three-dimensional morphological characteristics of the target medium are extracted, and three-dimensional views are generated from different perspectives to display the geological structure and physical properties. Color gradients and texture mapping are used to enhance the visual sense of geological structure and physical properties, and interactive visualization is provided. Users can dynamically interact with the three-dimensional view by rotating, zooming and translating, and determine the accurate spatial position and scale of the target medium.
[0052] Embodiment 1:
[0053] The present invention transmits short pulse electromagnetic waves and receives electromagnetic wave reflection signals through ground detection radar, defines thresholds and processing strategies, and uses Gaussian filtering to optimize electromagnetic wave reflection signals to effectively improve the quality of electromagnetic wave reflection signals collected by ground detection radar. The processing of missing values and outliers ensures the validity of electromagnetic wave reflection signals, while the application of Gaussian filtering further smoothes the signals, reduces noise interference, and greatly improves the analysis accuracy of electromagnetic wave reflection signals in complex geological environments, which helps to more accurately identify and analyze geological features.
[0054] The present invention defines the spatial particles and their physical properties of the target medium, establishes a three-dimensional elastic wave equation and a spring model, and uses the finite difference method and frequency response characteristic analysis to calculate the spectral energy value of the target medium and extract the quantitative wave behavior and frequency response characteristics. It can systematically analyze and obtain the spectral energy value and frequency response characteristics of the target medium, providing a scientific basis for a deep understanding of the dynamic behavior of the medium under the action of electromagnetic waves. It has obvious advantages in practical applications and can provide important technical support and decision-making basis for geological exploration, resource development and engineering construction. Through spectrum analysis, it can better adapt to complex geological environmental changes and effectively improve the efficiency of obtaining and processing geological information.
[0055] The present invention generates a characteristic matrix by selecting a Hamming window function and performing Fourier transform, identifies the main frequency components of the target medium, performs coordinate transformation and three-dimensional coordinate diagram drawing, calculates the frequency response function and its amplitude and phase spectrum, and analyzes the dynamic response characteristics of the target medium at different frequencies. Its dynamic response characteristics are analyzed. Through coordinate transformation and three-dimensional coordinate diagram drawing, the distribution of particles in space and their frequency characteristics are intuitively displayed. At the same time, the frequency response function and its amplitude and phase spectrum are calculated, and the resonant frequency and phase delay characteristics of the target medium are deeply analyzed. This method not only improves the understanding of the dynamic response of the target medium, but also provides important theoretical basis and data support for subsequent geological exploration and engineering applications. Through frequency response analysis, it can better adapt to complex geological environment changes.
[0056] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.
Claims
1. Frequency response signal analysis and target medium spectrum energy value calculation method, characterized in that: The following steps are involved: Step 1: Use the ground detection radar to emit short pulse electromagnetic waves and receive the electromagnetic wave reflection signal, define the threshold and processing strategy, and use Gaussian filtering to optimize the electromagnetic wave reflection signal; Step 2: Define the spatial particles and their physical properties of the target medium, establish a three-dimensional elastic wave equation and spring model, and use the finite difference method and frequency response characteristic analysis to calculate the spectral energy value of the target medium and extract the quantitative wave behavior and frequency response characteristics; Step 3: Generate a characteristic matrix by selecting a Hamming window function and perform Fourier transform, identify the main frequency components of the target medium, perform coordinate transformation and three-dimensional coordinate plotting, calculate the frequency response function and its amplitude and phase spectra, and analyze the dynamic response characteristics of the target medium at different frequencies; Step 4: By combining the coordinates of the spatial particles with their corresponding frequency response spectrum energy values, a point cloud data set is formed and reconstructed into a three-dimensional view, which is visualized using color gradient and texture mapping to provide interactive operations to dynamically display the spatial position and scale of the target medium.
2. The method for frequency response signal analysis and target medium spectrum energy value calculation according to claim 1, characterized in that: In the step one, a short pulse electromagnetic wave is emitted by a ground detection radar and an electromagnetic wave reflection signal is received, and a grid radar array is set according to the electromagnetic wave propagation characteristics, the working frequency range of the ground detection radar is matched to be twice the target medium response frequency, the missing value of the electromagnetic wave reflection signal and the reasonable upper and lower limit thresholds of the ground detection radar are detected, the outlier abnormal value of the electromagnetic wave reflection signal is detected, the repeated record of the electromagnetic wave reflection signal is detected, the standard deviation of the electromagnetic wave reflection signal is selected as a Gaussian filter parameter, the Gaussian filter kernel is constructed by using the weight through the Gaussian function, and the filter window is generated according to the Gaussian filter parameter for convolution.
3. The method for frequency response signal analysis and target medium spectrum energy value calculation according to claim 2, characterized in that: The specific formula for constructing the Gaussian filter kernel is: Among them, G(x,y) represents the value of the two-dimensional Gaussian kernel, σ represents the Gaussian filter parameter, x and y represent the distance between the Gaussian filter kernel and the filter center in the horizontal direction and the vertical direction respectively, and the specific formula for generating the filter window for convolution is: Among them, f filtered [n] represents the filtered electromagnetic wave reflection signal at a distance n from the filter center, f[nm] represents the electromagnetic wave reflection signal at a distance nm from the filter center, G[m] represents the weight at a distance m from the filter center, and k represents half of the filter window.
4. The method for frequency response signal analysis and target medium spectrum energy value calculation according to claim 1, characterized in that: In the step 2, the spatial particles of the target medium are defined, the mass of each particle in the medium is determined according to the geophysical properties, the elastic modulus of the spatial particle structure is selected according to the uniformity of the electromagnetic wave reflection signal, the density of the target medium is adaptively defined according to the electromagnetic wave reflection signal, a three-dimensional elastic wave equation is established, the coupling relationship of the particles is defined according to the interaction between the particles, a spring model is established by applying Newton's second law, the initial displacement and velocity field are defined and the boundary conditions are set, the spring model is discretized by the finite difference method, the geometric characteristics of the target medium are defined, the wave behavior of the target medium is described by the spring model, the spectral energy value of the target medium in the spatial particle structure is calculated and the frequency response characteristics of the target medium are extracted, the spectral energy ratio intensity values of different target media on the spectrum diagram are accurately calculated according to the spectral energy value of the target medium, and the frequency response characteristics of different target media are deconstructed.
5. The method for frequency response signal analysis and target medium spectrum energy value calculation according to claim 4, characterized in that: The specific formula of the three-dimensional elastic wave equation is: Among them, β represents the density of the target medium, u represents the displacement of the particle in space, t represents the time of displacement of the particle in space, and T represents the internal stress of the target medium.
6. The method for frequency response signal analysis and target medium spectrum energy value calculation according to claim 4, characterized in that: The specific formula for calculating the target medium spectral energy value in the spatial particle structure is: Among them, A n represents the spectral energy value of the target medium, ω1 and ω2 represent the resonance frequency response bandwidth of the target medium, and f n (ω) represents the frequency response spectrum of the target medium, F(ω) represents the full-domain spectrum of the reflected wave signal, and ω represents the angular frequency.
7. The method for frequency response signal analysis and target medium spectrum energy value calculation according to claim 1, characterized in that: In the step three, a Fourier transform is performed on each Hamming window to generate a characteristic matrix represented by a time-frequency response, significant peaks are identified through short-time spectrum, the main frequency components of the target medium are determined, and the corresponding time positions are recorded, coordinate transformation is performed according to the geometric characteristics of the target medium and the electromagnetic wave reflection signal, a three-dimensional coordinate diagram of the particle is drawn and marked using the center moment and frequency response of the Hamming window, an excitation signal corresponding to the electromagnetic wave reflection signal is added to the spatial particle of the target medium, a frequency response function is calculated according to the excitation in the frequency response domain and the response signal corresponding to the electromagnetic wave reflection signal, the resonant frequency of the target medium is identified according to the amplitude spectrum of the frequency response function, the phase delay characteristics of the target medium under different frequency responses are analyzed by observing the phase response of the frequency response function, and the amplitude and phase curves of the frequency response function are drawn to observe the dynamic response characteristics of the target medium under different frequencies.
8. The method for frequency response signal analysis and target medium spectrum energy value calculation according to claim 1, characterized in that: In the step 4, the coordinates of each spatial particle are combined with the corresponding frequency response spectrum energy value to form a point cloud data set containing three-dimensional coordinates and corresponding energy values and reconstructed to extract the three-dimensional morphological features of the target medium, and generate three-dimensional views from different perspectives to display the geological structure and physical properties. The color gradient and texture mapping are used to enhance the visual sense of the geological structure and physical properties, and interactive visualization is provided. The user can dynamically interact with the three-dimensional view by rotating, zooming and translating, and determine the accurate spatial position and scale of the target medium.