A gridded seismic image road detection method and system

Abstract

This invention discloses a gridded seismic image road detection method and system, comprising the following steps: S1: acquiring sensor detection data; S2: converting t window The detected data within a time interval is segmented into individual .sgy files to obtain segmented data. This process is repeated across multiple time intervals to obtain multiple segments. S3: Each segment is convolved using a wavelet basis, and the modulus of the convolution result is taken to obtain the time-frequency energy map P of the seismic waves acquired by each sensor. S4: For each time sampling point t, the energy of low-frequency, mid-frequency, and high-frequency waves is statistically analyzed to generate energy features S. S5: The energy features S acquired by each sensor at each time point are combined into a two-dimensional feature map S(x,t), and displayed as a two-dimensional color image to obtain the seismic image result. This detection method and system form a three-dimensional detection image, improving the effective detection range and data resolution of the equipment.

Description

Technical Field

[0001] This invention relates to the field of seismic image detection technology, and in particular to a gridded seismic image road detection method and system. Background Technology

[0002] The main characteristics of underground cavities include their high degree of concealment, numerous causes, and difficulty in early prediction. Large-scale detection of underground cavities and defects can not only reveal the structural layers of buildings but also prevent ground subsidence, providing strong protection for the sustainable development of cities and the safety of people's lives and property.

[0003] In road quality inspection, road defects such as cracks, cavities, and leaks may be buried deep underground. Therefore, controlled seismic sources are generally used to generate artificial vibration signals and receive the seismic echoes from the ground surface. By analyzing the echo signals, the medium below the ground surface can be detected and described. This method is called the seismic method.

[0004] Traditional single-point seismic imaging schemes are commonly used. These schemes offer advantages such as low cost, simple principle, and strong anti-interference capabilities. The principle is as follows: The seismic source and sensor are arranged at optimal offsets along the road's extension direction. After the seismic source is excited, echo signals are collected, and the entire observation system is moved forward, repeating the measurement process. Echo signals from a single measurement line are used to create a seismic map. Direct analysis of the seismic map identifies potential waveform time-frequency distribution anomalies, thereby inferring the spatial location of the anomaly underground.

[0005] However, single-point seismic imaging schemes only use one excitation-receiver pair, and the offset is generally set to the optimal offset, which makes the offset large, making it difficult to deploy in two dimensions on roads and unable to perform three-dimensional imaging of underground structures. Summary of the Invention

[0006] Based on the technical problems existing in the background technology, the present invention proposes a gridded seismic imaging road detection method and system, which forms a three-dimensional detection image and improves the effective detection range and data resolution of the equipment.

[0007] The present invention proposes a gridded seismic image road detection method, comprising the following steps:

[0008] S1: Acquire sensor detection data, which is the data detected by the sensor on the ground after the seismic source is excited;

[0009] S2: t window The probe data within a time interval is segmented into individual sgy files to obtain segmented data. This process is repeated across multiple time intervals to obtain multiple segments of data, where the time interval t is... window The interval is time t iup to time t i +t window The time interval between;

[0010] S3: Convolve each segment of data based on wavelet basis, and take the modulus of the convolution result to obtain the time-frequency energy map P of the seismic waves collected by each sensor;

[0011] S4: For each time t0, the energy of low frequency, medium frequency, and high frequency is statistically analyzed to generate energy feature S;

[0012] S5: Combine the energy features S collected by each sensor at each time moment into a two-dimensional feature map S(x,t), and display the two-dimensional feature map S(x,t) as a two-dimensional color image to obtain the seismic image results.

[0013] Furthermore, when acquiring sensor detection data in step S1, source data is acquired simultaneously, and the source data is the vibration signal emitted by the source when it is excited;

[0014] When the source data is greater than the preset vibration signal data, acquire and store the sensor detection data;

[0015] If the source data is less than or equal to the preset vibration signal data, the source data and sensor detection data are reacquired.

[0016] Furthermore, the source data and sensor detection data are data uploaded based on the same detection unit, which is a detection device formed by combining a source and a sensor.

[0017] Furthermore, in step S3, the convolution formula is as follows:

[0018]

[0019] Where w(k) represents the result after convolution, u(j) represents the wavelet basis, v(k-j+1) represents the segmented data, k represents the index of the discrete sequence after convolution, j represents the cycle index of the multiplication in the convolution process, and j takes the value of all integers that make u(j) and v(k-j+1) non-zero.

[0020] Furthermore, step S5 is detailed as follows:

[0021] The energy features S = [Pl, Pm, Ph] collected by each sensor at each moment are combined into a two-dimensional feature map S(x,t), S(x,t) = [Pl(x,t), Pm(x,t), Ph(x,t)], where x represents the spatial coordinates of the sensor and t represents the time axis of the sensor's detection data;

[0022] The values ​​of Pl(x,t), Pm(x,t), and Ph(x,t) are mapped to the 0-255 color value range of the red, green, and blue channels, respectively, and the two-dimensional feature map S(x,t) is displayed as a two-dimensional color image.

[0023] A gridded seismic image road detection system includes an acquisition module, a cutting and traversal module, a convolutional model extraction module, a frequency statistics module, and a combined display module;

[0024] The acquisition module is used to acquire sensor detection data, which is the data detected by the sensor on the ground after the earthquake source is excited.

[0025] The cutting and traversal module is used to process t window The probe data within a time interval is segmented into individual sgy files to obtain segmented data. This process is repeated across multiple time intervals to obtain multiple segments of data, where the time interval t is... window The interval is time t i up to time t i +t window The time interval between;

[0026] The convolution modulus module is used to perform convolution processing on each segment of data based on wavelet basis, and to extract the modulus of the convolution result to obtain the time-frequency energy map P of the seismic waves collected by each sensor;

[0027] The frequency statistics module is used to perform statistical analysis on the energy of low frequency, medium frequency, and high frequency for each time sampling point t, and generate energy feature S;

[0028] The combined display module is used to combine the energy features S collected by each sensor at each time moment into a two-dimensional feature map S(x,t), and display the two-dimensional feature map S(x,t) as a two-dimensional color image to obtain the seismic image results.

[0029] Furthermore, the acquisition module synchronously acquires source data, which is the vibration signal emitted by the source when it is excited; when the source data is greater than a preset vibration signal data, the sensor detection data is acquired and stored; when the source data is less than or equal to the preset vibration signal data, the source data and sensor detection data are reacquired.

[0030] A computer-readable storage medium storing a plurality of classification programs, the plurality of classification programs being invoked by a processor and executed as described above, a gridded seismic image road detection method.

[0031] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0032] The advantages of the gridded seismic imaging road detection method and system provided by this invention are as follows: The gridded seismic imaging road detection method and system provided by this invention solves the problems of difficult data analysis and waste of effective detection area in three-dimensional seismic schemes on roads, while also addressing the shortcomings of single-point seismic imaging schemes that can only be deployed in one dimension. Furthermore, it introduces a new two-dimensional analysis and imaging algorithm to process two-dimensional seismic imaging data and form three-dimensional detection results, thereby improving the effective detection range and data resolution of the equipment. Ultimately, it solves the problems of poor effect and low efficiency when using traditional seismic methods to detect roads. Attached Figure Description

[0033] Fig. 1 This is a schematic diagram of the structure of the present invention;

[0034] Fig. 2 For detailed flowchart. Detailed Implementation

[0035] The technical solution of the present invention will now be described in detail through specific embodiments. Many specific details are set forth in the following description to provide a thorough understanding of the invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0036] like Figs. 1-2 As shown, the present invention proposes a gridded seismic imaging road detection method, which includes the following steps:

[0037] S1: Acquire sensor detection data, which is the data detected by the sensor on the ground after the seismic source is excited;

[0038] In step S1, when acquiring sensor detection data, earthquake source data is acquired simultaneously. The earthquake source data is the vibration signal emitted by the earthquake source when it is excited.

[0039] When the source data is greater than the preset vibration signal data, acquire and store the sensor detection data;

[0040] If the source data is less than or equal to the preset vibration signal data, the source data and sensor detection data are reacquired.

[0041] By judging the threshold of the seismic source data, it is determined whether to continue acquiring sensor detection data. This ensures that the signal energy of each seismic source excitation is large, thereby resulting in a high signal-to-noise ratio and ensuring the effectiveness of sensor detection data acquisition.

[0042] It is understandable that the source data and sensor detection data are uploaded based on the same detection unit, which is a detection device formed by combining a source and a sensor. In the actual detection process, multiple detection devices can be set up according to actual needs. Multiple detection devices can form a grid to complete the seismic image scanning of a region. Therefore, the acquired source data and sensor detection data can be data uploaded by one detection device or data uploaded by multiple detection devices, depending on the actual number of detection devices set up. This setting of one or more detection devices avoids complex data processing procedures. At the same time, the combination of detection units is convenient and can form a detection network, improving detection efficiency.

[0043] It should be noted that when multiple detection devices are used, the seismic source is excited sequentially to obtain source data and sensor detection data of the detection devices. The combination structure of detection units effectively avoids the dynamic and static correction problem in the three-dimensional seismic scheme, simplifies the data processing process, and reduces the damage to the original data information. In addition, the repeated combination of detection unit structures (i.e., multiple detection devices) effectively covers the underground medium of the detection area, increases the seismic ray density, reduces the ineffective detection area, and improves detection efficiency.

[0044] It is important to understand that while ensuring that seismic rays fully cover the underground medium within the detection equipment, the data acquisition system has an upper limit on the number of channels. When multiple detection devices are combined, the total number of sensors may exceed the support limit of the data acquisition system. In order to facilitate the combination of detection units, a single detection device is generally equipped with 6 sensors arranged in a hexagonal pattern, with the seismic source located in the middle.

[0045] S2: t window The probe data within a time interval is segmented into individual sgy files to obtain segmented data. This process is repeated across multiple time intervals to obtain multiple segments of data, where the time interval t is... window The interval is time t i up to time t i +t window The time interval between;

[0046] Understandably, the seismic image data analysis system calculates the entire data upload time in t increments. window The time interval is divided into multiple segments, and the segmented data is stored in the main control system.

[0047] S3: Convolve each segment of data based on wavelet basis, and take the modulus of the convolution result to obtain the time-frequency energy map P of the seismic waves collected by each sensor;

[0048] The convolution formula is as follows:

[0049]

[0050] Where w(k) represents the result after convolution, u(j) represents the wavelet basis, which is a constant, v(k-j+1) represents the segmented data, k represents the index of the discrete sequence after convolution, j represents the cycle index of the multiplication in the convolution process, and j takes the value of all integers that make u(j) and v(k-j+1) non-zero.

[0051] In the process of taking the modulus of the convolution result, the result includes data with real and imaginary parts. By taking the modulus, the result of the convolution is converted into a real number to obtain the time-frequency energy map P = P(x,t,f) of the seismic waves collected by each sensor, where x represents the spatial coordinates of the sensor corresponding to the data collected by each sensor, t represents the time axis of the sensor detection data, and f represents the center frequency corresponding to each wavelet basis.

[0052] S4: For each time sampling point t0, classify f into low frequency, medium frequency and high frequency, and statistically analyze the energy of low frequency, medium frequency and high frequency to generate energy feature S = [Pl, Pm, Ph];

[0053] By statistically analyzing the energy distribution at low, medium, and high frequencies, the energy distribution can be converted into a three-channel data set, which can then be easily converted into three channels of a color signal, thereby transforming the sensor's detection data into a color visual image.

[0054] S5: Combine the energy features S collected by each sensor at each time moment into a two-dimensional feature map S(x,t), and display the two-dimensional feature map S(x,t) as a two-dimensional color image to obtain the seismic image results. The seismic image results are clearly and intuitively presented as follows:

[0055] The energy features S = [Pl, Pm, Ph] collected by each sensor at each time moment are combined into a two-dimensional feature map S(x,t), where S(x,t) = [Pl(x,t), Pm(x,t), Ph(x,t)], x represents the spatial coordinates of the sensor corresponding to the data collected by each sensor, and t represents the time axis of the seismic data. The values ​​of Pl(x,t), Pm(x,t), and Ph(x,t) are mapped to the red, green, and blue three-channel color value range of 0-255 respectively, and the two-dimensional feature map S(x,t) is displayed as a two-dimensional color image.

[0056] It is important to understand that, in order to display earthquake data intuitively, the spectral information of the earthquake data needs to be compressed into the RGB three-channel information of a general color digital signal. Therefore, the original spectral signal is divided into low frequency, mid frequency, and high frequency. The low frequency Pl(x,t) is mapped to the R channel (red channel), the mid frequency Pm(x,t) is mapped to the G channel (green channel), and the high frequency Ph(x,t) is mapped to the B channel (blue channel). Then, the three channels are combined for color three-dimensional display. This realizes the conversion of spectral signal to color signal, which makes it convenient for data processors to observe earthquake data through color vision, making data processing more intuitive and efficient.

[0057] Through steps S1 to S5, a data processing flow suitable for seismic image arrays was designed, making the detection process and results clear and concise, and improving the construction efficiency of the method. Through a seismic image data analysis system suitable for array-type seismic image data, a lateral road scanning capability similar to ground-penetrating radar was obtained, improving lane coverage. While solving the problems of difficult data analysis and wasted effective detection area in 3D seismic schemes on roads, it also addresses the shortcomings of single-point seismic image schemes that can only be deployed in one dimension. A new 2D analysis and imaging algorithm is introduced to process 2D seismic image data to form 3D detection results, improving the effective detection range and data resolution of the equipment. Ultimately, it solves the problems of poor effect and low efficiency when using seismic methods to detect roads in the traditional way.

[0058] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A gridded seismic imaging road detection method, characterized in that, Includes the following steps: S1: Acquire sensor detection data, which is the data detected by the sensor on the ground after the seismic source is excited; S2: t window The probe data within a time interval is segmented into individual sgy files to obtain segmented data. This process is repeated across multiple time intervals to obtain multiple segments of data, where the time interval t is... window The interval is time t i up to time t i +t window The time interval between; S3: Convolve each segment of data based on wavelet basis, and take the modulus of the convolution result to obtain the time-frequency energy map P of the seismic waves collected by each sensor; S4: For each time t0, the energy of low frequency, medium frequency, and high frequency is statistically analyzed to generate energy feature S; S5: Combine the energy features S collected by each sensor at each time moment into a two-dimensional feature map S(x,t), and display the two-dimensional feature map S(x,t) as a two-dimensional color image to obtain the seismic image results.

2. The gridded seismic imaging road detection method according to claim 1, characterized in that, In step S1, when acquiring sensor detection data, earthquake source data is acquired simultaneously. The earthquake source data is the vibration signal emitted by the earthquake source when it is excited. When the source data is greater than the preset vibration signal data, acquire and store the sensor detection data; If the source data is less than or equal to the preset vibration signal data, the source data and sensor detection data are reacquired.

3. The gridded seismic imaging road detection method according to claim 1, characterized in that, The source data and sensor detection data are data uploaded based on the same detection unit, which is a detection device formed by combining a source and a sensor.

4. The gridded seismic imaging road detection method according to claim 1, characterized in that, In step S3, the convolution formula is as follows: w(k)=∑u(j)v(k-j+1) Where w(k) represents the result after convolution, u(j) represents the wavelet basis, v(k-j+1) represents the segmented data, k represents the index of the discrete sequence after convolution, j represents the cycle index of the multiplication in the convolution process, and j takes the value of all integers that make u(j) and v(k-j+1) non-zero.

5. The gridded seismic imaging road detection method according to claim 1, characterized in that, Step S5 is as follows: The energy features S = [Pl, Pm, Ph] collected by each sensor at each moment are combined into a two-dimensional feature map S(x,t), S(x,t) = [Pl(x,t), Pm(x,t), Ph(x,t)], where x represents the spatial coordinates of the sensor and t represents the time axis of the sensor's detection data; The values ​​of Pl(x,t), Pm(x,t), and Ph(x,t) are mapped to the 0-255 color value range of the red, green, and blue channels, respectively, and the two-dimensional feature map S(x,t) is displayed as a two-dimensional color image.

6. A gridded seismic imaging road detection system, characterized in that, It includes an acquisition module, a cutting and traversal module, a convolution modulus extraction module, a frequency statistics module, and a combined display module; The acquisition module is used to acquire sensor detection data, which is the data detected by the sensor on the ground after the earthquake source is excited. The cutting and traversal module is used to process t window The probe data within a time interval is segmented into individual sgy files to obtain segmented data. This process is repeated across multiple time intervals to obtain multiple segments of data, where the time interval t is... window The interval is time t i up to time t i +t window The time interval between; The convolution modulus module is used to perform convolution processing on each segment of data based on wavelet basis, and to extract the modulus of the convolution result to obtain the time-frequency energy map P of the seismic waves collected by each sensor; The frequency statistics module is used to perform statistical analysis on the energy of low frequency, medium frequency, and high frequency for each time sampling point t, and generate energy feature S; The combined display module is used to combine the energy features S collected by each sensor at each time moment into a two-dimensional feature map S(x,t), and display the two-dimensional feature map S(x,t) as a two-dimensional color image to obtain the seismic image results.

7. The gridded seismic imaging road detection system according to claim 6, characterized in that, The acquisition module synchronously acquires earthquake source data, which is the vibration signal emitted by the earthquake source when it is excited. When the earthquake source data is greater than the preset vibration signal data, the sensor detection data is acquired and stored. When the earthquake source data is less than or equal to the preset vibration signal data, the earthquake source data and sensor detection data are acquired again.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of classification programs, which are used by a processor to execute the gridded seismic image road detection method as described in any one of 1 to 5.

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