Advanced geological forecasting method based on high-precision frequency of geological radar

Abstract

The invention relates to the technical field of geological exploration, in particular to an advanced geological forecasting method based on high-precision frequency of a geological radar. A geological radar high-precision frequency-based advanced geological forecasting method comprises the following steps: S1, performing improved WVD time-frequency analysis processing on radar depth domain data to obtain a time-frequency spectrum, and performing first-moment integral calculation based on the time-frequency spectrum to obtain a main frequency of a sampling point; s2, highlighting low-frequency data on the data profile of the main frequency in a variable density mode; and S3, based on the low-frequency data, determining distribution of geological anomalies in combination with early-stage geological data. According to the method, improved WVD time-frequency analysis is adopted, high-precision main frequency output is achieved, compared with the prior art, the high-precision main frequency has a more obvious effect on fracture water, karst and other geological anomalies, and the geological anomalies can be better recognized; moreover, a variable density mode is used, low-frequency data are highlighted, and early-stage geological data are combined, so that the interpretation precision of an abnormal position can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to an advanced geological prediction method based on high-precision frequency of geological radar. Background Art

[0002] Tunnel engineering is a concealed underground project. Due to its deep burial depth and complex surface topography, the accuracy of tunnel geology during initial surveys and designs is limited. Tunnel advanced geological prediction technology, based on preliminary surveys and combined with geological data from the tunnel excavation section, further explores the geology ahead of the tunnel excavation face. Common geological prediction methods include seismic wave reflection, geological radar, advanced pilot pits, and advanced horizontal drilling.

[0003] Geological radar technology first emerged in the early 20th century. With the deepening of theoretical knowledge and improvements in software and hardware in the mid-20th century, it rapidly developed in noise control and data processing, achieving promising results in archaeological research, goaf detection, and non-destructive testing. In the late 20th century, it began to be applied in tunnel geological prediction. In 2003, Wu Jun et al. applied geological radar technology to short-term geological prediction of highway tunnels. In 2005, Wang Zhengcheng et al. applied geological radar technology to the geological prediction of the Yiwu-Wanzhou Railway Tunnel, noting the advantages of high resolution and easy image recognition. Geological radar technology is based on the theory of electromagnetic wave propagation. A transmitting antenna transmits high-frequency electromagnetic waves into the object being detected. When the high-frequency electromagnetic waves reach the interface between two different media within the object, they are reflected and refracted due to the different dielectric constants of the two media. The propagation of the incident, reflected, and refracted waves follows the laws of reflection and refraction. The reflected signals are received by the receiving antenna, converted to digital-to-analog, and mapped on the instrument. In geological radar detection, the transmitted electromagnetic wave is similar to a uniform plane electromagnetic wave. The propagation speed and two-way travel time of the electromagnetic wave are key factors affecting detection progress. During propagation, the radar electromagnetic wave encounters different media. Due to the different dielectric constants of the media, the electromagnetic wave is reflected and projected at the interface between the media, and the reflected signal is received by the receiving antenna. The strength of the reflected signal depends on the reflection coefficient. The processing of geological radar data mainly involves removing interference signals and noise through various filter functions and highlighting the valid signal by selecting an appropriate gain value. The ultimate goal of geological radar data processing is to visualize the collected electromagnetic wave signal, thereby identifying the phase axis variation pattern, amplitude, frequency characteristics, and other useful information of the reflected wave, thereby interpreting the geological conditions of the detection area. In tunnel advanced geological prediction, the limited width of the tunnel face limits the length of the radar survey line. The electromagnetic wave is easily interfered with by the metal environment inside the tunnel, such as the arch and trolley, which increases the interference of the reflected wave data. These tunnel environmental factors increase the difficulty of geological radar data processing. In the process of geological radar prediction, data is usually processed through background removal, FK filtering, frequency domain filtering, and gain adjustment, and geological interpretation is performed through the phase axis characteristics and amplitude characteristics of the radar image.

[0004] Because the interference during geological radar forecasting is strong, in order to improve the interpretation accuracy, the data can be processed and interpreted from different attributes, and the comprehensive results can be compared to improve the accuracy of the forecast results. Although many technologies have been adopted in radar data processing, the amplitude and phase axis characteristics of the processed data are difficult to meet actual needs, and conventional frequency extraction technology cannot effectively describe geological anomalies due to insufficient accuracy.

[0005] Prior art discloses a method and device for rapidly identifying unfavorable geological conditions in tunnel construction based on longitudinal waves (Publication No.: CN116719083A). The method comprises: obtaining raw seismic longitudinal wave data from a target area, preprocessing the raw seismic longitudinal wave data to obtain first seismic longitudinal wave data; decomposing and reconstructing the first seismic longitudinal wave data using shear wave transform to form second seismic longitudinal wave data; obtaining time-frequency spectrum data of the second seismic longitudinal wave data using the maximum entropy WVD method; calculating the logarithmic gradient of the amplitude at each time point in the second seismic longitudinal wave data based on the time-frequency spectrum data; plotting a profile gradient map based on the logarithmic gradient of the amplitude; and determining unfavorable geological areas within the target area based on the profile gradient map. This invention uses shear transform in conjunction with the maximum entropy WVD method to rapidly identify unfavorable geological bodies in the target area (i.e., the tunnel face). Although prior art methods can identify unfavorable geological areas using the WVD method, the amplitude and event characteristics of the processed data are difficult to meet practical needs, resulting in conventional frequency extraction techniques being unable to effectively describe geological anomalies due to insufficient accuracy. Summary of the Invention

[0006] The purpose of the present invention is to overcome the problem in the prior art that geological anomalies cannot be effectively described due to insufficient accuracy of frequency extraction, and to provide an advanced geological prediction method based on high-precision frequency of geological radar.

[0007] In a first aspect, the present invention provides an advanced geological prediction method based on high-precision frequency of geological radar, comprising:

[0008] S1. Using improved WVD time-frequency analysis to process radar depth domain data to obtain a time-frequency spectrum, and using first-order moment integral calculation to obtain the main frequency of the sampling point based on the time-frequency spectrum;

[0009] S2. Using a variable density method to adjust the data profile of the main frequency to highlight the low-frequency data;

[0010] S3. Determine the distribution of geological anomalies based on the low-frequency data and in combination with previous geological data.

[0011] Preferably, before S1, it is also necessary to collect the original geological radar data and the preliminary geological data.

[0012] Further preferably, the preliminary geological data include: fault activity in the study area, lithology distribution and weathering, fissure water and karst.

[0013] Further preferably, it is necessary to perform signal processing on the geological radar raw data to obtain the radar depth domain data;

[0014] The signal processing includes: digital filtering, zero point return, energy balance, time-depth conversion and file annotation.

[0015] Preferably, the improved WVD time-frequency analysis process in S1 includes:

[0016]

[0017] Where W0 is the Wigner-Ville distribution, z(t) is the analytical signal of the original signal, t is time, f is frequency, z*(t) is the complex conjugate of z(t), and τ is the integration variable.

[0018] Further preferably, the kernel function in the improved WVD time-frequency analysis process is z(t)z * (t), it is also necessary to calculate the kernel function z(t)z * (t) Extrapolation was performed using the maximum entropy Burg method.

[0019] Further preferably, after the maximum entropy Burg method is used for extrapolation, the kernel function z(t)z needs to be * (t) Perform discrete Fourier transform.

[0020] Preferably, the first-order moment integral calculation in S1 is used to obtain the main frequency of the sampling point, including:

[0021]

[0022] Where: is the main frequency of the sampling point, W0 is the Wigner-Ville distribution, t is time, and f is frequency.

[0023] Preferably, the step S2 is specifically to display the low-frequency data in color using a variable density method on the data profile of the main frequency, and to highlight the low-frequency data by adjusting the color scale.

[0024] An advanced geological prediction device based on high-precision frequency of geological radar includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute any of the methods described above.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The present invention adopts improved WVD time-frequency analysis, specifically by extrapolating the kernel function through the maximum entropy Burg method to achieve high-precision main frequency output. Compared with the existing technology, the high-precision main frequency has a more obvious effect on geological anomalies such as fissure water and karst, and can better identify geological anomalies. In addition, the present invention uses a variable density method to highlight low-frequency data, and combined with previous geological data, it can effectively improve the accuracy of interpreting anomaly locations compared with the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of an advanced geological prediction method based on high-precision frequency of geological radar in Example 1.

[0028] Figure 2 This is a radar reflection wave-line scan diagram of the traditional processing method in Example 1.

[0029] Figure 3 This is the radar reflection wave-wave train diagram of the traditional processing method in Example 1.

[0030] Figure 4 This is a high-precision main frequency profile diagram in Example 1.

[0031] Figure 5 This is a diagram of the jet-like water discharge from the spandrel of the tunnel face in Example 1.

[0032] Figure 6 This is a schematic diagram of an advanced geological prediction device based on high-precision frequency of geological radar in Example 2. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments, as all technologies implemented based on the present invention fall within the scope of the present invention.

[0034] Unless otherwise specified, in the description of the specific embodiments of the present invention, the terms indicating the orientation or positional relationship, such as "upper", "lower", "left", "right", "center", "inside", and "outside", are based on the expressions of the orientation or positional relationship shown in the accompanying drawings, or are the orientation or positional relationship in which the invented product / device / apparatus is placed when it is conventionally used. These terms of orientation or positional relationship are merely for the purpose of facilitating the description of the scheme of the present invention or simplifying the description of the specific embodiments to facilitate the rapid understanding of the scheme by technicians, and do not indicate or imply that a specific device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship, and therefore should not be understood as limiting the present invention.

[0035] In addition, if the terms "horizontal", "vertical", "overhanging", "parallel" and the like appear, it does not mean that the corresponding devices / components / elements are required to be absolutely horizontal or vertical or overhanging or parallel, but may be slightly tilted or have deviations. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but may be slightly tilted. Alternatively, it can be simply understood that the corresponding devices / components / elements are set in directions such as "horizontal", "vertical", "overhanging", and "parallel", and can have an error / deviation of ±10% relative to the corresponding direction setting, more preferably an error / deviation within ±8%, more preferably an error / deviation within ±6%, more preferably an error / deviation within ±5%, and more preferably an error / deviation within ±4%. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its role in the solution of the present invention.

[0036] In addition, the expressions “first”, “second”, “third”, etc. in the terms are merely used to distinguish the description of the same or similar components, and should not be understood as emphasizing or implying the relative importance of specific components.

[0037] In addition, in the description of the embodiments of the present invention, "several," "plurality," and "a number" represent at least two. It can also be any number such as two, three, four, five, six, seven, eight, nine, or even more than nine.

[0038] Furthermore, in the description of the technical solution of the present invention, unless otherwise expressly specified, defined, or restricted, the terms "disposed," "installed," "connected," "connected," "provided with," "laid," and "arranged" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections. They may include welding, riveting, bolting, threading, and other commonly used connection methods in the field of geological exploration. Such connections may be mechanical, electrical, or communication; they may be direct, indirect via an intermediate medium, or internal communication between two components.

[0039] Example 1

[0040] The present invention provides an advanced geological prediction method based on high-precision frequency of geological radar, the flow chart is as follows Figure 1 As shown, specifically including:

[0041] S1. Using an improved WVD (Wigner-Ville distribution) time-frequency analysis method to process radar depth domain data to obtain a time-frequency spectrum, and using a first-order moment integral calculation to obtain the main frequency of the sampling point based on the time-frequency spectrum;

[0042] S2. Using a variable density method to adjust the data profile of the main frequency to highlight the low-frequency data;

[0043] S3. Determine the distribution of geological anomalies based on the low-frequency data and in combination with previous geological data.

[0044] Before S1, it is also necessary to collect the original geological radar data and the preliminary geological data.

[0045] The preliminary geological data include: fault activity, lithology distribution and weathering, fissure water and karst in the study area.

[0046] It is also necessary to perform signal processing on the geological radar raw data to obtain the radar depth domain data;

[0047] Among them, it is also necessary to perform signal processing on the original data of the geological radar. The signal processing process adopted in this embodiment includes: data transmission, file editing, digital filtering, zero point return, energy balance, time-depth conversion and file annotation, and finally outputs the radar depth domain data through signal processing.

[0048] The improved WVD time-frequency analysis process described in S1 includes:

[0049]

[0050] Where W0 is the Wigner-Ville distribution, z(t) is the analytical signal of the original signal, t is time, f is frequency, z*(t) is the complex conjugate of z(t), and τ is the integration variable.

[0051] Furthermore, the analytical signal of the time domain signal x(n) is defined as z(n), where n=0,1,...,N s -1, N s is the total number of samples, and the analytical signal z(n) constructs the kernel function of WVD:

[0052]

[0053] Where: K n (l) is the kernel function, z(n) is the analytical signal, n is time, and l is the time-shifted variable.

[0054] The long kernel function sequence has a good suppression on the interference of WVD cross terms. Therefore, the maximum entropy Burg method is used to extrapolate the kernel function. M (i), i=1,2,…,M}, then the kernel function K is extrapolated by the formula n (l) Extrapolation:

[0055]

[0056] Where: is the extrapolated kernel function, a M(i), is the coefficient of the M-order prediction filter;

[0057] Extrapolation from To begin, extrapolate the kernel function through the extrapolation formula And take Kernel function for extrapolation By performing discrete Fourier transform, we can obtain the time-frequency spectrum W0(t,f) with cross-interference eliminated.

[0058] In the processing of digital signals, the primary frequency is usually obtained by performing Fourier transform on the signal to obtain the phase spectrum and then taking the derivative of the phase. However, when the signal contains noise, the obtained result is very unstable and its accuracy will be significantly reduced. In the time-frequency analysis of non-stationary signals, the frequency changes with time. In this embodiment S1, the primary frequency of the sampling point is calculated using the first-order moment integral, as follows:

[0059]

[0060] Where: is the main frequency of the sampling point, W0 is the Wigner-Ville distribution, t is time, and f is frequency.

[0061] Specifically, S2 is to display the low-frequency data in color using a variable density method on the data profile of the main frequency, and to highlight the low-frequency data by adjusting the color scale. Specifically, in this embodiment, the low-frequency data is set to be data below 70 Hz. By adjusting the color scale, the data below 70 Hz is displayed in a different color from other data, thereby highlighting the low-frequency data (the low-frequency data corresponds to areas with collapse risks such as fracture zones, karst channels, or loose soil layers);

[0062] Furthermore, this embodiment also requires circling the low-frequency area in the profile and combining it with previous geological data to ultimately determine the distribution of the geological anomaly. Specifically, it is necessary to combine the existing stratigraphic lithology map and drilling data and other previous geological data of the low-frequency area to confirm whether the low-frequency area coincides with a known fragile geological unit (such as a limestone distribution area). If so, the low-frequency area is determined to be a distribution of geological anomalies.

[0063] This embodiment can accurately identify geological anomalies such as karst and groundwater reflected in radar data. Combining traditional geological radar analysis technology can effectively improve the accuracy of interpreting anomaly locations, providing a new anomaly identification technology for geological radar data interpretation.

[0064] Figure 2 The radar reflection wave-line scan diagram of the traditional processing method, Figure 3This is a radar reflection wave train diagram obtained using traditional processing. The results obtained using this method show that the radar reflection wave signal has poor overall continuity in its event axis and significant amplitude variations in this forecast segment. The radar reflection wave energy in the segment PDK252+513 to +532 is strong, with the main anomaly located to the left of the tunnel face. This section is inferred to have fractured surrounding rock, well-developed joints and fissures, and water content.

[0065] Figure 4 This is the high-precision main frequency profile obtained in this embodiment. The results of high-precision frequency technology processing show that the frequency abnormal area is mainly concentrated in the PDK252+512~+537 section, and the main abnormal area is on the left side of the tunnel face. Among them, the frequency abnormality on the left side of the PDK252+526~+537 section is obvious. It is speculated that the joints are densely developed, which is easy to conduct groundwater and increase the water volume.

[0066] Figure 5 This is a diagram of water jetting from the spandrels of the tunnel face. Tunnel excavation revealed that the surrounding rock integrity in the PDK252+513 to +535 section was relatively poor. The left side of the surrounding rock was fractured, with multiple tensile water-conducting fissures developing. High-pressure fissure water jetted out from the PDK252+525 to +535 section (where frequency anomalies were significant), and the water volume gradually cleared over time. This indicates that the high-precision frequency technology used in this embodiment is more responsive to water-conducting fissure anomalies. Comprehensive analysis shows that the anomaly range of the high-precision frequency technology processing results is consistent with the anomaly location of the traditional processing method, verifying the reliability of the high-precision frequency technology.

[0067] The above description is only a detailed description of the specific embodiments of the invention, and does not limit the invention. Various substitutions, modifications and improvements made by those skilled in the relevant art without departing from the principles and scope of the invention should be included in the scope of protection of the invention.

[0068] Example 2

[0069] like Figure 6 As shown, an electronic device includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the advanced geological prediction method based on high-precision frequency of geological radar described in the above embodiment. The input and output interfaces may include a display, a keyboard, a mouse, and a USB interface for inputting and outputting data; the power supply is used to provide power to the electronic device.

[0070] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, and other media that can store program codes.

[0071] When the above-mentioned integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

Claims

1. A method for advanced geological prediction based on high-precision frequency of geological radar, characterized in that: include: S1. Using improved WVD time-frequency analysis to process radar depth domain data to obtain a time-frequency spectrum, and using first-order moment integral calculation to obtain the main frequency of the sampling point based on the time-frequency spectrum; S2. Using a variable density method to adjust the data profile of the main frequency to highlight the low-frequency data; S3. Determine the distribution of geological anomalies based on the low-frequency data and in combination with previous geological data.

2. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 1, characterized in that: Before S1, it is also necessary to collect the original geological radar data and the preliminary geological data.

3. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 2, characterized in that: The aforementioned preliminary geological data include: fault activity in the study area, lithology distribution and weathering, fissure water and karst.

4. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 2, characterized in that: It is also necessary to perform signal processing on the geological radar raw data to obtain the radar depth domain data; The signal processing includes: digital filtering, zero point return, energy balance, time-depth conversion and file annotation.

5. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 1, characterized in that: The improved WVD time-frequency analysis process described in S1 includes: Where W0 is the Wigner-Ville distribution, z(t) is the analytical signal of the original signal, t is time, f is frequency, z*(t) is the complex conjugate of z(t), and τ is the integration variable.

6. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 5, characterized in that: The kernel function in the improved WVD time-frequency analysis process is z(t)z * (t), it is also necessary to calculate the kernel function z(t)z * (t) Extrapolation was performed using the maximum entropy Burg method.

7. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 6, characterized in that: After extrapolating using the maximum entropy Burg method, the kernel function z(t)z needs to be * (t) Perform discrete Fourier transform.

8. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 1, characterized in that: The main frequency of the sampling point is obtained by using the first-order moment integral calculation in S1, including: Where: is the main frequency of the sampling point, W0 is the Wigner-Ville distribution, t is time, and f is frequency.

9. The method for advanced geological prediction based on high-precision frequency of geological radar according to claim 1, characterized in that: Specifically, the step S2 is to display the low-frequency data in color using a variable density method on the data profile of the main frequency, and to highlight the low-frequency data by adjusting the color scale.

10. An advanced geological prediction device based on high-precision frequency of geological radar, characterized in that: The invention comprises at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 9.

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