Road slope intelligent detection method based on passive source frequency imaging

Abstract

The invention discloses a road slope intelligent detection method based on passive source frequency imaging, and the method comprises the steps: deploying a plurality of two-dimensional exploration survey lines along the steps of a road slope in a road slope detection region; obtaining passive source frequency data of the road slope through a plurality of deployed two-dimensional exploration survey lines; preprocessing the obtained road slope passive source frequency data to obtain resonant frequency amplitude data; generating a geologic structure map of the road slope based on the resonant frequency amplitude data; performing definition and readability optimization on the geological structure map of the road slope; correcting the optimized geologic structure map of the road side slope, and eliminating the influence of topographic relief on the geologic structure map of the road side slope; identifying an impedance interface in the geologic structure map of the corrected road slope according to a frequency domain imaging result; and calibrating and explaining the identified impedance interface. The detection result is not influenced by experience and subjective judgment of researchers, so that the reliability and the consistency of the detection result are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of slope detection, and particularly to an intelligent detection method for road slopes based on passive source frequency imaging. Background Art

[0002] Landslide refers to the process and phenomenon in which the rock and soil mass on a slope, under the action of gravity, is triggered by external factors such as rainfall, earthquake, and human engineering activities, deforms and finally fails and collapses. The occurrence of a landslide is usually a gradual process. Initially, it may be manifested as local cracks or small-scale slips. With the continuous action of external conditions, it may eventually lead to large-scale sliding of the rock and soil mass. Landslides not only damage the natural environment and ecological system, but also pose a serious threat to the safety of human life and property. China has a complex terrain with a vast area of mountains and hills. Many highway, railway and other transportation infrastructures have to be built on or beside slopes. Such geographical conditions make these transportation lines directly face the potential threat of landslides. During the construction of highways and railways, the filling of roadbeds and the construction of heavy machinery will significantly increase the vertical load on the slope and change the original stress distribution state. In addition, if the drainage system is not well designed or maintained, rainwater may accumulate on or in the slope, increasing the water content of the rock and soil mass and reducing its shear strength, thus inducing landslides. Highways and railways built on or beside slopes are extremely prone to becoming high-incidence areas of landslide disasters due to the above factors. Landslides not only cause road interruptions and traffic paralysis, but may also trigger secondary disasters such as debris flows and barrier lakes, further exacerbating the severity of the disasters. Therefore, studying reliable slope detection methods to timely identify and warn of landslide risks is of great significance for ensuring road safety and reducing disaster losses.

[0003] The existing slope detection methods mainly adopt manual patrol inspections. Researchers use manual tools such as geological hammers to conduct on-site inspections, resulting in problems such as a large amount of manpower and material resources being consumed in the detection process, the inspection results relying on the subjective judgment of researchers, and the inability to achieve real-time monitoring, seriously affecting the detection results and detection efficiency. That is, there are the following defects: 1. The results of manual inspections highly depend on the experience and subjective judgment of researchers. Different personnel may draw different conclusions about the stability of the same slope, making it difficult to ensure the reliability and consistency of the detection results. 2. Manual inspections require a large amount of manpower and time, especially in the case of complex terrain and large slope ranges, the detection efficiency is extremely low. In addition, manual inspections cannot achieve real-time monitoring, making it difficult to capture the dynamic changes of slopes in a timely manner, resulting in the inability to provide effective warnings before landslides occur. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides an intelligent detection method for road slopes based on passive source frequency imaging, which realizes the detection of the geological structure of road slopes by combining geophysical exploration technology and sensor detection technology.

[0005] To achieve the above object, the technical solution provided by the present invention is as follows:

[0006] An intelligent detection method for road slopes based on passive source frequency imaging, comprising:

[0007] Deploy multiple two-dimensional exploration lines along the road slope steps in the road slope detection area;

[0008] Obtain the passive source frequency data of the road slope through the deployed multiple two-dimensional exploration lines;

[0009] Preprocess the obtained passive source frequency data of the road slope to obtain resonance frequency amplitude data;

[0010] Generate a geological structure map of the road slope based on the resonance frequency amplitude data;

[0011] Optimize the clarity and readability of the geological structure map of the road slope;

[0012] Correct the optimized geological structure map of the road slope to eliminate the influence of terrain undulation on the geological structure map of the road slope;

[0013] Identify the impedance interface in the corrected geological structure map of the road slope through the frequency domain imaging method;

[0014] Calibrate and interpret the identified impedance interface.

[0015] Furthermore, before obtaining the passive source frequency data of the road slope through the deployed multiple two-dimensional exploration lines, perform a consistency check test on the exploration equipment of the multiple two-dimensional exploration lines;

[0016] When performing the consistency check test, calculate the consistency of all exploration equipment by combining square array passive source observation and correlation calculation.

[0017] Furthermore, calculating the consistency of all exploration equipment by combining square array passive source observation and correlation calculation includes:

[0018] Level and north-point the exploration equipment before the test, and observe for a continuous period of time during the test;

[0019] Calculate the consistency of all exploration equipment using the following formula:

[0020]

[0021] Where X1i and X 2i are the i-th measurement values of the exploration device 1 and the exploration device 2 respectively, and are the measurement means of the exploration device 1 and the exploration device 2 respectively.

[0022] Furthermore, preprocess the obtained passive source frequency data of the road slope, including:

[0023] Suppress abnormal amplitude noise;

[0024] Suppress surface wave noise;

[0025] Extract the resonance frequency.

[0026] Furthermore, suppressing abnormal amplitude noise specifically means removing the passive source frequency data that exceeds several times the absolute median difference of the median;

[0027] Suppose the passive source frequency data X is obtained, and it is arranged from small to large or from large to small. The number in the middle is the median, denoted as Median(X). The formula for the absolute median difference is:

[0028] Median(|X i -Median(X)|)

[0029] where X i is the passive source frequency data obtained from the i-th measurement.

[0030] Furthermore, suppress surface wave noise through a Butterworth band-pass filter, and its frequency response formula is:

[0031]

[0032] where H0 is the passband gain, is the center frequency, ω1 and ω2 are the lower cut-off frequency and the upper cut-off frequency respectively, and are the quality factors, BW = ω2 - ω1 is the bandwidth.

[0033] Furthermore, extracting the resonance frequency includes:

[0034] Use the fast Fourier transform algorithm to perform frequency domain analysis on the digital signal to obtain the frequency domain characteristics of the signal. The formula used is as follows:

[0035] Fourier transform formula:

[0036]

[0037] where F(ω) is the frequency domain signal, f(t) is the time domain signal, ω is the angular frequency, j is the imaginary unit, e-jωt is a complex exponential function;

[0038] Inverse Fourier transform formula:

[0039]

[0040] where is a normalization factor.

[0041] Furthermore, the geological structure diagram of the optimized road slope is corrected, including:

[0042] Correction model establishment: According to the observation data and surface elevation information in the collected data information, a correction model is established to describe the relationship between the observation data and the surface elevation;

[0043] Correction calculation: The correction model is used to perform correction calculations on the observation data. The seismic data is corrected from the surface to a unified reference plane (usually sea level or a certain horizontal plane) through Δt = h / v to eliminate the influence of terrain undulation on the geological structure diagram of the road slope. Here, h is the elevation difference between the geophone or shot point and the reference plane, and v is the near-surface shear wave velocity.

[0044] Compared with the prior art, the principles and advantages of this technical solution are as follows:

[0045] 1. By combining geophysical exploration technology and sensor detection technology, the detection of the geological structure of the road slope is realized, which is not affected by the experience and subjective judgment of researchers, ensuring the reliability and consistency of the detection results.

[0046] 2. By obtaining the passive source frequency data of the road slope through multiple two-dimensional exploration lines deployed along the road slope steps, the detection efficiency can be greatly improved, and real-time monitoring can be realized, enabling the dynamic changes of the slope to be captured in a timely manner, providing an effective early warning before a landslide occurs.

[0047] 3. The passive source frequency imaging method adopted utilizes the naturally existing seismic wave field and does not require artificial excitation of the seismic source, and can be applied to working areas with rugged terrain or urban areas with strong electromagnetic interference.

[0048] 4. By observing the geological background field in a long-term continuous observation manner, the interference caused by accidental events can be effectively removed, with the characteristics of strong anti-noise ability, especially suitable for the exploration of shallow geological structures, and having the characteristics of "simple observation, non-destructive detection, environmental protection and high efficiency".

[0049] 5. The effect of the frequency imaging data preprocessing method adopted is relatively good. After denoising, noises such as abnormal amplitude noise, surface waves, and single-frequency interference on the original record are effectively suppressed, greatly improving the signal-to-noise ratio of the original record, making the signal clearer and easier to identify. In a weak signal environment, the signal can be more reliably distinguished from the background noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the services required for the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 is the principle flowchart of an intelligent road slope detection method based on passive source frequency imaging of the present invention;

[0052] Figure 2 is a schematic diagram of deploying multiple two-dimensional exploration lines along the steps of the road slope;

[0053] Figure 3 is Figure 2 the frequency imaging interpretation profile of the horizontal line 1 (left), horizontal line 2 (middle), and horizontal line 3 (right);

[0054] Figure 4 is the generated geological structure diagram of the road slope. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The present invention will be further described below in conjunction with specific embodiments:

[0056] As Figure 1 shown, an intelligent road slope detection method based on passive source frequency imaging described in this embodiment includes the following steps:

[0057] S1. In the road slope detection area, deploy five two-dimensional exploration lines along the steps of the road slope;

[0058] The total length of the five two-dimensional exploration lines is 252 meters, the point spacing is 2 meters, and the number of physical points is 127. The layout of each two-dimensional exploration line covers the key areas of the road slope.

[0059] S2. Conduct a consistency check test on the exploration equipment of the five deployed two-dimensional exploration lines;

[0060] During the consistency check test, the consistency of all exploration equipment is calculated by using the method of square array passive source observation combined with correlation calculation. The process includes:

[0061] Before the test, the exploration and detection equipment is leveled and oriented northward, and the test lasts for continuous observation for 10 minutes;

[0062] The consistency of all exploration and detection equipment is calculated using the following formula:

[0063]

[0064] where, X 1i and X 2i are the i-th measurement values of exploration and detection equipment 1 and exploration and detection equipment 2 respectively, and are the mean values of the measurements of exploration and detection equipment 1 and exploration and detection equipment 2 respectively.

[0065] S3. Obtain the passive source frequency data of the road slope through five deployed two-dimensional exploration lines, and the continuous observation duration is 30 minutes;

[0066] S4. Preprocess the obtained passive source frequency data of the road slope to obtain the resonance frequency amplitude data; this step specifically includes:

[0067] S4-1. Suppress the abnormal amplitude noise and eliminate the passive source frequency data that exceeds several times the absolute median difference of the median;

[0068] Suppose the obtained passive source frequency data is X, arrange it from small to large or from large to small, and the number in the middle is the median, denoted as Median(X), and the formula for the absolute median difference is:

[0069] Median(|X i - Median(X)|)

[0070] where, X i is the passive source frequency data obtained from the i-th measurement.

[0071] S4-2. Suppress the surface wave noise through a Butterworth band-pass filter, and its frequency response formula is:

[0072]

[0073] where, H0 is the passband gain, is the center frequency, ω1 and ω2 are the lower cut-off frequency and the upper cut-off frequency respectively, and are the quality factors, BW = ω2 - ω1 is the bandwidth.

[0074] S4-3. Extract the resonance frequency, and the process includes:

[0075] Use the fast Fourier transform algorithm to perform frequency domain analysis on the digital signal to obtain the frequency domain characteristics of the signal, and the formula used is as follows:

[0076] Fourier transform formula:

[0077]

[0078] where F(ω) is the frequency-domain signal, f(t) is the time-domain signal, ω is the angular frequency, j is the imaginary unit, and e -jωt is the complex exponential function;

[0079] Inverse Fourier transform formula:

[0080]

[0081] where is the normalization factor.

[0082] S5. Generate a geological structure diagram of the road slope based on the resonance frequency amplitude data;

[0083] S6. Optimize the clarity and readability of the geological structure diagram of the road slope;

[0084] S7. Calibrate the optimized geological structure diagram of the road slope, including:

[0085] S7-1. Calibration model establishment: Establish a calibration model that describes the relationship between the observed data and the surface elevation information based on the observed data and the surface elevation information in the collected data information;

[0086] S7-2. Calibration calculation: Use the calibration model to perform calibration calculations on the observed data, and correct the seismic data from the surface to a unified reference plane (usually the sea level or a certain horizontal plane) through Δt = h / v to eliminate the influence of terrain undulation on the geological structure diagram of the road slope. Where h is the elevation difference between the geophone or shot point and the reference plane, and v is the near-surface shear wave velocity.

[0087] S8. Identify the impedance interface in the calibrated geological structure diagram of the road slope; when the impedance of the underground medium changes, seismic waves will be reflected to form a reflection interface, that is, the impedance interface. This interface is the interface between different geological bodies and reflects the physical property differences of geological bodies.

[0088] S9. Calibrate and interpret the identified wave impedance interface, determine its geological significance, and provide a basis for slope stability analysis. Calibration is to compare the seismic data with known geological information (such as drilling and logging data) to verify the accuracy of seismic imaging and adjust the model parameters. Compare the drilling data (such as acoustic logging and density logging) with the seismic data to establish the correspondence between seismic reflections and formation interfaces. Interpretation is the process of converting seismic data into geological information, including analysis of strata, structures, lithologies, etc., including stratigraphic interpretation, structural interpretation, and lithologic interpretation.

[0089] In this embodiment, by combining geophysical exploration technology and sensor detection technology, the detection of the geological structure of the road slope is realized, which is not affected by the experience and subjective judgment of researchers, ensuring the reliability and consistency of the detection results.

[0090] In this embodiment, the passive source frequency data of the road slope are obtained through multiple two-dimensional exploration lines deployed along the steps of the road slope, which can greatly improve the detection efficiency, and can realize real-time monitoring, timely capture the dynamic changes of the slope, and provide an effective early warning before the landslide occurs.

[0091] The passive source frequency imaging method adopted in this embodiment utilizes the naturally existing seismic wave field and does not require artificial excitation of the seismic source, and can be applied to working areas with rugged terrain or strong electromagnetic interference such as cities.

[0092] In this embodiment, the geological background field is observed by means of long-term continuous observation, which can effectively remove the interference caused by accidental events, has the characteristics of strong anti-noise ability, is particularly suitable for the exploration of shallow geological structures, and has the characteristics of "simple observation, non-destructive detection, environmental protection and high efficiency".

[0093] In this embodiment, the effect of the frequency imaging data preprocessing method adopted is good. After denoising processing, abnormal amplitude noise, surface waves, single-frequency interference and other noises on the original record are effectively suppressed, greatly improving the signal-to-noise ratio of the original record, making the signal clearer and easier to be recognized, and being able to more reliably distinguish the signal from the background noise in a weak signal environment.

[0094] The above-described embodiments are only the preferred embodiments of the present invention, and do not limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A road slope intelligent detection method based on passive source frequency imaging, characterized in that: include: In the road slope detection area, multiple two-dimensional survey lines are deployed along the road slope steps; Obtain the passive source frequency data of road slopes through the deployment of multiple two-dimensional survey lines; Preprocess the acquired road slope passive source frequency data to obtain resonance frequency amplitude data; Generate geological structure map of road slope based on resonant frequency amplitude data; Optimize the clarity and readability of geological structure maps of road slopes; Correct the optimized geological structure map of the road slope to eliminate the influence of terrain undulation on the geological structure map of the road slope; The impedance interface in the corrected geological structure map of the road slope is identified by frequency domain imaging method; The identified impedance interfaces are labeled and interpreted.

2. According to claim 1, a road slope intelligent detection method based on passive source frequency imaging is characterized in that: Before acquiring the passive source frequency data of the road slope through the deployed multiple two-dimensional survey lines, a consistency check test is conducted on the survey equipment of the multiple two-dimensional survey lines; During the consistency check test, the consistency of all exploration equipment is calculated by combining array passive source observation with correlation calculation.

3. The method for intelligent detection of road slopes based on passive source frequency imaging according to claim 2 is characterized in that: The consistency of all exploration equipment is calculated by combining square array passive source observation with correlation calculation, including: The exploration equipment shall be leveled and pointed to the north before the test, and the test observation shall be continued for a period of time; The consistency of all surveying equipment is calculated using the following formula: Among them, X 1i and X 2i are the i-th measurement values ​​of exploration equipment 1 and exploration equipment 2 respectively, and are the measurement means of surveying equipment 1 and surveying equipment 2 respectively.

4. The method for intelligent detection of road slopes based on passive source frequency imaging according to claim 1 is characterized in that: The acquired road slope passive source frequency data is preprocessed, including: Suppress abnormal amplitude noise; Suppress surface wave noise; Extract the resonant frequencies.

5. The method for intelligent detection of road slopes based on passive source frequency imaging according to claim 4 is characterized in that: Suppressing abnormal amplitude noise specifically involves removing passive source frequency data that exceeds the median by several times the absolute median difference; Suppose we obtain the passive source frequency data X, and arrange them from small to large or from large to small. The number in the middle is the median, recorded as Median(X). The absolute median difference calculation formula is: Median(|X i -Median(X)|) Among them, X i is the passive source frequency data obtained from the i-th measurement.

6. The method for intelligent detection of road slopes based on passive source frequency imaging according to claim 4 is characterized in that: The surface wave noise is suppressed by the Butterworth bandpass filter, and its frequency response formula is: Where H0 is the passband gain, is the center frequency, ω1 and ω2 are the lower cutoff frequency and the upper cutoff frequency respectively, and is the quality factor, BW=ω2-ω1 is the bandwidth.

7. The method for intelligent detection of road slopes based on passive source frequency imaging according to claim 4 is characterized in that: Extracting the resonant frequency includes: Use the fast Fourier transform algorithm to perform frequency domain analysis on the digital signal to obtain the frequency domain characteristics of the signal. The formula used is as follows: Fourier transform formula: Among them, F(ω) is the frequency domain signal, f(t) is the time domain signal, ω is the angular frequency, j is the imaginary unit, and e -jωt is a complex exponential function; Inverse Fourier transform formula: in, is the normalization factor.

8. The method for intelligent detection of road slopes based on passive source frequency imaging according to claim 1 is characterized in that: Correction of the geological structure map of the optimized road slope, including: Correction model establishment: Based on the observation data and surface elevation information in the collected data information, a correction model describing the relationship between the observation data and the surface elevation is established; Correction calculation: The correction model is used to perform correction calculation on the observed data. The seismic data is corrected from the surface to a unified reference surface through Δt=h / v to eliminate the influence of terrain undulation on the geological structure map of the road slope, where h is the elevation difference between the detection point or shot point and the reference surface, and v is the near-surface shear wave velocity.

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