Railway subgrade and underlying stratum multi-physical field on-line detection and evaluation method in frozen soil area

Abstract

The application discloses a kind of frozen soil area railway subgrade and underlying stratum multi-physical field on-line detection evaluation method, through periodic excitation, observation direct current electric field and seismic wave field, network upload data, fast processing direct current electric field and seismic wave field observation data, realize the real-time detection of railway subgrade structure density and the upper and lower limit of underground frozen soil layer, short interval, nondestructive, fast, accurately grasp the change situation of road basic body structure and underground frozen soil layer, provide safety early warning for railway infrastructure, and provide early warning reference standard.Observation system cost is low, installation is convenient, once installation benefits for life, detection method has the advantages such as large detection depth, real-time, few people, intelligent detection, can meet the engineering operation detection demand of plateau railway, highway and the like, provide safety early warning method and equipment for railway, highway and the like subgrade engineering.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of railway engineering exploration, and particularly relates to a method for on-line detection and evaluation of a railway subgrade and underlying strata in a frozen soil area. BACKGROUND

[0002] Railway subgrade underlying strata measurement can be divided into two stages of construction period investigation and operation period monitoring. In long-time monitoring in the operation period, railway engineering often uses electromagnetic method for stratum investigation work, but at present, the work is mainly carried out by using the way of evenly distributed measuring points, especially for area exploration work, which is time-consuming and difficult to guarantee the investigation timeliness, and difficult to meet the needs of rapid and high-frequency detection of plateau railway engineering and other engineering, thereby affecting the subsequent engineering investigation and design work, and the cost of large-area point exploration is relatively high.

[0003] The conventional method is mostly "one-time" or "stage" detection, which cannot realize continuous and time-lapse monitoring, and is difficult to capture the dynamic changes of frozen soil and subgrade state in time. Therefore, it is necessary to study a new investigation method and algorithm to improve the investigation efficiency and save exploration expenses. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a method and system for on-line detection and evaluation of a railway subgrade and underlying strata in a frozen soil area, to solve the problem that the internal structure health condition of the railway subgrade in a complex geological area such as frozen soil cannot be monitored in real time, non-destructively and comprehensively in the operation period, and to realize intelligent safety warning.

[0005] In order to achieve the above purpose, the method for on-line detection and evaluation of a railway subgrade and underlying strata in a frozen soil area provided by the present application comprises the following steps:

[0006] S1, an electric shock distributed detection device is arranged along the line on both sides of the roadbed shoulder, the electric shock distributed detection device comprises a resistivity and seismic wave integrated detector and a shock and electricity integrated sensor;

[0007] S2, a direct current electric field and a seismic wave field are periodically applied, and direct current electric field data and seismic wave field data are periodically received and preprocessed;

[0008] S3, the preprocessed direct current electric field data and seismic wave field data are subjected to direct current and seismic wave double field joint inversion to obtain an electrical conductivity model and a Rayleigh wave velocity model of the underground space;

[0009] S4, a resistivity profile of the subgrade and underlying strata is generated based on the electrical conductivity model, and a Rayleigh wave velocity profile is generated based on the Rayleigh wave velocity model;

[0010] S5. Calculate the roadbed compaction based on the Rayleigh wave velocity profile, and calculate the upper and lower limits of the frozen soil layer based on the resistivity profile.

[0011] S6. Conduct time-shift analysis of subgrade compaction and the upper and lower limits of the frozen soil layer;

[0012] S7. Based on the time-shift analysis results, conduct railway subgrade safety early warning and status reporting.

[0013] More preferably, in S2, the preprocessing includes the following steps:

[0014] Delete abnormal DC electric field data; use the retained DC electric field data to calculate apparent resistivity;

[0015] Bandpass filtering and wavebody removal are performed on seismic wavefield data to remove noise data;

[0016] A two-dimensional Fourier transform was performed on the preserved seismic wavefield data to obtain the power spectrum, and the dispersion curve was extracted using the power spectrum.

[0017] Further preferably, in S3, the preprocessed DC electric field data and seismic wave field data are subjected to a joint DC electric-seismic wave dual-field inversion, including:

[0018] S301. Construct the joint inversion objective function;

[0019]

[0020] in: and These are the fitting terms for DC electric field and seismic wave field observation data, respectively. ,in , It is the cross gradient term; It is the DC electric field conductivity of the model; It is the Rayleigh wave velocity of seismic waves;

[0021] S302. Initialize each grid point in the underground space based on empirical values, assigning each grid point an initial conductivity and an initial Rayleigh wave velocity;

[0022] S303. Input the obtained initial conductivity and initial Rayleigh wave velocity into the conductivity model and wave velocity model respectively, and use the conductivity model to perform forward modeling to obtain the theoretical electric field response value; use the wave velocity model to perform forward modeling to obtain the theoretical wave velocity response value;

[0023] S304. Input the calculated theoretical electric field, wave velocity response value, preprocessed DC electric field data, and seismic wave field data into the constructed joint inversion objective function, and calculate the residuals.

[0024] S305. Determine whether the residual meets the accuracy requirements. If it does, output the conductivity model and wave velocity model at this time.

[0025] If the condition is not met, the derivatives of the objective function with respect to each model parameter will be jointly inverted to form the Jacobian matrix;

[0026] S306. Solving for DC electric field conductivity using the Jacobian matrix. and seismic wave velocity The cross gradient function is used to obtain the update of the DC electric field conductivity. and the update amount of seismic wave and thunderwave velocity. ;

[0027] S307. After updating the conductivity and Rayleigh wave velocity according to the update amount, input the updated conductivity and Rayleigh wave velocity into the conductivity model and wave velocity model, and repeat the above S304-S306 to calculate the residuals again until the residuals converge.

[0028] More preferably, in S4, when generating the resistivity profile and Rayleigh wave velocity profile of the subgrade and underlying strata, the following steps are taken: converting the conductivity model obtained after joint inversion into a resistivity model; representing the resistivity value of each grid with different colors or contour lines using horizontal distance as the abscissa and depth as the ordinate to form a resistivity profile map; and using the Rayleigh wave velocity model output by inversion; representing the wave velocity value of each grid with different colors or contour lines using horizontal distance as the abscissa and depth as the ordinate to form a Rayleigh wave velocity profile map.

[0029] Furthermore, in S5, when calculating the roadbed compaction, the Rayleigh wave velocity is based on the Rayleigh wave velocity profile. Calculate the shear wave velocity inside the roadbed structure ,

[0030] in, v Poisson's ratio, soil subgrade v If we take 0.25, then , The unit is m / s.

[0031] Further preferred, the shear wave velocities inside the roadbed structure are classified according to the following classification levels:

[0032] When Vs < 150 m / s, the subgrade structure area is divided into a loose, low bearing capacity area;

[0033] When Vs = 150~300 m / s, the subgrade structure area is classified as a medium-dense zone;

[0034] When Vs > 300 m / s, the subgrade structure area is classified as a high-density zone.

[0035] More preferably, in S5, determining the upper and lower limits of the frozen soil layer based on the resistivity profile includes:

[0036] On the resistivity profile, draw the resistivity-depth curve from the surface downwards;

[0037] Identify the first inflection point on the resistivity-depth curve where the resistivity value increases rapidly, and determine the depth corresponding to the first inflection point as the upper limit of the frozen soil.

[0038] Continuing downwards from the upper limit of the permafrost, identify the second inflection point on the resistivity-depth curve where the resistivity value drops rapidly, and determine the depth corresponding to the second inflection point as the lower limit of the permafrost layer.

[0039] The above process is repeated at multiple measuring points along the roadbed, and the upper limit of the frozen soil at all measuring points is connected to form the upper limit line of the frozen soil, and the lower limit of the frozen soil at all measuring points is connected to form the lower limit of the frozen soil.

[0040] More preferably, the time-shift analysis of roadbed compaction and the upper and lower limits of the frost layer includes:

[0041] Shear wave velocity change rate analysis, including:

[0042] Will t 1 Shear wave velocity profile at time t and t 2 The shear wave velocity profiles at time t are subtracted to obtain the shear wave velocity change profiles over the time interval Δt.

[0043] The profile is divided into a preset grid, and the rate of change of shear wave velocity in each grid is calculated.

[0044] Cross-sectional diagrams were drawn based on the changes in shear wave velocity and rate of change, respectively.

[0045] Resistivity change rate analysis, including:

[0046] use resistivity at time and resistivity at time Perform the difference to obtain Cross-section of resistivity change over time Calculate the total rate of change of resistivity over the time interval Δt. ,

[0047] The profile was divided into 0.1m × 0.1m grids, and the resistivity of each region was... According to the formula The rate of change of resistivity was obtained. , This indicates that the water content inside the roadbed has decreased and the structure has become stronger. This indicates that the increased water content and weakened strength of the roadbed are reflected in changes in resistivity. and rate of change Draw cross-sectional views for each attribute;

[0048] Analysis of the changes in the upper and lower limits of frozen soil elevation:

[0049] The changes in the upper and lower limits of the frozen soil elevation at time t1 and at time t2 are obtained by subtracting them from the upper and lower limits of the frozen soil elevation at time t2.

[0050] This invention also provides an online multi-physics field detection and evaluation system for railway subgrade and underlying strata in permafrost regions, comprising:

[0051] Multiple resistivity seismic wave integrated detectors are deployed on both sides of the roadbed shoulders to periodically excite and receive DC electric fields and seismic wave fields.

[0052] Two cables are connected to each of the detectors, and the cables are laid along the line direction; multiple integrated seismoelectric sensors are laid on the cables for collecting DC electric field data and seismic wave field data;

[0053] The data processing and early warning server is used to receive the collected DC electric field data and seismic wave field data, and execute the steps of the method described above to generate early warning information.

[0054] More preferably, the data processing and early warning server integrates algorithms for performing DC-seismic wave dual-field joint inversion, roadbed compaction calculation, determination of the upper and lower limits of the frozen soil layer, and time-lapse analysis.

[0055] The multi-physics field online detection and evaluation method and system for railway subgrade and underlying strata in permafrost areas disclosed in this application have the following beneficial effects:

[0056] 1. This application utilizes a vertical magnetic coil to transmit a magnetic field at a single frequency and a receiving coil to move and scan, eliminating the need for grounding in the exploration system and resulting in high exploration efficiency;

[0057] 2. Single-frequency regional exploration data: By looking up the table in the pre-calculated resistivity and polarizability vector map, the resistivity and polarizability of the earth can be found, thereby quickly delineating abnormal sections and target areas, which can effectively improve exploration efficiency.

[0058] 3. In the rapidly delineated anomaly zones, further multi-frequency static data acquisition and stratigraphic inversion were carried out, enriching the anomaly zone data and ensuring the accuracy of the exploration results and data. Finally, the exploration results combined horizontal and vertical profiles, which not only ensured exploration efficiency but also guaranteed exploration accuracy. Under the premise of ensuring exploration accuracy, the exploration cost was also significantly reduced. Attached Figure Description

[0059] Figure 1 This is a flowchart of a multi-physics field online detection and evaluation method for railway subgrade and underlying strata in permafrost areas according to the present invention;

[0060] Figure 2 This is a layout diagram of the online detection and data acquisition system for multi-physics fields of railway subgrade and underlying strata in permafrost regions according to the present invention;

[0061] Figure 3 This is a flowchart of the joint inversion process of DC electric field data and seismic wave field data according to the present invention;

[0062] Figure 4 This is a DC electric field resistivity profile of the present invention;

[0063] Figure 5 This is a Rayleigh wave velocity cross-section diagram of the present invention;

[0064] Figure 6 This is a cross-sectional view of the shear wave velocity of the present invention;

[0065] Figure 7 This is the resistivity-depth curve of the present invention;

[0066] Figure 8 This is a cross-sectional diagram of the shear wave velocity variation and rate of change of the present invention;

[0067] Figure 9 This is a cross-sectional view of the resistivity change and the rate of change of the present invention;

[0068] Figure 10 This is a cross-sectional view of the upper and lower limits of frozen soil in this invention. Detailed Implementation

[0069] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] like Figure 1 As shown, one embodiment of the present invention provides a multi-physics field online detection and evaluation method for railway subgrade and underlying strata in permafrost areas, comprising the following steps:

[0071] S1. Install distributed seismic monitoring devices along the shoulders on both sides of the roadbed, such as... Figure 2As shown, the distributed seismic detection device includes an integrated resistivity seismic wave detector and an integrated seismoelectric sensor. During deployment, the integrated resistivity seismic wave detector is typically placed at intervals along the roadway on both sides of the roadbed's earthen shoulders, with a spacing generally between 200 and 1000 meters. This spacing depends on the roadbed condition and the required observation accuracy; the higher the roadbed grade and the worse its condition, the smaller the spacing should be. In addition to the integrated resistivity seismic wave detector, cables and integrated seismoelectric sensors are also installed along the roadway on both sides of the detector. The sensor's tail is an electrode that receives a DC electric field, and a seismic detector is nested inside the sensor to receive seismic wave fields. Two cables, each 22 meters long, are used, along with 20 sensors spaced 1 meter apart. The cables and sensors need to be buried approximately 10 cm below the surface of the roadbed shoulder. The integrated resistivity seismic wave detector is fixed to the roadbed shoulder and is powered by an internally equipped lithium battery. Simultaneously, solar panels embedded on the detector's surface charge the internal lithium battery.

[0072] S2. Periodically apply DC electric field and seismic wave field, and periodically receive DC electric field data and seismic wave field data for preprocessing; The resistivity seismic wave integrated detector in this application can emit DC current and seismic waves, with a maximum excitation voltage of 1000V. In the time-lapse detection of roadbed structures, the stratum is detected by periodically exciting DC electric field and seismic wave field. The excitation period can be adjusted according to the importance and state of the roadbed. For important roadbeds or high-risk conditions, a short period is preferred, such as excitation once every 1 hour. For general roadbeds and low-risk conditions, a long period is preferred, such as excitation once every 12 hours. The seismic wave is excited by the exciter in the central position, and the DC current is supplied to the stratum through the two outermost sensors. The resistivity-seismic wave integrated detector can not only excite direct current and seismic waves, but also receive the ground electric field and seismic waves sensed by the integrated seismoelectric sensor. For each seismic wave excitation, 20 sensors simultaneously receive the seismic wave; the direct current electric field is received by 18 sensors (excluding A and B). With the midpoint as the center, the sensors are paired up, and the potential difference is observed in a gradient manner, for a total of 9 sets of potential differences. .

[0073] More preferably, in S2, the preprocessing includes the following steps:

[0074] Deleting abnormal DC electric field data; calculating apparent resistivity using the retained DC electric field data; data preprocessing, including data quality evaluation and removal of abnormal data. Abnormal data generally originates from environmental interference. The principle for removing data is to remove values ​​that deviate from daily observation values ​​by more than ±3 times.

[0075] Apparent resistivity is calculated based on the formula. Nine sets of potential differences for each station Perform apparent resistivity calculation, where Apparent resistivity (unit) ), To measure potential difference (unit: V), Excitation current (unit: A) This is the device coefficient (dimensionless, related to the electrode spacing). , For power supply electrodes, For measuring electrodes, (Electrode distance).

[0076] Bandpass filtering and wavebody removal are performed on seismic wavefield data to remove noise data;

[0077] A two-dimensional Fourier transform is performed on the preserved seismic wavefield data to obtain the power spectrum, which is then used to extract dispersion curves. Specifically, this includes:

[0078] Data preprocessing includes noise removal and volume wave removal. Noise removal is performed using bandpass filtering with a frequency band of 1-200Hz. Volume wave removal is performed by selecting the time domain data region to preserve the Rayleigh wave field.

[0079] Dispersion curve extraction, the dispersion curve attribute values ​​are derived from phase velocity Sure, It is the power spectrum In frequency Peak wavenumber at that location.

[0080] Power spectrum The two-dimensional Fourier transform was performed on 20 seismic wavefield data. ,in Earthquake records (time-space domain) Frequency (Hz) Wave number (1 / m) : number of channels, Number of time sampling points.

[0081] S3. Perform a joint inversion of the preprocessed DC electric field data and seismic wave field data to obtain the conductivity model and Rayleigh wave velocity model of the underground space.

[0082] like Figure 3 As shown, the preprocessed DC electric field data and seismic wave field data are subjected to a joint DC electric-seismic wave dual-field inversion, including:

[0083] S301. Construct the joint inversion objective function

[0084]

[0085] in: and These are the fitting terms for DC electric field and seismic wave field observation data, respectively. ,in , It is the cross gradient term; It is the DC electric field conductivity of the model; It is the Rayleigh wave velocity of seismic waves;

[0086] S302. Initialize each grid point in the underground space based on empirical values, assigning each grid point an initial conductivity and an initial Rayleigh wave velocity; record the initial conductivity. and initial Rayleigh wave velocity ;

[0087] S303. Input the obtained initial conductivity and initial Rayleigh wave velocity into the conductivity model and wave velocity model respectively. Use the conductivity model for forward modeling to obtain the theoretical electric field response value; use the wave velocity model for forward modeling to obtain the theoretical wave velocity response value; input the initial conductivity value into the model. And Rayleigh speed Input the values ​​from each grid, and then calculate the forward modeling responses of the DC electric field and the seismic wave field. The forward modeling response of the DC electric field is shown below. By solving the DC electric field potential Satisfied Poisson equation get. δ is the current intensity, and δ is the Dirac function. and These are the source point locations.

[0088] Forward Modeling Response of Seismic Wave Field Through the elastic wave equation The solution is obtained, where It is the model density. It is the i-th displacement component. It is the stiffness matrix. It is the focal point.

[0089] S304. Input the calculated theoretical electric field, wave velocity response value, preprocessed DC electric field data, and seismic wave field data into the constructed joint inversion objective function, and calculate the residuals.

[0090] residual in, This is the theoretical electric field response value. This is the theoretical wave velocity response value; This is the forward modeling response to a DC electric field. This is the forward modeling response of the seismic wave field.

[0091] S305. Determine whether the residual meets the accuracy requirements. If it does, output the conductivity model and wave velocity model at this time.

[0092] If the conditions are not met, the derivatives of the objective function with respect to each model parameter will be jointly inverted to form the Jacobian matrix; it will then be determined whether the set target value has been reached. If it has, proceed to step S5 and output the inversion result; otherwise, the Jacobian matrix will be calculated. Calculation;

[0093] S306. Solving for DC electric field conductivity using the Jacobian matrix. and seismic wave velocity The cross gradient function is used to obtain the update of the DC electric field conductivity. and the update amount of seismic wave and thunderwave velocity. ;

[0094] Jacobian matrix It is the objective function For formation conductivity And Rayleigh speed The derivatives of the two parameters are calculated using the following formula:

[0095]

[0096] After unfolding:

[0097]

[0098] Indicates the real part.

[0099]

[0100] Therefore, the Jacobian matrix Represented as:

[0101]

[0102] DC electric field conductivity and seismic wave velocity The cross gradient is expressed as

[0103]

[0104] In the case of a two-dimensional roadbed model, the cross gradient function can be expressed as:

[0105]

[0106] Solve the equation Obtain the model modification amount ,Right now and .

[0107] S307. After updating the conductivity and Rayleigh wave velocity based on the update amount, input the updated conductivity and Rayleigh wave velocity into the conductivity model and wave velocity model, and repeat S304-S306 above to recalculate the residuals until the residuals converge. Then update the physical quantities of each grid in the model. , Repeat steps S304-S306 above to calculate the residuals again until the residuals converge.

[0108] S4. Generate resistivity profiles of the roadbed and underlying strata based on the conductivity model, and generate Rayleigh wave velocity profiles based on the Rayleigh wave velocity model.

[0109] When generating resistivity profiles and Rayleigh wave velocity profiles for the roadbed and underlying strata, the process includes converting the conductivity model obtained after joint inversion into a resistivity model; using horizontal distance as the abscissa and depth as the ordinate, the resistivity values ​​of each grid are represented by different colors or contour lines, forming a pattern such as... Figure 4 The resistivity profile shown; using the Rayleigh wave velocity model from the inversion output; with horizontal distance as the x-axis and depth as the y-axis, the wave velocity value of each grid is represented by different colors or contour lines, forming a pattern as shown. Figure 5 The diagram shows the Rayleigh wave velocity profile.

[0110] More preferably, in S4, when calculating the roadbed compaction, the Rayleigh wave velocity is based on the Rayleigh wave velocity profile. Calculate the shear wave velocity inside the roadbed structure and draw as follows Figure 6 The diagram shows the shear wave velocity profile.

[0111] in, v Poisson's ratio, soil subgrade v If we take 0.25, then , The unit is m / s.

[0112] Further preferred, the shear wave velocities inside the roadbed structure are classified according to the following classification levels:

[0113] When Vs < 150 m / s, the subgrade structure area is divided into a loose, low bearing capacity area;

[0114] When Vs = 150~300 m / s, the subgrade structure area is classified as a medium-dense zone;

[0115] When Vs > 300 m / s, the subgrade structure area is classified as a high-density zone.

[0116] S5. Calculate the roadbed compaction based on the Rayleigh wave velocity profile, and calculate the upper and lower limits of the frozen soil layer based on the resistivity profile.

[0117] More preferably, in S5, determining the upper and lower limits of the frozen soil layer based on the resistivity profile includes:

[0118] On a resistivity profile, drawn downwards from the surface as shown... Figure 7 The resistivity-depth curve shown;

[0119] Identify the first inflection point on the resistivity-depth curve where the resistivity value increases rapidly, and determine the depth corresponding to the first inflection point as the upper limit of the frozen soil.

[0120] Continuing downwards from the upper limit of the permafrost, identify the second inflection point on the resistivity-depth curve where the resistivity value drops rapidly, and determine the depth corresponding to the second inflection point as the lower limit of the permafrost layer.

[0121] Repeat the above process at multiple measuring points along the roadbed, and connect the upper limits of the frozen soil at all measuring points to form the upper limit line of the frozen soil, and connect the lower limits of the frozen soil at all measuring points to form the lower limit line of the frozen soil.

[0122] S6. Conduct time-lapse analysis of roadbed compaction and the upper and lower limits of the frozen soil layer, specifically including shear wave velocity change rate analysis, resistivity change rate analysis, and frozen soil layer upper and lower limit elevation change analysis.

[0123] The specific process of analyzing the rate of change of shear wave velocity is as follows:

[0124] Will t 1 Shear wave velocity profile at time t and t 2 The shear wave velocity profiles at time t are subtracted to obtain the shear wave velocity change profiles over the time interval Δt.

[0125] That is, get Cross-section of shear wave velocity variation over time And further calculate the rate of change of shear wave velocity. First, the profile was divided into 0.1m × 0.1m grids, and the shear wave velocity in each region was... Then according to the formula The rate of change of shear wave velocity was obtained. rate of change Positive and negative values ​​can be observed; positive values ​​indicate increased roadbed strength, while negative values ​​indicate decreased roadbed strength. The final value reflects the change in shear wave velocity. and rate of change For the attributes, draw them respectively as follows Figure 8 The cross-sectional view shown has depth (in meters) on the vertical axis and mileage (in meters) on the horizontal axis.

[0126] use resistivity at time and resistivity at time Perform the difference to obtain Cross-section of resistivity change over time ,calculate Total resistivity change rate over time period ,

[0127] The profile was divided into 0.1m × 0.1m grids, and the resistivity of each region was... According to the formula The rate of change of resistivity was obtained. , This indicates that the water content inside the roadbed has decreased and the structure has become stronger. This indicates that the increased water content and weakened strength of the roadbed are reflected in changes in resistivity. and rate of change For the attributes, draw them respectively as follows Figure 9 The cross-sectional view shown;

[0128] Will The upper and lower limits of the permafrost layer at time are respectively related to The difference between the upper and lower limits of the permafrost layer at time t is obtained. Changes in the upper and lower limits of the frozen soil layer within a given period;

[0129] The process mainly consists of two parts: drawing the upper and lower time-lapse profiles. The upper limit represents the freeze-thaw boundary of the permafrost layer, and the lower limit represents the lower interface of the permafrost layer. The specific process of drawing the time-lapse profile is as follows: using... The lower limit elevation of the permafrost layer at any time and The difference between the lower limit elevation of the permafrost layer and the actual elevation is obtained. Cross-section of changes in the upper and lower limits of frozen soil elevation over a period of time , , drawing as Figure 10 The displacement profile shown in the figure reveals the upper limit of the permafrost layer, thus allowing us to determine the changes in the permafrost elevation.

[0130] S7. Based on the time-shift analysis results, conduct railway subgrade safety early warning and status reporting.

[0131] Based on the time-shift detection index (shear wave velocity change rate) generated in step S7 resistivity change rate Changes in the upper and lower limits of the frozen soil layer , (and density conditions), and conduct railway subgrade safety early warning and status reporting through the network.

[0132] When the rate of change of shear wave velocity resistivity change rate More than 5% considered it a slight change, more than 10% considered it a moderate change, and more than 20% considered it a severe change. and When one or two items reach a moderate or severe level of change, a safety warning should be issued in a timely manner; when there is a slight change, only a status report should be issued.

[0133] The compaction of the subgrade is determined by the shear wave velocity. Generally, a shear wave velocity Vs < 150 m / s indicates that the subgrade soil is loose and has low bearing capacity; Vs = 150~300 m / s indicates medium compaction; and Vs > 300 m / s indicates high compaction, which is suitable for high-grade subgrades.

[0134] Changes in the upper and lower limits of the frozen soil layer , The changes are classified as slight, moderate, and severe when the changes reach 0.5m, 1m, and 2m, respectively, and a safety warning is issued when the changes reach 1m and 2m.

[0135] This invention also provides an online multi-physics field detection and evaluation system for railway subgrade and underlying strata in permafrost regions, such as... Figure 2 As shown, the system includes: multiple resistivity seismic wave integrated detectors deployed on both sides of the roadbed shoulders for periodically exciting and receiving DC electric fields and seismic wave fields; two cables connected to each detector, the cables being laid along the route; multiple seismoelectric integrated sensors deployed on the cables for collecting DC electric field data and seismic wave field data; and a data processing and early warning server for receiving the collected DC electric field data and seismic wave field data, executing the above-described method, and generating early warning information.

[0136] More preferably, the data processing and early warning server integrates algorithms for performing DC-seismic wave dual-field joint inversion, roadbed compaction calculation, determination of the upper and lower limits of the frozen soil layer, and time-lapse analysis.

[0137] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A multi-physics field online detection and evaluation method for railway subgrade and underlying strata in permafrost regions, characterized in that, Includes the following steps: S1. Install distributed electro-seismic detection devices along the road shoulders on both sides of the roadbed. The distributed electro-seismic detection devices include an integrated resistivity seismic wave detector and an integrated seismo-electric sensor. S2. Periodically apply DC electric field and seismic wave field, and periodically receive DC electric field data and seismic wave field data for preprocessing; S3. Perform a joint inversion of the preprocessed DC electric field data and seismic wave field data to obtain the conductivity model and Rayleigh wave velocity model of the underground space. The preprocessed DC electric field data and seismic wave field data are subjected to a joint DC electric-seismic wave dual-field inversion, including: S301. Construct the joint inversion objective function in: and These are the fitting terms for DC electric field and seismic wave field observation data, respectively. ,in It is the cross gradient term; It is the DC electric field conductivity of the model; It is the Rayleigh wave velocity of seismic waves; S302. Initialize each grid point in the underground space based on empirical values, assigning each grid point an initial conductivity and an initial Rayleigh wave velocity; S303. Input the obtained initial conductivity and initial Rayleigh wave velocity into the conductivity model and wave velocity model respectively, and use the conductivity model to perform forward modeling to obtain the theoretical electric field response value; use the wave velocity model to perform forward modeling to obtain the theoretical wave velocity response value; S304. Input the calculated theoretical electric field, wave velocity response value, preprocessed DC electric field data, and seismic wave field data into the constructed joint inversion objective function, and calculate the residuals. S305. Determine whether the residual meets the accuracy requirements. If it does, output the conductivity model and wave velocity model at this time. If the condition is not met, the derivatives of the objective function with respect to each model parameter will be jointly inverted to form the Jacobian matrix; S306. Solving for DC electric field conductivity using the Jacobian matrix. and seismic wave velocity The cross gradient function is used to obtain the update of the DC electric field conductivity. and the update amount of seismic wave and thunderwave velocity. ; S307. After updating the conductivity and Rayleigh wave velocity according to the update amount, input the updated conductivity and Rayleigh wave velocity into the conductivity model and wave velocity model, and repeat the above S304-S306 to calculate the residuals again until the residuals converge. S4. Generate resistivity profiles of the roadbed and underlying strata based on the conductivity model, and generate Rayleigh wave velocity profiles based on the Rayleigh wave velocity model. S5. Calculate the roadbed compaction based on the Rayleigh wave velocity profile, and calculate the upper and lower limits of the frozen soil layer based on the resistivity profile. S6. Conduct time-lapse analysis of subgrade compaction and the upper and lower limits of the frost layer; including: Shear wave velocity change rate analysis; Will t 1 Shear wave velocity profile at time t and t 2 The shear wave velocity profiles at time t are subtracted to obtain the shear wave velocity change profiles over the time interval Δt. The profile is divided into a preset grid, and the rate of change of shear wave velocity in each grid is calculated. Cross-sectional diagrams were drawn based on the changes in shear wave velocity and rate of change, respectively. Resistivity change rate analysis includes: use resistivity at time and The resistivity at each time step is subtracted to obtain... resistivity change profile over time Calculate the total rate of change of resistivity over the time interval Δt. , The profile was divided into 0.1m × 0.1m grids, and the resistivity of each region was... According to the formula The rate of change of resistivity was obtained. , This indicates that the water content inside the roadbed has decreased and the structure has become stronger. This indicates that the internal water content of the roadbed has increased and its strength has weakened, as reflected in the change in resistivity. and rate of change Draw cross-sectional views for each attribute; Analysis of the changes in the upper and lower limits of frozen soil elevation: The difference between the upper and lower limits of the frozen soil elevation at time t1 and the upper and lower limits of the frozen soil elevation at time t2 is used to obtain the change in the upper and lower limits of the frozen soil elevation during the time period Δt. S7. Based on the time-shift analysis results, conduct railway subgrade safety early warning and status reporting.

2. The online multi-physics field detection and evaluation method for railway subgrade and underlying strata in permafrost areas according to claim 1, characterized in that, In S2, the preprocessing includes the following steps: Delete abnormal DC electric field data; use the retained DC electric field data to calculate apparent resistivity; Bandpass filtering and wavebody removal are performed on seismic wavefield data to remove noise data; A two-dimensional Fourier transform was performed on the preserved seismic wavefield data to obtain the power spectrum, and the dispersion curve was extracted using the power spectrum.

3. The online multi-physics field detection and evaluation method for railway subgrade and underlying strata in permafrost areas according to claim 1, characterized in that, In S4, when generating resistivity profiles and Rayleigh wave velocity profiles of the subgrade and underlying strata, the following steps are taken: converting the conductivity model obtained after joint inversion into a resistivity model; representing the resistivity value of each grid with different colors or contour lines using horizontal distance as the abscissa and depth as the ordinate to form a resistivity profile map; and using the Rayleigh wave velocity model output from the inversion, representing the wave velocity value of each grid with different colors or contour lines using horizontal distance as the abscissa and depth as the ordinate to form a Rayleigh wave velocity profile map.

4. The online multi-physics field detection and evaluation method for railway subgrade and underlying strata in permafrost areas according to claim 1, characterized in that, In S5, when calculating the roadbed compaction, the Rayleigh wave velocity is based on the Rayleigh wave velocity profile. Calculate the shear wave velocity inside the roadbed structure , Where ν is Poisson's ratio, and for soil subgrade ν is taken as 0.25, then , The unit is m / s.

5. The online multi-physics field detection and evaluation method for railway subgrade and underlying strata in permafrost areas according to claim 4, characterized in that, The shear wave velocities inside the roadbed structure are classified according to the following classification levels: When Vs < 150 m / s, the subgrade structure area is divided into a loose, low bearing capacity area; When Vs = 150~300 m / s, the subgrade structure area is classified as a medium-dense zone; When Vs > 300 m / s, the subgrade structure area is classified as a high-density zone.

6. The online multi-physics field detection and evaluation method for railway subgrade and underlying strata in permafrost areas according to claim 4, characterized in that, In S5, based on the resistivity profile, the upper and lower limits of the frozen soil layer are determined, including: On the resistivity profile, draw the resistivity-depth curve from the surface downwards; Identify the first inflection point on the resistivity-depth curve where the resistivity value increases rapidly, and determine the depth corresponding to the first inflection point as the upper limit of the frozen soil. Continuing downwards from the upper limit of the permafrost, identify the second inflection point on the resistivity-depth curve where the resistivity value drops rapidly, and determine the depth corresponding to the second inflection point as the lower limit of the permafrost layer. The above process is repeated at multiple measuring points along the roadbed, and the upper limit of the frozen soil at all measuring points is connected to form the upper limit line of the frozen soil, and the lower limit of the frozen soil at all measuring points is connected to form the lower limit line of the frozen soil.

7. A multi-physics field online detection and evaluation system for railway subgrade and underlying strata in permafrost regions, characterized in that, include: Multiple resistivity seismic wave integrated detectors are deployed on both sides of the roadbed shoulders to periodically excite and receive DC electric fields and seismic wave fields. Two cables are connected to each of the detectors, and the cables are laid along the line direction; Multiple integrated seismoelectric sensors are deployed on the cable to collect DC electric field data and seismic wave field data; A data processing and early warning server is used to receive collected DC electric field data and seismic wave field data, and execute the method described in any one of claims 1-6 to generate early warning information.

8. The online multi-physics field detection and evaluation system for railway subgrade and underlying strata in permafrost areas according to claim 7, characterized in that, The data processing and early warning server integrates algorithms for performing DC-seismic wave dual-field joint inversion, roadbed compaction calculation, determination of upper and lower limits of frozen soil layer, and time-lapse analysis.

Images