Loess collapsibility evaluation method and system based on geophysical testing technology

Abstract

The application discloses a loess collapsibility evaluation method and system based on geophysical prospecting test technology, relates to the field of land exploration, and comprises the following steps: arranging a regular detection grid in a loess site to be evaluated, and constructing a site geographic coordinate system; collecting Rayleigh wave signals in real time, inverting a shear wave velocity profile, and collecting apparent resistivity data; drilling shallow soil samples, measuring soil parameters, constructing a model according to the mapping relationship between site geophysical parameters and soil parameters; inputting the collected data into a wave velocity-moisture content correlation model and a resistivity-dry density correlation model, generating a moisture content distribution field and a dry density distribution field; calculating the collapsibility of loess in each detection grid based on a geophysical response equation of the collapsibility, outputting a collapsibility contour map, and determining the collapsibility grade of each grid and the distribution range of collapsible loess. The application has the advantages that: through accurate non-invasive data collection and model analysis, a collapsibility distribution field and a contour map are generated, and a scientific basis is provided for engineering construction.

Description

Technical Field

[0001] This invention relates to the field of land exploration, and in particular to a method and system for evaluating the collapsibility of loess based on geophysical testing technology. Background Technology

[0002] Loess collapsibility refers to the volume change of loess under moist or varying moisture conditions due to water adsorption, which may lead to foundation settlement or failure, affecting the safety of engineering structures. With the advancement of urbanization, engineering construction in loess areas is gradually increasing; therefore, loess collapsibility assessment has become an important aspect of ensuring engineering safety. Geophysical testing techniques can effectively obtain the physical parameters and collapsibility characteristics of loess layers. Through geophysical testing, information on loess collapsibility can be quickly obtained without damaging the soil structure, helping engineers determine the stability and adaptability of the foundation.

[0003] Current methods for assessing loess collapsibility based on geophysical testing techniques largely rely on manual sampling and local borehole analysis. Furthermore, the assessment results are often limited to point data, failing to comprehensively cover the entire site and introducing spatial errors. In addition, existing methods often neglect the variations in soil properties at different depths, resulting in limited depth and breadth of assessment. Moreover, many existing methods require extensive on-site investigation and excavation, potentially causing environmental damage and increasing engineering costs and construction time. They also cannot provide the same level of data integration and visualization as this method, resulting in relatively low accuracy and efficiency, and may be unsuitable for complex site conditions. Summary of the Invention

[0004] To improve existing methods and systems, this paper presents a method and system for evaluating the collapsibility of loess based on geophysical testing technology. This method efficiently generates collapsibility distribution fields and contour maps through precise non-invasive data acquisition and model analysis, providing a scientific basis for engineering construction. Compared with traditional methods, it is lower in cost, higher in accuracy, and more applicable, significantly improving the efficiency and reliability of the assessment.

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

[0006] Methods for evaluating the collapsibility of loess based on geophysical testing technology include:

[0007] In the loess site to be evaluated, a site geographic coordinate system is constructed by setting up a detection grid according to the rules, and the site grid location data is mapped to the coordinate system;

[0008] Rayleigh wave signals were collected in real time along the grid nodes using a surface wave instrument. The shear wave velocity profile in the 0-20m depth range was inverted using the dispersion curve. Apparent resistivity data were collected along the same grid using the high-density resistivity method.

[0009] Shallow soil samples were drilled from each detection grid to determine soil parameters, including water content and dry density. Based on the mapping relationship between site geophysical parameters and soil parameters, models were constructed, including wave velocity-water content correlation model and resistivity-dry density correlation model.

[0010] The acquired shear wave velocity data and apparent resistivity data are input into the wave velocity-water content correlation model and the resistivity-dry density correlation model, and interpolation is used to generate the water content distribution field and the dry density distribution field.

[0011] The collapsibility of loess within each detection grid is calculated based on the geophysical response equation of the collapsibility coefficient, and the collapsibility coefficient contour map is output in layers according to depth to determine the collapsibility level of each grid and the distribution range of collapsible loess.

[0012] Preferably, the step of laying out a regular detection grid on the loess site to be evaluated, constructing a site geographic coordinate system, and mapping the site grid location data to the coordinate system specifically includes:

[0013] Based on the topography and landform of the loess site, square grids were selected for layout, the grid size was determined, and a unique identifier was added to each grid.

[0014] Based on the selected site geographic coordinate system, the local coordinate data of each grid is mapped to the geographic coordinate system.

[0015] Preferably, the step of acquiring Rayleigh wave signals in real time along the grid nodes using a surface wave instrument, inverting the shear wave velocity profile in the 0-20m depth range underground using dispersion curves, and acquiring apparent resistivity data along the same grid using the high-density resistivity method specifically includes:

[0016] A surface wave meter receiver is deployed at each grid node, and the distance between the measurement nodes is determined based on the spacing set in the grid.

[0017] Rayleigh wave signals are collected along each grid node. Data is recorded by receiving vibration signals from the ground, and dispersion curves are generated using a dispersion analysis algorithm.

[0018] The dispersion curves were processed by nonlinear inversion to obtain shear wave velocity profiles at different depths underground.

[0019] The electrode array required for the high-density resistivity method is deployed at the same grid nodes, and data is collected at each grid node. The apparent resistivity value at each depth is calculated based on the collected current and voltage signal data.

[0020] Preferably, the step of drilling shallow soil samples based on each detection grid, determining soil parameters including water content and dry density, and constructing models based on the mapping relationship between site geophysical parameters and soil parameters, including wave velocity-water content correlation models and resistivity-dry density correlation models, specifically includes:

[0021] Based on the deployed detection grid, borehole sampling is performed at representative locations around the grid nodes;

[0022] The collected soil samples were analyzed in the laboratory to obtain soil properties, including: determining the moisture content of the soil samples by the drying method and determining the dry density of the soil samples by the mass method.

[0023] Based on the transverse wave velocity and soil moisture content data of different grid nodes, a wave velocity-moisture content correlation model was constructed by fitting the relationship between wave velocity and moisture content through regression analysis.

[0024] Based on the apparent resistivity data of different grid nodes and the dry density of the corresponding soil samples, a resistivity-dry density correlation model is constructed by fitting the relationship between resistivity and dry density through regression analysis.

[0025] Preferably, the step of inputting the acquired shear wave velocity data and apparent resistivity data into the wave velocity-water content correlation model and the resistivity-dry density correlation model, and interpolating to generate the water content distribution field and the dry density distribution field specifically includes:

[0026] The real-time collected shear wave velocity data is input into the wave velocity-water content correlation model to calculate the water content data of each grid node;

[0027] The apparent resistivity data is input into the resistivity-dry density correlation model to calculate the dry density data for each grid node;

[0028] Based on the obtained moisture content and dry density data of each grid node, the moisture content distribution field and dry density distribution field of the entire site are obtained by interpolation using spline interpolation.

[0029] Preferably, the calculation of the collapsibility of loess within each detection grid using the geophysical response equation based on the collapsibility coefficient, and the output of collapsibility coefficient contour maps by depth layer, to determine the collapsibility level and distribution range of collapsible loess in each grid specifically includes:

[0030] The collapsibility coefficient is calculated based on the ratio of the expansion of soil volume with water change to the initial volume under saturated water conditions.

[0031] Based on the relationship between the physical properties of loess and the collapsibility coefficient, the wave velocity-water content model and the resistivity-dry density model are used to calculate and construct the geophysical response equation.

[0032] The geophysical data and soil parameters of each grid are collected and input into the geophysical response equation to calculate the collapsibility coefficient in each detection grid.

[0033] The loess site is divided into layers based on depth, and the collapsibility coefficient of each layer is calculated to obtain a continuous collapsibility coefficient distribution field and generate a collapsibility coefficient contour map.

[0034] The degree of collapse is determined based on the magnitude of the collapse coefficient;

[0035] Based on the collapsibility coefficient and collapsibility level of each grid, the spatial distribution of collapsible loess is obtained.

[0036] Furthermore, a loess collapsibility evaluation system based on geophysical testing technology is proposed, including:

[0037] Detection grid and coordinate system module: The detection grid and coordinate system module lays out a regular detection grid in the loess site to be evaluated, and constructs a site geographic coordinate system, mapping the grid location data to the coordinate system;

[0038] Data acquisition module: The data acquisition module acquires Rayleigh wave signals along the grid nodes using a surface wave instrument and acquires apparent resistivity data using the high-density resistivity method for analysis of underground soil physical parameters;

[0039] Soil Parameters and Model Module: The Soil Parameters and Model Module constructs wave velocity-water content correlation models and resistivity-dry density correlation models by drilling shallow soil samples and measuring soil parameters;

[0040] Distribution field generation module: The distribution field generation module inputs the collected shear wave velocity data and apparent resistivity data into the correlation model, and generates water content distribution field and dry density distribution field through interpolation;

[0041] Collapsibility Calculation Module: The collapsibility calculation module calculates the collapsibility of loess in each detection grid based on the collapsibility coefficient geophysical response equation, and outputs collapsibility coefficient contour maps in layers according to depth to determine the collapsibility level;

[0042] Collapsible loess distribution module: The collapsible loess distribution module determines the spatial distribution of collapsible loess based on collapsibility coefficient and collapsibility grade data, and completes the collapsibility assessment;

[0043] Processor: The processor is used to handle the calculation process of each formula and the construction calculation process of each model.

[0044] Compared with the prior art, the advantages of the present invention are:

[0045] By combining a regular detection grid with a geographic coordinate system, the spatial characteristics of the site are accurately located, ensuring the systematic nature and accuracy of data acquisition. Rayleigh wave signals and apparent resistivity data are jointly acquired using a surface wave instrument and a high-density resistivity method, enabling precise inversion of shear wave velocity and resistivity within a depth range of 0–20 m, providing comprehensive and efficient data coverage. A wave velocity-water content and resistivity-dry density correlation model, established by combining borehole sampling and laboratory analysis, accurately fits soil physical parameters through regression analysis, demonstrating high model reliability. Spline interpolation is used to generate water content and dry density distribution fields. Combined with the geophysical response equation for the collapsibility coefficient, collapsibility is calculated, and depth-layered contour maps are generated, visually displaying the collapsibility level and distribution range. This method eliminates the need for large-scale excavation, reducing environmental damage, and is low-cost and highly efficient, making it suitable for rapid assessment of complex terrain sites. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the method proposed in this invention;

[0047] Figure 2 This is a schematic diagram illustrating the division of the detection grid and the construction of the site geographic coordinate system proposed in this invention;

[0048] Figure 3 This is a schematic diagram illustrating the method for obtaining site geophysical parameters proposed in this invention;

[0049] Figure 4 This is a schematic diagram of the construction model proposed in this invention;

[0050] Figure 5 This is a schematic diagram of the generated water content distribution field and dry density distribution field proposed in this invention;

[0051] Figure 6 This is a schematic diagram illustrating the determination of collapsibility level and the distribution of collapsible loess proposed in this invention. Detailed Implementation

[0052] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0053] A loess collapsibility evaluation system based on geophysical testing technology includes:

[0054] Detection grid and coordinate system module: The detection grid and coordinate system module lays out a regular detection grid in the loess site to be evaluated, and constructs a site geographic coordinate system, mapping the grid location data to the coordinate system;

[0055] Data acquisition module: The data acquisition module acquires Rayleigh wave signals along the grid nodes using a surface wave instrument and acquires apparent resistivity data using the high-density resistivity method for analysis of underground soil physical parameters;

[0056] Soil Parameters and Model Module: The Soil Parameters and Model Module constructs wave velocity-water content correlation models and resistivity-dry density correlation models by drilling shallow soil samples and measuring soil parameters;

[0057] Distribution field generation module: The distribution field generation module inputs the collected shear wave velocity data and apparent resistivity data into the correlation model, and generates water content distribution field and dry density distribution field through interpolation;

[0058] Collapsibility Calculation Module: The collapsibility calculation module calculates the collapsibility of loess in each detection grid based on the collapsibility coefficient geophysical response equation, and outputs collapsibility coefficient contour maps in layers according to depth to determine the collapsibility level;

[0059] Collapsible loess distribution module: The collapsible loess distribution module determines the spatial distribution of collapsible loess based on collapsibility coefficient and collapsibility grade data, and completes the collapsibility assessment;

[0060] Processor: The processor is used to handle the calculation process of each formula and the construction calculation process of each model.

[0061] See Figure 1 As shown, the loess collapsibility evaluation method based on geophysical testing technology includes:

[0062] Step 1: Layout a regular detection grid in the loess site to be evaluated, construct a site geographic coordinate system, and map the site grid location data into the coordinate system;

[0063] Step 2: Rayleigh wave signals are collected in real time along the grid nodes using a surface wave instrument. The shear wave velocity profile in the 0-20m depth range is inverted using the dispersion curve. Apparent resistivity data are collected along the same grid using the high-density resistivity method.

[0064] Step 3: Drill shallow soil samples based on each detection grid, determine soil parameters, including water content and dry density, and construct models based on the mapping relationship between site geophysical parameters and soil parameters, including wave velocity-water content correlation model and resistivity-dry density correlation model.

[0065] Step 4: Input the acquired shear wave velocity data and apparent resistivity data into the wave velocity-water content correlation model and the resistivity-dry density correlation model, and interpolate to generate the water content distribution field and the dry density distribution field;

[0066] Step 5: Calculate the collapsibility of loess in each detection grid based on the geophysical response equation of the collapsibility coefficient, and output the collapsibility coefficient contour map by depth to determine the collapsibility level of each grid and the distribution range of collapsible loess.

[0067] See Figure 2As shown, in the detection grid laid out according to the rules of the loess site to be evaluated, a site geographic coordinate system is constructed, and the site grid location data is mapped to the coordinate system, specifically including:

[0068] Based on the topography and landform of the loess site, square grids were selected for layout, the grid size was determined, and a unique identifier was added to each grid.

[0069] Based on the selected site geographic coordinate system, the local coordinate data of each grid is mapped to the geographic coordinate system.

[0070] See Figure 3 As shown, Rayleigh wave signals are acquired in real time along the grid nodes using a surface wave instrument. The shear wave velocity profile within the 0–20 m depth range is inverted using dispersion curves. Apparent resistivity data is acquired along the same grid using the high-density resistivity method. Specifically, this includes:

[0071] A surface wave meter receiver is deployed at each grid node, and the distance between the measurement nodes is determined based on the spacing set in the grid.

[0072] Rayleigh wave signals are collected along each grid node. Data is recorded by receiving vibration signals from the ground, and dispersion curves are generated using a dispersion analysis algorithm.

[0073] The dispersion curves were processed by nonlinear inversion to obtain shear wave velocity profiles at different depths underground.

[0074] The electrode array required for the high-density resistivity method is deployed at the same grid nodes, and data is collected at each grid node. The apparent resistivity value at each depth is calculated based on the collected current and voltage signal data.

[0075] Specifically, ground vibration signals are collected at each grid node, mainly Rayleigh wave signals. Rayleigh waves are surface waves that propagate along the ground. Ground sensors can record Rayleigh wave vibration data. Signal acquisition can be performed using accelerometers, velocity meters, or displacement sensors. The recorded signals are usually time series.

[0076] Based on the collected Rayleigh wave signals, dispersion analysis was performed to obtain dispersion curves. These curves represent the relationship between wave velocity and frequency, indicating the propagation speed of Rayleigh waves at different frequencies. The dispersion relationship formula is as follows:

[0077] ;

[0078] in, Let f be the propagation speed at frequency f. Angular frequency, Wave number;

[0079] Based on the dispersion curve f and obtained from the experiment It can be used to infer the transverse wave velocity at different depths underground;

[0080] Nonlinear inversion calculations were performed using dispersion curves to obtain the subsurface shear wave velocity profile;

[0081] The resistivity method involves arranging an electrode array at the same grid nodes, applying a known current through the electrode array, measuring the voltage difference, recording the current and voltage signals between each electrode, and calculating the apparent resistivity using the following formula:

[0082] ;

[0083] in, Apparent resistivity The distance between the electrodes. For the applied current, The measured voltage difference;

[0084] By continuously changing the configuration of the electrodes and the applied current, the apparent resistivity values ​​at different depths are obtained, and the resistivity profiles at different underground depths are calculated based on the resistivity data.

[0085] See Figure 4 As shown, shallow soil samples were drilled based on each detection grid to determine soil parameters, including water content and dry density. Based on the mapping relationship between site geophysical parameters and soil parameters, models were constructed, including a wave velocity-water content correlation model and a resistivity-dry density correlation model. Specifically, these models include:

[0086] Based on the deployed detection grid, borehole sampling is performed at representative locations around the grid nodes;

[0087] The collected soil samples were analyzed in the laboratory to obtain soil properties, including: determining the moisture content of the soil samples by the drying method and determining the dry density of the soil samples by the mass method.

[0088] Based on the transverse wave velocity and soil moisture content data of different grid nodes, a wave velocity-moisture content correlation model was constructed by fitting the relationship between wave velocity and moisture content through regression analysis.

[0089] Based on the apparent resistivity data of different grid nodes and the dry density of the corresponding soil samples, a resistivity-dry density correlation model is constructed by fitting the relationship between resistivity and dry density through regression analysis.

[0090] Specifically, the moisture content is analyzed by the drying method, which involves taking a certain mass of soil sample, placing it in an oven, drying it at 105℃±5℃ for 24 hours, taking it out and cooling it, and then measuring its dry weight. The dry density is analyzed by the mass method, which involves calculating the dry density of the soil sample based on its dry weight and volume.

[0091] Data was collected at different grid nodes based on the shear wave velocity obtained from surface wave detection and the soil moisture content obtained from laboratory analysis.

[0092] The relationship between shear wave velocity and water content is fitted using regression analysis, with the following formula:

[0093] ;

[0094] in, This refers to the natural water content of loess. Let be the transverse wave velocity, a be the logarithmic coefficient, b be the power function coefficient, and c be the power exponent.

[0095] Data were collected at different grid nodes using apparent resistivity data obtained by resistivity method and dry density obtained by laboratory analysis.

[0096] A regression method is used to fit the relationship between apparent resistivity and dry density, as shown in the formula:

[0097] ;

[0098] in, The dry density of loess, Apparent resistivity As a scale factor, The resistivity index is... This represents the background density offset.

[0099] See Figure 5 As shown, the acquired shear wave velocity data and apparent resistivity data are input into the wave velocity-water content correlation model and the resistivity-dry density correlation model. The interpolation to generate the water content distribution field and the dry density distribution field specifically includes:

[0100] The real-time collected shear wave velocity data is input into the wave velocity-water content correlation model to calculate the water content data of each grid node;

[0101] The apparent resistivity data is input into the resistivity-dry density correlation model to calculate the dry density data for each grid node;

[0102] Based on the obtained moisture content and dry density data of each grid node, the moisture content distribution field and dry density distribution field of the entire site are obtained by interpolation using spline interpolation.

[0103] Specifically, for the transverse wave velocity of each grid node, the wave velocity-water content correlation model is substituted to calculate the corresponding water content; for the apparent resistivity of each grid node, the resistivity-dry density correlation model is substituted to calculate the corresponding dry density.

[0104] Based on the moisture content and dry density data of the grid nodes, spline interpolation, usually cubic spline interpolation, is used to interpolate in a two-dimensional plane across the entire site to generate a continuous moisture content distribution field and dry density distribution field.

[0105] The coefficients of cubic spline interpolation are determined by the following conditions: nodal value condition: the interpolation function passes through the data of all grid nodes; continuity condition: the first and second derivatives of the function are continuous at the nodes; boundary conditions: natural boundary conditions or fixed boundary conditions are usually used.

[0106] Interpolation yields the moisture content distribution field and dry density distribution field of the entire site, which represent the moisture content and dry density values ​​at any point within the site.

[0107] See Figure 6 As shown, the collapsibility of loess within each detection grid is calculated based on the geophysical response equation of the collapsibility coefficient, and the collapsibility coefficient contour maps are output in layers according to depth. The collapsibility level and distribution range of collapsible loess in each grid are determined, specifically including:

[0108] The collapsibility coefficient is calculated based on the ratio of the expansion of soil volume with water change to the initial volume under saturated water conditions.

[0109] Based on the relationship between the physical properties of loess and the collapsibility coefficient, the wave velocity-water content model and the resistivity-dry density model are used to calculate and construct the geophysical response equation.

[0110] The geophysical data and soil parameters of each grid are collected and input into the geophysical response equation to calculate the collapsibility coefficient in each detection grid.

[0111] The loess site is divided into layers based on depth, and the collapsibility coefficient of each layer is calculated to obtain a continuous collapsibility coefficient distribution field and generate a collapsibility coefficient contour map.

[0112] The degree of collapse is determined based on the magnitude of the collapse coefficient;

[0113] Based on the collapsibility coefficient and collapsibility level of each grid, the spatial distribution of collapsible loess is obtained.

[0114] Specifically, the collapsibility coefficient refers to the ratio of the volume expansion of soil under saturated water conditions to the initial volume of the soil. Based on the relationship between the physical properties of loess and the collapsibility coefficient, the collapsibility coefficient is estimated using existing wave velocity-water content correlation models and resistivity-dry density correlation models. It is expressed by the geophysical response equation, with the following formula:

[0115] ;

[0116] in, This is the collapsibility coefficient. This represents the difference in transverse wave velocity between the saturated state and the natural state. The natural transverse wave velocity, This represents the difference in resistivity between the saturated state and the natural state. Apparent resistivity in its natural state. Contribute weights to wave velocity attenuation. Contribute weights to resistivity changes. The resistivity nonlinear response index;

[0117] The collected geophysical data and soil parameters of each grid are input into the geophysical response equation. Based on these data, the collapsibility coefficient of each grid node is obtained.

[0118] Based on the depth of the loess site, the layers are divided. The collapsibility coefficient of each layer can be calculated from geophysical data at different depths. By calculating the collapsibility coefficient at different depths, the collapsibility coefficient distribution field of the entire area is obtained. Based on the calculated collapsibility coefficient distribution field, a contour map of the collapsibility coefficient is generated. The collapsibility is graded according to the magnitude of the collapsibility coefficient. Usually, a threshold is set to divide the collapsibility coefficient into several grades. Based on the collapsibility coefficient and collapsibility grade of each grid, the spatial distribution of collapsible loess is obtained.

[0119] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating loess collapsibility based on geophysical testing technology, characterized in that, The method comprises the following steps: A regular detection grid is laid out in the loess site to be evaluated, a site geographic coordinate system is constructed, and site grid position data is mapped into the coordinate system; Rayleigh wave signals are collected in real time along the grid nodes by a surface wave instrument, a shear wave velocity profile in the range of 0-20m underground is inverted by a dispersion curve, and apparent resistivity data are collected along the same grid by a high-density resistivity method; Based on the laid-out detection grid, representative positions around the grid nodes are selected for drilling sampling; The collected soil samples are analyzed in the laboratory to obtain soil parameters, including: the water content of the soil samples is determined by drying method, and the dry density of the soil samples is determined by mass method; Based on the shear wave velocity and water content data of different grid nodes, a regression analysis is performed to fit the relationship between the shear wave velocity and the water content, and a wave velocity-water content correlation model is constructed, and the formula is: ； wherein, is the natural water content of loess, is the transverse wave velocity, a is the logarithmic term coefficient, b is the power function coefficient, and c is the power index; The apparent resistivity data obtained by the resistivity method and the dry density obtained by laboratory analysis are collected at different grid nodes; A regression method is used to fit the relationship between the apparent resistivity and the dry density, and a resistivity-dry density correlation model is constructed, and the formula is: ； wherein, is the dry density of loess, is the apparent resistivity, is the scale factor, is the resistivity exponent, is the background density shift; The collected shear wave velocity data and apparent resistivity data are input into the wave velocity-water content correlation model and the resistivity-dry density correlation model, and the water content distribution field and the dry density distribution field are interpolated; Based on the ratio of the volume expansion of the soil under saturated water state to the initial volume, the collapse coefficient is calculated and obtained; The collapse coefficient is the ratio of the volume expansion of the soil under saturated water state to the initial volume of the soil, and according to the relationship between the physical properties of loess and the collapse coefficient, the collapse coefficient is calculated by the existing wave velocity-water content correlation model and resistivity-dry density correlation model, and the geophysical response equation is represented, and the formula is: ； wherein, is the coefficient of compression, is the difference between the saturated and natural state shear wave velocity, is the natural state shear wave velocity, is the difference between the saturated and natural state resistivity, is the natural state apparent resistivity, is the wave velocity decay contribution weight, is the resistivity change contribution weight, is the resistivity non-linear response exponent; The geophysical data and soil parameters of each grid are input into the geophysical response equation, and the collapse coefficient in each detection grid is calculated and obtained; According to the depth of the loess site area, hierarchical division is performed, the collapse coefficient of each layer is calculated by the geophysical data at different depths, the collapse coefficient distribution field of the entire region is obtained by calculating the collapse coefficients at different depths, based on the calculated collapse coefficient distribution field, the contour map of the collapse coefficient is generated, the grade of collapsibility is determined according to the size of the collapse coefficient, and the collapse coefficient is usually divided into several grades by setting a threshold, and based on the collapse coefficient and the collapse grade of each grid, the spatial distribution of collapsible loess is obtained.

2. The loess collapsibility evaluation method based on geophysical test technology according to claim 1, characterized in that, The method comprises the following steps: Based on the terrain and topography of the loess site, a square grid is selected for layout, the size of the grid is determined, and a unique identifier is added to each grid; Based on the selected site geographic coordinate system, the local coordinate data of each grid is mapped into the geographic coordinate system.

3. The loess collapsibility evaluation method based on geophysical test technology according to claim 1, characterized in that, The method comprises the following steps: The surface wave instrument receiver is arranged at each grid node, and the distance between the measurement nodes is determined based on the interval of the grid arrangement; Rayleigh wave signals are collected along each grid node, data is recorded by receiving the vibration signals of the ground, and a dispersion curve is generated by a frequency dispersion analysis algorithm; The dispersion curve is processed by nonlinear inversion to obtain the shear wave velocity profile data at different depths of the underground; The electrode array required by the high-density resistivity method is arranged at the same grid node, data is collected at each grid node, and the apparent resistivity value at each depth is calculated based on the collected current and voltage signal data.

4. The loess collapsibility evaluation method based on geophysical test technology according to claim 1, characterized in that, The collected shear wave velocity data and apparent resistivity data are input into the wave velocity-moisture content correlation model and the resistivity-dry density correlation model, and the moisture content distribution field and the dry density distribution field are generated by interpolation, which specifically includes: The real-time collected shear wave velocity data are input into the wave velocity-moisture content correlation model to calculate the moisture content data of each grid node; The apparent resistivity data are input into the resistivity-dry density correlation model to calculate the dry density data of each grid node; Based on the obtained moisture content data and dry density data of each grid node, the moisture content distribution field and the dry density distribution field of the entire site are obtained by spline interpolation.

5. A loess collapsibility evaluation system based on geophysical testing technology, used to implement the loess collapsibility evaluation method based on geophysical testing technology according to any one of claims 1-4, characterized in that, It includes: The detection grid and coordinate system module: The detection grid and coordinate system module arranges a regular detection grid in the loess site to be evaluated and constructs a site geographic coordinate system, and maps the grid position data to the coordinate system; The data acquisition module: The data acquisition module collects Rayleigh wave signals along the grid nodes by the surface wave instrument and collects apparent resistivity data by the high-density resistivity method, which is used for underground soil physical parameter analysis; The soil property parameter and model module: The soil property parameter and model module constructs the wave velocity-moisture content correlation model and the resistivity-dry density correlation model by drilling shallow soil samples and measuring soil property parameters; The distribution field generation module: The distribution field generation module inputs the collected shear wave velocity data and apparent resistivity data into the correlation model to generate the moisture content distribution field and the dry density distribution field by interpolation; The collapsibility calculation module: The collapsibility calculation module calculates the collapsibility of the loess in each detection grid based on the collapsibility coefficient geophysical response equation, and outputs the collapsibility coefficient contour map by depth layering to determine the collapsibility grade; The collapsible loess distribution module: The collapsible loess distribution module determines the spatial distribution of collapsible loess based on the collapsibility coefficient and collapsibility grade data to complete the collapsibility evaluation; The processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

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