A detection system and method for spatial distribution of shallow karst groundwater

Abstract

The present invention discloses a detection system and method for the spatial distribution of shallow karst groundwater. The host module, pulse electromagnetic wave transmitting module, pulse electromagnetic wave receiving module and positioning module of the detection system are connected to the traction module through a holding cable, and are respectively connected to the master control machine through a data transmission line arranged inside the holding cable. During the specific detection, the groundwater distribution in the formation medium is detected based on the pulse electromagnetic wave, and the distribution range of the karst groundwater and the three-dimensional spatial distribution characteristics of the groundwater are determined according to the distribution range of the relatively low-energy abnormal area of ​​the electromagnetic wave energy slice, and the possible karst groundwater runoff channel is further delineated, and the qualitative change of the water content is determined according to the relative size of the electromagnetic wave energy value in the relatively low-energy area. This scheme is sensitive to the existence of karst groundwater, has the advantages of non-destructive, high efficiency, low cost, strong urban applicability, etc., and can realize the three-dimensional refined detection of the spatial distribution of shallow karst groundwater in cities.

Description

Technical Field

[0001] The invention belongs to the technical field of karst groundwater detection, and in particular relates to a system and method for detecting the spatial distribution range and formation water content of shallow karst groundwater. Background Art

[0002] With the acceleration of urban modernization, the development and utilization of underground space has become a necessary means of construction for urban development, such as subway tunnels, underground shopping malls, underground parking lots, etc. The main construction layers are mostly located in shallow strata less than 50m. In areas where underground karst is relatively developed, groundwater in karst strata mainly relies on karst spaces such as cracks, pipelines, and caves in the strata for storage and migration. It has highly nonlinear characteristics and its distribution state is difficult to predict. The complex karst groundwater system brings huge safety risks to urban underground engineering construction, such as tunnel water inrush and foundation pit water gushing. At the same time, drainage during the construction and operation of underground engineering may seize the source of karst water supply, resulting in ecological and environmental problems such as threats to downstream spring water gushing and vegetation survival. Therefore, identifying the spatial distribution characteristics of shallow karst groundwater is of great significance to ensure the construction of underground engineering and ecological and environmental protection in karst areas.

[0003] At present, the investigation methods for karst areas mainly include electrical resistivity tomography (ERT), ground penetrating radar (GPR), seismic wave method, transient electromagnetic method (TEM), geophysical logging, etc.:

[0004] (1) The electrical resistivity tomography (ERT) method is mainly based on the resistivity difference between dry caves (higher resistivity) or saturated karst development areas (lower resistivity) and bedrock, and reveals the karst distribution of the strata by obtaining the spatial distribution of resistivity in the detection area. The detection depth of this method is controlled by the length of the survey line, and its spatial resolution decreases rapidly with increasing depth. In addition, the large-scale hardened ground and complex road conditions in cities make it difficult to provide ground electrodes and survey lines for deployment. It has the limitations of high implementation difficulty, small data volume, and high large-scale acquisition costs.

[0005] (2) The ground penetrating radar (GPR) method mainly determines the distribution characteristics of karst in the formation based on the abnormal changes in high-frequency radio waves caused by the significant electrical differences between karst cavities, water-filled or clay-filled karst formations and the surrounding formation media. By analyzing the characteristics of the radar reflection wave image and the image abnormality characteristics of the radar reflection wave profile, such as hyperbolic reflection anomaly, phase axis displacement, and reflection wave waveform distortion, the distribution of karst in the formation can be identified. However, geological anomalies such as complex pipelines, formation fractures, sand bodies, karst cavities, and water-filled caves in urban formations are all included in the radar image abnormality characteristics, making it difficult to accurately identify the distribution of karst groundwater. In addition, radar image anomalies are difficult to track continuously in different radar profiles, making it difficult to achieve three-dimensional characterization of the distribution of karst groundwater.

[0006] (3) The seismic wave method mainly identifies karst cavities, filled karst, etc. through the refraction and reflection anomalies caused by the influence of different medium density, wave velocity and other properties when the seismic waves propagate in the strata. The detection principle is similar to that of GPR, and it mainly identifies image anomaly features through seismic wave profiles. When applied to the detection of groundwater distribution in shallow karst areas in cities, it has the disadvantages of poor anti-interference ability and great implementation difficulty.

[0007] (4) The principle of transient electromagnetic method (TEM) is to emit a pulsed primary electromagnetic field into the ground and identify the abnormal distribution characteristics of the underground medium by monitoring the temporal and spatial distribution characteristics of the secondary electromagnetic field generated by the underground eddy current field induced by the pulsed electromagnetic field. When it is applied to the spatial distribution detection of shallow karst groundwater in cities, it has the limitations of difficulty in obtaining three-dimensional data and the existence of a detection blind spot in the shallow part of 0 to 50 meters.

[0008] (5) Geophysical logging methods such as natural potential logging, resistivity logging, and sonic logging are to obtain physical parameters of the formation at different depths along the borehole wall by setting up boreholes at target points. They have high accuracy in describing the distribution layers and hydrodynamic characteristics of karst groundwater in the formation. However, they are fixed-point survey methods, and the cost of large-scale implementation is extremely high. In addition, the data is discontinuous, making it difficult to achieve three-dimensional characterization of the distribution of karst groundwater.

[0009] It can be seen that the current geological survey methods are mainly aimed at the detection of geological anomalies such as karst development degree, stratigraphic structure, and structural fragmentation. Karst groundwater and stratum karst distribution usually have a nonlinear correlation, that is, the water-richness of karst-developed strata is not necessarily good, and the distribution of groundwater runoff channels cannot be accurately inferred only by karst development. In addition, the existing geological survey methods have certain limitations in terms of urban applicability, data volume, detection accuracy, and three-dimensional imaging energy, making it difficult to achieve the three-dimensional refined detection goal of the spatial distribution of shallow karst groundwater.

[0010] In summary, it is necessary to develop a three-dimensional detection technology for karst groundwater distribution that is non-destructive, rapid, accurate, and applicable to urban environments, so as to provide technical support for underground engineering construction and ecological environmental protection in karst areas. Summary of the invention

[0011] In order to solve the defects of the prior art such as difficulty in implementing the spatial distribution detection of karst groundwater and poor detection accuracy, the present invention provides a detection system and method for the spatial distribution of shallow karst groundwater, so as to realize in-situ, non-destructive, rapid and accurate measurement of the three-dimensional spatial distribution of shallow karst groundwater in the planned development area of ​​urban underground engineering.

[0012] The present invention is implemented by adopting the following technical scheme: a detection system for the spatial distribution of shallow karst groundwater, comprising a traction module, a host module, a handheld remote control, a measurement module, a positioning module and a holding cable, wherein the measurement module comprises a pulse electromagnetic wave transmitting module and a pulse electromagnetic wave receiving module; the host module is arranged in the inner space of the traction module, and the holding cable is connected to the traction module through a lock; the pulse electromagnetic wave transmitting module, the positioning module and the pulse electromagnetic wave receiving module are connected to the traction module through the holding cable, and are respectively connected to the master control machine through the data transmission line arranged inside the holding cable, and the pulse electromagnetic wave transmitting module, the pulse electromagnetic wave receiving module, the pulse electromagnetic wave transmitting module and the positioning module are all wrapped with a wear-resistant PVC material shell to ensure that the device is subjected to ground friction without being damaged during the measurement and dragging process;

[0013] The traction module includes a remote control signal receiving module, a motor control module, a drive motor and a drive wheel. The traction module is used to provide forward power for the measurement system. The traction module can control the rotation direction and speed of the drive motor through a handheld remote control through a radio frequency signal.

[0014] The host module includes a master control machine, a data storage module, a central control unit, an amplifier, a filter and an A / D module. The power module is connected to the master control machine and supplies power to each power-consuming unit through the master control machine. The pulse electromagnetic wave transmitting module, the pulse electromagnetic wave receiving module, the data storage module, the amplifier, the filter and the A / D module are all connected to the central control unit. The central control unit controls the pulse electromagnetic wave receiving module to adjust the signal strength of the received electromagnetic wave signal through the amplifier, and then performs signal separation and noise reduction processing through the filter. Finally, the electromagnetic wave analog signal is converted into a digital signal through the A / D module and stored in the data storage module.

[0015] When the system is working, the power module first supplies power to the master control machine, which then sends a power-on command to the central control unit, the positioning module, and the data storage module. The central control unit controls the pulse electromagnetic wave transmitting module to continuously transmit electromagnetic wave signals underground with specified parameters. The positioning module measures the real-time latitude and longitude coordinates and elevation at a frequency of once per meter, and transmits them to the data storage module for recording.

[0016] The surveyor uses a handheld remote control to send forward instructions to the master control machine and the traction module and determine the forward speed. The traction module drags the measuring device at a constant speed through the holding cable. The central control unit controls the pulse electromagnetic wave receiving module to adjust the signal strength of the received electromagnetic wave signal through the signal operation amplifier module, and then separates and reduces the noise through the filter. Finally, the electromagnetic wave analog signal is converted into a digital signal through the A / D module and stored in the SD card of the data storage module.

[0017] The location of the collection point is the center of the line connecting the electromagnetic wave signal transmitting and receiving modules, and the positioning module records the real-time latitude and longitude coordinates and elevation of the collection point once every meter;

[0018] Through the above steps, data collection is carried out in a covering manner in the survey area. In urban areas, survey lines are deployed along available roads to form a survey line network that covers the survey area as much as possible. In open areas, horizontal survey lines are laid out at equal intervals to cover the survey area (with an interval of 0.5 to 5 m).

[0019] The present invention also proposes a method for detecting groundwater distribution in karst areas using the above three-dimensional electromagnetic wave energy spectrum acquisition system.

[0020] Step A, performing noise removal and shaping processing on the collected two-dimensional electromagnetic wave data set, and reorganizing the data set into three-dimensional coordinates according to the real spatial position of each electromagnetic wave to form a three-dimensional electromagnetic wave data set;

[0021] (1) Based on the measured two-dimensional electromagnetic wave data set collected by the detection system, the data format is (x, t, a), and basic denoising and shaping processing is performed, including zero point correction to remove direct waves, background elimination to eliminate noise, bandpass filtering to remove non-transmitted frequency band signals, inter-channel equalization to balance the signal strength between data points, and standardization of balanced data point intervals;

[0022] (2) Combined with GPS data, the processed two-dimensional data volume, the data format is (x, t, a)*number of survey lines, and the three-dimensional coordinates are reorganized according to the real space position. The coordinate information of each data collection point is obtained by interpolating the coordinate data recorded by the positioning module during the collection process, and finally a three-dimensional electromagnetic wave data set covering the survey area is formed. The data of any collection point contains the information on the change of the amplitude intensity of the electromagnetic wave signal at that point over time. At this time, the data format of the three-dimensional electromagnetic wave data set is (x, y, t, a);

[0023] Step B: The three-dimensional electromagnetic wave data set obtained in step A is subjected to time slicing, horizontal two-dimensional interpolation, time-depth conversion, energy standardization, three-dimensional space interpolation and three-dimensional terrain correction to obtain a three-dimensional electromagnetic wave energy distribution model of the survey area. Specifically:

[0024] (1) The three-dimensional electromagnetic wave data obtained in step A is concentrated into t i ~t i The absolute amplitude data within the range +Δt are averaged to create time slices, with Δt defined as the slice thickness, It is defined as the slice overlap. The parameters Δt and m are set according to the actual situation to obtain the electromagnetic wave energy distribution data (time slice data) in different time planes. At this time, the data format is t i ~t i +Δt(x,y, ), where t i ~t i +Δt is the time interval, x and y are the longitude and latitude coordinates, is the absolute amplitude value (representing the energy of electromagnetic waves);

[0025] (2) Perform two-dimensional spatial interpolation calculation on the electromagnetic wave energy distribution data (time slice data) obtained through time slicing, set relevant parameters such as grid size, search radius and interpolation algorithm, and obtain interpolated time slice data after interpolation of different time slices. The data format is t i ~t i +Δt(x,y,A estimated ), and further give the electromagnetic wave velocity v according to the empirical value or the in-situ measured value, perform time-depth conversion on the interpolated time slice data, convert the horizontal slices at different times into horizontal slices at different depths, and obtain the depth slice data. At this time, the data format is converted to z i ~z i +Δz(x,y,A estimated ), where z i ~z i +Δz is the depth interval;

[0026] The data bodies in the same depth slice have the same electromagnetic wave propagation distance and the same energy attenuation degree, so the electromagnetic wave data are at the same energy comparison level, and the difference in amplitude at different horizontal positions reflects the difference in water content at different horizontal positions of the formation. However, the two-dimensional data bodies sliced ​​at different depths have experienced different electromagnetic wave propagation distances. The longer the propagation distance, the higher the electromagnetic wave energy attenuation degree, which results in the two-dimensional data bodies sliced ​​at different depths being at different energy levels, and the data cannot be directly compared;

[0027] (3) Based on the above steps, slices of different depths are obtained. Each depth slice needs to be standardized separately, that is, the data in the same depth slice is mapped to a linear interval of 0 to 100% according to the size ratio in the slice for energy standardization to obtain standardized depth slice data. At this time, the data format is converted to z i ~z i +Δz(x,y,A nor ); perform three-dimensional spatial interpolation on the standardized depth slice data to form a three-dimensional electromagnetic wave energy data point cloud (x, y, z, A nor ), at this time A nor is the standardized energy value (0≤A nor ≤100%), dimensionless, A nor It only represents the relative size of the electromagnetic wave energy at a data point among the data points at the same level, so the A at different levels nor Can be compared with each other;

[0028] (4) Finally, a three-dimensional terrain correction process is performed on the obtained three-dimensional electromagnetic wave energy data point cloud to obtain a three-dimensional electromagnetic wave energy distribution model of the survey area. The specific steps are as follows:

[0029] 1) Constructing the terrain data file of the survey area according to the coordinate information collected by the positioning module during the acquisition process, interpolating the terrain data, and its gridding parameters are consistent with those in step (2). At this time, the terrain data file format is (x, y, h);

[0030] 2) The three-dimensional data volume obtained in step (3), i.e., the standardized depth slice data (x, y, z, A nor ) is corrected to the elevation coordinates H, H according to the elevation at the same position. i =h i -z i , at this time the three-dimensional electromagnetic wave data volume format is converted to (x, y, H, A nor );

[0031] Step C, generating a three-dimensional energy isosurface rendering and a two-dimensional energy slice diagram through three-dimensional data visualization, and interpreting the data based on the energy threshold of the selected groundwater distribution area;

[0032] The obtained three-dimensional electromagnetic wave data volume (x, y, H, A nor ) to render a three-dimensional visualization model, including energy isosurface rendering, arbitrary angle two-dimensional slice rendering, arbitrary point single-channel data curve drawing, etc., select appropriate energy threshold and color scale, and identify the area below the energy threshold (low energy abnormal area) as the karst groundwater distribution area;

[0033] The energy threshold is determined as follows:

[0034] Considering that when the electromagnetic wave signal moves from the dry karst stratum to the karst groundwater distribution area, the electromagnetic wave energy value will show an abnormal decrease near the interface, and the contour lines will become denser at the interface position. According to the above abnormal characteristics, the approximate energy threshold range of the groundwater distribution area is identified through the contour map of the measured data, and then hydrological holes are set on both sides of the boundary of the groundwater distribution area for pumping tests, and the energy threshold is further calibrated according to the water-rich characteristics of the formation.

[0035] Step D, delineating the three-dimensional spatial distribution range and water content change of karst groundwater in the measurement area;

[0036] Based on the three-dimensional visualization results obtained in step C, first determine whether there is a relatively low-energy area in the energy slice. A relatively low-energy area refers to an area in which the electromagnetic wave energy is generally lower than that of the adjacent position. If there is a relatively low-energy area, the relatively low-energy area in the stratum is defined as the distribution range of karst groundwater. The relative size of the energy in the relatively low-energy area is determined as the relative water content. If the electromagnetic wave energy is large, the water content is relatively small. Conversely, if the electromagnetic wave energy is small, the water content is high.

[0037] Compared with the prior art, the advantages and positive effects of the present invention are:

[0038] (1) This scheme detects the distribution of groundwater in the formation medium based on pulsed electromagnetic waves. The distribution range of karst groundwater is determined according to the distribution range of the relatively low-energy abnormal area of ​​the electromagnetic wave energy slice. The three-dimensional spatial distribution characteristics of karst groundwater in the survey area are constructed according to the distribution range of karst groundwater at different depths. The possible karst groundwater runoff channels are further delineated, and the qualitative changes in water content can be determined according to the relative size of the electromagnetic wave energy value in the relatively low-resistance area.

[0039] (2) In addition, this scheme can achieve the goal of fine-grained detection of the three-dimensional spatial distribution of groundwater in karst areas. The detection depth can reach 60m. The detection results have good three-dimensional visualization capabilities. The measurement process does not damage the stratum and does not cause disturbing noise. It has excellent site passability, especially for urban conditions with dense buildings and complex roads. The detection range can be flexibly changed with the layout of the survey line, with high detection efficiency and good economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the structure of the detection system according to an embodiment of the present invention;

[0041] Figure 2 It is a schematic diagram of the principle of the detection system according to an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a method for detecting the distribution range of karst groundwater according to an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of the layout of three-dimensional electromagnetic wave energy spectrum measurement lines according to an embodiment of the present invention;

[0044] Figure 5 For Figure 4 The rendering of the three-dimensional electromagnetic wave energy isosurface obtained by the survey line layout plan;

[0045] Figure 6 For Figure 4 Horizontal slices of electromagnetic wave energy at different elevations obtained by the survey line layout scheme;

[0046] Among them: 1. traction module; 2. host module; 3. driving wheel; 4. power supply; 5. handheld remote control; 6. pulse electromagnetic wave transmitting module; 7. pulse electromagnetic wave receiving module; 8. positioning module; 9. holding cable; 100. ground; 200. water-filled cave; 300. filling cave. DETAILED DESCRIPTION

[0047] In order to be able to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and Examples. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. In the following description, a lot of specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] First of all, the theoretical basis of the present invention needs to be explained: the propagation of electromagnetic waves of the same frequency underground is mainly affected by the dielectric constant, conductivity and magnetic permeability of the formation. For non-magnetic formations, the dielectric conductivity and dielectric constant of the medium are mainly considered. The larger the dielectric constant and conductivity, the stronger the attenuation of electromagnetic wave energy, and the dielectric constant and conductivity of the formation medium increase with the increase of water content: the dielectric constant of relatively dry limestone is 4-8, clay is 5-40, and water is 80; the resistivity of relatively dry limestone (which is the reciprocal of conductivity) is 100-1000, clay is 5-20, and water is 15-35. With the increase of formation water content, the dielectric constant and conductivity of the formation increase, which leads to the rapid attenuation of electromagnetic wave energy. Therefore, the attenuation characteristics of electromagnetic wave energy can sensitively reflect the difference in formation water content.

[0049] Embodiment 1: A detection system for the spatial distribution of shallow karst groundwater, such as Figure 1-2As shown, the detection system includes a traction module 1, a host module 2, a power module 4, a handheld remote control 5, a pulse electromagnetic wave transmitting module 6, a positioning module 8, a pulse electromagnetic wave receiving module 7 and a holding cable 9. The host module 2 and the power module 4 are arranged in the internal space of the traction module 1. The host module 2 and the pulse electromagnetic wave transmitting module 6, the positioning module 8, and the pulse electromagnetic wave receiving module 7 are used for data transmission and power supply through a dedicated line fixed inside the holding cable 9; the holding cable 9 is connected to the traction module 1 through a lock, bears all traction forces, is woven with Kevlar fiber, has the characteristics of wear resistance, waterproofness, and coilability, and a connecting line is placed in the hollow inside. When in the measuring state, the whole is in a straight line shape under the dragging of the traction module.

[0050] Among them, the traction module 1 includes a housing, a remote control signal receiving module, a motor control module, a drive motor and a drive wheel 3. After the remote control signal receiving module receives the radio frequency signal transmitted by the external handheld remote control 5, it parses the command contained in the radio frequency signal, identifies the forward, backward, left offset, right offset, stop, speed increase, speed decrease and other commands, and transmits them to the motor control module. The motor control module controls the rotation of the drive motor according to the command. The drive wheel is connected to the drive motor through a transmission gear, thereby carrying the host and power supply, and pulling the rear measurement module along the established measurement line through the holding cable. The traction module is powered by the master control machine, and the motor control module uploads the motor motion status and control log to the master control machine in real time.

[0051] The host module 2 includes a master control machine, a data storage module, a central control unit, an amplifier, a filter and an A / D module. The power module 4 is composed of a 12V, 30AH high-power lithium battery and is connected to the master control machine through a dedicated connection line. The master control machine is powered by the power module, and is connected to the positioning module, the central control unit and the motor control module respectively, and supplies power, communicates and controls each unit respectively. The pulse electromagnetic wave transmitting module 6, the pulse electromagnetic wave receiving module 7, the data storage module, the amplifier, the filter and the A / D module are all connected to the central control unit.

[0052] After receiving the acquisition instruction from the master control machine, the central control unit controls the pulse electromagnetic wave transmitting module 6 to continuously emit a pulse electromagnetic wave signal with a center frequency of 50Mhz underground, and controls the pulse electromagnetic wave receiving module 7 to receive the returned pulse electromagnetic wave reflection signal, forming an analog signal and transmitting it to the amplifier to perform operational amplification on the signal. The amplified analog signal is transmitted to the filter for signal separation and noise reduction processing. The electromagnetic wave analog signal after filtering is transmitted to the A / D module to be converted into a digital signal. Each electromagnetic wave signal records the change data of the electromagnetic wave amplitude over time. Each data is arranged in the acquisition order to form two-dimensional electromagnetic wave profile data. The data volume of each survey line is the number of signal sample points × acquisition frequency × survey line length. The number of electromagnetic wave signal sample points / channel is set to 625 and the acquisition frequency is set to 25 channels / meter. During the data acquisition process, the central control unit writes the two-dimensional electromagnetic wave profile data into the data storage module. In this embodiment, the circuit structure of each module adopts a mature circuit module, which will not be elaborated in detail here.

[0053] Among them, the positioning module 8 is located at the center of the connection line between the pulse electromagnetic wave transmitting module 6 and the pulse electromagnetic wave receiving module 7. The latitude and longitude coordinates and elevation information of the location of the GPS positioning module 8 are recorded every 1m, and the coordinate data is stored in the data storage module in an independent file. During the post-processing of the electromagnetic wave data, the x, y coordinates and elevation h will be assigned to each electromagnetic wave data according to the GPS data.

[0054] Based on the acquisition system formed by this scheme, the measurement module (pulse electromagnetic wave transmitting module 6, pulse electromagnetic wave receiving module 7) and related cables are flexible, the device has a linear structure, presents a slender shape, occupies a small area, has good passability, can adapt to complex urban working conditions, and the foldable and coiled characteristics make it portable. The entire measurement process is driven by a motor, saving time and effort, and the forward speed and movement trajectory of the device can be accurately controlled and recorded by the circuit to ensure the stability of data acquisition and the accuracy of the measurement line.

[0055] When collecting data, first design a coverage survey line deployment plan according to the working conditions of the survey area. The survey lines should cover the entire survey area in a checkerboard pattern as much as possible. They should be deployed along available roads in urban areas, and parallel survey lines should be laid out at equal intervals (0.5-5m) in open areas. Then place the equipment at the starting point of the ground survey line, with the traction module in front and the measuring device in the back. Turn on the main control machine and control each device to be in a power supply working state. The handheld remote control sends a forward command to the master control machine and the motor control module and adjusts the speed to an appropriate level. Then the traction module drags the acquisition system along the predetermined survey line, and the measuring module and the positioning module start working. , the electromagnetic wave reflection echo signal is received and recorded through the measurement module to obtain the actual measured data of the electromagnetic wave energy spectrum in the survey area, the GPS positioning module is used to determine the location coordinates and elevation information of the data collection point, and the two-dimensional electromagnetic wave profile data and GPS data are continuously written to the data storage module. When changing the next survey line or pausing the measurement, the handheld remote control can send a stop command to the master control machine and the motor control module. At this time, the system will stop collecting and name and store the data; after completing the measurement of all survey lines, the handheld remote control sends a stop command to the master control machine and the motor control module, turns off the power of the master control machine, and ends the measurement.

[0056] Embodiment 2, based on the detection system described in Embodiment 1, this embodiment proposes a corresponding detection method, combining Figure 3 As shown, the following steps are included:

[0057] The following is the data processing process of the three-dimensional electromagnetic wave energy spectrum method included in the present invention. The detection target of the three-dimensional spatial distribution of karst groundwater can be finally achieved through the following processing method:

[0058] Step A, performing denoising and shaping processing on the two-dimensional measured data, and reorganizing the data set into three-dimensional coordinates according to the real spatial position of each electromagnetic wave data, so as to form a three-dimensional electromagnetic wave data set;

[0059] The SD card in the data storage module is taken out to export the data, and the original two-dimensional electromagnetic wave profile data is obtained, and a two-dimensional electromagnetic wave data set consisting of multiple measurement line data is obtained, and the data format is (x, t, a).

[0060] In the data processing scheme provided by the present invention, the amplitude attribute of the electromagnetic wave signal (i.e., the electromagnetic wave energy attribute) is mainly selected as the characteristic value to characterize the distribution range and relative water content of karst groundwater, which solves the limitation of traditional electromagnetic methods using electromagnetic wave time domain signal profiles for image feature analysis.

[0061] (1) Perform basic denoising and shaping processing on the acquired original two-dimensional electromagnetic wave data set (original two-dimensional electromagnetic wave profile data), including zero point correction to remove direct waves, background elimination to eliminate noise, bandpass filtering to remove non-transmitted frequency band signals, inter-channel equalization to balance the signal strength between data points, and standardization of the channel spacing of balanced electromagnetic wave signals. This process can be implemented by existing commercial electromagnetic wave data processing software, such as Reflexw, IDSP7, etc., and finally obtain the processed two-dimensional electromagnetic wave data set, the data format of which is (x, t, a)*number of survey lines.

[0062] (2) Combined with GPS data, the processed two-dimensional electromagnetic wave data set is reorganized into three-dimensional coordinates according to the real space position. The coordinate information of each data collection point is obtained by interpolating the coordinate data recorded by the positioning module during the collection process, and finally a three-dimensional electromagnetic wave data set covering the measurement area is formed. The single-channel data of any collection point contains the change information of the amplitude intensity of the electromagnetic wave signal at that point over time and the position information. At this time, the data format of the three-dimensional electromagnetic wave data set is (x, y, t, a);

[0063] Step B: The three-dimensional electromagnetic wave data set is subjected to time slicing, horizontal two-dimensional interpolation, time-depth conversion, energy standardization, three-dimensional space interpolation and three-dimensional terrain correction to obtain a three-dimensional electromagnetic wave energy distribution model of the survey area:

[0064] (1) Convert the amplitude data a in the three-dimensional electromagnetic wave data set obtained in step A into absolute amplitude data A, and calculate the absolute amplitude data A of t in the data set (x, y, t, A). i ~t i The A value of a single channel electromagnetic wave signal within the range of +Δt is averaged to create a horizontal time slice, and Δt is defined as the slice thickness. Defined as the slice overlap, set the parameters Δt and m according to the actual situation to obtain the average electromagnetic wave energy distribution data in different time planes, that is, time slice data. At this time, the data format is where t i ~t i +Δt is the time interval, x and y are the longitude and latitude coordinates, is the average amplitude value (this data can reflect the level of electromagnetic wave energy of the selected thickness);

[0065] Traditional electromagnetic wave time domain profile image feature information is messy and has many invalid interference anomalies, making it difficult to obtain effective information on the distribution of karst groundwater. This processing step can convert electromagnetic wave data from the time domain to the energy domain for analysis, and innovatively use the strong attenuation characteristics of groundwater on electromagnetic wave signals to characterize the distribution characteristics of shallow karst groundwater.

[0066] (2) The time slice data obtained in step (1) is subjected to two-dimensional spatial interpolation calculation to obtain interpolated time slice data. In this embodiment, the grid size is set to 3m×3m, the search radius is set to 50m (determined according to the maximum survey line interval), and the interpolation algorithm selects the inverse distance interpolation method. The following formula gives the estimated value A of the surrounding data points after using the distance weighted index calculation: estimated ,Right now:

[0067]

[0068] Among them, w i =1 / h^a;

[0069] h is the distance between the nearby point and the point to be estimated on the grid;

[0070] a is the smoothing factor (usually set to 2);

[0071] is the electromagnetic wave reflection intensity at the interpolation point;

[0072] The inverse distance interpolation method performs better for situations where the data points are distributed irregularly. This method can flexibly interpolate according to the distance between data points, better reflect the spatial changes of the data, and emphasize the influence of nearby data points during the interpolation process. It is more sensitive to the interpolation of local features. In addition, when the distance weight needs to be adjusted according to actual conditions, this method provides a free parameter a for users to adjust, making it more flexible to adapt to different data distributions and spatial characteristics.

[0073] The format of the interpolated time slice dataset obtained at this time is t i ~t i +Δt(x,y,A estimated );

[0074] In order to enable the detection data to continuously track the energy anomaly characteristics in the horizontal space and further reflect the continuous changes of the detection target in space, this technical solution adopts a spatial interpolation algorithm to spatially continuous the two-dimensional discrete data in the same time slice, providing a prerequisite for subsequent inter-layer energy balance and three-dimensional spatial interpolation.

[0075] Furthermore, the two-dimensional data volume composed of different time slices obtained above (interpolated time slice data) is converted into horizontal slices of different depths by using the electromagnetic wave velocity v given by empirical value or in-situ measured value. The time-depth conversion formula is z=vt / 2, and the depth slice data is obtained. At this time, the data format is converted into z i ~z i +Δz(x,y,A estimated ), where z i ~zi +Δz is the depth interval;

[0076] (3) Standardizing the data with the same depth in the two-dimensional data volume (depth slice data) composed of slices of different depths obtained in step (2), mapping the slice data of the same depth to a linear interval of 0 to 100% according to the size ratio, and obtaining standardized depth slice data. The specific method is as follows:

[0077]

[0078] At this time, the data format is converted to z i ~z i +Δz(x,y,A nor ), at this time A nor is the standardized energy value (0≤A nor ≤100%), dimensionless, A nor It only represents the energy attenuation degree of a data point after removing the influence of natural attenuation, so A at different levels nor Can be compared with each other;

[0079] Due to the natural attenuation of electromagnetic wave energy with depth, data at different depths are at different energy levels and cannot be directly compared with each other. In order to correct the data at different depths to the same energy level, this step innovatively uses a standardized algorithm to balance the energy between layers of the data, and normalizes the energy data of all depths to 0-100%. At this time, the natural attenuation effect of electromagnetic waves with propagation distance is eliminated, and the value of each data point represents the degree of energy attenuation of the point except for the natural attenuation factor (mainly karst groundwater in the formation). nor The closer the value is to 100%, the smaller the energy attenuation effect of karst groundwater on this point is, and vice versa.

[0080] Then the data volume after interlayer energy balance obtained above, that is, the standardized depth slice data, is interpolated in three-dimensional space. The interpolation method still uses the inverse distance interpolation method to form a three-dimensional electromagnetic wave energy data point cloud (x, y, z, A nor );

[0081] Based on this step, it is possible to continuously track the energy anomaly characteristics in three-dimensional space, further reflect the continuous changes of the detection target in three-dimensional space, and provide a prerequisite for subsequent terrain correction processing.

[0082] (4) Perform terrain correction on the three-dimensional electromagnetic wave energy data point cloud obtained by F to obtain a three-dimensional electromagnetic wave energy distribution model of the survey area. The specific steps are as follows:

[0083] 1) Constructing the terrain data file of the survey area according to the coordinate information collected by the positioning module during the acquisition process, interpolating the terrain data, and its gridding parameters are consistent with those in step (2). At this time, the terrain data file format is (x, y, h);

[0084] 2) The three-dimensional data volume obtained in step (3), i.e., the standardized depth slice data (x, y, z, A nor ) is corrected to the elevation coordinate H according to the elevation at the same position. The correction formula is H i =h i -z i , at this time, the three-dimensional electromagnetic wave energy data point cloud format is converted to (x, y, H, A nor );

[0085] Due to the ups and downs of the terrain in the survey area, the starting point of each data is different, which makes it impossible to circle the real spatial position of the data anomaly, and the distribution form of the anomaly will also be distorted. In this step, the three-dimensional data body is corrected for terrain based on the GPS data collected by the system in the early stage.

[0086] Step C, generating a three-dimensional energy isosurface rendering and a two-dimensional energy slice diagram through three-dimensional data visualization, and interpreting the data based on the selected groundwater distribution energy threshold;

[0087] The obtained three-dimensional electromagnetic wave data point cloud (x, y, H, A nor ) to render the three-dimensional visualization model, including energy isosurface rendering, arbitrary angle two-dimensional slice rendering, arbitrary point single-channel data curve drawing, etc., select appropriate energy threshold and color scale, and identify the area below the energy threshold (low energy abnormal area) as the karst groundwater distribution area. This step can use the OpenGL library of Matlab software for graphics rendering and the above-mentioned visualization effect;

[0088] The method for determining the energy threshold is as follows: According to the principle of this method, when the electromagnetic wave signal moves from the surrounding dry karst strata to the karst groundwater distribution area, the electromagnetic wave energy value will rapidly decrease near the interface, and the contour lines will become denser at the interface. According to the above abnormal characteristics, the approximate energy threshold range of the groundwater distribution area can be identified through the contour line map of the measured data, and then hydrological holes are set on both sides of the boundary of the groundwater distribution area for pumping tests. The energy threshold is further calibrated according to the water-rich characteristics of the stratum, and the karst groundwater distribution range is corrected.

[0089] Step D, delineating the three-dimensional spatial distribution range and water content change of karst groundwater in the measurement area;

[0090] Based on the three-dimensional visualization results obtained in step C, first determine the approximate energy threshold range of the water-bearing area based on the data contour line characteristics in the energy slice, and circle the relatively low-energy area, which means that the electromagnetic wave energy in the area is generally lower than that in the adjacent position; define the relatively low-energy area in the stratum as the distribution range of karst groundwater, and judge the relative size of energy in the relatively low-energy area as the relative amount of water content. If the electromagnetic wave energy is large, the water content is relatively small, and vice versa, if the electromagnetic wave energy is small, the water content is large; finally, based on the three-dimensional isosurface rendering technology, perform three-dimensional modeling of the distribution of karst groundwater, and the constructed model can be used for relevant groundwater numerical simulation.

[0091] Combination Figure 4 As shown in the figure, taking the Quancheng Square survey area as an example, the in-situ three-dimensional electromagnetic wave energy isosurface rendering model is obtained Figure 5 ,The 3D geological body in the figure is the area where the electromagnetic wave energy is higher than 70%, corresponding to the lower water content of the strata, the low degree of karst development of the strata, and no continuous karst groundwater distribution. The electromagnetic wave energy in the blank area is lower than 30%, corresponding to the more developed karst formation, the higher water content of the formation, and the existence of continuous karst groundwater distribution areas; Figure 5 The horizontal energy slice map at an elevation of 4m is also given. In the figure, blue represents low-energy areas. The approximate energy threshold range for identifying groundwater distribution areas based on the contour map is 25% to 30%. The low-energy anomaly areas are mainly distributed on the west and northeast sides of the survey area. It is inferred that they are the main karst groundwater distribution areas in the survey area. The detection results are consistent with the distribution position of springs. The blue arrow marks the possible direction of water inflow.

[0092] Figure 6 It is a horizontal slice diagram of electromagnetic wave energy at different elevations. According to the changes in low-energy anomaly areas at different elevation depths, it can reflect the distribution range of karst groundwater in different layers. The results show that the electromagnetic wave energy is low in the southwest of the survey area, near Baotu Spring and near Heihu Spring, and the corresponding stratum water content is relatively high. It can be seen that the detection process of the present invention is simple, fast, non-destructive, accurate, and low-cost. The results have three-dimensional visualization capabilities and can be detected in real time and repeatedly.

[0093] The above description is only a preferred embodiment of the present invention and does not limit the present invention in other forms. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A detection method for a detection system for the spatial distribution of shallow karst groundwater, characterized in that: The detection system includes a traction module, a host module, a handheld remote control, a measurement module, a positioning module and a holding cable, the host module is arranged in the internal space of the traction module, one end of the holding cable is connected to the traction module, the traction module is used to provide forward power for the measurement system, the measurement module includes a pulse electromagnetic wave transmitting module and a pulse electromagnetic wave receiving module, the pulse electromagnetic wave transmitting module, the positioning module and the pulse electromagnetic wave receiving module are arranged on the holding cable in sequence, and are electrically connected to the host module through the data transmission line inside the holding cable; The detection method comprises the following steps: Step A, performing noise removal and shaping processing on the two-dimensional electromagnetic wave data set collected by the detection system, and reorganizing the data set into three-dimensional coordinates according to the real spatial position of each electromagnetic wave to form a three-dimensional electromagnetic wave data set; Step B, sequentially performing time slicing, horizontal two-dimensional interpolation, time-depth conversion, energy standardization, three-dimensional space interpolation and three-dimensional terrain correction processing on the three-dimensional electromagnetic wave data set obtained in step A to obtain a three-dimensional electromagnetic wave energy distribution model of the survey area; Step B1: collect the three-dimensional electromagnetic wave data obtained in step A into i ~t i The absolute amplitude data within the range of +Δt are averaged to create time slices, where Δt is the slice thickness, Defined as slice overlap, set parameters Δt and m to obtain electromagnetic wave energy distribution data in different time planes. The data format is where t i ~t i +Δt is the time interval, x and y are the longitude and latitude coordinates, is the absolute amplitude value; Step B2: Perform two-dimensional spatial interpolation calculation on the electromagnetic wave energy distribution data obtained through time slicing to obtain interpolated time slice data after interpolation of different time slices. The data format is t i ~t i +Δt(x,y,A estimated ), and combined with the given electromagnetic wave velocity v, the interpolation time slice data is converted into time-depth, and the horizontal slices at different times are converted into horizontal slices at different depths to obtain the depth slice data. At this time, the data format is converted to z i ~z i +Δz(x,y,A estimated ), where z i ~z i +Δz is the depth interval; Step B3: Based on the depth slice data obtained above, the data in the same depth slice is mapped to a linear interval of 0 to 100% according to the size ratio in the slice to perform energy standardization to obtain standardized depth slice data. At this time, the data format is converted to z i ~z i +Δz(x,y,A nor ); perform three-dimensional spatial interpolation on the standardized depth slice data to form a three-dimensional electromagnetic wave energy data point cloud (x, y, z, A nor ), at this time A nor is the standardized energy value, 0≤A nor ≤100%; Step B4: Finally, perform three-dimensional terrain correction processing on the obtained three-dimensional electromagnetic wave energy data point cloud to obtain a three-dimensional electromagnetic wave energy distribution model of the survey area. Specifically: 1) Constructing a terrain data file of the survey area according to the coordinate information collected by the positioning module during the acquisition process, and performing interpolation calculation on the terrain data. The gridding parameters are consistent with those in step B2. At this time, the terrain data file format is (x, y, h); 2) The three-dimensional data volume obtained in step B3, i.e., the standardized depth slice data (x, y, z, A nor ) is corrected to the elevation coordinates H, H according to the elevation at the same position. i =h i -z i , at this time the three-dimensional electromagnetic wave data volume format is converted to (x, y, H, A nor ); Step C, according to the electromagnetic wave energy distribution model obtained in step B, generate a three-dimensional energy isosurface rendering map and a two-dimensional energy slice map through three-dimensional data visualization, and interpret the data based on the energy threshold of the selected groundwater distribution area; Step D: Based on the three-dimensional visualization results obtained in step C, the three-dimensional spatial distribution range and water content change of the karst groundwater in the measurement area are delineated: Firstly, based on the data contour line characteristics in the energy slice, the energy threshold range of the water-bearing area is determined, and the relatively low-energy area is circled. The relatively low-energy area refers to the area where the electromagnetic wave energy is generally lower than that of the adjacent position. The relatively low-energy area in the stratum is delineated as the distribution range of karst groundwater, and the relative size of the energy in the relatively low-energy area is judged as the relative water content. If the electromagnetic wave energy is large, the water content is relatively small. Conversely, if the electromagnetic wave energy is small, the water content is high.

2. The detection method of the detection system for the spatial distribution of shallow karst groundwater according to claim 1, characterized in that: The step A is implemented in the following manner: (1) Based on the two-dimensional electromagnetic wave data set collected by the detection system, the data format is (x, t, a), and basic denoising and shaping processing is performed to obtain a two-dimensional data volume with a data format of (x, t, a)*number of survey lines. The denoising and shaping processing includes zero point correction to remove direct waves, background elimination to eliminate noise, bandpass filtering to remove signals in non-transmitted frequency bands, inter-channel equalization to balance the signal strength between data points, and standardization of balanced data point intervals; (2) Combined with GPS data, the processed two-dimensional data volume is reorganized into three-dimensional coordinates according to the real space position. The coordinate information of each data collection point is obtained by interpolating the coordinate data recorded by the positioning module during the collection process, and finally a three-dimensional electromagnetic wave data set covering the survey area is formed. The data of any collection point contains the information on the change of the amplitude intensity of the electromagnetic wave signal at that point over time. At this time, the data format of the three-dimensional electromagnetic wave data set is (x, y, t, a).

3. The detection method of the detection system for the spatial distribution of shallow karst groundwater according to claim 1, characterized in that: In the step C, the obtained three-dimensional electromagnetic wave data volume (x, y, H, A nor ) is used to render the three-dimensional visualization model. The rendering methods include energy isosurface rendering, arbitrary angle two-dimensional slice rendering, and arbitrary point single-channel data curve drawing. The quantity threshold and color scale are selected to identify the area below the energy threshold as the karst groundwater distribution area.

4. The detection method of the detection system for the spatial distribution of shallow karst groundwater according to claim 1 is characterized in that: The method for determining the energy threshold in step C is as follows: Considering that when the electromagnetic wave signal moves from the karst groundwater distribution area to the surrounding dry karst strata, the electromagnetic wave energy value will show an abnormal decrease near the interface, and the contour lines will become denser at the interface. According to the above abnormal decrease characteristics, the energy threshold range of the groundwater distribution area is identified through the contour line map of the measured data, and then hydrological holes are set on both sides of the boundary of the groundwater distribution area for pumping tests, and the energy threshold is further calibrated according to the water-rich characteristics of the formation.

5. The detection method of the detection system for the spatial distribution of shallow karst groundwater according to claim 1, characterized in that: The host module includes a master control machine, a central control unit and a data storage module. The pulse electromagnetic wave transmitting module, the pulse electromagnetic wave receiving module and the data storage module are all connected to the central control unit, and the central control unit is connected to the master control machine. The central control unit receives the acquisition instructions of the master control machine, controls the pulse electromagnetic wave transmitting module to continuously transmit pulse electromagnetic wave signals underground, controls the pulse electromagnetic wave receiving module to receive the transmitted pulse electromagnetic wave reflection signals, and processes them to form a two-dimensional electromagnetic wave data set and stores it in the data storage module.

6. The detection method of the detection system for the spatial distribution of shallow karst groundwater according to claim 1, characterized in that: The traction module includes a remote control signal receiving module, a motor control module, a driving motor and a driving wheel. The remote control signal receiving module communicates with the handheld remote control to control the motor control module to control the driving motor through a radio frequency signal, and then control the rotation direction and speed of the driving wheel, so as to realize the movement of the measurement module along a predetermined measuring line by traction of a force-bearing cable.

Images