High-density electrical method combined with microseismic exploration method for near-surface water-rich area

Abstract

This invention provides a method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity tomography (EDS) and micro-motion, belonging to the field of engineering geophysical exploration technology. The technical solution is as follows: a method for exploring near-surface water-rich areas using a combination of high-density EDS and micro-motion includes: conducting rock physics studies of the survey area to obtain the relationship between surrounding rock porosity and shear wave velocity under saturated water conditions; conducting high-density EDS to roughly determine the location of water-rich areas; implementing micro-motion detection; establishing an initial model for surface wave inversion using the high-density EDS results as the initial model; setting the initial model for shear wave velocity inversion using the high-density EDS results, providing initial estimates of shear wave velocity and layer thickness; and calculating the near-surface shear wave velocity structure of the survey area to determine the location of water-rich areas. This method effectively solves the problems of inaccurate depth measurement with high-density EDS and the problem of providing a suitable initial model for micro-motion detection, achieving an effective combination of high-density EDS and micro-motion detection for accurate detection of water-rich areas.

Description

Technical Field

[0001] This invention relates to the field of engineering geophysical exploration technology, and in particular to a method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity tomography and micro-motion. Background Technology

[0002] Water-rich areas such as underground anomalous water bodies and mining subsidence zones may pose hidden dangers such as foundation instability during engineering construction. Relying solely on drilling and geological surveys to conduct large-scale explorations of water-rich areas is extremely costly, time-consuming, and unlikely to meet demand, posing a significant challenge to surface exploration in these areas. Current surface exploration methods for near-surface water-rich areas primarily include electrical resistivity tomography (EDM) and high-density electrical resistivity tomography (HMT). However, these methods cannot accurately measure depth; they can only roughly depict the location based on electrical property differences. Furthermore, if the surrounding rock of the water-rich area has fissures, the HMT cannot accurately determine the extent of the water-rich area due to fissure water filling, resulting in low resolution. In saturated water conditions, significant differences in electrical properties and surface wave velocities are observed.

[0003] In recent years, a geophysical method called micro-motion exploration has emerged, which avoids some of the drawbacks of electrical logging. It uses natural earth micro-motion as a signal source and has the advantages of flexible construction, less site limitation, non-destructive operation, and high efficiency. It has begun to be widely used in engineering geophysical exploration. However, micro-motion exploration also has certain drawbacks. It requires dispersion curve extraction and inversion. If the initial model is not set properly during the inversion process, it is easy to get trapped in local extrema and cause detection deviation. Summary of the Invention

[0004] To address the shortcomings of existing high-density electrical resistivity tomography (EDS) and micro-motion detection technologies in the detection of water-rich areas, the present invention aims to provide a combined high-density EDS and micro-motion method for near-surface water-rich area exploration. Based on the linear relationship between porosity (water content) and shear wave velocity in a rock physics model, this method can accurately locate water-rich areas.

[0005] This invention is achieved through the following technical solution: a method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods, comprising the following steps:

[0006] S1: Data Collection: Collect relevant geological survey data and rock physical information for the survey area;

[0007] S2: Rock physical simulation study: Conduct rock physical studies on the survey area to obtain the relationship between the porosity of the surrounding rock and the transverse wave velocity under saturated water conditions.

[0008] S3: Preliminary high-density electrical resistivity tomography: High-density electrical resistivity tomography is conducted in the survey area to roughly determine the location of water-rich areas by arranging survey lines in different directions;

[0009] S4: Micro-motion detection: Based on the location of the water-rich area shown in S3, determine the optimal detection line and implement micro-motion detection;

[0010] S5: Surface wave inversion initial model establishment: Using the high-density electrical resistivity tomography (EDT) results in S3 as the initial model, and combining the relationship between surrounding rock porosity and shear wave velocity obtained in S2, the initial model for surface wave inversion is established; the initial model setting for shear wave velocity inversion is performed using the measured high-density EDT results, the low-velocity zone is divided, and initial estimates of shear wave velocity and layer thickness within the high-density EDT water body range are given;

[0011] S6: Shear wave velocity profile inversion: Extract the dispersion curve and, based on the inversion initial model described in S5, invert and calculate the near-surface shear wave velocity structure of the region to determine the location of the water-rich area.

[0012] Furthermore, S2 specifically refers to:

[0013] S21. A rock physical study was conducted in the survey area to obtain relevant rock physical parameters: the Xu-White model was used to calculate the saturated rock shear modulus, and the pores of the argillaceous sandstone were divided into mudstone pores with a smaller pore aspect ratio and sandstone pores with a larger pore aspect ratio. This was further analyzed using the Kuster-... The shear modulus of rocks saturated with pore fluid is calculated using a model and differential equivalent medium theory (DEM); the specific calculation formula is as follows:

[0014]

[0015]

[0016]

[0017] In the formula: Total porosity and V represents the porosity of mudstone and sandstone. c and V s This represents the corresponding volume percentage content, where K0 is the bulk modulus of the rock mineral, and μ... dry μ0 and μ fl α represents the shear modulus of dry rock, rock minerals, and pore fluid, respectively, and F(α) is a function related to the pore aspect ratio α.

[0018] S22. To improve computational efficiency, the solution to the KT equation is transformed into the solution of a system of linear ordinary differential equations to obtain the elastic modulus of the rock skeleton:

[0019]

[0020]

[0021] S23. To accurately determine the relationship between the porosity of the surrounding rock and the transverse wave velocity under saturated water conditions in this test area and to determine the corresponding rock physical parameters, a rock physical study of the test area is required. Based on the rock physical parameters obtained in S21 and S23, a saturated water rock mass model is designed. The porosity variation range in the experiment is 5% to 30%, and the sampling interval is 1%. To accurately fit the measured data, the porosity aspect ratio variation range is 0.05 to 0.20. The relationship between the transverse wave velocity and porosity is calculated using the Xu-White model.

[0022] Furthermore, S3 specifically involves: deploying multiple high-density electrical resistivity tomography (ERT) lines in different directions according to the specific conditions of the survey area. Each high-density ERT line includes: several spaced measuring electrodes and several observation devices; performing high-density ERT line detection on the survey area to obtain a high-density ERT profile; and determining the approximate location of the water-rich area based on the obtained high-density ERT profile.

[0023] Furthermore, the measuring electrodes are non-polarized electrodes, and the electrodes are treated with water or an auxiliary grounding device. The spacing between adjacent measuring electrodes is 3 to 5 meters, and 60 electrodes are arranged in a single row.

[0024] Furthermore, the observation device adopts one or two of the following: the Winner α device, the Winner β device, and the Winner device.

[0025] The single-point measurement time is 0.5s to 2.0s, and the measurement cycle is 1 to 3 times. A reasonable isolation coefficient should be set to ensure the detection depth penetrates at least 20m underground. Let n be the maximum isolation coefficient, a be the electrode distance, and the overlap length of the measuring line be determined according to (3*n-1)*a. Generally, an electrode distance of 3m and a maximum isolation coefficient n≥20 for 60 electrodes are sufficient to meet the requirements. The approximate location of the water-rich area is determined based on the obtained high-density electrical resistivity tomography profile.

[0026] Furthermore, during the micro-motion detection, data is collected by arranging stations into a circular detection array. One station is located at the detection point, and the remaining stations are located on concentric circles with a fixed detection radius centered on the central array. They are generally arranged in an equilateral triangle configuration, with detectors overlapping between measurement points. The deepest depth measured can reach three to four times the detection radius, meaning the detection depth primarily depends on the size of the detection radius. If the detection depth is approximately 25 meters, the measurement point spacing can be set to 10 meters, employing a multiple detection radius approach: an outer circle detection radius of 11.43 meters and an inner circle detection radius of 5.76 meters.

[0027] Furthermore, S6 specifically involves expanding the initial surface wave inversion model of S5 by 50% both upwards and downwards as the search range for the genetic algorithm. The model data with the best fitting effect is selected iteratively as the optimal solution, ultimately yielding the inversion results of the shear wave velocity profile at each measuring point. During the inversion process, P-wave velocity and density are set as the correlation function of shear wave velocity. Lateral data interpolation is performed on the inversion results of each measuring point to obtain the two-dimensional shear wave velocity profile of the survey area. Water-rich areas generally exhibit significantly lower velocities, contrasting sharply with the high velocities of the surrounding rock, with a difference of approximately 2-3 times. In cold weather, water-rich areas at the surface exhibit significantly higher velocities due to freezing, showing a clear difference in shear wave velocity compared to the surrounding fill.

[0028] The beneficial effects of this invention are as follows: This method effectively solves the problems of inaccurate depth measurement by high-density electrical resistivity tomography and the problem of providing a given initial model for micro-motion detection. Based on the relationship between the porosity of rock saturated water and the transverse wave velocity, it achieves an effective combination of high-density electrical resistivity tomography and micro-motion detection, accurately detects the location of water-rich areas, produces intuitive maps, is convenient to implement, and has low cost, thus possessing good technical advantages and application prospects. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method;

[0030] Figure 2 The effect of porosity on shear wave velocity for rock type 1;

[0031] Figure 3 The effect of porosity on shear wave velocity for rock type 2;

[0032] Figure 4 The effect of rock type 3 porosity on shear wave velocity;

[0033] Figure 5 The effect of rock type 4 porosity on shear wave velocity;

[0034] Figure 6 The results of high-density electrical resistivity tomography (OTT) survey line layout and detection.

[0035] Figure 7 The detection results are a two-dimensional profile of the high-density resistivity of the detection area.

[0036] Figure 8 Two-dimensional profile of the detection results - contour map of the micro-motion detection results;

[0037] Figure 9 This is to compare the results of micro-motion detection with borehole data. Detailed Implementation

[0038] To clearly illustrate the technical features of this solution, the following detailed implementation method will be used to explain the solution.

[0039] Example 1, as Figure 1 As shown, the present invention is achieved through the following technical solution: a method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity tomography and micro-motion, comprising the following steps:

[0040] S1: Data Collection: Collect relevant geological survey data and rock physical information for the survey area;

[0041] S2: Rock physical simulation study: Conduct rock physical studies on the survey area to obtain the relationship between the porosity of the surrounding rock and the transverse wave velocity under saturated water conditions.

[0042] S3: Preliminary high-density electrical resistivity tomography: High-density electrical resistivity tomography is conducted in the survey area to roughly determine the location of water-rich areas by arranging survey lines in different directions;

[0043] S4: Micro-motion detection: Based on the location of the water-rich area shown in S3, determine the optimal detection line and implement micro-motion detection;

[0044] S5: Surface wave inversion initial model establishment: Using the high-density electrical resistivity tomography (EDT) results in S3 as the initial model, and combining the relationship between surrounding rock porosity and shear wave velocity obtained in S2, the initial model for surface wave inversion is established; the initial model setting for shear wave velocity inversion is performed using the measured high-density EDT results, the low-velocity zone is divided, and initial estimates of shear wave velocity and layer thickness within the high-density EDT water body range are given;

[0045] S6: Shear wave velocity profile inversion: Extract the dispersion curve and, based on the initial inversion model described in S5, invert and calculate the near-surface shear wave velocity structure of the region to determine the location of the water-rich area.

[0046] Example 2, as Figure 2-9 As shown, a method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity tomography and micro-motion includes the following steps:

[0047] S1: Data Collection: Collect relevant geological survey data and rock physical information for the survey area;

[0048] S2: Rock physical simulation study: Conduct rock physical studies in the survey area to obtain the relationship between the porosity of the surrounding rock and the transverse wave velocity under saturated water conditions;

[0049] S21. A rock physical study was conducted in the survey area to obtain relevant rock physical parameters: the Xu-White model was used to calculate the saturated rock shear modulus, and the pores of the argillaceous sandstone were divided into mudstone pores with a smaller pore aspect ratio and sandstone pores with a larger pore aspect ratio. This was further analyzed using the Kuster-... The shear modulus of rocks saturated with pore fluid is calculated using a model and differential equivalent medium theory (DEM); the specific calculation formula is as follows:

[0050]

[0051]

[0052]

[0053] In the formula: Total porosity and V represents the porosity of mudstone and sandstone. c and V s This represents the corresponding volume percentage content, where K0 is the bulk modulus of the rock mineral, and μ... dry μ0 and μ fl α represents the shear modulus of dry rock, rock minerals, and pore fluid, respectively, and F(α) is a function related to the pore aspect ratio α.

[0054] S22. To improve computational efficiency, the solution to the KT equation is transformed into the solution of a system of linear ordinary differential equations to obtain the elastic modulus of the rock skeleton:

[0055]

[0056]

[0057] S23. To accurately determine the relationship between porosity and shear wave velocity in the surrounding rock under saturated water conditions in this survey area and to identify the corresponding rock physical parameters, a rock physical study of the survey area is required. The Quaternary strata in this area mainly consist of Holocene artificial fill layers with a relatively small thickness. The bedrock is composed of rhyolitic tuff from the Lower Cretaceous Shiqianzhuang Formation (KqS) and sandstone, siltstone, and basalt from the Bamudi Formation (KqB). Tuff, sandstone, and siltstone are widely distributed, while basalt is less abundant. Based on the rock physical parameters obtained in S21 and S23, a saturated water rock mass model was designed to investigate the influence of porosity on shear wave velocity. The porosity variation range in the experiment was 5%–30%, with a sampling interval of 1%. To accurately fit the measured data, the porosity aspect ratio ranged from 0.05 to 0.20. The relationship between shear wave velocity and porosity was calculated using the Xu-White model as follows: Figures 2-5 As shown, under the condition of saturated rock mass, the rock shear wave velocity and porosity exhibit a good linear relationship, and different rock masses have a good fitting relationship with different pore aspect ratios.

[0058] In water-rich areas, there is a significant difference in porosity between the target area and the surrounding rock. Under saturated water conditions, this results in significant differences in electrical properties and surface wave velocities. In practical engineering, high-density resistivity methods are often used to detect water-rich goaf areas, but this method cannot accurately measure depth. Based on the relationship between rock shear wave velocity and saturated rock mass porosity, this scheme will adopt a combination of high-density electrical resistivity and micro-motion exploration.

[0059] S3: Preliminary High-Density Electrical Resonance Testing: High-density electrical resonance testing is conducted in the survey area to roughly determine the location of water-rich regions by arranging survey lines in different directions; for example... Figure 6 As shown, a high-density electrical resistivity tomography method was used to conduct a "rice"-shaped detection of the detection area. Four measuring lines were laid out in the detection area, and measuring line 2 was determined to be the optimal micro-motion detection measuring line based on the detection results.

[0060] S4: Micro-motion Detection: Based on the location of the water-rich area shown in S3, determine the optimal detection line and implement micro-motion detection. During this detection, stations are arranged in a circular array for data acquisition. One station is located at the detection point, and the remaining stations are located on concentric circles with a fixed detection radius centered on the central array. Generally, they are arranged in an equilateral triangle formation, using a method of overlapping detectors between measurement points. Specifically, three instruments S2-S4 are placed at equal intervals on the circumference of a circle with radius r1 and S1 at the center point. In actual observation, arrays with different radii are used for multi-radius micro-motion observation. The deepest depth measured can reach three to four times the detection radius, meaning the detection depth mainly depends on the size of the detection radius. In this case, the detection depth is approximately 25 meters, so the measurement point spacing can be set to 10 meters. A multi-radius detection method is adopted, with an outer circle detection radius of 11.43 meters and an inner circle detection radius of 5.76 meters.

[0061] S5: Surface wave inversion initial model establishment: Using the high-density electrical resistivity tomography (EDT) results in S3 as the initial model, and combining the relationship between surrounding rock porosity and shear wave velocity obtained in S2, the initial model for surface wave inversion is established; the initial model setting for shear wave velocity inversion is performed using the measured high-density EDT results, the low-velocity zone is divided, and initial estimates of shear wave velocity and layer thickness within the high-density EDT water body range are given;

[0062] S6: Shear wave velocity profile inversion: Extract dispersion curves and, based on the initial inversion model described in S5, invert and calculate the near-surface shear wave velocity structure of the region to determine the location of water-rich areas. Extend the initial model in S5 by 50% both above and below as the search range for the genetic algorithm. Iterate and select the model data with the best fit as the optimal solution to finally obtain the shear wave velocity profile inversion results for each measuring point. During the inversion process, P-wave velocity and density are set as the correlation function of shear wave velocity; for example... Figure 7-9As shown, the two-dimensional shear wave velocity profile of the survey area can be obtained by lateral data interpolation of the inversion results of each measuring point. Water-rich areas generally exhibit significantly low velocities, contrasting sharply with the high velocities of the surrounding rock, with a difference of approximately 2-3 times. In cold weather, water-rich areas at the surface exhibit significantly high velocities due to freezing, showing a clear difference in shear wave velocity compared to the surrounding fill. The water-rich areas show a clear anomalous low-velocity phenomenon, and the detection results correspond well with the high-density resistivity method, both showing two distinct water-rich mined-out areas. Because the high-density resistivity method cannot accurately measure depth, the detection results show uneven sides in the low-resistivity areas (approximately 70 meters), while the slope of the actual quarry is relatively gentle. Therefore, the detection results combining the two methods are more consistent with the actual situation.

[0063] Example 3: Based on Example 2, the measuring electrodes are further described as non-polarized electrodes, and the electrodes are treated with water or an auxiliary grounding device is used. The spacing between adjacent measuring electrodes is 3 to 5 meters, and 60 electrodes are arranged in a single row.

[0064] Furthermore, the observation device adopts one or two of the following: the Winner α device, the Winner β device, and the Winner device.

[0065] The single-point measurement time is 0.5s to 2.0s, and the measurement cycle is 1 to 3 times. A reasonable isolation coefficient should be set to ensure the detection depth penetrates at least 20m underground. Let n be the maximum isolation coefficient, a be the electrode distance, and the overlap length of the measuring line be determined according to (3*n-1)*a. Generally, an electrode distance of 3m and a maximum isolation coefficient n≥20 for 60 electrodes are sufficient to meet the requirements. The approximate location of the water-rich area is determined based on the obtained high-density electrical resistivity tomography profile.

[0066] In the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0067] The technical features of this invention not described can be implemented by or using existing technology, and will not be repeated here. Of course, the above description is not a limitation of this invention, and this invention is not limited to the examples above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of this invention should also be within the protection scope of this invention.

[0068] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

Claims

1. A method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity tomography and micro-motion, characterized in that, Includes the following steps: S1: Data Collection: Collect relevant geological survey data and rock physical information for the survey area; S2: Rock physical simulation study: Conduct rock physical studies in the survey area to obtain the relationship between the porosity of the surrounding rock and the transverse wave velocity under saturated water conditions; S3: Preliminary high-density electrical resistivity tomography: High-density electrical resistivity tomography is conducted in the survey area to roughly determine the location of water-rich areas by arranging survey lines in different directions; S4: Micro-motion detection: Based on the location of the water-rich area shown in S3, determine the optimal detection line and implement micro-motion detection; S5: Establishment of the initial model for surface wave inversion: Using the high-density electrical resistivity tomography results in S3 as the initial model, and combining the relationship between the surrounding rock porosity and shear wave velocity obtained in S2, the initial model for surface wave inversion is established. S6: Shear wave velocity profile inversion: Extract dispersion curves and, based on the initial model obtained in S5, invert and calculate the near-surface shear wave velocity structure of the survey area to determine the location of water-rich areas.

2. The method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods according to claim 1, characterized in that, Specifically, S2 is: S21. Conduct rock physics research on the survey area to obtain the corresponding rock physics parameters: use the Xu-White model to calculate the saturated rock shear modulus, divide the pores of argillaceous sandstone into mudstone pores with a smaller pore aspect ratio and sandstone pores with a larger pore aspect ratio, and calculate the shear modulus of pore fluid-saturated rocks using the Kuster-Toksöz model and differential equivalent medium theory (DEM). S22. The elastic modulus of the rock skeleton is obtained by solving a system of linear ordinary differential equations. S23. Based on the rock physical parameters obtained in S21 and S23, design a saturated water rock mass model and calculate the relationship between shear wave velocity and porosity using the Xu-White model.

3. The method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods according to claim 1, characterized in that, S3 specifically involves: setting up multiple high-density electrical resistivity tomography (EPT) lines in different directions in the survey area, each high-density EPT line including: several spaced measuring electrodes and several observation devices; performing high-density EPT line detection on the survey area to obtain a high-density EPT profile; and determining the approximate location of the water-rich area based on the obtained high-density EPT profile.

4. The method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods according to claim 3, characterized in that... The measuring electrodes are non-polarized electrodes, and the distance between adjacent measuring electrodes is 3~5m.

5. The method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods according to claim 3, characterized in that, The observation device is one or two of the following: the Winner α device, the Winner β device, and the Winner device.

6. The method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods according to claim 1, characterized in that, When performing the micro-motion detection, the stations are arranged into a circular detection array for data acquisition. One station is located at the detection point, and the other stations are located on concentric circles with a fixed detection radius centered on the central array.

7. The method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods according to claim 1, characterized in that, S5 also includes setting the initial model for transverse wave velocity inversion using the measured high-density electrical resistivity tomography (EDT) data, dividing the low-velocity zone region, and providing initial estimates of transverse wave velocity and layer thickness within the water body detected by the high-density EDT.

8. The method for exploring near-surface water-rich areas using a combination of high-density electrical resistivity and micro-motion methods according to claim 1, characterized in that, Specifically, S6 involves expanding the initial surface wave inversion model of S5 by 50% both vertically and horizontally as the search range of the genetic algorithm. By iteratively selecting the model data with the best fitting effect as the optimal solution, the inversion results of the shear wave velocity profile at each measuring point are finally obtained. During the inversion process, the P-wave velocity and density are set as the correlation function of the shear wave velocity. The two-dimensional shear wave velocity profile of the measuring area can be obtained by performing lateral data interpolation on the inversion results of each measuring point.

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