WO2022078516A1 - 基于深度分辨率的隧道电阻率超前探测优化方法及*** - Google Patents
基于深度分辨率的隧道电阻率超前探测优化方法及*** Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 43
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- 238000005259 measurement Methods 0.000 claims abstract description 40
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- 238000004364 calculation method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 238000003384 imaging method Methods 0.000 description 5
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- 238000013480 data collection Methods 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
- G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
- G01R27/20—Measuring earth resistance; Measuring contact resistance, e.g. of earth connections, e.g. plates
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/02—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with propagation of electric current
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- the present disclosure belongs to the technical field of tunnel resistivity advance detection and observation, and in particular relates to a depth resolution-based tunnel resistivity advance detection optimization method and system.
- tunnels the geological survey of which is more difficult.
- higher resolution and more difficult targets are required, which poses higher requirements and challenges for the advance detection accuracy of tunnels.
- the traditional surface exploration conditions and technical level are difficult to meet the needs of the project for the detection depth and refined exploration.
- tunnel resistivity advanced detection methods are vague, undetectable, and inaccurate in the identification of the imaging boundary of the water body, and for Some sub-meter or even decimeter-level water-conducting structures are difficult to identify effectively. Therefore, a new tunnel resistivity advanced detection device is needed, which can perform fine imaging of sub-meter-level water-bearing structures, that is, "electrode power supply in the hole, palm “Sub-surface array measurement” fine advance detection and observation device for tunnel borehole resistivity.
- the tunnel borehole resistivity advanced detection and observation device has more advantages in refined imaging: 1) It has a longer sounding depth; 2) It can obtain richer and more direct information about abnormal bodies around the borehole . Therefore, the advanced detection of tunnel borehole resistivity has broad application prospects in the advanced detection of tunnel resistivity.
- observation work requires a lot of time and manpower.
- the large amount of data obtained from the basic observation mode poses a great challenge to the inversion calculation. observation efficiency.
- the current conventional optimization method is to improve the overall model resolution, which is a uniform improvement, and cannot solve the problem of excessive loss of deep model resolution.
- the present disclosure proposes a method and system for the advance detection of tunnel resistivity based on depth resolution.
- the present disclosure optimizes the observation system, and selects the measurement electrode points that contribute more to the model resolution. On the basis of the model resolution, the optimized electrode arrangement is finally obtained, which simplifies the number of electrodes and improves the detection efficiency.
- the present disclosure adopts the following technical solutions:
- a depth-resolution-based tunnel resistivity advance detection optimization method comprising the following steps:
- step (1) drill holes are arranged on the tunnel face, electrodes are arranged in the drill holes, and the length of the drill holes and the electrode spacing are determined; according to the detection accuracy requirements, the forward and inversion grids are determined. Size and number and arrangement of electrodes on the palm face.
- step (1) data collection is performed by using the full-space hole-tunnel resistivity method.
- step (2) all potential data that can be collected by the two measuring electrodes on the upper and lower surfaces of the face are selected as the initial set.
- the depth resolution balance matrix is composed of resolution balance factors, and each resolution balance factor is determined according to the inversion depth.
- the depth resolution balance goodness function of each temporary subset is:
- the relative model resolution of the initial set at this time is the main diagonal element of the model resolution matrix of the initial set and the main diagonal element of the model resolution matrix of the comprehensive set at this time.
- the average relative model resolution is obtained by averaging the elements in the relative model resolution.
- the specific process of judging whether the average relative model resolution of the initial set at this time meets the optimization requirements is to judge the value of the average relative model resolution of the initial set at this time Is it greater than the set value.
- An advanced detection and optimization system of tunnel resistivity based on depth resolution comprising:
- the model resolution matrix used to calculate the comprehensive set a module that selects several electrode measurement data from the comprehensive set to form an initial set
- a tunnel resistivity advance detection and observation system includes a plurality of measurement electrodes, and the number and positions of the measurement electrodes are determined according to the depth resolution-based tunnel resistivity advance detection optimization method.
- the present disclosure is a method for optimizing the form of a tunnel resistivity advanced detection device.
- the present disclosure uses the DRB method of depth resolution balance optimization on the basis of the uniform arrangement of electrodes in the hole and the basic type of electrode measurement in the face array.
- the observation device is optimized, and the measurement electrode points that contribute more to the model resolution are selected.
- the optimized face electrode arrangement is finally obtained, which simplifies the number of electrodes and improves the detection efficiency.
- the present disclosure introduces the depth resolution balance matrix H into the depth resolution balance goodness function, which can be combined with prior information to adjust the resolution balance factor of different mesh model resolutions in the DRB calculation process.
- Properly increasing the resolution balance of a certain depth region makes the model resolution of the grid in this region more important in the calculation of DRB, and finally enables the algorithm to give priority to improving the model resolution of this depth region.
- Fig. 1 is a flow chart of a method for optimizing the depth resolution balance of a tunnel borehole resistivity advance detection device
- Fig. 2 is the schematic diagram of electrode distribution in basic mode of borehole resistivity observation device
- Fig. 3 is the variation curve of the average relative model resolution with the iteration number
- Fig. 4 is the schematic diagram of the electrode distribution of the face measurement electrode of the observation device after optimization
- FIG. 5 is a schematic diagram of the distribution of the depth resolution balance factor
- Fig. 6 is the relative model resolution figure after optimization
- Fig. 7 is the geoelectric model diagram used when carrying out numerical simulation
- Fig. 8 is an inversion result diagram based on the optimized tunnel borehole observation model device.
- orientation or positional relationship is based on the orientation or positional relationship shown in the drawings, and is only a relational word determined for the convenience of describing the structural relationship of each component or element of the present disclosure, and does not specifically refer to any component or element in the present disclosure, and should not be construed as a reference to the present disclosure. public restrictions.
- a method for optimizing the depth resolution balance of a tunnel resistivity advance detection device includes the following steps:
- this embodiment is only an example, and does not only mean that the drilling depth, number of electrodes, spacing, and total number of data in other embodiments should be consistent with this embodiment, and the above data can be Reasonable changes are made according to specific observation requirements and environments, which are easily thought of by those skilled in the art, and should belong to the protection scope of the present disclosure.
- the maximum depth of the drill hole is set to 60m, the power supply electrodes in the hole are arranged at equal intervals, and the spacing between electrodes is 2m, so there are 30 power supply electrodes in the drill hole.
- the measurement electrodes on the face are arranged in an array. As shown in Figure 2, there are 8 rows of electrodes, of which the 1st and 8th rows each have only 3 electrodes, and the 4th and 5th rows each have 9 electrodes. The remaining rows each have 7 electrodes.
- the data collection adopts the dipole method of power supply in the borehole and reception on the face, and a total of 1560 measurement data are collected.
- the basic comprehensive set S c that the optimization relies on is the 1560 potential data generated when all 82 electrodes participate in power supply and measurement.
- all the potential data that can be collected by the two measuring electrodes on the face of the tunnel are selected as the initial set Si , which contains 60 potential data.
- M is the model resolution matrix to be calculated
- G is the Jacobian matrix
- C is the constraint matrix.
- Each of the remaining measurement electrodes is added to the initial set as an observation device to form 80 temporary subsets S t , and the corresponding model resolution matrix is calculated according to the model resolution matrix calculation formula.
- the depth resolution balance matrix is specifically:
- the depth resolution balance matrix H contains the resolution balance factors of all grids. By adding this matrix, the resolution balance of different grid model resolutions in the DRB calculation process is adjusted. In the preliminary study of the model resolution distribution of the borehole resistivity observation device, it was found that with the increasing depth, the model resolution of the deep area grid will continue to decrease. In order to ensure that the deep model resolution is not excessively lost, a depth resolution balance matrix is added. In the embodiment, the inversion depth is 60m, so h is taken as 30m. When the depth is greater than 30m, the resolution balance factor H of all grids is 1.2, and the resolution balance factor of the grids within 30m in front of the tunnel The value is 1.0, and the specific schematic diagram is shown in Figure 5.
- the depth-resolution balance goodness function is specifically:
- M t stores the main diagonal element of the resolution of the temporary subset model, representing the resolution vector of the temporary subset
- M b stores the main diagonal element of the resolution vector of the initial set model, representing the resolution vector of the initial set
- H is the depth resolution balance matrix vector.
- m represents the number of elements in the above vector
- M t (j) represents the j-th element in the temporary subset resolution vector.
- the value of the DRB function represents the improvement of the resolution of the original basic set model by the newly added observation device. In one iteration, the highest ranked observation device is selected and added to the initial set to form a new initial set. That is, in the embodiment, at the end of each optimization iteration, a new measurement electrode point will be added to the new initial set.
- M b and M c respectively store the main diagonal elements of the resolution matrix of the initial set and the comprehensive set model at this time, which are respectively expressed as the initial set resolution vector and the comprehensive set resolution vector.
- the division in the formula means that each element in M b is divided by the element in the corresponding position of M c .
- M r measures the closeness of the initial set model resolution and the comprehensive set model resolution at this time, and its elements are all values between 0 and 1, and obviously when these values are closer to 1, it indicates that At this time, the closer the model resolution of the initial set is to that of the comprehensive set, the better the model resolution.
- each remaining observation electrode is added to the updated initial set as an observation device to form 79 temporary subsets, and the above steps are repeated.
- FIG. 3 shows the curve of the average relative model resolution changing with the number of optimization iterations.
- Figure 6 shows the relative model resolution distribution after 20 optimizations.
- the relative model resolution value of each part in the deep part is close to 1, indicating that the model resolution of the selected subset is very close to the comprehensive set, and the model resolution of the deep part is very close. get a big boost.
- the optimized position and number of face electrodes are obtained, as shown in Figure 4.
- the figure shows that, compared with before optimization, the optimized face measurement electrodes have removed more redundant electrodes, the middle vertical column of measurement electrodes are all selected, and the rest of the selected electrodes are symmetrically distributed on the surrounding edges. There are a total of 22 measuring electrodes on the face.
- Such an arrangement of measuring electrodes can improve the overall model resolution more uniformly, and combined with the 30 power supply electrodes in the borehole, a total of 660 potential data can be collected, which is smaller than the comprehensive set of data. half, the observation efficiency is greatly improved, and the engineering applicability is improved.
- Figure 7 is the geoelectric model for the inversion calculation.
- the inversion area is 30m*30m*60m
- the background resistivity is set to 1000 ⁇ .m
- a low-resistance anomaly is set
- the size is 4m*5m*6m
- its resistance is 10 ⁇ .m.
- the inversion result diagram is obtained, as shown in Figure 8.
- the numerical simulation results show that the optimal method for the depth resolution balance of the tunnel borehole resistivity advanced detection device simplifies the number of electrodes, reduces the amount of data, and improves the inversion efficiency. On the basis, the effect of inversion imaging is still guaranteed.
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Abstract
Description
Claims (11)
- 一种基于深度分辨率的隧道电阻率超前探测优化方法,其特征是:包括以下步骤:(1)将采集的所有电极测量数据整合为综合集;(2)计算综合集的模型分辨率矩阵,从综合集中选择若干电极测量数据形成初始集;(3)将不在初始集中电极的测量数据添加到初始集中,形成多个临时子集;(4)根据模型分辨率矩阵,计算每个临时子集的深度分辨率平衡优度函数,选择深度分辨率平衡优度函数值最优的临时子集作为新的初始集,判断此时的初始集的平均相对模型分辨率是否满足优化要求;若不满足要求,则返回步骤(3),否则输出此时的初始集;(5)根据更新后的初始集确定掌子面测量电极的数目和位置,得到优化后的钻孔电阻率超前探测的有效观测模式。
- 如权利要求1所述的一种基于深度分辨率的隧道电阻率超前探测优化方法,其特征是:所述步骤(1)中,在隧道掌子面上布置钻孔,钻孔内布置电极,确定钻孔长度和电极间距;依据探测精度要求,确定正反演网格大小和掌子面上的电极数量和排列。
- 如权利要求1所述的一种基于深度分辨率的隧道电阻率超前探测优化方法,其特征是:所述步骤(1)中,使用全空间孔隧电阻率方法进行数据采集。
- 如权利要求1所述的一种基于深度分辨率的隧道电阻率超前 探测优化方法,其特征是:所述步骤(2)中,选取掌子面上下两个测量电极所能采集的全部电位数据作为初始集。
- 如权利要求1所述的一种基于深度分辨率的隧道电阻率超前探测优化方法,其特征是:所述步骤(4)中,深度分辨率平衡矩阵由分辨率平衡因子组成,且各分辨率平衡因子根据反演深度确定。
- 如权利要求1所述的一种基于深度分辨率的隧道电阻率超前探测优化方法,其特征是:所述步骤(4)中,此时的初始集的相对模型分辨率为此时的初始集模型分辨率矩阵的主对角线元素与综合集模型分辨率矩阵的主对角线元素的比值。
- 如权利要求1或7所述的一种基于深度分辨率的隧道电阻率超前探测优化方法,其特征是:所述步骤(4)中,平均相对模型分辨率计算为对相对模型分辨率中的元素求平均得到的。
- 如权利要求1所述的一种基于深度分辨率的隧道电阻率超前探测优化方法,其特征是:所述步骤(4)中,判断此时的初始集的平均相对模型分辨率是否满足优化要求的具体过程是,判断此时的初始集的平均相对模型分辨率的值是否大于设定值。
- 一种基于深度分辨率的隧道电阻率超前探测优化***,其特征是:包括:用于将采集的所有电极测量数据整合为综合集的模块;用于计算综合集的模型分辨率矩阵,从综合集中选择若干电极测量数据形成初始集的模块;用于将不在初始集中电极的测量数据添加到初始集中,形成多个临时子集的模块;用于根据模型分辨率矩阵,计算每个临时子集的深度分辨率平衡优度函数,选择深度分辨率平衡优度函数值最优的临时子集作为新的初始集,判断此时的初始集的平均相对模型分辨率是否满足优化要求;若不满足要求,则返回重新添加另外电极的测量数据形成新的临时子集,否则输出此时的初始集的模块;用于根据更新后的初始集确定掌子面测量电极的数目和位置的模块。
- 一种隧道电阻率超前探测观测***,其特征是:包括多个测量电极,所述测量电极的数目和位置根据权利要求1-9中任一项所述的一种基于深度分辨率的隧道电阻率超前探测优化方法确定。
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CN114778948A (zh) * | 2022-06-17 | 2022-07-22 | 中铁大桥科学研究院有限公司 | 动水隧道岩体电阻率监测方法及相关设备 |
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