WO2022078516A1 - 基于深度分辨率的隧道电阻率超前探测优化方法及系统 - Google Patents

基于深度分辨率的隧道电阻率超前探测优化方法及系统 Download PDF

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WO2022078516A1
WO2022078516A1 PCT/CN2021/124212 CN2021124212W WO2022078516A1 WO 2022078516 A1 WO2022078516 A1 WO 2022078516A1 CN 2021124212 W CN2021124212 W CN 2021124212W WO 2022078516 A1 WO2022078516 A1 WO 2022078516A1
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resolution
initial set
depth
model
resistivity
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PCT/CN2021/124212
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English (en)
French (fr)
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聂利超
陈磊
周勇
王川
许新骥
栗剑
刘征宇
张宁
白鹏
张永恒
解冬东
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山东大学
山东高速集团有限公司
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Priority to US18/031,715 priority Critical patent/US20230384473A1/en
Publication of WO2022078516A1 publication Critical patent/WO2022078516A1/zh

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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
    • 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
    • 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
    • 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

一种基于深度分辨率的隧道电阻率超前探测优化方法及系统，将采集的所有电极测量数据整合为综合集，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；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；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，则不断更新临时子集，否则输出此时的初始集；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。

Description

基于深度分辨率的隧道电阻率超前探测优化方法及系统 技术领域
本公开属于隧道电阻率超前探测观测技术领域，具体涉及一种基于深度分辨率的隧道电阻率超前探测优化方法及系统。
背景技术
本部分的陈述仅仅是提供了与本公开相关的背景技术信息，不必然构成在先技术。
随着隧道建设重点逐步向地形地质复杂的西部山区和水域阻隔的东部海峡地区转移，艰险山区深埋隧道和跨江越海隧道大量涌现，对于各类具有洞线长、埋深大等特点的深长隧道，对其进行地质调查更加困难。对于深埋长大隧道，由于各类不良地质赋存更隐蔽、致灾性更高，要求分辨率更高、目标更难探，对于隧道超前探测精度提出了更高的要求和挑战。目前传统的地表勘查条件和技术水平难以满足工程对探测深度和精细化探查的需求，而当前主流的隧道电阻率超前探测方法对含水体成像边界识别模糊、探不到、探不准，且对于一些亚米级甚至分米级的含导水构造难以有效识别，因此，需要一种新的隧道电阻率超前探测装置，能够对亚米级含水构造进行精细成像，即“孔中电极供电、掌子面阵列式测量”的隧道钻孔电阻率精细超前探测观测装置。
与隧道常规观测模式相比，隧道钻孔电阻率超前探测观测装置在精细化成像方面更具优势：1)具有更远的测深；2)可以获取钻孔周边异常体更丰富更直接的信息。因此，隧道钻孔电阻率超前探测在隧道电阻率超前探测中具有广阔的应用前景。
然而，据发明人了解，目前隧道钻孔电阻率超前探测基本观测模式仍存在以下关键问题尚未解决：
为了保证反演成像的效果，需要通过布设大量的电极来获得大量的观测数据，观测工作需要耗费大量的时间和人力，基本观测模式得到的大量数据对反演计算造成了极大的挑战，降低了观测效率。
同时，当前常规优化方法是提升整体的模型分辨率，是均一的提升，不能解决深部模型分辨率过分损失的问题。
发明内容
本公开为了解决上述问题，提出了一种基于深度分辨率的隧道电阻率超前探测优化方法及系统，本公开对观测系统进行优化，优选出对模型分辨率贡献较大的测量电极点，在保证模型分辨率的基础上最终得到优化后的掌子面电极排列，精简了电极数量，提高了探测效率。
根据一些实施例，本公开采用如下技术方案：
一种基于深度分辨率的隧道电阻率超前探测优化方法，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。
作为可选择的实施方式，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。
作为可选择的实施方式，所述步骤(1)中，使用全空间孔隧电阻率方法进行数据采集。
作为可选择的实施方式，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。
作为可选择的实施方式，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。
作为可选择的实施方式，所述步骤(4)中，每个临时子集的深度分辨率平衡优度函数为：
Figure PCTCN2021124212-appb-000001
作为可选择的实施方式，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。
作为可选择的实施方式，所述步骤(4)中，平均相对模型分辨率为对相对模型分辨率中的元素求平均得到的。
作为可选择的实施方式，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系统，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系统，包括多个测量电极，所述测量电极的数目和位置根据所述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。
与现有技术相比，本公开的有益效果为：
1、本公开一种隧道电阻率超前探测装置形式优化方法，本公开在孔中电极等间距均匀布置、掌子面阵列电极测量的基本型式的基础上，使用深度分辨率平衡优化的DRB方法对观测装置进行优化，优选出对模型分辨率贡献较大的测量电极点，在保证模型分辨率的基础上最终得到优化后的掌子面电极排列，精简了电极数量，提高了探测效率。
2、本公开在深度分辨率平衡优度函数中，引入了深度分辨率平衡矩阵H，可以结合先验信息，调整不同网格模型分辨率在DRB计算过程中的分辨率平衡度因子，可以通过适当增大某一深度区域的分辨率平衡度，使得这一区域网格的模型分辨率在DRB的计算中更为重要，最终使得算法能够优先提升这一深度区域的模型分辨率。
附图说明
构成本公开的一部分的说明书附图用来提供对本公开的进一步理解，本公开的示意性实施例及其说明用于解释本公开，并不构成对本公开的不当限定。
图1是基于隧道钻孔电阻率超前探测装置深度分辨率平衡优化方法流程图；
图2是钻孔电阻率观测装置基本模式电极分布示意图；
图3是平均相对模型分辨率随迭代次数的变化曲线；
图4是优化后观测装置的掌子面测量电极分布示意图；
图5是深度分辨率平衡因子分布示意图；
图6是优化后的相对模型分辨率图；
图7是进行数值模拟时使用的地电模型图；
图8是基于优化后的隧道钻孔观测模型装置的反演结果图。
具体实施方式：
下面结合附图与实施例对本公开作进一步说明。
应该指出，以下详细说明都是例示性的，旨在对本公开提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本公开所属技术领域的普通技术人员通常理解的相同含义。
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一种隧道电阻率超前探测装置深度分辨率平衡优化方法，如图1所示，包括以下步骤：
(1)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；
在具体实例中，当然，需要说明的是，本实施例仅为示例，并不仅代表其他实施例的钻孔深度、电极数量、间距和数据总数等需要和本实施例一致，上述数据都是可以根据具体观测要求和环境进行合理变更的，此为本领域技术人员容易想到的，理应属于本公开的保护范围。
钻孔最大深度设定为60m，孔内供电电极以等间距的方式排布，电极间间距为2m，则钻孔内共有30个供电电极。掌子面测量电极则呈阵列式排布，如图2所示共设置有8排电极，其中第1与第8排各 自仅有3个电极，第4与第5排各自有9个电极，其余各排各自拥有7个电极。
(2)使用全空间孔隧电阻率方法进行数据采集；
具体地，数据采集采取钻孔内供电、掌子面接收的二极法型式，共采集到1560个测量数据。
(3)对掌子面测量电极进行优化和精简：①计算综合集的模型分辨率矩阵M c，选取某个子集作为优化的初始集S i；②将每个其他的观测装置与初始集结合形成众多临时子集S t，计算出相应的模型分辨率矩阵M t；③确定深度分辨率平衡矩阵H，计算深度分辨率平衡优度函数DRB优选出排位靠前的观测装置,并将其添加到构成初始集的该子集中形成新的子集；④计算相对模型分辨率M r，求出平均相对模型分辨率，判断是否满足优化要求；⑤若不满足要求，将新的子集作为优化的初始集S i，返回步骤②继续重复以上步骤，若满足条件，则输出当前的子集作为优化的最终结果。
具体地，优化所依赖的基础综合集S c即为82个电极全部参与供电与测量时所产生的1560个电位数据。本次优化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S i,其中包含有60个电位数据。
计算模型分辨率矩阵M，其线性计算方法与估算公式为：
M＝(G TG+C) -1G TG
式中，M为所要计算的模型分辨率矩阵，G为雅各比矩阵，C为 约束矩阵。实施例中约束项使用常规约束C＝λI，其中约束因子λ取值为2.5×10 -6。
将其余每个测量电极作为一个观测装置分别添加到初始集中，形成80个临时子集S t，并依据模型分辨率矩阵计算公式计算出相应的模型分辨率矩阵。
所述的深度分辨率平衡矩阵具体为：
Figure PCTCN2021124212-appb-000002
深度分辨率平衡矩阵H中包含了所有网格的分辨率平衡因子，通过加入这一矩阵调整不同网格模型分辨率在DRB计算过程中的分辨率平衡度。在钻孔电阻率观测装置模型分辨率分布的初步研究中发现，随着深度的不断增加，深部区域网格的模型分辨率会不断减小。为了保证深部的模型分辨率不致过分损失，加入深度分辨率平衡矩阵。在实施例中反演深度为60m，因此h取为30m，当深度大于30m时，令所有网格的分辨率平衡度因子H为1.2，而隧道前方30m范围内网格的分辨率平衡度因子取值为1.0，具体的示意图如图5所示。
利用DRB函数将除初始集内的所有观测装置进行排序，深度分辨率平衡优度函数具体为：
Figure PCTCN2021124212-appb-000003
M t储存着临时子集模型分辨率的主对角线元素,表示临时子集 的分辨率向量，M b储存着初始集模型分辨率向量的主对角线元素，表示初始集的分辨率向量,H为深度分辨率平衡矩阵向量。m表示上述向量中的元素个数，M t(j)表示临时子集分辨率向量中的第j个元素。DRB函数的值表征了新加入观测装置对原有基本集模型分辨率的提升,在一次迭代中选取排序最靠前的一个观测装置加入初始集中形成新的初始集。即在实施例中每次优化迭代结束时将会有一个新的测量电极点加入新的初始集中。
计算新的初始集的相对模型分辨率，其公式为：
Figure PCTCN2021124212-appb-000004
其中M b与M c分别储存着此时的初始集与综合集模型分辨率矩阵的主对角线元素，分别表示为初始集分辨率向量和综合集分辨率向量。公式中的除法代表M b中的每个元素分别除以M c对应位置的元素。所得结果M r即衡量了此时的初始集模型分辨率与综合集模型分辨率的接近程度，其元素均是介于0-1之间的数值，且显然当这些值越接近于1时表明此时的初始集的模型分辨率与综合集的越接近，其模型分辨率越好。
计算新的初始集的平均相对模型分辨率，判断新的初始集的平均相对模型分辨率是否满足优化要求，即平均模型分辨率大小是否不小于0.9。
具体地，在本实施例中，一次优化不能满足优化要求，将其余的每个观测电极分别作为一个观测装置添加到该更新后的初始集中，构 成79个临时子集，重复以上步骤。
具体地，本实施例中当优化迭代次数为20次时，平均相对模型分辨率刚好满足了大于0.9的要求，平均相对模型分辨率随优化迭代次数变化的曲线如附图3所示。图6为优化20次后的相对模型分辨率分布图，深处各部分的相对模型分辨率数值接近于1，表明优选出的子集的模型分辨率与综合集非常接近，深部的模型分辨率得到很大的提升。
(4)根据优化结果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。
得到优化后的掌子面电极位置和数目，如图4所示。该图表明，优化后的掌子面测量电极相较于优化前，去除了较多多余电极，中间竖直的一列测量电极全被选中，其余被选中电极则较为对称地分布在四周边缘处，掌子面上共包含有22个测量电极，这样的测量电极布置能够较为均匀的提升整体的模型分辨率，而且结合钻孔中的30个供电电极共能采集660个电位数据，小于综合集数据的一半，观测效率大大提升，提高了工程适用性。
图7是进行反演计算的地电模型，反演区域为30m*30m*60m，背景电阻率设置为1000Ω.m，其中设置了一个低阻异常体，尺寸为4m*5m*6m，其电阻率为10Ω.m。得到反演结果图，如图8所示，该数值模拟结果表明，该隧道钻孔电阻率超前探测装置深度分辨率平衡优化方法在精简了电极数目，减少了数据量，提高了反演效率的基础 上，依然保证了反演成像的效果。
以上所述仅为本公开的优选实施例而已，并不用于限制本公开，对于本领域的技术人员来说，本公开可以有各种更改和变化。凡在本公开的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开的保护范围之内。
上述虽然结合附图对本公开的具体实施方式进行了描述，但并非对本公开保护范围的限制，所属领域技术人员应该明白，在本公开的技术方案的基础上，本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本公开的保护范围以内。

Claims (11)

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。
  6. 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，每个临时子集的深度分辨率平衡优度函数为：
    Figure PCTCN2021124212-appb-100001
    M t储存着临时子集模型分辨率的主对角线元素,表示临时子集的分辨率向量，M b储存着初始集模型分辨率向量的主对角线元素，表示初始集的分辨率向量,H为深度分辨率平衡矩阵向量；m表示上述向量中的元素个数，M t(j)表示临时子集分辨率向量中的第j个元素。
  7. 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。
  8. 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。
  9. 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。
  10. 一种基于深度分辨率的隧道电阻率超前探测优化系统，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。
  11. 一种隧道电阻率超前探测观测系统，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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