A method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring

Abstract

A method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring automates the entire process from data collection and model building to real-time monitoring, significantly improving the accuracy and automation level of electrode grounding status identification, achieving scientific, accurate, and real-time identification of electrode grounding status, and significantly enhancing the effectiveness of mine safety monitoring systems.

Description

Technical Field

[0001] The invention belongs to the technical field of geophysical exploration, and in particular relates to a method for distinguishing a poorly grounded transmitting electrode in mine time-lapse resistivity monitoring. Background Art

[0002] Coal has long been my country's primary energy source, and the country's coal resources are complex. Time-lapse resistivity monitoring in mines is an important tool for predicting and preventing mine geological hazards. It works by transmitting a current of a specific frequency into the ground and measuring the time-varying resistivity distribution. This allows for the detection of stratum structure, groundwater conditions, ore body distribution, and potential geological hazard indicators (such as goaf collapse, karst collapse, and fault activity). A monitoring system typically consists of ground-based transmitting and receiving electrodes, as well as signal acquisition and processing equipment. Proper grounding of the transmitting electrode is a key factor in ensuring the accuracy of monitoring data, directly impacting the effective injection of current and the precise measurement of resistivity. Poor grounding of the transmitting electrode can lead to increased ground resistance, current leakage, or poor impedance matching, resulting from factors such as corrosion, loose contact, and changes in geological structure. These issues can cause the following adverse effects: 1. Data bias: Poor grounding prevents effective current injection into the ground, causing the measured resistivity value to deviate from the true value and affecting the accuracy of geological structure analysis. 2. Monitoring blind spots: Current leakage can weaken or even eliminate the resistivity signal in a localized area, creating monitoring blind spots and preventing the effective detection of potential geological hazards. 3. System stability: Poor grounding may lead to unstable current loop, causing monitoring signal fluctuations, increased noise, and reducing the overall stability of the system.

[0003] Traditional methods for identifying poor grounding of transmitting electrodes rely primarily on manual inspections, periodic ground resistance testing, and empirical judgment. These methods have the following limitations: 1. Lag: Manual inspections are time-consuming, making it difficult to detect sudden poor grounding problems in a timely manner. 2. Subjectivity: Empirical judgments are easily affected by factors such as individual skill level and site conditions, resulting in low accuracy. 3. Inefficiency: Conventional ground resistance testing requires interrupting normal monitoring operations and only reflects the grounding status at a specific moment, failing to achieve continuous monitoring. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide a method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring, so as to solve the problems of lag, subjectivity, inefficiency and other problems in the existing methods for identifying poorly grounded transmitting electrodes, which cannot meet the needs of real-time and accurate monitoring.

[0005] To achieve the above-mentioned purpose, the technical solutions adopted by the present invention include:

[0006] A method for identifying a poorly grounded transmitting electrode in mine time-lapse resistivity monitoring comprises the following steps:

[0007] Step 1: Collect the transmitting current sets corresponding to N groups of receiving electrodes, including the transmitting electrodes in the M group with good ground coupling and the transmitting electrodes in the NM group with poor ground coupling. Select the transmitting current set corresponding to the transmitting electrodes in the M0 group with good ground coupling and the transmitting current set corresponding to the transmitting electrodes in the M1 group with poor ground coupling as the training set, and select the transmitting current set corresponding to the transmitting electrodes in the N0 group with good ground coupling and the transmitting current set corresponding to the N1 group with poor ground coupling as the test set.

[0008] Wherein, M0+N0=M, M1+N1=NM; the transmitting current set corresponding to any group of receiving electrodes includes the transmitting currents corresponding to n pairs of receiving electrodes;

[0009] Step 2: Calculate the good contact mean vector μ0 and the bad contact mean vector μ1 for the corresponding transmitting current set of the receiving electrodes in the M0 group with good ground coupling and the corresponding transmitting current set of the receiving electrodes in the M1 group with poor ground coupling in the training set, and calculate the intra-class scatter matrix S w ; Calculate the mean vector μ for all the receiving electrodes corresponding to the transmitting current set in the training set, and calculate the inter-class divergence matrix S b ;

[0010] Step 3: Set the criterion function as formula (3) and calculate when J weighted The projection vector corresponding to the maximum value of (w) is the optimal projection vector w;

[0011] J weighted(w) =λ(w T S b w)-(1-λ)(w T S w w) (3)

[0012] Among them, λ represents the weight factor, λ∈[0,1];

[0013] Step 4: Project the transmitting current x corresponding to the receiving electrode whose coupling state is to be determined onto the optimal projection vector w using formula (4) to obtain a one-dimensional projection value z;

[0014] z=w T ×x (4)

[0015] If z is greater than the discrimination threshold of 0.5, it is predicted that the transmitting electrode ground coupling is poor; if z≤0.5, it is predicted that the transmitting electrode ground coupling is good.

[0016] Preferably, λ in step 3 = 0.6.

[0017] Preferably, in step 2, the intra-class scatter matrix S is calculated by formula (1): w ;

[0018]

[0019] Among them, x i represents the characteristic vector of the transmitting current set corresponding to the i-th group of receiving electrodes; y i Represents the category label of the i-th group of data, when y i = 0, indicating that the transmitting electrode is well coupled to the ground. i =1 indicates that the transmitting electrode ground coupling is poor.

[0020] Preferably, in step 2, the inter-class scatter matrix S is calculated by formula (2): b ;

[0021] S b =M0(μ0-μ)(μ0-μ) T +M1(μ1-μ)(μ1-μ) T (2).

[0022] Compared with the prior art, the advantages of the present invention are:

[0023] The present application discloses a method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring, which automates the entire process from data collection, model construction to real-time monitoring, significantly improving the accuracy and automation level of electrode grounding status identification, achieving scientific, accurate, and real-time identification of electrode grounding status, and significantly improving the efficiency of mine safety monitoring systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the present invention but do not constitute a limitation of the present invention. In the accompanying drawings:

[0025] Figure 1 Verification accuracy for different λ values. DETAILED DESCRIPTION

[0026] The invention is not limited to the following specific embodiments, and all equivalent modifications made on the basis of the technical solution of this application fall within the protection scope of the present invention.

[0027] Unless otherwise specified, all components and devices in the present invention are components and devices known in the prior art.

[0028] Example

[0029] A method for identifying a poorly grounded transmitting electrode in mine time-lapse resistivity monitoring comprises the following steps:

[0030] Step 1: Collect the transmitting current sets corresponding to N groups of receiving electrodes, including the transmitting electrodes in the M group with good ground coupling and the transmitting electrodes in the NM group with poor ground coupling. Select the transmitting current set corresponding to the transmitting electrodes in the M0 group with good ground coupling and the transmitting current set corresponding to the transmitting electrodes in the M1 group with poor ground coupling as the training set, and select the transmitting current set corresponding to the transmitting electrodes in the N0 group with good ground coupling and the transmitting current set corresponding to the N1 group with poor ground coupling as the test set.

[0031] Wherein, M0+N0=M, M1+N1=NM; the transmitting current set corresponding to any group of receiving electrodes includes the transmitting currents corresponding to n pairs of receiving electrodes.

[0032] This embodiment uses a monopole-dipole device to monitor the resistivity of a single tunnel mine. Before carrying out the time-lapse resistivity monitoring project, supplementary data collection is carried out on a targeted basis. A reasonable resistivity monitoring experiment is designed to simulate different ground coupling states and record the corresponding transmission current changes of the receiving electrode.

[0033] Before the mine resistivity monitoring project began, 20 sets of receiving electrodes corresponding to the transmitting current set were measured. Among them, 10 sets of transmitting electrodes had good ground coupling and 10 sets of transmitting electrodes had poor ground coupling. The collected data are shown in Table 1. In Table 1, a transmitting electrode ground coupling state of 0 indicates good ground coupling (marked as category 0), and a ground coupling state of 1 indicates poor ground coupling (marked as category 1). 8 sets of data with good transmitting electrodes and 8 sets of data with poor transmitting electrodes were selected as the training set, and the remaining 2 sets of data with good transmitting electrodes and 2 sets of data with poor transmitting electrodes were selected as the validation data set.

[0034] Table 1 Collected emission electrode current data and its coupling state

[0035]

[0036]

[0037] Step 2: For the 8 sets of receiving electrodes corresponding to the good ground coupling state in the training set and the 8 sets of receiving electrodes corresponding to the poor ground coupling state in the training set, the good contact mean vector μ0 and the poor contact mean vector μ1 are calculated respectively, and the intra-class divergence matrix S is calculated by formula (1): w ;

[0038]

[0039] Among them, x iRepresents the eigenvector of the transmitting current set corresponding to the i-th group of receiving electrodes;

[0040] y i Represents the category label of the i-th group of data, when y i = 0, indicating that the transmitting electrode is well coupled to the ground. i =1 indicates that the transmitting electrode ground coupling is poor.

[0041] The mean vector μ is calculated for all the transmitting current sets corresponding to the receiving electrodes in the training set, and the inter-class divergence matrix Sb is calculated by formula (2);

[0042] S b =M0(μ0-μ)(μ0-μ) T +M1(μ1-μ)(μ1-μ) T (2)

[0043] Step 3: Set the criterion function as formula (3) and calculate when J weighted The projection vector corresponding to the maximum value of (w) is the optimal projection vector w;

[0044] J weighted(w) =λ(w T S b w)-(1-λ)(w T S w w) (3)

[0045] Where λ represents the weight factor, λ∈[0,1]. When λ tends to 1, it emphasizes inter-class separation; when λ tends to 0, it emphasizes intra-class compactness. The λ value range is selected in [0.1, 0.2, ..., 0.9]. The optimal λ value is determined by combining the validation dataset. For each λ value, the weighted discriminant analysis function is performed on the training set to calculate J. weighted (w) and obtain the corresponding projection vector w. Using the obtained w, the verification data is projected into the new feature space and the classification accuracy is calculated. The accuracy of verification for different λ values ​​is as follows Figure 1 shown. Figure 1 It can be seen that when λ=0.6, all four sets of verification data are correct. Therefore, in the subsequent test data of this embodiment, λ=0.6 is used for discriminant analysis.

[0046] Step 4: Project the transmitting current x corresponding to the receiving electrode whose coupling state is to be determined onto the optimal projection vector w using formula (4) to obtain a one-dimensional projection value z;

[0047] z=w T ×x (4)

[0048] If z is greater than the discrimination threshold of 0.5, it is predicted that the transmitting electrode ground coupling is poor;

[0049] If z≤0.5, it is predicted that the transmitting electrode is well coupled to the ground.

[0050] In the time-lapse resistivity monitoring project of this embodiment, the current data of the two transmitting electrodes collected in real time are shown in Table 2.

[0051] Table 2 Transmitter electrode current data to be determined for the transmitter electrode state

[0052]

[0053] S3.2: Contact coupling state determination: Substituting the real-time current data in Table 2 into equation (4), the projection value of sample number 1 to be determined is 0.375, which is predicted to be good contact. The projection value of sample number 2 to be determined is 0.64, which is predicted to be poor contact.

[0054] As the monitoring project progresses and new data accumulates, the discriminant function is regularly retrained or updated monthly to adapt to changes in geological and environmental factors. The discriminant model's performance on new data is continuously monitored. If performance deteriorates, the dataset should be updated or the discriminant method should be changed. If the discrimination results indicate the presence of a poorly grounded electrode, the system immediately generates an alert and notifies relevant personnel via SMS, email, or app push notifications.

[0055] The method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring disclosed in this embodiment realizes full-process automation from data collection, model building to real-time monitoring, and significantly improves the accuracy and automation level of electrode grounding status identification.

[0056] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details of the above embodiments. Within the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the scope of protection of the present disclosure.

[0057] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0058] In addition, the various different implementation methods disclosed in this solution can also be arbitrarily combined, as long as they do not violate the ideas of this disclosure, they should also be regarded as the contents of the invention of this disclosure.

Claims

1. A method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring, characterized in that: The steps include: Step 1: Collect the transmitting current sets corresponding to N groups of receiving electrodes, including the transmitting electrodes in the M group with good ground coupling and the transmitting electrodes in the NM group with poor ground coupling. Select the transmitting current set corresponding to the transmitting electrodes in the M0 group with good ground coupling and the transmitting current set corresponding to the transmitting electrodes in the M1 group with poor ground coupling as the training set, and select the transmitting current set corresponding to the transmitting electrodes in the N0 group with good ground coupling and the transmitting current set corresponding to the N1 group with poor ground coupling as the test set. Wherein, M0+N0=M, M1+N1=NM; the transmitting current set corresponding to any group of receiving electrodes includes the transmitting currents corresponding to n pairs of receiving electrodes; Step 2: Calculate the good contact mean vector μ0 and the bad contact mean vector μ1 for the corresponding transmitting current set of the receiving electrodes in the M0 group with good ground coupling and the corresponding transmitting current set of the receiving electrodes in the M1 group with poor ground coupling in the training set, and calculate the intra-class scatter matrix S w ; The mean vector μ is calculated for all the receiving electrodes corresponding to the transmitting current set in the training set, and the inter-class divergence matrix S is calculated. b ; Step 3: Set the criterion function as formula (3) and calculate when J weighted The projection vector corresponding to the maximum value of (w) is the optimal projection vector w; J weighted(w) =λ(w T S b w)-(1-λ)(w T S w w) (3) Among them, λ represents the weight factor, λ∈[0,1]; Step 4: Project the transmitting current x corresponding to the receiving electrode whose coupling state is to be determined onto the optimal projection vector w using formula (4) to obtain a one-dimensional projection value z; with T ×x (4) If z is greater than the discrimination threshold of 0.5, it is predicted that the transmitting electrode ground coupling is poor; If z≤0.5, it is predicted that the transmitting electrode is well coupled to the ground.

2. The method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring according to claim 1, characterized in that: In step 3, λ=0.

6.

3. The method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring according to claim 1, characterized in that: In step 2, the intra-class scatter matrix S is calculated by formula (1): w ; Among them, x i Represents the eigenvector of the transmitting current set corresponding to the i-th group of receiving electrodes; y i Represents the category label of the i-th group of data, when y i = 0, indicating that the transmitting electrode is well coupled to the ground. i =1 indicates that the transmitting electrode ground coupling is poor.

4. The method for identifying poorly grounded transmitting electrodes in mine time-lapse resistivity monitoring according to claim 1, wherein: In step 2, the inter-class scatter matrix S is calculated by formula (2): b ; S b =M0(μ0-μ)(μ0-μ) T +M1(μ1-μ)(μ1-μ) T (2)。

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