A quality control process for downhole direct current method monitoring data

Abstract

The application discloses a quality control process for downhole direct current method monitoring data, which comprises the following steps: step 1: taking a single transmitting electrode as a group of evaluation units to evaluate the size and stability of transmitting current and eliminate unqualified data; step 2: performing Fourier transform on full waveform data of each measuring point to calculate the signal-to-noise ratio of the data of each measuring point and eliminate unqualified measuring point data; step 3: if the measuring points of a single group of evaluation units account for a small proportion after step 2, directly entering step 4; otherwise, forming measured curves from the data of each measuring point to evaluate the stability of the data in space, and then entering the monitoring data processed in step 2 into step 4; step 4: repeatedly performing steps 1-3 to obtain monitoring data of multiple sub-stations; and step 5: taking a single measuring point as an evaluation unit to calculate the relative mean square error of each measuring point and evaluate the stability of the data in time. The application can comprehensively reflect the noise interference level and provide a reliable basis for subsequent electrical method data processing.

Description

Technical Field

[0001] This invention relates to the field of downhole DC electrical resistivity tomography (DCE) monitoring technology, specifically a quality control process for downhole DCE monitoring data. Background Technology

[0002] Coal mine water hazards are a serious geological problem affecting the safe mining of coal faces. Real-time dynamic monitoring of water hazard hazards and assessment of water hazard risks are urgently needed in coal mines. Underground DC electrical resistivity tomography (DCE) monitoring in coal mines can reveal the dynamic development process of roof and floor damage and water conduit channels during mining, achieving the goal of water hazard risk assessment during the mining process. It provides accurate and reliable technical basis for timely implementation of prevention and control measures in coal mines, preventing water hazard risks before they occur. This is of paramount importance for ensuring safe coal mine production and preventing water hazards, thereby saving manpower and material costs, ensuring safe mining, and improving economic efficiency.

[0003] Underground DC resistivity tomography (DC tomography) monitoring offers advantages such as automation, intelligence, all-weather operation, and uninterrupted data acquisition across all waveforms. However, the unique electromagnetic environment of underground coal mines also increases the difficulty of acquiring effective signals. Compared to surface DC tomography, underground monitoring involves more complex electromagnetic noise types, extremely high intensity, and significant variations depending on spatial location. Furthermore, safety restrictions on emission current and limitations on electrode placement within the confined underground space contribute to relatively weaker effective signals. Compared to underground DC tomography detection, monitoring requires continuous observation and cannot avoid normal mining activities, making a large amount of monitoring data susceptible to strong electromagnetic noise. Therefore, it is necessary to manage and evaluate the raw data from underground DC tomography monitoring to ensure that the final acquired data meets the quality requirements for subsequent DC tomography monitoring data processing and interpretation.

[0004] Currently, there are three common data management methods for DC electrical resistivity tomography (DCIP). The first method involves multiple samplings at each measuring point for data quality management. The principle is that stationary random noise following a Gaussian distribution can be suppressed using mathematical statistics. Its advantage lies in improving data quality through arithmetic averaging and evaluating the data quality at each measuring point through relative mean square error. The more samplings, the greater the improvement in data quality, and the more accurate the management and evaluation. However, this method also has drawbacks. First, it requires a long acquisition time, reducing work efficiency; second, the full waveform data acquisition method, with its large number of sampling points, puts pressure on the instrument's storage space; and third, it causes some data loss. The second method involves system detection for raw data quality management. According to the "Coal Coal Electrical Resistivity Exploration Code" (MT / T 898-2000), the arithmetic mean relative error is used to evaluate the system detection result of a single measuring point; the mean square relative error is used to evaluate the system detection result of a detection point (segment); and the arithmetic mean of the mean square relative errors of all detection points (segments) is used to evaluate the system detection result of the entire area. The drawback of this method is that the detection and original measurement at the detection points should generally be carried out at different times by different operators using different instruments, and the number of points re-laid out should not be less than 2 / 3 of the total number of detection points. These requirements are incompatible with the automated, intelligent, and uninterrupted data acquisition characteristics of downhole DC electrical resistivity monitoring, making them difficult to meet in practice. The third method involves data quality control of the raw data through survey curve rating. This method is common in various electromagnetic exploration procedures. Its drawback is that it is a qualitative analysis, susceptible to human factors, and relatively difficult to achieve automatic rating. Its use alone in downhole DC electrical resistivity monitoring has limitations. Currently, there are few standards related to downhole DC electrical resistivity monitoring, and the data quality evaluation methods used in conventional DC electrical resistivity detection are still in use. Furthermore, a search using the combined constraint terms of "electrical resistivity monitoring" and "data quality" yielded no relevant patents or published papers.

[0005] In summary, downhole DC electrical resistivity tomography (DCET) monitoring suffers from weak effective signals, strong electromagnetic interference, and low signal-to-noise ratio, necessitating effective evaluation and management of raw data. However, existing DCET data management methods are primarily designed for exploration missions and are less applicable and limited in scope when dealing with monitoring data. Currently, no relevant technologies have been developed to meet the needs of downhole DCET monitoring data management. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a quality control process for downhole DC electrical resistivity tomography (DCET) monitoring data. This process offers advantages such as strong targeting, diverse methods, and a comprehensive reflection of noise interference, thus solving the problem of insufficient quality control of mine DCET monitoring data under existing technological conditions.

[0007] The technical solution adopted in this invention is,

[0008] A quality control process for downhole DC electrical resistivity tomography monitoring data includes the following steps:

[0009] Step 1: Evaluate the transmission current: Select monitoring data from a single substation, pre-set the maximum and minimum thresholds for the transmission current, and evaluate the magnitude and stability of the transmission current of a single transmitting electrode as a group of evaluation units. After completing all evaluations, remove unqualified data and proceed to the next step with qualified data.

[0010] Step 2: Evaluate the noise level of the raw data: For the monitoring data of the current substation after processing in Step 1, perform Fourier transform on the full waveform data of each measuring point to obtain the spectrum of different frequencies, and then calculate the signal-to-noise ratio (SNR) of the data at each measuring point. Evaluate the noise level of the raw monitoring data of different measuring points one by one, and remove the potential (difference) data corresponding to the unqualified measuring points in the evaluation unit. Qualified data will proceed to the next step.

[0011] Step 3: Spatially evaluate data stability: For the monitoring data of the current substation after processing in Step 2, each transmitting electrode is used as an evaluation unit. If the proportion of measurement points retained in a single evaluation unit after Step 2 is small, proceed directly to Step 4; otherwise, the potential V of each measurement point is... i or potential difference ΔV i The measured curves are generated, and the data stability is evaluated spatially using a single transmitting electrode as an evaluation unit. Then, the monitoring data of the current substation after processing in step 2 is entered into step 4; at this point, the data processing of a single front substation is completed.

[0012] Step 4: Repeat steps 1-3 to obtain monitoring data from multiple substations;

[0013] Step 5: Evaluate data stability over time: From the monitoring data obtained in Step 4, select monitoring data from multiple substations with adjacent acquisition times within a certain time period. Using a single measuring point as the evaluation unit, plot the potential (difference) change curve of the monitoring data of a single measuring point over time, calculate the relative mean square error of each measuring point, and evaluate the data stability over time based on the relative mean square error.

[0014] Furthermore, in step 1, the maximum threshold I of the emission current max The minimum threshold I is the maximum achievable transmit current of the monitoring instrument. min During the deployment phase of the monitoring system, the potential (difference) is estimated through field tests. If the field test results are unsatisfactory, the threshold is set based on experience. When the emission current exceeds the threshold, the potential (difference) data corresponding to the unqualified measuring points in that evaluation unit are removed. The estimation of I through field tests... min The formula is as follows:

[0015] I min = minI s / A smin

[0016] Among them, I min The minimum threshold for the emission current; A min The smallest signal that the monitoring instrument can distinguish; I s The emission current for the field test; A smin This represents the minimum potential (difference) received at each measuring point during the field test.

[0017] Furthermore, in step 1, the stability of the transmission current is evaluated by the relative mean square error of the transmission current. If the relative mean square error exceeds the threshold, the transmission current is considered unstable, and all data corresponding to that evaluation unit are discarded. The calculation formula is as follows:

[0018]

[0019] Where, m I The relative mean square error of the emitted current is given by I, where n is the number of measurement points corresponding to the selected emitting electrode. j This is the emission current data for a single measurement point. The average value of the emission current data from n measurement points.

[0020] Furthermore, the relative mean square error threshold for the transmit current is 5%.

[0021] Furthermore, in step 2, the signal-to-noise ratio calculation formula for the measurement point is as follows:

[0022]

[0023] Among them, V signal The spectrum of the transmission frequency is obtained by performing a Fourier transform on the full waveform data; V noise This is the spectrum of noise near the transmission frequency obtained by performing a Fourier transform on the full waveform data; the frequency band near the transmission frequency is a frequency band with a certain bandwidth centered on the transmission frequency.

[0024] Furthermore, the bandwidth of the frequency band near the transmission frequency should be determined based on the sampling frequency and sampling duration, and should include at least 10 frequency points; the signal-to-noise ratio threshold of the data should not be lower than 10dB.

[0025] Furthermore, in step 3, if the retention rate of the measurement points of the single evaluation unit after step 2 is less than a threshold, it is considered that the retention rate is low. The threshold value ranges from 60% to 80%.

[0026] Furthermore, in step 3, the data stability evaluation criteria include: under the condition of a single emitting electrode, the potential or potential difference curves of each measuring point should be smooth, continuous, and have a clear curve shape; under the condition of adjacent emitting electrodes, the potential or potential difference curves of each measuring point should have a good regularity.

[0027] Furthermore, in step 5, the monitoring data from multiple stations with adjacent acquisition times within a certain time period are monitoring data under the same downhole production environment conditions within 24 hours or 48 hours.

[0028] Furthermore, monitoring data under the same underground production environment conditions, i.e., data distinguishing between production shifts and maintenance shifts, should not be data that shows underground production for some time periods and underground shutdown for others.

[0029] Furthermore, in step 5, the formula for calculating the relative mean square error of the potential (difference) at a single measuring point is:

[0030]

[0031] Where, m s S represents the relative mean square error of the potential (difference) at a single measuring point, where n is the number of selected substations. i This is the potential (difference) monitoring data for a single substation. This represents the average value of the potential (difference) monitoring data from n substations.

[0032] Furthermore, the relative mean square error threshold of the potential (difference) at a single measuring point can be set within the range of 5% to 10%.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] This invention's data management process is based on the automated, intelligent, and uninterrupted full-waveform data acquisition characteristics of downhole DC electrical resistivity tomography (DCIP) monitoring. It employs qualitative and quantitative evaluation methods to assess and manage the raw data from DCIP monitoring, focusing on three aspects: transmission current, raw data noise, and data stability. First, transmission current issues are not present in the data processing stage of conventional electrical resistivity tomography and can usually be detected and avoided during the on-site construction phase. However, downhole DCIP monitoring spans the entire working face construction process. Due to the impact of mining activities, the transmitting and receiving electrodes and wires are easily damaged, thus requiring routine management of the transmission current. Second, to address the impact of continuous and relatively stable noise, a quantitative evaluation of the raw data noise level is performed, directly yielding the signal-to-noise ratio of the transmission frequency. Finally, given the difficulty of manually setting up detection points during automated monitoring, this process utilizes the gradual spatial and temporal variations in monitoring data to manage data quality for short-term random electromagnetic noise. In summary, the data management process proposed in this invention is highly targeted, applicable to downhole electrical resistivity monitoring data, and offers a variety of evaluation methods, which can comprehensively reflect the level of noise interference and provide a reliable foundation for subsequent electrical resistivity data processing. Attached Figure Description

[0035] Figure 1 This is a data management flowchart of the present invention;

[0036] Figure 2 This refers to the transmission current of all raw data in a single substation in an embodiment of the present invention;

[0037] Figure 3 This refers to the signal-to-noise ratio of the original data in a single substation after processing in step 1 in an embodiment of the present invention;

[0038] Figure 4 This refers to the potential of the original data in a single substation after processing in step 2 in an embodiment of the present invention;

[0039] Figure 5 This refers to a portion of the monitoring data from eight consecutive stations after processing in step 3 in an embodiment of the present invention.

[0040] Figure 6 These are monitoring data curves before (top) and after (bottom) quality control in an embodiment of the present invention. Detailed Implementation

[0041] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0042] Please see Figure 1 The quality control process for downhole DC electrical resistivity tomography monitoring data provided by this invention specifically includes the following steps:

[0043] Step 1: Evaluate the transmission current: Select monitoring data from a single substation, pre-set the maximum and minimum thresholds for the transmission current, and evaluate the magnitude and stability of the transmission current of a single transmitting electrode as a group of evaluation units. After completing all evaluations, remove unqualified data and proceed to the next step with qualified data.

[0044] Where: the maximum threshold of the emission current I max The minimum threshold I is the maximum achievable transmit current of the monitoring instrument. min During the monitoring system deployment phase, the potential (difference) is estimated through field tests. If the field test results are unsatisfactory, the threshold is set based on experience. When the emission current exceeds the threshold, the potential (difference) data corresponding to the unqualified measuring points within that evaluation unit are removed. Specifically, I is estimated through field tests. min The formula is as follows:

[0045] I min =A min I s / A smin

[0046] Among them, I min The minimum threshold for the emission current, and A smin Under the same device conditions; A min The smallest signal that the monitoring instrument can distinguish; I s The emission current for the field test; A smin This represents the minimum potential (difference) received at each measuring point during the field test.

[0047] Preferably, the maximum threshold value I for the emission current is set. max 80mA, minimum threshold I min A current of 30mA is considered acceptable for received data if the transmit current is within this range.

[0048] The stability of the emission current is evaluated by the relative mean square error of the emission current, and the calculation formula is as follows:

[0049]

[0050] Where, m I The relative mean square error of the emitted current is given by I, where n is the number of measurement points corresponding to the selected emitting electrode. j This is the emission current data for a single measurement point. The average value of the emission current data from n measurement points.

[0051] In this embodiment, the relative mean square error threshold of the transmission current is set to 5%. If the error exceeds this threshold, the transmission current is considered unstable, and all data corresponding to this evaluation unit are discarded.

[0052] Step 2: Evaluate the noise level of the raw data: For the monitoring data of the current substation after processing in Step 1 (i.e. the qualified data obtained in Step 1), perform Fourier transform on the full waveform data of each measuring point to obtain the spectrum of different frequencies, and then calculate the signal-to-noise ratio (SNR) of the data at each measuring point. Evaluate the noise level of the raw monitoring data of different measuring points one by one, and remove the potential (difference) data corresponding to the unqualified measuring points in the evaluation unit. The qualified data proceeds to the next step.

[0053] In step 2, the signal-to-noise ratio of each measuring point is calculated using the following formula:

[0054]

[0055] Among them, V signal The spectrum of the transmission frequency is obtained by performing a Fourier transform on the full waveform data; V noise This is the spectrum of noise near the transmission frequency obtained by performing a Fourier transform on the full waveform data. The frequency band near the transmission frequency is a frequency band with a certain bandwidth centered on the transmission frequency.

[0056] Specifically, the bandwidth of the frequency band near the transmission frequency is determined based on the sampling frequency and sampling duration, and should include at least 10 frequency points. In this embodiment, the bandwidth of the frequency band near the transmission frequency is set to 5Hz; the signal-to-noise ratio threshold for the data is set to 10dB, and data exceeding this value is considered to be of acceptable quality.

[0057] Step 3: Spatially evaluate data stability: For the monitoring data of the current substation after processing in Step 2 (i.e., the qualified data obtained in Step 2), each transmitting electrode is used as an evaluation unit. If the proportion of measurement points retained after Step 2 in a single evaluation unit is small (less than 80% in this embodiment), proceed directly to Step 4; otherwise, the potential V of each measurement point is... i or potential difference ΔV i A measured curve (i.e., a potential (difference) change curve) is generated. The stability of the data is evaluated spatially by taking a single emitting electrode as a group of evaluation units. Then, the monitoring data of the current substation after processing in step 2 is entered into step 4. At this time, the data processing of a single substation is completed.

[0058] Among them, the data stability standard includes: within a set of evaluation units, the potential (difference) curves of each measuring point should be smooth, continuous, and have a clear curve shape; in two adjacent sets of evaluation units, the potential (difference) curves of each measuring point should have a good regularity.

[0059] Step 3 does not discard data. This step generates curves for data from single evaluation units with a high percentage (80% or more) of qualified measurement points obtained in Step 2, and assesses the spatial stability of these curves to form evaluation conclusions. These conclusions serve as the basis for subsequent steps after the method of this invention is completed. These subsequent steps process the data based on the spatial stability evaluation conclusions (i.e., curve characteristics) of the potential (difference) curves of different evaluation units obtained in Step 3. Data from a single emitting electrode with a low percentage (less than 80%) of qualified measurement points obtained in Step 2 directly proceeds to Step 5 of this invention. Step 3 primarily serves to observe whether static effects, electrode polarization, or insufficient infinity distance exist during the construction process. It cannot be used when there are few qualified measurement points within a single evaluation unit.

[0060] Step 4: Repeat steps 1-3 to obtain monitoring data from multiple substations;

[0061] Step 5: Evaluate data stability over time: From the monitoring data obtained in Step 4, select monitoring data from multiple stations with adjacent acquisition times within 48 hours (preferably 8 stations with adjacent acquisition times). Using a single measuring point as the evaluation unit, plot the potential (difference) change curve of the monitoring data of a single measuring point over time, calculate the relative mean square error of each measuring point, and evaluate the data stability over time.

[0062] In this embodiment, monitoring data from eight substations were selected within a 48-hour timeframe, ensuring temporal continuity. Temporal continuity means that the monitoring data from multiple substations should be selected sequentially in chronological order. Furthermore, the monitoring data from multiple substations should be collected under the same underground production environment conditions, distinguishing between production shifts and maintenance shifts; data should not be collected for periods of underground production and periods of underground shutdown. This step is predicated on the number of monitoring substations within a 24-hour period being three or more, and the number of monitoring substations within a 48-hour period being five or more.

[0063] The specific formula for calculating the relative mean square error of the potential (difference) at a single measuring point is as follows:

[0064]

[0065] Where, m s S represents the relative mean square error of the potential (difference) at a single measuring point, where n is the number of selected substations (8 in this embodiment). i This is the potential (difference) monitoring data for a single substation. This represents the average value of the potential (difference) monitoring data from n substations.

[0066] Wherein: the relative mean square error threshold for the potential (difference) of a single measuring point is set at 5%, and data within this threshold range are considered stable; when the relative mean square error of a single measuring point is m sWhen the deviation is greater than 5%, and the data with a large deviation is located in the middle of the curve, it is necessary to add multi-station monitoring data processing and other related steps to the subsequent data processing of the method of the present invention. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0067] Figure 2 This refers to the transmission current of all raw data from a single substation. In this embodiment, the evaluation criterion considers raw data with a transmission current below 30mA to not contain a transmission frequency signal. In subsequent processing stages, data with a transmission current below 30mA will be removed for noise analysis, and the remaining data will proceed to the data quality analysis in the following steps.

[0068] In this embodiment, the evaluation criterion considers the relative mean square error m of a single set of emission currents. I When the error is ≤5%, the emission current is considered stable, and the corresponding raw data can be directly used for data quality analysis in the following steps; the relative mean square error (m) of a single set of emission currents. I If the value is greater than 5%, the transmission current is unstable, and the corresponding data will be discarded and will not enter the subsequent data quality control process and other related data processing processes.

[0069] Figure 3 This represents the signal-to-noise ratio (SNR) of all raw data from a single substation. The SNR of the measurement points at this substation is generally between -40 and 60 dB. Specifically, the SNR of measurement points 900 and earlier is generally between 20 and 60 dB, indicating that the noise is less than 10% of the effective signal amplitude. The SNR of measurement points 901 and later is generally between -20 and 10 dB, indicating that the noise is roughly equivalent to the effective signal amplitude.

[0070] The evaluation criteria consider raw data with a signal-to-noise ratio below 10dB to be severely affected by interference, and this part of the data will not be included in the subsequent data quality control process.

[0071] Figure 4 This represents the potential (difference) of all raw data from a single substation. Measurement points with smaller numbers show larger potential values, clearer curve shapes, and better regularity in their curve types. Measurement points with larger numbers show smaller potential values, harder-to-identify curve shapes, and poorer regularity in their curve types. This may be due to the transmitting electrode gradually approaching infinity, resulting in a weakened received signal and a reduced signal-to-noise ratio.

[0072] Figure 5 This is a partial set of monitoring data from 8 consecutive stations. The mean value of the 8 raw data points from monitoring point 89 is 19.48 μV, and the relative mean square error is m. s The mean value was 2.04%; the mean value of the 8 raw data points at measurement point 90 was 16.98 μV, and the relative mean square error was m. sThe error was 6.18%, with the fifth raw data showing a relatively large deviation of 14.62 μV; the mean of the eight raw data from measuring point 91 was 27.55 μV, with a relative mean square error of m. s The deviation was 16.18%, with the 8th original data showing a relatively large deviation of 32.65 μV.

[0073] The evaluation criteria state that when the relative mean square error of the measuring point is m s When the deviation is greater than 5% and the data is located in the middle of the curve, it can be considered that the measuring point is subjected to a large short-term random electromagnetic interference, and related steps such as multi-station monitoring data processing need to be added in the subsequent data processing.

[0074] The principle of this embodiment will be introduced below.

[0075] To ensure safe production in underground working faces, underground electrical resistivity tomography (EDT) instruments are typically equipped with overcurrent protection. Because the grounding conditions remain constant for the same transmitting electrode, the transmitting current is generally stable. However, when the transmitting electrode is changed, a step-like phenomenon occurs. Therefore, it is necessary to evaluate the transmitting current on a group basis, using individual transmitting electrodes as a group. If the transmitting current is too high, it indicates a problem with the instrument; if the transmitting current is too low, it indicates that the transmitting wire or electrode was damaged during mining; if the transmitting current is unstable, it is likely that the transmitting electrode is loose, the grounding conditions have changed significantly, or the transmitting instrument is unstable.

[0076] When evaluating the noise level of raw data, the raw data spectrum mainly includes single or multiple transmitted frequency signals, strong interference power frequency signals and their harmonics, and the remaining background noise is close to Gaussian white noise. The intensity of the background noise is related to the number and intensity of electromagnetic interference sources in the tunnels where the transmitter and receiver are located. At substations 901 to 1800, the background noise is significantly stronger, the signal-to-noise ratio is lower, and the data quality is poor. The transmission is conducted by electrodes in the main transport roadway, and the reception is conducted by electrodes in the auxiliary transport roadway.

[0077] When evaluating data stability spatially, under single transmitting electrode conditions, the potential (difference) at different measuring points exhibits a gradual and smooth change. A sawtooth pattern indicates that the raw data is affected by external factors, possibly due to a loose receiving electrode or poor grounding. If the same problem persists after replacing the receiving electrode, it may be due to a static effect caused by the receiving electrode being located on an electrically inhomogeneous body, or polarization of the receiving electrode. Appropriate correction steps need to be added in subsequent data processing. Under adjacent transmitting electrode conditions, the receiving curves at different measuring points should have similar or gradually changing shapes, consistent with theory. In this figure, the overall potential at the measuring points is smaller under the condition of the larger transmitting electrode, which is inconsistent with theory. This may be due to insufficient distance at infinity, requiring appropriate correction steps in subsequent data processing.

[0078] When evaluating data stability over time, the damage to the roof and floor during mining and the development of water-conducting channels are gradual, and therefore can be approximated as static over shorter periods. Drastic fluctuations in the curve shape indicate that the measuring point is subject to continuous interference, with high interference intensity and poor stability. The appearance of a single or a few distorted points in the middle of the curve indicates that the measuring point is subject to short-term interference. If the tail of the curve shows continuous and significant changes (at least 3 points), it is necessary to consider whether the anomaly is caused by damage to the roof and floor and the development of water-conducting channels during mining.

[0079] The following comparative experiment will illustrate this point.

[0080] The following comparative experiment was designed: monitoring data from eight consecutive stations at a mine were subjected to quality control according to the process described in this article, and compared with monitoring data from one of the stations that were not subject to quality control. Figure 6 The figures show the monitoring data curves before (top) and after (bottom) quality control. It can be seen that after quality control of the monitoring data using the process described in this article, the signal-to-noise ratio of the retained monitoring data is generally above 30dB, making it usable and effective data. Furthermore, the control process provides a basis for selecting subsequent processing methods for raw data under different circumstances, achieving the goal of effectively controlling the quality of monitoring data.

Claims

1. A quality control process for downhole DC electrical resistivity tomography (DCE) monitoring data, characterized in that, Specifically, the following steps are included: Step 1: Evaluate the transmission current: Select monitoring data from a single substation, pre-set the maximum and minimum thresholds for the transmission current, and evaluate the magnitude and stability of the transmission current of a single transmitting electrode as a group of evaluation units. After completing all evaluations, remove unqualified data and proceed to the next step with qualified data. The maximum threshold of the emission current I max To monitor the maximum achievable transmit current and minimum threshold of the instrument. I min During the deployment phase of the monitoring system, the potential is estimated through field tests. If the field test results are unsatisfactory, the settings are adjusted based on experience. When the emission current exceeds the threshold, the potential or potential difference data corresponding to the unqualified measuring points in that evaluation unit are removed. The estimation method described above is based on field tests. I min The formula is as follows: in, I min This is the minimum threshold for the emission current; A min The smallest signal that the monitoring instrument can distinguish; I s This refers to the emission current during the field test. A smin This refers to the minimum potential or potential difference received at each measuring point during the field test. Step 2: Evaluate the noise level of the raw data: For the monitoring data of the current substation processed in Step 1, perform Fourier transform on the full waveform data of each measuring point to obtain the spectrum of different frequencies, and then calculate the signal-to-noise ratio of the data at each measuring point. SNR The noise level of the original monitoring data of different measuring points is evaluated one by one. The potential or potential difference data corresponding to the unqualified measuring points in the evaluation unit are removed, and the qualified data are entered into the next step. Step 3: Spatially evaluate data stability: For the monitoring data of the current substation after processing in Step 2, each transmitting electrode is used as an evaluation unit. If the proportion of measurement points retained in a single evaluation unit after Step 2 is small, proceed directly to Step 4; otherwise, the potential of each measurement point is... V i or potential difference Δ V i The measured curves are generated, and the data stability is evaluated spatially using a single transmitting electrode as an evaluation unit. Then, the monitoring data of the current substation after processing in step 2 is entered into step 4; at this point, the data processing of a single front substation is completed. Step 4: Repeat steps 1-3 to obtain monitoring data from multiple substations; Step 5: Evaluate data stability over time: From the monitoring data obtained in Step 4, select monitoring data from multiple substations with adjacent acquisition times within a certain time period. Using a single measuring point as the evaluation unit, plot the potential or potential difference change curve of the monitoring data of a single measuring point over time, calculate the relative mean square error of each measuring point, and evaluate the data stability over time based on the relative mean square error.

2. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 1, characterized in that, In step 1, the stability of the transmission current is evaluated by the relative mean square error of the transmission current of the evaluation unit. If the relative mean square error is higher than the threshold, the transmission current is considered unstable, and all data corresponding to that evaluation unit are removed. The calculation formula is as follows: in, m I This represents the relative mean square error of the emitted current. n To select the number of measurement points corresponding to the transmitting electrode, I j This is the emission current data for a single measurement point. for n The average value of the emission current data at each measuring point.

3. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 1 or 2, characterized in that, The relative mean square error threshold for the emission current is 5%.

4. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 1, characterized in that, In step 2, the signal-to-noise ratio calculation formula for the measurement point is as follows: in, V signal The spectrum of the transmission frequency obtained by performing a Fourier transform on the full waveform data; V noise This is the spectrum of noise near the transmission frequency obtained by performing a Fourier transform on the full waveform data; the frequency band near the transmission frequency is a frequency band with a certain bandwidth centered on the transmission frequency.

5. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 4, characterized in that, The bandwidth of the frequency band near the transmission frequency is determined according to the sampling frequency and sampling duration, and should include at least 10 frequency points; the signal-to-noise ratio threshold of the data should not be lower than 10dB.

6. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 1, characterized in that, In step 3, if the proportion of measurement points retained by the single evaluation unit after step 2 is less than a threshold, it is considered small. The threshold can be set in the range of 60% to 80%.

7. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 1, characterized in that, In step 5, the monitoring data from multiple stations with adjacent acquisition times within a certain time period refers to monitoring data under the same downhole production environment conditions within 24 hours or 48 hours.

8. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 1, characterized in that, In step 5, the monitoring data under the same underground production environment conditions, i.e., the data distinguishing between production shifts and maintenance shifts, should not be a period of underground production and a period of underground shutdown.

9. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 1, characterized in that, In step 5, the formula for calculating the relative mean square error of the potential or potential difference at a single measuring point is: in, m s The relative mean square error of the potential or potential difference at a single measuring point. n For the selected number of substations, S i This refers to the potential or potential difference monitoring data for a single substation. for n The average value of the potential or potential difference monitoring data of each substation.

10. The quality control process for downhole DC electrical resistivity tomography monitoring data as described in claim 9, characterized in that, in: The threshold value for the relative mean square error of the potential or potential difference at a single measuring point ranges from 5% to 10%.

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