Time domain resistivity monitoring and alarming method

Abstract

The invention discloses a time-domain resistivity monitoring and alarming method. The method comprises the following steps of: 1, processing data obtained by detection each time into an apparent resistivity data body, and dividing a three-dimensional grid; 2, calculating an apparent resistivity background value; 3, performing anomaly grading on the (n + 1) th detection apparent resistivity data volume of each grid; 4, judging whether an abnormal area exists or not according to the condition of (n + 1) th detection, if yes, marking the abnormal area and entering the step 5, and if not, updating the background value and entering the step 6; 5, evaluating a time period with a relatively low apparent resistivity value in a time dimension, and giving an alarm; and 6, repeatedly executing the step 1 for next detection, and repeatedly executing the steps 3, 4 and 5 to realize warning in the process of dynamically detecting the apparent resistivity. According to the invention, the comprehensive change of the apparent resistivity of the time dimension and the space dimension is used as the basis of water disaster alarm, and the alarm precision and timeliness are improved.

Description

Technical Field

[0001] The invention belongs to the field of underground coal mine time domain resistivity monitoring, and in particular relates to a time domain resistivity monitoring alarm method. Background Art

[0002] Coal mine water hazards refer to the process and result of groundwater developed in the direct or indirect aquifers of the main mining coal seams entering the mine through the water channels formed by natural or artificial disturbances during the mining process and causing disasters to the mine. The types of coal mine water hazards in my country are complex, such as fault water inrush, karst collapse column water inrush, fissure seepage water inrush, etc. In recent years, due to the continuous increase in mining depth, the threat of pressurized water has become increasingly serious. Therefore, real-time dynamic monitoring of coal mine water hazards, early warning and taking corresponding control measures are necessary for coal mine safety production.

[0003] Time domain resistivity monitoring is an effective method for monitoring water hazards in coal mines, and its main observation parameter is the apparent resistivity value. Since water channels are a necessary condition for water inrush, common water channels include faults, karst collapse columns, fissures, and artificial channels. Under mining disturbance, the water channels may be opened due to the rupture of rock formations, and groundwater in the aquifer may enter the mining area through the water channels, which will cause the apparent resistivity value in the area to decrease. Therefore, areas threatened by water hazards are characterized by a decrease in apparent resistivity values, which appear as abnormal areas on the detection results map. Conversely, the apparent resistivity value is higher.

[0004] The time domain resistivity monitoring system used in the above method is a complete set of devices for signal transmission, signal reception, data uploading, etc. for time domain resistivity monitoring, for example, the ZDJ11 mine electrical monitoring device commonly used by the applicant, or the parallel electrical monitoring system for mines of other manufacturers. It is installed and deployed near the detection target area, and a single detection of the target area is performed, or continuous detection is performed at a specified interval. Both are used to observe the resistivity changes in the area under mining disturbance. The time domain resistivity monitoring system detects the target area at a specified interval and uploads the collected data to the ground server in real time. Correspondingly, the data processing software deployed on the server automatically processes and images the data of the target area. The processing imaging results can be analyzed for a single detection or for continuous detection. If it is the former, the imaging results reflect the distribution and changes of the apparent resistivity values ​​in the detection area. If it is the latter, it reflects the changes in the apparent resistivity of the same area at different time periods, and timely classifies and alarms the abnormality level according to the speed of the apparent resistivity change.

[0005] Among them, the transmitting electrode is a device used by the time domain resistivity monitoring system to supply power to the underground; the receiving electrode is a device used by the time domain resistivity monitoring system to receive the voltage signal carrying geological information after being transmitted underground.

[0006] At present, this method monitors more of the analysis of single detection, that is, it can only observe the distribution of apparent resistivity within a specific spatial range at a certain point in time; even for continuous detection, it is only limited to the changes in the apparent resistivity of individual grids corresponding to several adjacent detections, and does not consider the important factor of spatial influence range. Therefore, there is a problem of low accuracy and untimely alarm due to the lack of influence on spatial range. Summary of the invention

[0007] The purpose of the present invention is to provide a time domain resistivity monitoring alarm method to solve the problem of low graded alarm accuracy and untimely alarm due to lack of influence on spatial range.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A time domain resistivity monitoring and alarm method comprises the following steps:

[0010] Step 1: Store the data obtained from each detection of the time domain resistivity monitoring system as a data file, perform inversion processing on each data file to obtain an apparent resistivity data volume, and divide the detection area into three-dimensional grids of equal size as needed. The apparent resistivity data volume contains the three-dimensional coordinates and apparent resistivity values ​​of all grids;

[0011] Step 2: Calculate the apparent resistivity background value in the detection area without apparent resistivity anomaly;

[0012] Step 3: classify the anomaly of the apparent resistivity data volume of each grid for the n+1th detection, and select the grid with the highest anomaly level;

[0013] Step 4: Use the situation of the n+1th detection to determine whether there is an abnormal area. If yes, mark the abnormal area and proceed to step 5. If no, update the background value and proceed to step 6.

[0014] Step 5: Evaluate the time period with low apparent resistivity value in the time dimension, and issue an alarm and record the situation where the same abnormal area appears multiple times continuously;

[0015] Step 6: Repeat step 1 for the next detection, and repeat steps 3, 4, and 5 to implement the alarm during the dynamic detection of apparent resistivity.

[0016] Furthermore, in step 1, the apparent resistivity data volume ρ is expressed as:

[0017]

[0018] Among them, m is the total number of grids in the detection area, the first three columns of data in the apparent resistivity data body represent the three-dimensional coordinates of grids 1 to grid m, and the fourth column of data is the apparent resistivity value of grids 1 to grid m.

[0019] Further, the specific operations of step 2:

[0020] In the continuous detection mode, the apparent resistivity value obtained by inverting the data file obtained from the previous n detections of each grid is selected, n>3, and the average value of the apparent resistivity value of each grid is calculated respectively. The average value of the apparent resistivity of all grids in the detection area constitutes the apparent resistivity background value.

[0021]

[0022] Furthermore, in step 3: the value of the n+1th detection is:

[0023]

[0024] The method of anomaly classification is: by calculating the average apparent resistivity of all grids detected in the n+1th time and variance σ;

[0025]

[0026]

[0027] The grids in the apparent resistivity data volume obtained by the n+1th detection are divided into four levels according to the resistivity values. The resistivity value ranges of the four levels of grids are: Among them, the resistivity value is The interval is the grid with the lowest apparent resistivity value, that is, the grid with the highest anomaly level, and the three-dimensional coordinates of all grids with the highest anomaly level are recorded.

[0028] Further, the specific operation of step 4 is as follows: Specifically, the apparent resistivity value of each grid in the n+1th detection is subtracted from the apparent resistivity average value of the corresponding grid in the apparent resistivity background value to obtain the relative apparent resistivity value of all grids in the n+1th detection, among which the relative apparent resistivity value corresponding to the grid with the highest abnormal level recorded in step 3 is selected. p is the grid number in the grid with the highest abnormality level;

[0029] If the relative apparent resistivity value of any grid in the grid with the highest anomaly level is less than the anomaly threshold, that is, Then the relative apparent resistivity values ​​of the six grids in front, behind, left, right, top and bottom of the grid are judged. If all of them are less than It is considered that there is a larger area with a higher abnormal level, and the three-dimensional coordinates of the current grid and the corresponding 6 grids are marked as abnormal areas; until all grids in the grid with the highest abnormal level are traversed to obtain all marked abnormal areas; execute step 5;

[0030] If the relative apparent resistivity values ​​of all grids in the grid with the highest anomaly level are greater than the anomaly threshold, that is, It is considered that there is no abnormality in this detection, and the apparent resistivity data volume obtained by this inversion is combined with the apparent resistivity data volume of the 1st to nth times to calculate the new apparent resistivity average value. Indicates, and uses the same method as step 2 to obtain the current apparent resistivity background value; execute step 6.

[0031] Further, the specific operation of step 5 is: the same operation as step 3 and step 4 is performed on the apparent resistivity data obtained by the n+2nd and n+3rd detections and inversions respectively. If an abnormal area that overlaps with the abnormal area of ​​the (n+1)th time appears each time, an alarm is issued and the abnormal area of ​​the alarm is returned.

[0032] Furthermore, the specific operation of step six is: repeat step one for the next detection, and repeat steps three, four, and five to realize the alarm in the process of dynamic detection of apparent resistivity. In step four, the apparent resistivity background value used when calculating the relative apparent resistivity value is the current apparent resistivity background value, and when there is no abnormality in the previous detection, the apparent resistivity data body obtained by this inversion is combined with the apparent resistivity data body without abnormalities in all previous detections to calculate a new apparent resistivity average value, thereby realizing the alarm in the process of dynamic detection of apparent resistivity.

[0033] Compared with the existing technology, the present invention has the following beneficial effects:

[0034] (1) The present invention uses the comprehensive changes in apparent resistivity in the time dimension and the space dimension as the basis for water disaster warning, thereby improving the accuracy and timeliness of the warning.

[0035] (2) The apparent resistivity data volume is first classified, and then the changes in the apparent resistivity values ​​in the area with the highest abnormality level are analyzed, which improves the calculation efficiency while ensuring the calculation accuracy.

[0036] (3) Incorporating areas without abnormalities in the apparent resistivity data volume into the calculation of new background values ​​improves the accuracy of the background values ​​and thus improves the accuracy of the alarm. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a time domain resistivity monitoring alarm flow chart;

[0038] Figure 2 It is to divide the detection area into grids of equal size;

[0039] Figure 3 It is a schematic diagram of a certain grid and the grids in front, behind, left, right, top and bottom. DETAILED DESCRIPTION

[0040] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] The time domain resistivity monitoring and alarm method provided by the present invention has a detection time of each time domain resistivity monitoring, which depends on the size of the detection area and the transmission time, etc. Specifically, it includes the following steps:

[0042] Step 1: Store the data obtained from each detection (i.e., each acquisition cycle) of the time domain resistivity monitoring system as a data file, and perform inversion processing on each data file to obtain an apparent resistivity data volume. During inversion, the detection area is divided into three-dimensional grids of equal size as needed. The apparent resistivity data volume contains the three-dimensional coordinates and apparent resistivity values ​​of all grids.

[0043] Specifically, the apparent resistivity data volume ρ is expressed as:

[0044]

[0045] Among them, m is the total number of grids in the detection area, the first three columns of data in the apparent resistivity data body represent the three-dimensional coordinates of grids 1 to grid m, and the fourth column of data is the apparent resistivity value of grids 1 to grid m.

[0046] Step 2: Calculate the apparent resistivity background value in the detection area without apparent resistivity anomaly.

[0047] Specifically, in the continuous detection mode, the apparent resistivity values ​​obtained by inverting the data files obtained from the previous n (n>3) detections of each grid are selected, and the average value of the apparent resistivity value of each grid is calculated respectively. The average value of the apparent resistivity of all grids in the detection area constitutes the apparent resistivity background value.

[0048]

[0049] Step 3: Classify the anomaly of the apparent resistivity data volume of the n+1th detection of each grid and select the grid with the highest anomaly level.

[0050] Specifically, the value of the n+1th detection is:

[0051]

[0052] Specifically, the method of anomaly classification is as follows: by calculating the average apparent resistivity of all grids detected in the n+1th time and variance σ, and divide the grids in the apparent resistivity data volume obtained by the n+1th detection into four levels according to the resistivity values. The resistivity value ranges of the four levels of grids are: Among them, the resistivity value is The interval is the grid with the lowest apparent resistivity value, that is, the grid with the highest anomaly level, and the three-dimensional coordinates of all grids with the highest anomaly level are recorded.

[0053] Specifically,

[0054]

[0055] Step 4: Determine the area with lower apparent resistivity in the spatial dimension.

[0056] Specifically, the apparent resistivity value of each grid in the n+1th detection is subtracted from the average apparent resistivity value of the corresponding grid in the apparent resistivity background value to obtain the relative apparent resistivity value of all grids in the n+1th detection, among which the relative apparent resistivity value corresponding to the grid with the highest abnormal level recorded in step 3 is selected. p is the grid number in the grid with the highest abnormality level.

[0057] If the relative apparent resistivity value of any grid in the grid with the highest anomaly level is less than the anomaly threshold, that is, Then the relative apparent resistivity values ​​of the six grids in front, behind, left, right, top and bottom of the grid are judged. If all of them are less than It is considered that there is a larger area with a higher abnormal level, and the three-dimensional coordinates of the current grid and the corresponding 6 grids are marked as abnormal areas; until all grids in the grid with the highest abnormal level are traversed to obtain all marked abnormal areas; execute step 5;

[0058] If the relative apparent resistivity values ​​of all grids in the grid with the highest anomaly level are greater than the anomaly threshold, that is, It is considered that there is no abnormality in this detection, and the apparent resistivity data volume obtained by this inversion is combined with the apparent resistivity data volume of the 1st to nth times to calculate the new apparent resistivity average value. Indicates, and uses the same method as step 2 to obtain the current apparent resistivity background value; execute step 6;

[0059] The purpose of step 4 is to first determine the abnormal area in three-dimensional space, and then make a judgment on the determined area in the time dimension in the subsequent steps, as shown in step 5;

[0060] Step 5: Evaluate the time periods with lower apparent resistivity values ​​in the time dimension.

[0061] Specifically, the same operations as steps 3 and 4 are performed on the apparent resistivity data obtained by the n+2nd and n+3rd detections and inversions. If an abnormal area that overlaps with the abnormal area of ​​the n+1th time appears for each detection (i.e., a low resistivity abnormal area appears at the same 7 grid positions for more than 3 consecutive detections), an alarm is issued and the 3D coordinate values ​​of the 7 grids in the abnormal area of ​​the alarm are returned.

[0062] Step 6: Repeat step 1 for the next detection, and repeat steps 3, 4, and 5. In step 4, the apparent resistivity background value used when calculating the relative apparent resistivity value is the current apparent resistivity background value (no need to perform step 2 to obtain the apparent resistivity background value), and when there is no abnormality in the previous detection, the apparent resistivity data volume obtained by this inversion is combined with the apparent resistivity data volume without abnormalities in all previous detections to calculate a new apparent resistivity average value. This realizes the alarm in the process of dynamic detection of apparent resistivity.

[0063] Example:

[0064] Step 1: If Figure 2 As shown, the detection area is divided into 10m*10m*10m three-dimensional grids. The time domain resistivity monitoring and acquisition system collects time domain resistivity data and obtains the apparent resistivity data volume after inversion. The data volume contains the three-dimensional coordinates of each three-dimensional grid and the apparent resistivity value at each three-dimensional grid. Assuming that the total number of grids in the apparent resistivity data volume is m, the apparent resistivity data volume can be expressed as a matrix, in which the first three columns are the three-dimensional coordinates of the grid, and the fourth column is the apparent resistivity value of the grid obtained by inversion:

[0065]

[0066] Among them, m is the total number of grids in the detection area, the first three columns of data in the apparent resistivity data body represent the three-dimensional coordinates of grids 1 to grid m, and the fourth column of data is the apparent resistivity value of grids 1 to grid m.

[0067] Step 2: Calculate the apparent resistivity background value in the detection area without apparent resistivity anomaly.

[0068] Specifically, in the continuous detection mode, the apparent resistivity values ​​obtained by the previous n detection inversions of each grid are selected, and the average value of the apparent resistivity value of each grid is calculated respectively. The average value of the apparent resistivity of all grids in the detection area constitutes the apparent resistivity background value.

[0069]

[0070] in, Represents the set of apparent resistivity background values ​​for all grids.

[0071] Step 3: Classify the apparent resistivity data volume for the n+1th detection according to the apparent resistivity value, and select the grid with the highest abnormal level.

[0072] Among them, the apparent resistivity value of the n+1th detection is:

[0073]

[0074] The grading method is as follows:

[0075] (1) Calculate the average apparent resistivity of all grids in the n+1th detection. The calculation method is:

[0076]

[0077] (2) Calculate its variance σ as follows:

[0078]

[0079] (3) The apparent resistivity data volume of the n+1th detection is divided into four levels according to the apparent resistivity value, namely: Among them, the apparent resistivity value is The interval is the grid with the lowest apparent resistivity value, that is, the grid with the highest anomaly level, and the three-dimensional coordinates of all grids with the highest anomaly level are recorded.

[0080] Step 4: Determine the area with lower apparent resistivity in the spatial dimension.

[0081] (1) Subtract the apparent resistivity value of each grid in the n+1th detection from the average apparent resistivity value of the corresponding grid in the apparent resistivity background value to obtain the relative apparent resistivity value of all grids in the n+1th detection, among which the relative apparent resistivity value corresponding to the grid with the highest abnormal level recorded in step 3 is selected. p is the grid number in the grid with the highest abnormality level.

[0082] (2) As a threshold for evaluating whether there is a low apparent resistivity anomaly, the relative apparent resistivity value of the pth grid in the grid with the highest anomaly level is Make a judgment:

[0083] If the apparent resistivity value of a grid in the grid with the highest anomaly level is less than the threshold, that is, Then the apparent resistivity values ​​of the six grids in front, behind, left, right, top and bottom of the grid are judged. If all of them are less than It is considered that there is a relatively large abnormal area of ​​low apparent resistivity, and the three-dimensional coordinates of the current grid and the corresponding six grids are marked as abnormal areas; until all grids in the grid with the highest abnormal level are traversed to obtain all marked abnormal areas; execute step five;

[0084] After all grids in the grid with the highest abnormal level are traversed, if the apparent resistivity values ​​of all grids are greater than It is considered that there is no abnormality in this detection, and the apparent resistivity data volume obtained by this inversion is combined with the data volume of the 1st to nth detections to calculate the average apparent resistivity value as the current apparent resistivity background value. And use the same method as step 2 to update the apparent resistivity background value and execute step 6.

[0085] like Figure 3 As shown in the figure, the detection area is divided into 10m*10m*10m grids. Assuming that the apparent resistivity value of a grid A is and less than Then judge the apparent resistivity values ​​of the six grids in front, behind, left, right, top and bottom. Assuming the three-dimensional coordinates of grid A are (x, y, z), the coordinates of the six grids in front, behind, left, right, top and bottom of grid A are (x, y + 10, z), (x, y-10, z), (x-10, y, z), (x + 10, y, z), (x, y, z + 10), (x, y, z-10), respectively. Judge the apparent resistivity values ​​of these six grids. If they are all less than It is considered that the apparent resistivity values ​​within this spatial range are all low, which is an abnormal area and needs to be marked.

[0086] Step 5: Evaluate the time period with lower apparent resistivity in the time dimension.

[0087] Specifically, the apparent resistivity data obtained by the n+2nd and n+3rd detections and inversions are respectively calculated in the same way as in step 3 and step 4. If an abnormal area that overlaps with the abnormal area of ​​the n+1th time appears for each detection (i.e., a low resistivity abnormal area appears at the same 7 grid positions for more than 3 consecutive detections), an alarm is issued and the 3D coordinate values ​​of the 7 grids in the abnormal area of ​​the alarm are returned. If it is less than 3 times, no alarm is issued.

[0088] Step 6: Repeat steps 1, 3, 4, and 5. The apparent resistivity background value in step 4 is the current apparent resistivity background value (no need to perform step 2 to obtain the apparent resistivity background value). This realizes the alarm during the dynamic detection of apparent resistivity.

[0089] Through the above steps, the comprehensive changes in apparent resistivity in the time dimension and the space dimension can be used as the basis for water disaster warning, thereby improving the accuracy and timeliness of the warning; the apparent resistivity data body can be classified first, and then the changes in the apparent resistivity values ​​in the area with the highest abnormal level can be analyzed, thereby improving the calculation efficiency while ensuring the calculation accuracy; the areas without abnormalities in the apparent resistivity data body are also included in the calculation of the new background value, thereby improving the accuracy of the background value and thus improving the accuracy of the warning.

[0090] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A time domain resistivity monitoring and alarm method, characterized in that: The steps include: Step 1: Store the data obtained from each detection of the time domain resistivity monitoring system as a data file, perform inversion processing on each data file to obtain an apparent resistivity data volume, and divide the detection area into three-dimensional grids of equal size as needed. The apparent resistivity data volume contains the three-dimensional coordinates and apparent resistivity values ​​of all grids; Step 2: Calculate the apparent resistivity background value in the detection area without apparent resistivity anomaly; Step 3: classify the anomaly of the apparent resistivity data volume of each grid for the n+1th detection, and select the grid with the highest anomaly level; Step 4: Use the situation of the n+1th detection to determine whether there is an abnormal area. If yes, mark the abnormal area and proceed to step 5. If no, update the background value and proceed to step 6. Step 5: Evaluate the time period with low apparent resistivity value in the time dimension, and issue an alarm and record the situation where the same abnormal area appears multiple times continuously; Step 6: Repeat step 1 for the next detection, and repeat steps 3, 4, and 5 to implement the alarm during the dynamic detection of apparent resistivity.

2. The time domain resistivity monitoring and alarm method according to claim 1, characterized in that: In step 1, the apparent resistivity data volume ρ is expressed as: Among them, m is the total number of grids in the detection area, the first three columns of data in the apparent resistivity data body represent the three-dimensional coordinates of grids 1 to grid m, and the fourth column of data is the apparent resistivity value of grids 1 to grid m.

3. The time domain resistivity monitoring and alarm method according to claim 1, characterized in that: Specific operations of step 2: In the continuous detection mode, the apparent resistivity value obtained by inverting the data file obtained from the previous n detections of each grid is selected, n>3, and the average value of the apparent resistivity value of each grid is calculated respectively. The average value of the apparent resistivity of all grids in the detection area constitutes the apparent resistivity background value.

4. The time domain resistivity monitoring and alarm method according to claim 3, characterized in that: In step 3: the value of the n+1th detection is: The method of anomaly classification is: by calculating the average apparent resistivity of all grids detected in the n+1th time and variance σ; The grids in the apparent resistivity data volume obtained by the n+1th detection are divided into four levels according to the resistivity values. The resistivity value ranges of the four levels of grids are: Among them, the resistivity value is The interval is the grid with the lowest apparent resistivity value, that is, the grid with the highest anomaly level, and the three-dimensional coordinates of all grids with the highest anomaly level are recorded.

5. The time domain resistivity monitoring and alarm method according to claim 4, characterized in that: Specific operation of step 4: Specifically, subtract the apparent resistivity value of each grid in the n+1th detection from the average apparent resistivity value of the corresponding grid in the apparent resistivity background value to obtain the relative apparent resistivity value of all grids in the n+1th detection, among which the relative apparent resistivity value corresponding to the grid with the highest abnormal level recorded in step 3 is selected. p is the grid number in the grid with the highest abnormality level; If the relative apparent resistivity value of any grid in the grid with the highest anomaly level is less than the anomaly threshold, that is, Then the relative apparent resistivity values ​​of the six grids in front, behind, left, right, top and bottom of the grid are judged. If all of them are less than It is considered that there is a larger area with a higher abnormal level, and the three-dimensional coordinates of the current grid and the corresponding 6 grids are marked as abnormal areas; until all grids in the grid with the highest abnormal level are traversed to obtain all marked abnormal areas; execute step 5; If the relative apparent resistivity values ​​of all grids in the grid with the highest anomaly level are greater than the anomaly threshold, that is, It is considered that there is no abnormality in this detection, and the apparent resistivity data volume obtained by this inversion is combined with the apparent resistivity data volume of the 1st to nth times to calculate the new apparent resistivity average value. Indicates, and uses the same method as step 2 to obtain the current apparent resistivity background value; execute step 6.

6. The time domain resistivity monitoring and alarm method according to claim 5, characterized in that: Specific operation of step 5: perform the same operation as step 3 and step 4 on the apparent resistivity data obtained by the n+2nd and n+3rd detection and inversion. If an abnormal area that overlaps with the abnormal area of ​​the (n+1)th time appears each time, an alarm is issued and the abnormal area of ​​the alarm is returned.

7. The time domain resistivity monitoring and alarm method according to claim 6, characterized in that: Specific operation of step six: repeat step one for the next detection, and repeat steps three, four, and five to realize the alarm in the process of dynamic detection of apparent resistivity. In step four, the apparent resistivity background value used when calculating the relative apparent resistivity value is the current apparent resistivity background value, and when there is no abnormality in the previous detection, the apparent resistivity data body obtained by this inversion is combined with the apparent resistivity data body without abnormalities in all previous detections to calculate a new apparent resistivity average value, so as to realize the alarm in the process of dynamic detection of apparent resistivity.

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