A refined processing method and system for detecting data in goaf water accumulation areas of coal mines

Abstract

The present invention discloses a refined processing method and system for detecting data in the goaf water accumulation area of a coal mine, including the following steps: obtaining the transient electromagnetic original data, measurement data, and activated carbon radon measurement data of the coal mine area to be measured, drawing the activated carbon radon measurement plan view, and performing preliminary processing on the data to obtain multi-trace curves; selecting the trace range, drawing the corresponding multi-trace curve diagram and apparent resistivity pseudo-section diagram; performing transient electromagnetic bedding apparent resistivity slicing according to the elevation of the coal seam, selecting appropriate water-richness analysis methods and parameters to obtain the bedding apparent resistivity water-richness analysis diagram, and performing fusion processing on the data of the activated carbon radon measurement plan view to obtain the bedding apparent resistivity water-richness fusion diagram, selecting different filtering parameters, filtering the fusion diagram using guided filtering to obtain the fusion diagrams after filtering with different parameters and analyzing them to determine the final filtering parameters; using guided filtering to obtain the final fusion diagram for interpretation.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine detection, and in particular to a method and system for fine processing of detection data of coal mine goaf waterlogging areas. Background Art

[0002] In recent years, accidents affecting coal mine production safety due to water seepage in coal mine goafs have occurred from time to time, causing significant casualties and property losses. Since my country started to integrate mineral resources in September 2005, the number of coal mines has been reduced from 25,000 in 2005 to about 5,000 by the end of 2021. Most of the resource-integrated coal mines have problems such as serious lack of data on old goafs, unclear distribution of boundary ranges, unclear water accumulation area and water volume in goafs, and difficulty in conducting geophysical exploration underground in closed small coal mines, which poses a serious hidden danger to coal mine production safety. Therefore, it is urgent to use sophisticated detection technology to find out the distribution of water accumulation areas in coal mine goafs.

[0003] In the actual exploration of coal mine void water accumulation areas, the most commonly used geophysical methods are direct current method and transient electromagnetic method. The theoretical research of direct current method is relatively sufficient, and the working method is relatively complete, but there is a contradiction between its detection depth and efficiency. In deep detection, a large electrode distance is required, and only one depth of apparent resistivity can be obtained by moving the electrode once, which is inefficient. Compared with the direct current method, the advantages of transient electromagnetic method are flexible and light construction, high work efficiency, ability to penetrate high-resistance shielding layers, strong lateral resolution, sensitive response to low-resistance anomalies, and little influence by volume effect. Although transient electromagnetic method is less affected by volume effect, volume effect will still have a certain impact on data interpretation, resulting in large errors in the interpretation of coal mine void water accumulation areas. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a method and system for fine-grained processing of coal mine waterlogging area detection data, which is used to solve the technical problem of large errors in the interpretation of coal mine waterlogging areas by the existing transient electromagnetic method, thereby achieving the purpose of improving the accuracy of the interpretation of coal mine waterlogging areas.

[0005] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0006] A method for fine processing of detection data of coal mine goaf water accumulation area, comprising the following steps:

[0007] Obtain transient electromagnetic raw data, measurement data and activated carbon radon measurement data of the coal mine area to be tested;

[0008] Performing statistical analysis on the activated carbon radon measurement data, drawing an activated carbon radon measurement plane diagram, and performing preliminary processing on the transient electromagnetic raw data and the measurement data to obtain a multi-channel curve;

[0009] After selecting the survey channel range, removing the distorted data and performing filtering, a corresponding multi-channel curve graph and a pseudo-section graph of apparent resistivity are plotted;

[0010] According to the multi-channel curve graph and the pseudo-section graph of apparent resistivity, and performing transient electromagnetic bedding apparent resistivity slicing according to the elevation of the coal seam, by analyzing the characteristics of the goaf, selecting a suitable water-richness analysis method and analysis parameters, a bedding apparent resistivity water-richness analysis graph is obtained;

[0011] Fusing the data of the bedding apparent resistivity water-richness analysis graph and the data of the activated carbon radon measurement plan view to obtain a bedding apparent resistivity water-richness fusion graph, selecting different filtering parameters according to the activated carbon radon analysis results or known geological conditions, using guided filtering to filter the bedding apparent resistivity water-richness fusion graph, obtaining the bedding apparent resistivity water-richness fusion graph after filtering with different parameters and analyzing it, and through repeated calibration, determining the final filtering parameters;

[0012] According to the final filtering parameters, using the guided filtering to obtain the final bedding apparent resistivity water-richness fusion graph for interpretation.

[0013] As a preferred embodiment of the present invention, when acquiring the transient electromagnetic raw data and measurement data of the coal mine area to be measured, it includes:

[0014] Selecting a suitable transient electromagnetic exploration magnetic source device to emit a primary field to the coal mine area to be measured;

[0015] Under the excitation of the primary field, an induced eddy current is generated in the coal mine area to be measured, and a transient electromagnetic secondary field is generated as time changes;

[0016] Receiving the transient electromagnetic secondary field through the transient electromagnetic exploration magnetic source device, and obtaining an equivalent transient electromagnetic method induced voltage according to the transient electromagnetic secondary field;

[0017] Converting the transient electromagnetic method induced voltage into apparent resistivity, apparent longitudinal conductance and apparent depth.

[0018] As a preferred embodiment of the present invention, when obtaining an equivalent transient electromagnetic method induced voltage according to the transient electromagnetic secondary field, it includes:

[0019] The transient electromagnetic secondary field is equivalent to the transient electromagnetic method induced voltage generated by its action on the second loop, and the transient electromagnetic method induced voltage is proportional to the time derivative of the transient electromagnetic secondary field, specifically as shown in formula 1:

[0020] V2(t)∝(e -t / τ ) / τ (1);

[0021] Wherein, τ is the time constant of attenuation, the lower the resistivity, the larger the time constant, t is the attenuation time, and e is the natural constant;

[0022] Among them, the selected transient electromagnetic exploration magnetic source device is a co - point device. The co - point device includes a first loop and a second loop. The first loop is used to emit the primary field, the second loop is used to receive the transient electromagnetic secondary field, and the second loop has resistance and inductance.

[0023] As a preferred embodiment of the present invention, when converting the induced voltage of the transient electromagnetic method into apparent resistivity, it includes:

[0024] Obtaining the apparent resistivity according to the effective area of the receiving coil in the second loop, specifically as shown in Equation 2:

[0025] As shown in Equation 2:

[0026]

[0027] In the formula, t is the time window time, m is the transmitting magnetic moment, q is the effective area of the receiving coil, V(t) is the induced voltage, and u0 is the magnetic permeability of vacuum.

[0028] As a preferred embodiment of the present invention, when converting the induced voltage of the transient electromagnetic method into apparent longitudinal conductance and apparent depth, it includes:

[0029] Obtaining the apparent longitudinal conductance and the apparent depth according to the area of the first loop, specifically as shown in Equation 3 and Equation 4:

[0030] As shown in Equation 3 and Equation 4:

[0031]

[0032]

[0033] In the formula, V(t) / I is the normalized induced voltage, A is the area of the first loop, and d(V(t) / I) / dt is the derivative of the normalized induced voltage with respect to time.

[0034] As a preferred embodiment of the present invention, when selecting a suitable water - rich property analysis method and analysis parameters, it includes:

[0035] Analyzing the transient electromagnetic bedding - parallel apparent resistivity slice data by using different water - rich property analysis methods, and determining the adopted water - rich property analysis method according to the resolution and conformity of the mined - out water - accumulated area;

[0036] Determining the number of points used for water - rich property analysis according to the width of the mined - out area and the point spacing of the measuring points;

[0037] Determine the analysis parameters for water abundance analysis according to the variation of the low-resistance anomaly region with the change of analysis parameters.

[0038] As a preferred embodiment of the present invention, when obtaining the bedding apparent resistivity water abundance analysis map, it includes:

[0039] By adding variable components, more detailed characterization of the anomaly region morphology in the bedding apparent resistivity water abundance analysis map is carried out;

[0040] More clear characterization of small-scale water-bearing anomaly bodies in the bedding apparent resistivity water abundance analysis map is carried out;

[0041] The boundary position in the bedding apparent resistivity water abundance analysis map is corrected.

[0042] As a preferred embodiment of the present invention, when filtering the bedding apparent resistivity water abundance fusion map using guided filtering, it includes:

[0043] Assume that the guided filter is a local linear model within a two-dimensional window between the guidance image and the filtered output;

[0044] Discriminate the edges and regions in the bedding apparent resistivity water abundance analysis map through the information provided by the guidance image, and when filtering, smooth the regions and retain the edges.

[0045] As a preferred embodiment of the present invention, when determining the final filtering parameters, it includes:

[0046] Determine the bin parameters according to the ratio of the length and width of the known water-bearing geological body and the distance between measurement points;

[0047] Determine the regularization parameter according to the ratio between the water-bearing resistivity and the non-water-bearing resistivity obtained from the tests in the known area;

[0048] Wherein, the filtering parameters include the bin parameters and the regularization parameter, and the water-bearing geological body includes goaf water accumulation and roadway water accumulation.

[0049] A refined processing system for detecting data in coal mine goaf water accumulation areas includes:

[0050] Data acquisition unit: used to acquire the transient electromagnetic raw data, measurement data, and activated carbon radon measurement data of the coal mine area to be measured;

[0051] Drawing unit: It is used to statistically analyze the radon measurement data of activated carbon, draw the plane map of radon measurement of activated carbon, preliminarily process the transient electromagnetic raw data and the measurement data to obtain multi-channel curves; select the channel range, eliminate the distorted data and perform filtering, and then draw the corresponding multi-channel curve graph and apparent resistivity pseudo-section graph;

[0052] Analysis unit: It is used to perform transient electromagnetic bedding apparent resistivity slicing according to the multi-channel curve graph and the apparent resistivity pseudo-section graph and in accordance with the elevation of the coal seam, and by analyzing the characteristics of the goaf, select appropriate water-richness analysis methods and analysis parameters to obtain the bedding apparent resistivity water-richness analysis graph;

[0053] Filtering unit: It is used to fuse the data of the bedding apparent resistivity water-richness analysis graph and the plane map of radon measurement of activated carbon to obtain the fused graph of bedding apparent resistivity water-richness. Select different filtering parameters according to the radon measurement analysis result of activated carbon or known geological conditions, use guided filtering to filter the fused graph of bedding apparent resistivity water-richness, obtain the fused graph of bedding apparent resistivity water-richness after filtering with different parameters and analyze it. After repeated correction, determine the final filtering parameters; according to the final filtering parameters, use the guided filtering to obtain the final fused graph of bedding apparent resistivity water-richness for interpretation.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] (1) The present invention combines guided filtering for refined processing. Compared with the existing isotropic filtering, it can remove noise while retaining the overall trend of resistivity changes, thereby improving the accuracy of interpreting the goaf water accumulation area in coal mines;

[0056] (2) When analyzing the water-richness, the present invention combines the variable components, depicts the abnormal area morphology more carefully, depicts the water-containing abnormal bodies with smaller scales such as roadways and faults more clearly, and corrects the boundary positions, making the positions of the low-resistance areas more consistent with the actual goaf water accumulation areas and roadways;

[0057] (3) The interpretation results obtained by using the refined processing method provided by the present invention are more consistent with the actual goaf compared with the interpretation results obtained by the existing processing methods;

[0058] (4) The interpretation results obtained by using the refined processing method provided by the present invention are more consistent with the trend of the roadway alignment in the interpreted abnormal area range compared with the interpretation results obtained by the existing processing methods, and the abnormal range is depicted more precisely.

[0059] The present invention will be further described in detail below with reference to the drawings and specific embodiments. Description of the Drawings

[0060] Figure 1 - Schematic diagram of the overlapping loop device of the multi-turn small wire frame according to an embodiment of the present invention;

[0061] Figure 2 - TEM curve graphs of the 90°, 60°, and double vertical conductive thin plate models according to an embodiment of the present invention;

[0062] Figure 3 - Schematic diagram of the goaf and the overlying strata according to an embodiment of the present invention;

[0063] Figure 4 - Transient electromagnetic depth - apparent resistivity curve graph of the study area 1 according to an embodiment of the present invention;

[0064] Figure 5 - Transient electromagnetic multi - trace curve graph above the known goaf according to an embodiment of the present invention;

[0065] Figure 6 - Apparent resistivity section and geological profile of the known goaf according to an embodiment of the present invention;

[0066] Figure 7 - Bedding resistivity slice graph of the known goaf according to an embodiment of the present invention;

[0067] Figure 8 - Comparison graph of different water - rich property analysis methods according to an embodiment of the present invention;

[0068] Figure 9 - Comparison graph of different water - rich property analysis parameters according to an embodiment of the present invention;

[0069] Figure 10 - Comparison graph of the influence of variable components according to an embodiment of the present invention;

[0070] Figure 11 - Schematic diagram of the smoothing principle according to an embodiment of the present invention;

[0071] Figure 12 - Schematic diagram of the realization of the guided filtering according to an embodiment of the present invention;

[0072] Figure 13 - Original bedding resistivity slice graph before filtering according to an embodiment of the present invention;

[0073] Figure 14 - Bedding resistivity slice graphs after filtering with different parameters according to an embodiment of the present invention;

[0074] Figure 15 - Comparison graph of the guided filtering and the grid filtering according to an embodiment of the present invention;

[0075] Figure 16 - Residual comparison graph of the guided filtering and the grid filtering according to an embodiment of the present invention;

[0076] Figure 17 - is a comparison chart of different regularization parameters in the embodiments of the present invention;

[0077] Figure 18 - is a schematic diagram of the floor contour line and roadway distribution of Research Area 1 in the embodiments of the present invention;

[0078] Figure 19 - is the actual material map of transient electromagnetic exploration in Research Area 1 in the embodiments of the present invention;

[0079] Figure 20 - is the analysis result map of the water-richness of the bedding resistivity in Research Area 1 in the embodiments of the present invention;

[0080] Figure 21 - is the comparison chart before and after the fine processing of Research Area 1 in the embodiments of the present invention;

[0081] Figure 22 - is the comprehensive result map of mined-out area water accumulation in Research Area 1 in the embodiments of the present invention;

[0082] Figure 23 - is the actual material map of transient electromagnetic exploration in Research Area 2 in the embodiments of the present invention;

[0083] Figure 24 - is the analysis result map of the water-richness of the bedding resistivity in Research Area 2 in the embodiments of the present invention;

[0084] Figure 25 - is the fusion map of the water-richness of the bedding apparent resistivity in Research Area 2 in the embodiments of the present invention;

[0085] Figure 26 - is the final fusion map of the water-richness of the bedding apparent resistivity for interpretation in Research Area 2 in the embodiments of the present invention;

[0086] Figure 27 - is the comprehensive result map of mined-out area water accumulation in Research Area 2 in the embodiments of the present invention;

[0087] Figure 28 - is the step diagram of the fine processing method for the detection data of the mined-out area water accumulation in coal mines in the embodiments of the present invention. Detailed implementation manners

[0088] The fine processing method for the detection data of the mined-out area water accumulation in coal mines provided by the present invention, as Figure 28 shown, includes the following steps:

[0089] Step S1: Obtain the transient electromagnetic raw data, measurement data, and activated carbon radon measurement data of the coal mine area to be measured;

[0090] Step S2: Statistically analyze the radon measurement data of activated carbon, draw a plane radon measurement map of activated carbon, preliminarily process the original data and measurement data of transient electromagnetic method to obtain multi-channel curves;

[0091] Step S3: Select the channel range, eliminate the distorted data and perform filtering, and then draw the corresponding multi-channel curve diagram and apparent resistivity pseudo-section diagram;

[0092] Step S4: According to the multi-channel curve diagram and apparent resistivity pseudo-section diagram, and perform transient electromagnetic bedding apparent resistivity slicing according to the elevation of the coal seam. By analyzing the characteristics of the goaf, select appropriate water-richness analysis methods and analysis parameters to obtain a bedding apparent resistivity water-richness analysis map;

[0093] Step S5: Fuse the data of the bedding apparent resistivity water-richness analysis map and the activated carbon radon measurement plane map to obtain a fused bedding apparent resistivity water-richness map. Select different filtering parameters according to the activated carbon radon measurement analysis results or known geological conditions, use guided filtering to filter the fused bedding apparent resistivity water-richness map, obtain the fused bedding apparent resistivity water-richness map after filtering with different parameters and analyze it. After repeated correction, determine the final filtering parameters;

[0094] Step S6: According to the final filtering parameters, use guided filtering to obtain the final fused bedding apparent resistivity water-richness map for interpretation.

[0095] In the above Step S1, when obtaining the original data and measurement data of transient electromagnetic method for the coal mine area to be measured, it includes:

[0096] Select a suitable transient electromagnetic exploration magnetic source device to emit a primary field to the coal mine area to be measured;

[0097] Under the excitation of the primary field, an induced eddy current is generated in the coal mine area to be measured, and a transient electromagnetic secondary field is generated as time changes;

[0098] Receive the transient electromagnetic secondary field through the transient electromagnetic exploration magnetic source device, and obtain an equivalent induced voltage of transient electromagnetic method according to the transient electromagnetic secondary field;

[0099] Convert the induced voltage of transient electromagnetic method into apparent resistivity, apparent longitudinal conductance and apparent depth.

[0100] Furthermore, when obtaining the equivalent induced voltage of transient electromagnetic method according to the transient electromagnetic secondary field, it includes:

[0101] The transient electromagnetic secondary field is equivalent to the induced voltage of transient electromagnetic method generated by its action on the second loop. The induced voltage of transient electromagnetic method is proportional to the time derivative of the transient electromagnetic secondary field, specifically as shown in Formula 1:

[0102] V2(t)∝(e-t / τ ) / τ (1);

[0103] Where τ is the time constant of decay, the lower the resistivity, the larger the time constant, t is the decay time, and e is the natural constant;

[0104] Among them, the selected transient electromagnetic exploration magnetic source device is a co-located device. The co-located device includes a first loop and a second loop. The first loop is used to emit the primary field, and the second loop is used to receive the transient electromagnetic secondary field. The second loop has resistance and inductance.

[0105] Specifically, the transient electromagnetic exploration magnetic source device includes a co-located device, a dipole-dipole device, and a fixed-source loop device. The co-located device uses a square loop (1 turn or multiple turns) to emit the primary field, and uses another loop (1 turn or multiple turns) or a probe to receive the transient electromagnetic secondary field. The center points of the transmitting loop (the first loop) and the receiving loop (the second loop) coincide. Moving the transmitting loop and the receiving loop simultaneously indicates the completion of data acquisition at 1 measurement point. The co-located device includes an overlapping loop and a central loop. The difference between the overlapping loop and the central loop is that the laid receiving loop also coincides with the transmitting loop (the actual distance is less than 5 meters).

[0106] Preferably, the co-located device is an overlapping loop device of a multi-turn small wire frame, specifically as Figure 1 shown.

[0107] Furthermore, when converting the induced voltage of the transient electromagnetic method into apparent resistivity, it includes:

[0108] Obtaining the apparent resistivity according to the effective area of the receiving coil in the second loop, specifically as shown in formula 2:

[0109]

[0110] Where t is the time window time, m is the transmitting magnetic moment, q is the effective area of the receiving coil, V(t) is the induced voltage, and u0 is the vacuum permeability.

[0111] Furthermore, when converting the induced voltage of the transient electromagnetic method into apparent longitudinal conductance and apparent depth, it includes:

[0112] Obtaining the apparent longitudinal conductance and apparent depth according to the area of the first loop, specifically as shown in formula 3 and formula 4:

[0113]

[0114]

[0115] Where V(t) / I is the normalized induced voltage, A is the area of the first loop, and d(V(t) / I) / dt is the derivative of the normalized induced voltage with respect to time.

[0116] Specifically, the abnormal morphology and amplitude on the conductive thin plate are related to the dip angle of the conductor. Specifically, as Figure 2 shown, when the dip angle is 90°, due to the poor coupling between the loop and the conductor, the abnormal response is small, and the abnormal morphology is a double peak symmetric to the top of the conductor. Except for the differences in abnormal amplitude and range, the curves of different channels have the same characteristics as described above.

[0117] When the dip angle is less than 90°, as the dip angle decreases, the coupling between the loop and the conductor increases, and the abnormal response increases accordingly. However, the double peak is asymmetric, and the peak value on the side of the conductor dip is greater than the other side. The minimum value slightly increases as the dip angle decreases, and its position also moves slightly towards the anti-dip side.

[0118] In addition, the response of the transient electromagnetic secondary field is also affected by the conductive surrounding rock and the conductive overburden. The transient electromagnetic response of the conductive surrounding rock can be equivalent to the "smokering effect", that is, the electromagnetic induction secondary field generated by the conductive surrounding rock can be equivalent to the electromagnetic fields generated by a series of "current loops" that propagate deep into the earth and increase in radius with time. The existence of the current loops will generate electromagnetic induction on the inhomogeneous water-bearing geological body. The influence of the conductive surrounding rock on the transient electromagnetic response of the water-bearing geological body has four aspects:

[0119] (1) The response of the "circulating current" in the semi-infinite conductive surrounding rock;

[0120] (2) The current concentration effect generated by the convergence of the "current loops" in the surrounding rock towards the water-bearing geological body;

[0121] (3) The response of the "current loops" to the excitation of the water-bearing geological body to generate induced eddy currents;

[0122] (4) The induced polarization effect generated by the excitation of the "current loops" on the water-bearing geological body.

[0123] The relative relationship of these four additional responses is dominated by the resistivity of the conductive surrounding rock. When the resistivity of the surrounding rock is small, the energy of the current loops generated by the "smokering effect" is strong, and the current concentration effect and the induced polarization effect play a dominant role. The transient electromagnetic induction voltage of the overlapping loop on the water-bearing geological body has an abnormal characteristic of low in the middle and high on both sides. The current concentration effect and the induced polarization effect will make this characteristic more obvious. Negative transient electromagnetic secondary fields can often be seen in strongly water-bearing goafs and geological structures.

[0124] In the above step S3, the specific explanations for drawing the corresponding multi-channel curve graph and apparent resistivity pseudo-section graph are as follows:

[0125] After the coal seam in the strata is mined, a certain space is formed between the underground rock strata. The strata above the goaf collapse under the action of gravity, causing the overlying rock mass of the coal seam to lose its original balance state and undergo a certain degree of rock movement, destroying the integrity and continuity of the rock, resulting in the fragmentation of the strata and the appearance of a large number of voids and fractures. Specifically, as Figure 3 shown, when the voids in the goaf area are filled with water or mud, the resistivity at this place will be significantly lower than that of the surrounding intact rock, showing certain low-resistance characteristics. The goaf area is demarcated according to this electrical anomaly response.

[0126] Taking Research Area 1 as an example, the goaf is located in Coal Seam No. 9, with a depth of about 170 m. Figure 4 is the transient electromagnetic depth-apparent resistivity curve of Research Area 1. It can be seen from Figure 4 that the depth of Coal Seam No. 9 is 170 m, located between the 11th and 12th survey channels. Figure 5 is the transient electromagnetic multi-channel curve above the known goaf. It can be seen from Figure 5 that near the 11th survey channel, at the stake numbers of 600 - 1100 m, the induced voltage curve has obvious continuous undulating characteristics compared with other positions, proving that the electrical property here is uneven, which is the response characteristic of the goaf water accumulation area.

[0127] Figure 6 is the apparent resistivity section and geological profile of the known goaf. It can be seen from Figure 6 that the apparent resistivity near Coal Seam No. 9 at the stake numbers of 600 - 1100 m is significantly lower than that of other positions in the same layer, and there is an obvious low-resistance area above. After analysis, it is considered that there is water accumulation in the goaf of Coal Seam No. 9 here. After the goaf is mined, the roof collapses, resulting in a certain height of the "three zones" above the goaf. The increase in rock fractures leads to enhanced water content, and an obvious "shadow area" appears above, which also becomes an obvious characteristic for explaining the water accumulation in the goaf.

[0128] In the above-mentioned step S4, the specific interpretation of the transient electromagnetic bedding apparent resistivity slice according to the elevation of the coal seam is as follows:

[0129] Taking Research Area 1 as an example, there is a goaf in the northern part of the research area and a roadway in the northeastern part. As Figure 7 shown, the resistivity near the goaf and the roadway is lower than that of the surrounding area, and the resistivity in the northern part is even lower, which is consistent with the geological condition that the coal seam tilts northward. The goaf water accumulation area shows low-resistance characteristics in the bedding resistivity slice diagram.

[0130] Only from the bedding resistivity slice diagram, the location of the goaf water accumulation area can be roughly interpreted, but the boundary of the goaf and the location of the roadway cannot be accurately interpreted, and refined processing is required to improve the accuracy of the interpretation.

[0131] In the above-mentioned step S4, when selecting appropriate water-richness analysis methods and analysis parameters, it includes:

[0132] Analyze the transient electromagnetic bedding apparent resistivity slice data using different water-richness analysis methods, and determine the adopted water-richness analysis method according to the resolution and consistency of the mined-out water accumulation area.

[0133] Determine the number of points used for water-richness analysis based on the width of the mined-out area measured by radon measurement in activated carbon and the point spacing of the measuring points.

[0134] Determine the analysis parameters used for water-richness analysis according to the variation of the low-resistance anomaly area with the change of analysis parameters.

[0135] Specifically, the water-richness analysis method is to perform statistical analysis of water-richness on the transient electromagnetic bedding resistivity slice data by selecting different bins. The water-richness analysis methods include the average method, the variance method, and the extreme value method. The average method is a method that uses the average difference to measure the degree of difference between data. The average difference reflects the average difference between each data value and the arithmetic mean, and measures the central tendency of a set of data. The variance method is a measure of the degree of dispersion when measuring a set of data. The variance is the average of the sum of the squared deviations of each data from its arithmetic mean. The extreme value method refers to the difference between the maximum and minimum values within a set of data, reflecting the size of the change range of a set of data.

[0136] (1) Selection of water-richness analysis method

[0137] First, a comparative analysis was carried out on the three methods (average, variance, extreme value) of water-richness. Figure 8 is a comparative diagram of the three water-richness analysis methods. From Figure 8 it can be seen that the three methods have a good overall reflection on the low-resistance area anomaly, reflecting the consistency and effectiveness of the methods; then, combined with comprehensive analysis of geological data, it is considered that the extreme value water-richness analysis method has a higher resolution and better consistency for the mined-out water accumulation area. The subsequent work is carried out on the basis of this method.

[0138] (2) Testing and optimization of analysis parameters

[0139] Taking Research Area 1 as an example, the width of the known mined-out area in this research area is 260m, and the point spacing of the measuring points is 20m. It is inferred that using 13 points for water-richness analysis has better effects. During the research process, the parameters were fully tested and compared. As Figure 9 shown, as the parameter increases, the low-resistance anomaly area gradually becomes larger. The result of Parameter 13 fits well with the mined-out water accumulation area of the working face, and the result diagram of Parameter 7 fits well with the small mined-out water accumulation area generated by roadway tunneling.

[0140] In the above step S4, when obtaining the bedding apparent resistivity water-richness analysis map, it includes:

[0141] By adding varying components, the morphology of the abnormal area in the bedding apparent resistivity water-richness analysis map is depicted more meticulously;

[0142] The small-scale water-bearing abnormal bodies in the bedding apparent resistivity water-richness analysis map are depicted more clearly;

[0143] The boundary positions in the bedding apparent resistivity water-richness analysis map are corrected.

[0144] Specifically, water-bearing abnormal bodies mostly show low resistivity or sharp changes in resistivity in the apparent resistivity pseudo-section map. The varying component refers to the sharp change part of the resistivity. As Figure 10 shown, without adding the varying component, the overall contour of the anomaly can be seen. After adding the varying component, the morphology of the abnormal area is depicted more meticulously, the small-scale water-bearing abnormal bodies such as roadways and faults are depicted more clearly, and the boundary positions are corrected, making the position of the low-resistance area more consistent with the known mined-out water accumulation area and roadways.

[0145] In the above step S5, when filtering the bedding apparent resistivity water-richness fusion map using guided filtering, it includes:

[0146] Assume that the guided filter is a local linear model within a two-dimensional window between the guidance image and the filtered output;

[0147] Discriminate the edges and regions in the bedding apparent resistivity water-richness analysis map through the information provided by the guidance image, and when filtering, smooth the regions and retain the edges.

[0148] Specifically, in the process of transient electromagnetic data processing of the present invention, the inverted resistivity is single-point inversion. Affected by noise interference, the planar contour lines are messy, which affects the reflection of the mined-out water accumulation area. The present invention introduces the guided filtering technology into the refined processing to improve the detection accuracy of the coal seam mined-out water accumulation area.

[0149] Assume that there is some noise in the inverted resistivity. If we want to filter out this noise, the simplest and most basic method is to use some low-pass filters, such as simple smoothing (also called Box Filter) or Gaussian smoothing, etc. As Figure 11 shown, the resistivity value of the i-th point in the figure is obtained by averaging all the values in a window centered on the i-th point in the figure.

[0150] Whether it is simple smoothing or Gaussian smoothing, they all have a common weakness, that is, they all belong to isotropic filtering. The resistivity result map can be regarded as composed of (smooth transition, that is, small gradient) areas and (sharp transition, that is, large gradient) edges (also including image texture, details, etc.). Noise is an unfavorable factor affecting image quality, so it needs to be filtered out. The characteristic of noise is usually that the gradient is large in all directions centered on it, and the gradients are not much different. Edges are different. Edges will also have gradient changes compared to regions, but edges will only have large gradients in their normal direction, and small gradients in the tangential direction.

[0151] An important assumption relied on by the guided filter adopted by the present invention is that the guided filter is a local linear model in a two-dimensional window between the guided image and the filter output. This assumption is to hope that the information provided by the guided image is mainly used to indicate which are edges and which are regions. Therefore, during filtering, if the guided parameters tell us that this is within the region, then it is smoothed. If the guided image tells us that this is an edge, then in the final filtering result, we must try to retain this edge information. Figure 12 As shown, the input data p is filtered under the guidance of the guiding function I, and the output data q is finally obtained. In this way, the noise is removed and the anomaly is retained.

[0152] For isotropic filtering (such as simple smoothing or Gaussian smoothing), they treat noise and edge information in the same way. As a result, while the noise is removed, the important information indicating the water-bearing area in the resistivity is also smoothed out. However, the present invention, combined with guided filtering, can retain the overall trend of resistivity change while removing noise.

[0153] In the above step S6, when determining the final filtering parameters, it includes:

[0154] Determine the surface element parameters according to the ratio of the length, width and distance between the measuring points of the known water-bearing geological body;

[0155] Determine the regularization parameter based on the ratio between the resistivity containing water and the resistivity containing no water obtained from the test in the known area;

[0156] Among them, the filtering parameters include surface element parameters and regularization parameters, and the water-bearing geological bodies include water accumulation in goafs and water accumulation in tunnels.

[0157] Specifically, some data from a mine research area in a certain research area were selected for testing. Figure 13 This is the original layer resistivity slice map before filtering. The map is processed with guided filtering of 3, 5, 7 bins and regularization parameters of 0.02, 0.04, and 0.08. The processing effect is shown in Figure 14.Depend on Figure 14 It can be seen that the processing effect is controlled by the bin and the regularization parameter. As the regularization parameter increases, the image details become blurred, and as the bin increases, the image becomes smoother. Therefore, it is necessary to select appropriate parameters to preserve the details and make the image conform to geological laws.

[0158] Figure 15 is a comparison chart between guided filtering and grid filtering, Figure 15 It can be seen that when the number of facets is 5, the guided filter retains some details due to the limitation of the regularization parameters, while the grid filter directly smoothes the data, resulting in large differences between some areas and the original image, filtering out some useful information. When the number of facets is 7, the filtering difference is even greater.

[0159] Figure 16 is a comparison of the residuals of guided filtering and gridded filtering, Figure 16 It can be seen that compared with the original data, the residual of the guided filter is about ±22, and the residual of the gridded filter is -45 to 35. This shows that compared with the guided filter, the gridded filter indiscriminately filters out or smoothes the areas with large local changes, resulting in unclear boundaries of water accumulation in the goaf, which is not conducive to the interpretation of the scope of water accumulation in the goaf.

[0160] According to the above guidance filter analysis, combined with the geological information of the goaf, such as Figure 17 As shown, after comparing surface element 3 and regularization parameters of 0.01 to 0.04, surface element 3 and regularization parameter 0.03 are selected as filtering parameters, which have good smoothness and retain the goaf boundary information, so as to facilitate the subsequent refined processing and interpretation of the goaf waterlogging area.

[0161] The refined processing system for coal mine waterlogging area detection data provided by the present invention comprises:

[0162] Data acquisition unit: used to obtain transient electromagnetic raw data, measurement data and activated carbon radon detection data of the coal mine area to be tested;

[0163] Drawing unit: used to perform statistical analysis on the activated carbon radon measurement data, draw the activated carbon radon measurement plane diagram, perform preliminary processing on the transient electromagnetic raw data and measurement data, and obtain the multi-channel curve; select the channel range, remove the distorted data and perform filtering, and then draw the corresponding multi-channel curve diagram and apparent resistivity pseudo-section diagram;

[0164] Analysis unit: It is used to perform transient electromagnetic bedding apparent resistivity slicing according to the multi-channel curve diagram and apparent resistivity pseudo-section diagram and the elevation of the coal seam, analyze the characteristics of the goaf, select the appropriate water-rich analysis method and analysis parameters, and obtain the bedding apparent resistivity water-rich analysis diagram;

[0165] Filtering unit: It is used to fuse the data of the bedding apparent resistivity water-richness analysis map and the activated carbon radon measurement plan view to obtain the bedding apparent resistivity water-richness fusion map. Different filtering parameters are selected according to the activated carbon radon measurement analysis results or known geological conditions. The bedding apparent resistivity water-richness fusion map is filtered using guided filtering to obtain the bedding apparent resistivity water-richness fusion map after filtering with different parameters and analyze it. After repeated calibration, the final filtering parameters are determined. According to the final filtering parameters, the final bedding apparent resistivity water-richness fusion map for interpretation is obtained using guided filtering.

[0166] The following embodiments further illustrate the present invention, but the scope of the present invention is not limited thereto.

[0167] Embodiment 1

[0168] The coal-bearing strata in Research Area 1 mainly include the Upper Taiyuan Formation of the Carboniferous System and the Lower Shanxi Formation of the Permian System. The target layer of this exploration, the 9-1# coal seam, is located in the upper part of the Taiyuan Formation, with a burial depth of about 170 m and an average coal seam thickness of 5 m. In 2009, Research Area 1 was integrated from multiple coal mines. Before integration, the 9-1# coal seam was mined. Inclined shafts were used for development, wall mining was adopted, coal was cut by blasting, caving supports were used for the working face, and the full caving method was used to manage the roof, forming roadways and goafs of different areas. After integration, this area has not been mined, and there is water accumulation in the goaf. According to the collected data, only the 9-1# coal seam in this area has been mined and there is a goaf, specifically as Figure 18 shown. There is a large goaf in the southern part of the research area, with a width of about 260 m. Small roadway goafs exist in the western, central, and eastern parts of the research area respectively.

[0169] For exploration, a transient electromagnetic multi-turn small wire loop overlapping loop device was used. The transmitting coil has 15 turns, 2 m × 2 m, and the receiving coil has 10 turns, 1 m × 1 m. The measurement window is the SIROTEM time series 1-26, the gain is 32, the number of superpositions is 512, the current is 6.1 amperes, and the measurement delay is 80 microseconds.

[0170] The survey lines are arranged in the east-west direction, and the survey grid is 40 m (line spacing) × 20 m (point spacing). A total of 35 survey lines and 3,370 survey points were completed, specifically as Figure 19 shown.

[0171] By analyzing the collected geological data, there is a large goaf in the southern part of the research area, with a width of about 260 m, as Figure 19 shown. The point spacing of the transient electromagnetic method is 20 m, and the width of the goaf is about 13 survey point distances. The extreme water-richness method is selected, and the water-richness parameter is 13. In addition, small roadway goafs exist in the western, central, and eastern parts of the research area respectively. In order to take into account the abnormal reflection of the roadway goaf, the method of adding variable components is selected for water-richness analysis. The results of the bedding resistivity water-richness analysis are as Figure 20 shown.

[0172] Perform guided filtering with a regularization parameter of 0.03 on the water-richness analysis results for grid element 3 to obtain the results of this fine processing. Figure 21 It is a comparison chart of the water-richness analysis results of the bedding resistivity. Figure 21 It can be seen that compared with the original processing results, after fine processing, the west boundary position of the southern goaf is depicted more clearly, the strip shape of the roadway position is more obvious, the positions of the eastern roadway and the goaf are more consistent with the actual situation, and the boundary is clear. According to Figure 18 the coal seam floor contour lines shown, the overall trend is low in the north and high in the south, and it is easier to accumulate water in the deep part. Therefore, the anomalies in the north are more obvious.

[0173] Figure 22 It is the comprehensive result map of goaf water accumulation in Study Area 1. According to the fine processing this time, 7 interpreted anomaly areas are named 9-1 to 9-7 respectively. The location of anomaly area 9-1 is at the known goaf in the south, and it is interpreted as goaf water accumulation. Compared with the original interpretation results, the scope of this interpretation is more consistent with the known goaf, and the original interpreted anomaly area extends outside the southwest boundary of the goaf; anomaly areas 9-2, 9-3, 9-4, and 9-5 are all located at the known roadways. Compared with the original interpretation results, the scope of the interpreted anomaly areas is more consistent with the trend of the roadway; anomaly areas 9-6 and 9-7 are located at the eastern goaf, and the anomaly scope is depicted more precisely. The original interpreted anomaly areas combine these two areas into one, and the interpreted anomaly area scope is much larger than the known goaf. The comparison results of the goaf water accumulation area before and after refined processing are shown in Table 1.

[0174] Table 1 List of goaf water accumulation areas in Study Area 1 before and after refined processing

[0175]

[0176]

[0177] Example 2

[0178] In Study Area 2, a transient electromagnetic multi-turn small wire frame overlapping loop device is used. The transmitting coil has 15 turns with a size of 2m×2m, and the receiving coil has 20 turns with a size of 1m×1m. The measurement window is the SIROTEM time series 1-26, the gain is 32, the stacking times are 256, the current is 5.6 amperes, and the measurement delay is 40 microseconds. For the radon measurement with activated carbon, the buried time of the activated carbon collector is 4 days, and the measurement time is 3 minutes.

[0179] The survey lines are arranged in the east-west direction, and the survey grid is 20m (line spacing)×10m (point spacing). A total of 23 survey lines and 550 survey points are completed. The survey points of the two measurement methods coincide. The actual material map of Study Area 2 is as Figure 23 shown.

[0180] By analyzing the known goaf, the width (north-south) of the goaf is about 200m, the point distance is 20m, and the goaf range is about 11 measuring points wide. The extreme water-richness method is selected, and the water-richness parameter is 11. The variable components are added for water-richness analysis, and the bedding apparent resistivity water-richness analysis map of Research Area 2 is obtained, as Figure 24 shown.

[0181] The formation physical properties reflected by transient electromagnetic and activated carbon radon measurement data are different. Due to the limitations of a single method, the data processing adopts the attribute fusion processing method to improve the accuracy of predicting water-bearing abnormal areas. In this data fusion processing, the activated carbon radon measurement data, transient electromagnetic induction voltage data, and bedding resistivity data are used for fusion and analysis to obtain the bedding apparent resistivity water-richness fusion map ( Figure 25 right). The high-value parts in the figure are the areas where water-bearing anomalies develop.

[0182] Based on the bedding apparent resistivity water-richness fusion map ( Figure 26 left), guided filtering processing is carried out. By analyzing the goaf situation interpreted by activated carbon radon measurement, guided filtering with a bin size of 3 and a regularization parameter of 0.15 is performed on the attribute fusion map to obtain the final bedding apparent resistivity water-richness fusion map for interpretation ( Figure 26 right).

[0183] There are goaf data in the northern part of Research Area 2, and the data in the southern part is missing. A total of 3 goafs are interpreted through activated carbon radon measurement data. Among them, CK-3 basically coincides with the known goaf, CK-2 partially coincides with the known goaf, and CK-1 is the interpreted goaf. 5 water-rich areas are interpreted in the research area, namely 5-1 to 5-5 ( Figure 27 ). Among them, the water-rich area 5-1 is located in the low-lying area of the known goaf and is interpreted as water accumulation in the goaf; the water-rich areas 5-3 and 5-4 are located in CK-2. Through the known data, it is known that the two water-rich areas are located on the roadway or the extended line of the roadway, and the situation of cross-border mining cannot be excluded, so it is inferred that there is water accumulation in the mined-out area of the roadway; the water-rich areas 5-1 and 5-2 are located in CK-1 and are inferred to be suspected water accumulation areas in the mined-out area.

[0184] After verification, the water-rich areas 5-1, 5-3, and 5-5 are water accumulations in the mined-out area, and the water-rich areas 5-2 and 5-3 are not verified.

[0185] Compared with the existing technology, the beneficial effects of the present invention are as follows:

[0186] (1) The present invention combines guided filtering for refined processing. Compared with the existing isotropic filtering, it can remove noise while retaining the overall trend of resistivity changes, thereby improving the accuracy of interpreting water accumulation areas in coal mine goafs;

[0187] (2) When analyzing the water-rich property, the present invention combines variable components, depicts the abnormal area more meticulously, depicts the water-bearing abnormal bodies with smaller scales such as roadways and faults more clearly, and corrects the boundary positions, making the low-resistance area more consistent with the positions of the actual goaf water accumulation area and the roadway;

[0188] (3) Compared with the interpretation results obtained by the existing processing methods, the interpretation scope of the interpretation results obtained by using the refined processing method provided by the present invention is more consistent with the actual goaf;

[0189] (4) Compared with the interpretation results obtained by the existing processing methods, the interpretation results obtained by using the refined processing method provided by the present invention have the interpretation abnormal area range more consistent with the roadway trend, and the abnormal range is depicted more precisely.

[0190] The above embodiments are only the preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantive changes and substitutions made by those skilled in the art on the basis of the present invention fall within the scope of protection required by the present invention.

Claims

1. A refined processing method for detecting data in a goaf water accumulation area of a coal mine, characterized in that, It includes the following steps: Obtain the transient electromagnetic raw data, measurement data, and activated carbon radon measurement data of the coal mine area to be measured; Conduct statistical analysis on the activated carbon radon measurement data, draw a plan view of activated carbon radon measurement, and preliminarily process the transient electromagnetic raw data and the measurement data to obtain a multi-channel curve; Select the channel range, remove distorted data and perform filtering, and then draw the corresponding multi-channel curve diagram and apparent resistivity pseudo-section diagram; According to the multi-channel curve diagram and the apparent resistivity pseudo-section diagram, and perform transient electromagnetic bedding apparent resistivity slicing according to the elevation of the coal seam. By analyzing the characteristics of the goaf water accumulation area, select appropriate water-richness analysis methods and analysis parameters to obtain a bedding apparent resistivity water-richness analysis diagram; Fuse the data of the bedding apparent resistivity water-richness analysis diagram and the activated carbon radon measurement plan view to obtain a bedding apparent resistivity water-richness fusion diagram. Select different filtering parameters according to the activated carbon radon analysis results or known geological conditions, use guided filtering to filter the bedding apparent resistivity water-richness fusion diagram, obtain the bedding apparent resistivity water-richness fusion diagram after filtering with different parameters and analyze it. After repeated calibration, determine the final filtering parameters; According to the final filtering parameters, use the guided filtering to obtain the final bedding apparent resistivity water-richness fusion diagram for interpretation.

2. The refined processing method for the detection data of water accumulation areas in coal mine goafs according to claim 1, characterized in that, When obtaining the transient electromagnetic raw data and measurement data of the coal mine area to be measured, it includes: Select a suitable transient electromagnetic exploration magnetic source device to emit a primary field to the coal mine area to be measured; Under the excitation of the primary field, an induced eddy current is generated in the coal mine area to be measured, and a transient electromagnetic secondary field is generated as time changes; Receive the transient electromagnetic secondary field through the transient electromagnetic exploration magnetic source device, and obtain an equivalent transient electromagnetic method induced voltage according to the transient electromagnetic secondary field; Convert the transient electromagnetic method induced voltage into apparent resistivity, apparent longitudinal conductance, and apparent depth.

3. The refined processing method of the detection data of the goaf water accumulation area in coal mines according to claim 2, characterized in that, When obtaining an equivalent transient electromagnetic method induced voltage according to the transient electromagnetic secondary field, it includes: The transient electromagnetic secondary field is equivalent to the transient electromagnetic method induced voltage generated by its action on the second loop. The transient electromagnetic method induced voltage is proportional to the time derivative of the transient electromagnetic secondary field, specifically as shown in Formula 1: V 2 (t)∝(e -t / τ ) / τ （1）； In the formula, τ is the time constant of attenuation. The lower the resistivity, the larger the time constant. is the attenuation time, is the natural constant; Wherein, the selected transient electromagnetic exploration magnetic source device is a co-located device. The co-located device includes a first loop and a second loop. The first loop is used to emit the primary field, and the second loop is used to receive the transient electromagnetic secondary field. The second loop has resistance and inductance.

4. The refined processing method for detecting data of accumulated water areas in coal mine goafs according to claim 3, characterized in that, When converting the transient electromagnetic method induced voltage into apparent resistivity, it includes: Obtain the apparent resistivity according to the effective area of the receiving coil in the second loop, specifically as shown in Formula 2: （2）； In the formula, is the window time, is the transmitting magnetic moment, is the effective area of the receiving coil, is the induced voltage, is the permeability of vacuum.

5. The refined processing method for detecting data of accumulated water areas in coal mine goafs according to claim 4, wherein When converting the transient electromagnetic method induced voltage into apparent longitudinal conductance and apparent depth, it includes: Obtain the apparent longitudinal conductance and the apparent depth according to the area of the first loop, specifically as shown in Formulas 3 and 4: （3）； （4）； In the formula, is the normalized induced voltage, is the area of the first loop, is the derivative of the normalized induced voltage with respect to time.

6. The refined processing method for the detection data of water accumulation areas in coal mine goafs according to claim 1, characterized in that When selecting appropriate water-richness analysis methods and analysis parameters, it includes: Different water-richness analysis methods are used to analyze the transient electromagnetic bedding resistivity slice data, and the water-richness analysis method used is determined based on the resolution and consistency of the mined-out waterlogging area. Determine the number of points used for water-richness analysis based on the width of the goaf waterlogging area and the distance between the measuring points; The analysis parameters used for water-richness analysis are determined based on the changes in the low-resistance anomaly area as the analysis parameters change.

7. The refined processing method for the detection data of the mined-out water accumulation area in coal mines according to claim 1, characterized in that When obtaining the layer-by-layer apparent resistivity water-richness analysis diagram, it includes: By adding variable components, the abnormal area morphology in the layer-by-layer apparent resistivity water-richness analysis diagram is more carefully described; To more clearly depict the small-scale water-bearing anomaly in the bedding apparent resistivity water-richness analysis diagram; The boundary position in the layer-by-layer apparent resistivity water-richness analysis diagram is corrected.

8. The refined processing method for the detection data of the goaf water accumulation area in coal mines according to claim 1, characterized in that When the guide filter is used to filter the layer-by-layer apparent resistivity water-rich fusion map, it includes: Assume that the guided filter is a local linear model in a two-dimensional window between the guidance image and the filtered output; The edge and area in the layer-by-layer apparent resistivity water-richness analysis diagram are identified by the information provided by the guide image, and when filtering, the area is smoothed and the edge is retained.

9. The refined processing method for detecting data of accumulated water areas in coal mine goafs according to claim 1, characterized in that When determining the final filtering parameters, include: Determine the surface element parameters according to the ratio of the length, width and distance between the measuring points of the known water-bearing geological body; Determine the regularization parameter based on the ratio between the resistivity containing water and the resistivity containing no water obtained from the test in the known area; Wherein, the filtering parameters include the surface element parameters and the regularization parameters, and the water-bearing geological body includes water accumulation in the mined-out area and water accumulation in the tunnel.

10. A refined processing system for detecting data in a goaf water accumulation area of a coal mine, characterized in that, include: Data acquisition unit: used to obtain transient electromagnetic raw data, measurement data and activated carbon radon detection data of the coal mine area to be tested; Drawing unit: used for statistical analysis of activated carbon radon measurement data, drawing activated carbon radon measurement plane diagram, performing preliminary processing on the transient electromagnetic raw data and the measurement data, and obtaining a multi-channel curve; After selecting the measurement channel range, eliminating the distorted data and filtering, the corresponding multi-channel curve diagram and apparent resistivity pseudo-section diagram are drawn; Analysis unit: used for performing transient electromagnetic bedding apparent resistivity slicing according to the multi-channel curve diagram and the apparent resistivity pseudo-section diagram and the elevation of the coal seam, analyzing the characteristics of the goaf waterlogging area, selecting a suitable water-richness analysis method and analysis parameters, and obtaining a bedding apparent resistivity water-richness analysis diagram; Filtering unit: used for fusing the data of the layer apparent resistivity water-rich analysis map with the activated carbon radon measurement plane map to obtain a layer apparent resistivity water-rich fusion map, selecting different filtering parameters according to the activated carbon radon measurement analysis results or known geological conditions, filtering the layer apparent resistivity water-rich fusion map by using guided filtering, obtaining the layer apparent resistivity water-rich fusion map after filtering with different parameters and analyzing it, and determining the final filtering parameters after repeated corrections; according to the final filtering parameters, obtaining the final layer apparent resistivity water-rich fusion map for interpretation by using the guided filtering.

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