Coal mine extremely shallow goaf geophysical prospecting data analysis method based on transient electromagnetic method

Abstract

The invention provides a coal mine extremely shallow goaf geophysical prospecting data analysis method based on a transient electromagnetic method, which is characterized by comprising the following steps of: optimizing data acquisition parameters and correcting terrain; data preprocessing and noise suppression are carried out, multi-stage preprocessing is carried out on the original data, and preprocessing comprises outlier elimination, filtering noise reduction, null drift correction and background field deduction; time-depth conversion and resistivity inversion are carried out; quantitative analysis of goaf response characteristics; carrying out three-dimensional data fusion and visual modeling, integrating multi-measuring-line data and geological information, and constructing a goaf space distribution model; and the conclusion reliability is improved through field drilling verification and error source analysis. Aiming at the detection difficulty of an extremely shallow goaf, electromagnetic response details of a shallow medium are captured through small coil high-frequency excitation and microsecond-level signal acquisition. The terrain laser correction and the whole-period apparent resistivity inversion are combined, so that the tiny holes are accurately identified; and through drilling entity verification and error simulation, the misjudgment rate is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mining, in particular to a method for analyzing geophysical data of extremely shallow goaf areas in coal mines based on a transient electromagnetic method. Background Art

[0002] Detecting extremely shallow goaf in coal mines can timely identify and assess the location, scope and stability of these shallow underground cavities to prevent potential safety risks such as ground collapse, surface subsidence or gas explosion accidents, and ensure the safety of coal mine workers and surrounding residents; at the same time, this helps to monitor the impact of goaf on the surface environment, guide subsequent mining optimization, reclamation and management or safety sealing measures, and avoid waste of resources and operation interruption.

[0003] Therefore, geophysical data analysis of extremely shallow goafs in coal mines can significantly improve the safety level of coal mine production and reduce casualties and economic losses. Transient electromagnetic method is a time-domain geophysical exploration technology based on the principle of electromagnetic induction. It emits a pulsed electromagnetic field into the ground. During the pulse interval, the primary field is instantly turned off. According to Faraday's law of electromagnetic induction, an eddy current field that decays with time is induced in the underground conductor; these eddy current fields in turn generate a secondary induced electromagnetic field that changes with time and propagates toward the surface. The induced electromotive force and attenuation characteristics of the secondary field are measured by the ground receiving coil, and the electrical structure and geological structure of the medium at different depths underground can be inferred. It is applied to the fields of mineral resource exploration, hydrogeological survey, engineering geological survey and environmental geological survey.

[0004] However, existing technologies often use large coils and low-frequency currents for detection, resulting in the loss of early signals, weak recognition capabilities for goafs less than 50 meters, and difficulty in distinguishing goafs from shallow soil variations; at the same time, conventional filtering only uses smoothing or fixed notches, which cannot effectively suppress strong electromagnetic noise in mining areas such as inverter harmonics and track leakage, resulting in a high false alarm rate; geophysical exploration lacks effective additional verification methods, and geophysical exploration results cannot be used to correct the model in real time, resulting in errors in the positioning of goaf boundaries, and the design of prevention and control plans still relies on manual experience to judge risks. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a geophysical data analysis method for extremely shallow goafs in coal mines based on transient electromagnetic method, so as to solve the problems raised in the above-mentioned background technology. The present invention is aimed at the detection difficulties of extremely shallow goafs. This method effectively captures the electromagnetic response details of shallow media through high-frequency excitation of small coils and microsecond signal acquisition. Combined with terrain laser correction and full-period apparent resistivity inversion, the shallow layer resolution can be improved to 0.5 meters, and tiny cavities or water-bearing areas can be accurately identified; through drilling entity verification and error simulation, the uncertainty of the inversion results is quantified to a confidence interval, significantly reducing the misjudgment rate. The signal-to-noise ratio can still be maintained under strong interference environment in the mining area.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solution: a method for analyzing geophysical data of extremely shallow goaf areas in coal mines based on transient electromagnetic method, comprising the following steps: S1. Data acquisition parameter optimization and terrain correction. Small-sized transmitting coils and high-frequency transmitting currents were laid out. Measurement points were set up in a high-density measurement network. Small transmitting coils were laid on the surface in conjunction with high-frequency transmitters. Multi-component receiving coils were synchronously laid out using a central loop device. The coordinates and elevation of each measuring point were recorded. Bipolar square wave currents with an intensity of 20-50A were used for excitation. The off-time was controlled at 1-5μs to reduce shallow blind spots. The receiving system continuously recorded the secondary field attenuation signal in a time window of 0.01-10ms with an initial sampling interval of 0.01ms. The data was collected 3-5 times at each measuring point, and the signal-to-noise ratio was monitored in real time. S2, data preprocessing and noise suppression, multi-level preprocessing of the raw data, the preprocessing includes outlier removal, filtering noise reduction, zero drift correction and background field subtraction; S3, time-depth conversion and resistivity inversion, converts time-domain induced electromotive force data into depth-resistivity parameters, including time-depth conversion, inversion modeling and anomaly extraction; S4. Quantitative analysis of goaf response characteristics: establishing goaf identification indicators based on geological conditions and electromagnetic response characteristics. The identification indicators include attenuation curve analysis, multi-parameter fusion, and anisotropy analysis. S5. 3D data fusion and visualization modeling: integrating multi-line data with geological information to construct a spatial distribution model of goaf areas. This includes data interpolation, multi-source data fusion, and 3D visualization processing. This generates resistivity contour surfaces, annotates goaf locations, roof and floor depths, and scale parameters, and outputs a risk level zoning map. Scale parameters include volume and extension direction. S6. Improve the reliability of conclusions through field drilling verification and error source analysis. Select typical abnormal areas for drilling sampling. Compare the boundaries of the goaf interpreted by geophysical exploration with the actual exposure. Calculate positioning errors. Quantify the impact of noise interference, inversion parameter selection, and terrain simplification assumptions on the results. Simulate and evaluate the uncertainty distribution.

[0007] Furthermore, in step S1, a high-density survey network is designed and implemented based on the topographic map of the survey area, with a grid spacing ranging from 5×5 meters to 10×10 meters. The three-dimensional coordinates of each measuring point are accurately calibrated on-site using a GPS locator or a total station. At the same time, a small square transmitting coil is laid at the transmitting point. The length of the small square transmitting coil varies from 10m to 20m, and its position is adjusted so that the coil plane is flat and close to the ground surface. A multi-component receiving coil is synchronously laid out in conjunction with the central loop device, and is fixed on a special bracket and aligned with the center of the transmitting coil to maintain horizontality and directional consistency. During operation, the azimuth error of the point and the grounding status of the coil are checked in real time. The azimuth error is less than 1°. The surface undulation data is recorded by a laser rangefinder, and all positioning information is transmitted to the data acquisition system through a wireless module for storage to form a standardized measuring point data set. The entire process is verified on-site in real time.

[0008] Furthermore, during the coil laying process, the flatness and tightness of the coil is checked, and the inspection process includes: High-precision dual-axis tilt sensors are installed at five locations, including the four corners and the center point of the transmitting coil frame. The dual-axis tilt sensor has a range of ±5° and a resolution of 0.01°. The real-time tilt data is transmitted to the handheld terminal via the Bluetooth module. If the horizontal tilt angle at any point is greater than 0.3°, an alarm is triggered. A laser rangefinder is used to scan the gap between the coil and the ground surface at a grid interval of 20 cm, and the gap area exceeding 3 mm is marked and located. Three groups of elastic pressure-sensitive sheets are laid at equal intervals under the coil during laying. The thickness of each group of elastic pressure-sensitive sheets is 2 mm, 2 mm, and 3 mm, respectively. 5mm, 10mm. If the 10mm pressure-sensitive sheet is not completely compressed after pressure is applied, it is determined that there is partial suspension. After the coil is laid out, a 50kg counterweight is applied for 10 minutes, and the settlement stability is monitored by the inclination sensor. If the settlement fluctuation is greater than 0.1°, it needs to be re-leveled. Finally, a real-time impedance monitoring module is embedded in the data acquisition system. If the impedance suddenly increases or the time domain attenuation curve shows high-frequency oscillation greater than 5kHz, it is automatically determined as poor contact. All verification data are stored synchronously with the main measurement data to generate a coil coupling quality report for later inversion correction.

[0009] Furthermore, the laser rangefinder is used to eliminate the distortion effect of terrain on electromagnetic field signals. The process of recording surface undulation data includes: after the transient electromagnetic measuring points are laid out, a pulsed laser rangefinder is used to scan the terrain. The operator uses the current measuring point as the center and lays out 16 equi-angle radial scanning paths with a radius of 2 meters. The rangefinder is moved along each path with a step of 0.2 meters to obtain a total of 80 elevation sampling points; the laser beam is kept vertically downward during scanning, and the instrument tilt error is corrected in real time through the built-in tilt compensation module. The GPS positioning module is synchronously triggered to record the plane coordinates of each sampling point. The original distance data is transmitted to the tablet terminal in real time via Bluetooth on site, a millimeter-level digital elevation model is generated, and the terrain slope and curvature parameters are calculated; a point cloud density check is performed every time 5 measuring points are completed, and an automatic re-scanning program is started for the void area. Finally, the elevation data is linked with the transient electromagnetic measuring point coordinates to establish a spatial index, and a standardized data set with terrain correction parameters is output. The operation time for each measuring point is less than 3 minutes.

[0010] Furthermore, the outlier elimination adopts a dynamic threshold, with three times the standard deviation of the signal in the moving time window as the threshold, to automatically identify and delete abnormal jump points caused by high-voltage line pulses greater than 500mV or mechanical vibration; The background field deduction sets a benchmark point 20 meters outside the measurement area to collect pure formation response without interference from the goaf. After aligning the time domain waveforms using the cross-correlation algorithm, vector subtraction is performed from the measured data to increase the signal-to-noise ratio of low-resistance anomalies in water-bearing goafs by 3-8 times. A quality monitoring module is embedded to output parameters such as signal-to-noise ratio (SNR) greater than 20dB and waveform similarity greater than 0.85 in real time. The zero drift correction is based on the background magnetic field recorded by the reference coil, and the least square method is used to fit the baseline drift curve, and the offset is deducted point by point from the main signal. The baseline drift curve is linear or quadratic.

[0011] Furthermore, step S3 uses a full-period apparent resistivity iterative algorithm to convert the pre-processed induced electromotive force attenuation data, with a time range of 0.01-10ms, into depth-resistivity parameters: the initial apparent resistivity-depth curve is calculated through the formation model, and then constrained inversion modeling is performed to construct an unstructured three-dimensional grid containing terrain data, and the prior information revealed by the borehole is introduced as a regularization constraint to generate a resistivity profile; finally, intelligent anomaly extraction is performed to identify low-resistance anomaly areas, analyze and delineate the boundaries of the goaf, and output a three-dimensional resistivity anomaly body model with a burial depth error of ≤0.5 meters.

[0012] Furthermore, in step S4, the characteristics of the electromagnetic signal are analyzed to determine whether there is a goaf underground, including the following contents: obtaining the attenuation law of the electromagnetic wave. If the electromagnetic wave disappears quickly and the signal curve drops sharply, it is determined that the underground range of the current measurement area is a goaf, and multiple key indicators are extracted to assist in the judgment. The key indicators include the resistivity value of the fixed point, the severity of the magnetic field change, and the time difference when the signal reaches the peak value. The key indicator data are input into the analysis model to eliminate the error interference of individual data. After cross-validation in combination with known geological data, the range, depth and risk level of the goaf are marked.

[0013] Furthermore, step S5 is used to combine the scattered detection point data to generate a goaf map, including the following contents: S5.1. Input the resistivity data of hundreds of measurement points throughout the survey area into a computer, fill the gaps between points, and generate a continuous three-dimensional grid model to achieve a natural transition between the boundaries of the goaf; S5.2. Overlay and calibrate the resistivity model with known geological information to eliminate false anomalies; S5.3. Generate an intuitive 3D risk map: Use a blue gradient to represent formation resistivity, render confirmed goafs as semi-transparent purple voids suspended at the corresponding depth, and automatically annotate key parameters; generate a planar warning map, and use red, yellow, and green light areas to mark avoidance, monitored passage, and safety zone types.

[0014] 9. A method for analyzing geophysical data of extremely shallow goafs in coal mines based on transient electromagnetic method according to claim 8, characterized in that: during the field drilling verification process, 2-3 verification holes are drilled in the high-risk areas marked in the geophysical report, and the depth and size of the goaf actually revealed by the drilling are compared with the matching degree of the prediction model. If it is found that the predicted cavity is actually a rock crack, it is necessary to reversely check the data processing link; use a computer to simulate the detection process, quantify the influence of different interference factors, and finally generate an error distribution map, marking the credible range of the goaf boundary; adjust subsequent work according to the verification results, and the final report will not only mark the goaf location, but also attach a credibility score.

[0015] Furthermore, the process of constructing the verification hole is also included: based on the abnormal target area of ​​the goaf delineated by the transient electromagnetic method, typical verification points are selected on the geophysical profile, and the GPS positioning device is used for on-site layout, and the plane error is controlled to be ≤0.1 meters; a light geological drilling rig is used with a diamond drill bit to drill holes with a diameter of 75-110mm. Before drilling, the verticality of the drilling tower is calibrated with a total station. During the drilling process, parameters are recorded every 1 meter. The recorded parameters include: core recovery rate, flushing fluid consumption, drilling speed; when encountering the predicted goaf roof, the drilling rig is used to drill holes. When the depth reaches ±2 meters, the drilling speed is switched to slow drilling with a rotation speed of 1m / min. The water level, gas concentration and return slag composition in the hole are monitored in real time. After the goaf is exposed: downhole television or laser scanner is used to directly observe the height of the cavity and the filling material, which includes water, soil and coal slag; filling material samples are taken in layers to test the water content and density; if it is a cavity, the actual span is measured with a telescopic probe, and a PVC casing is inserted to protect the hole wall. A water injection test is carried out, and finally the hole is sealed to the surface with cement mortar, and the core catalog, image data and depth correction value are recorded simultaneously.

[0016] Beneficial effects of the present invention: This transient electromagnetic method for analyzing geophysical data in extremely shallow coal goafs addresses the challenges of detecting these areas. By utilizing high-frequency excitation from a small coil and microsecond-level signal acquisition, the method effectively captures the electromagnetic response details of shallow media. Combined with terrain laser correction and full-period apparent resistivity inversion, the method can improve shallow-layer resolution to 0.5 meters, enabling precise identification of tiny cavities or water-bearing areas.

[0017] 2. This transient electromagnetic method, based on geophysical data analysis for extremely shallow coal mine goafs, quantifies the uncertainty of inversion results to a confidence interval through borehole verification and error simulation, significantly reducing the false positive rate. It also maintains the signal-to-noise ratio even in high-interference environments within the mining area.

[0018] 3. This geophysical data analysis method for extremely shallow goafs in coal mines based on the transient electromagnetic method deeply integrates geological and geophysical data. By generating a three-dimensional resistivity model of the goaf and superimposing multi-source information such as drill hole lithology and seismic profiles, it achieves risk classification visualization and can also calculate the cavity volume, roof thickness, and water accumulation probability.

[0019] 4. This method can ensure that the coil plane is flat and close to the ground surface, so that signal stability is guaranteed. A ground impedance greater than 5Ω will introduce high-frequency oscillation noise. Tight fit can eliminate the additional impedance caused by the air gap, avoid signal jumps due to wind and rain vibrations, and ensure that the time domain attenuation curve is smooth and interpretable. The flat paving combined with laser ranging terrain correction can control the depth positioning error of the goaf within 0.5 meters. Therefore, the present invention uses the coil plane to be flat and close to the ground surface to improve the low-resistance anomaly amplitude and signal-to-noise ratio of the water-bearing goaf.

[0020] 5. The present invention provides an additional means of field drilling verification. Geophysical exploration methods such as transient electromagnetic method may be affected by stratum interference and produce false anomalies. Drilling can accurately distinguish between real goafs and geological interference bodies through core identification, direct imaging of cavities and filling material sampling, avoiding ineffective management investment caused by misjudgment. By comparing the geophysical predicted depth with the actual depth revealed by drilling, calculating the positioning error, and reversely calibrating the inversion algorithm parameters, the accuracy of subsequent detection is improved. During the drilling process, parameters such as gas outburst and water conductivity are monitored simultaneously to directly obtain first-hand data on the stability of the goaf and the risk of water inrush, providing support for the scope of grouting reinforcement. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of a method for analyzing geophysical data of extremely shallow goaf areas in coal mines based on transient electromagnetic method according to the present invention; Figure 2 This is a schematic diagram of the principle of geophysical data analysis of the present invention. DETAILED DESCRIPTION

[0022] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0023] See also Figures 1 to 2 The present invention provides the following technical solution: a method for analyzing geophysical data in extremely shallow coal mine goafs based on transient electromagnetic methods. This method, through high-resolution data acquisition, multi-parameter fusion modeling, and a dynamic verification mechanism, can effectively solve the problems of weak signals, high interference, and ambiguous interpretation in extremely shallow goaf detection, providing precise technical support for the prevention and control of hidden disasters in coal mines. Specifically, it includes the following steps: S1. Optimize data acquisition parameters and perform terrain correction. Lay small-sized transmitting coils and high-frequency transmitting currents. Set up measuring points in a high-density survey network. Lay small transmitting coils on the surface in conjunction with high-frequency transmitters. Use a central loop device to synchronously lay out multi-component receiving coils. Record the coordinates and elevation of each measuring point. Use a bipolar square wave current of 20-50A intensity to excite during transmission. The off time is controlled at 1-5μs to reduce shallow blind spots. The receiving system continuously records the secondary field attenuation signal in a time window of 0.01-10ms with an initial sampling interval of 0.01ms. Repeat the acquisition 3-5 times for each measuring point and monitor the signal-to-noise ratio in real time.

[0024] Based on the topographic map of the survey area, a high-density survey network was designed and implemented, with a grid spacing ranging from 5×5 meters to 10×10 meters. The three-dimensional coordinates of each measuring point were accurately calibrated on-site using a GPS locator or total station. At the same time, a small square transmitting coil with a variable length range of 10m to 20m was laid at the transmitting point. Its position was adjusted so that the coil plane was flat and close to the ground surface. In combination with the central loop device, a multi-component receiving coil was synchronously laid out and fixed on a special bracket to align with the center of the transmitting coil to maintain horizontality and directional consistency. During operation, the azimuth error of the point and the grounding status of the coil were checked in real time. The azimuth error was less than 1°. The surface undulation data was recorded using a laser rangefinder. All positioning information was transmitted to the data acquisition system through a wireless module for storage to form a standardized measuring point data set. The entire process was verified on-site in real time.

[0025] During the coil laying process, the flatness and tightness of the coil are checked. The inspection process includes: High-precision dual-axis tilt sensors are installed at five locations, including the four corners and the center of the transmitting coil frame. The dual-axis tilt sensors have a range of ±5° and a resolution of 0.01°. Real-time tilt data is transmitted to a handheld terminal via a Bluetooth module. If the horizontal tilt angle at any point is greater than 0.3°, an alarm is triggered. A laser rangefinder is used to scan the gap between the coil and the ground surface in a grid pattern with a spacing of 20 cm, marking and locating gaps exceeding 3 mm. During the laying process, three sets of elastic pressure-sensitive sheets are laid at equal intervals below the coils. The thickness of each set of elastic pressure-sensitive sheets is 2mm, 5mm, and 10mm respectively. If the 10mm pressure-sensitive sheet is not completely compressed after pressure is applied, it is determined that there is partial overhang. After the coils are laid, a 50kg counterweight is applied for 10 minutes. The settlement stability is monitored by an inclination sensor. If the settlement fluctuation is greater than 0.1°, re-leveling is required. Finally, a real-time impedance monitoring module is embedded in the data acquisition system. If the impedance suddenly increases or the time-domain attenuation curve shows high-frequency oscillation greater than 5kHz, it is automatically determined to be a poor contact. All verification data is stored synchronously with the main measurement data, and a coil coupling quality report is generated for later inversion and correction.

[0026] The laser rangefinder is used to eliminate the distortion effect of terrain on electromagnetic field signals. The process of recording surface undulation data includes: after the transient electromagnetic measuring points are laid out, a pulsed laser rangefinder is used to scan the terrain. The operator uses the current measuring point as the center and lays out 16 equi-angle radial scanning paths within a radius of 2 meters. The rangefinder is moved along each path with a step distance of 0.2 meters to obtain a total of 80 elevation sampling points; the laser beam is kept vertically downward during scanning, and the instrument tilt error is corrected in real time by the built-in tilt compensation module. The GPS positioning module is synchronously triggered to record the plane coordinates of each sampling point. The original distance data is transmitted to the tablet terminal in real time via Bluetooth on site, a millimeter-level digital elevation model is generated, and the terrain slope and curvature parameters are calculated; after every five measuring points are completed, a point cloud density check is performed, and an automatic re-scanning program is started for the void area. Finally, the elevation data is linked with the transient electromagnetic measuring point coordinates to establish a spatial index, and a standardized data set with terrain correction parameters is output. The operation time for each measuring point is less than 3 minutes. S2. Data preprocessing and noise suppression: performing multi-stage preprocessing on the raw data, including outlier removal, filtering noise reduction, zero drift correction and background field subtraction.

[0027] The outlier elimination adopts a dynamic threshold, taking three times the standard deviation of the signal in the moving time window as the threshold, to automatically identify and delete abnormal jump points caused by high-voltage line pulses greater than 500mV or mechanical vibration; Background field subtraction: A benchmark point is set 20 meters outside the measurement area to collect pure formation response without interference from goaf areas. After aligning the time domain waveforms using a cross-correlation algorithm, vector subtraction is performed from the measured data. This improves the signal-to-noise ratio of low-resistance anomalies in water-bearing goaf areas by 3-8 times. A quality monitoring module is embedded, which outputs parameters such as signal-to-noise ratio (SNR) greater than 20dB and waveform similarity greater than 0.85 in real time. Zero drift correction is based on the background magnetic field recorded by the reference coil. The baseline drift curve is fitted using the least squares method, and the offset is deducted point by point from the main signal. The baseline drift curve is linear or quadratic. S3, time-depth conversion and resistivity inversion, converts time-domain induced electromotive force data into depth-resistivity parameters, including time-depth conversion, inversion modeling and anomaly extraction: Time-to-depth conversion: Using the improved late-field formula or full-period apparent resistivity algorithm, combined with the formation propagation velocity model, calculate the apparent resistivity and equivalent depth corresponding to different time traces; Inversion modeling: Based on Occam inversion or Gauss-Newton iteration, a 2D or 3D resistivity profile is constructed. Prior geological constraints are introduced to suppress the multi-solution problem of inversion. In this embodiment, the prior geological constraints include coal seam thickness and surrounding rock resistivity range. Anomaly extraction: Identify low-resistance or high-resistance abnormal areas through resistivity gradient analysis or threshold segmentation technology.

[0028] In this embodiment, a full-period apparent resistivity iterative algorithm is used to convert pre-processed induced electromotive force decay data, with a time range of 0.01-10ms, into depth-resistivity parameters. An initial apparent resistivity-depth curve is calculated using a formation model. Constrained inversion modeling is then performed to construct an unstructured three-dimensional grid containing topographic data. Prior information revealed by the drill hole is introduced as a regularization constraint to generate a resistivity profile. Finally, intelligent anomaly extraction is performed to identify low-resistance anomaly areas, analyze and delineate goaf boundaries, and output a three-dimensional resistivity anomaly model with a buried depth error of ≤0.5 meters. S4. Quantitative analysis of goaf response characteristics: Based on geological conditions and electromagnetic response characteristics, goaf identification indicators are established. The identification indicators include attenuation curve analysis, multi-parameter fusion and anisotropy analysis.

[0029] Determining whether there are goafs underground is done by analyzing the characteristics of electromagnetic signals. This involves: obtaining the attenuation pattern of electromagnetic waves. If the electromagnetic waves disappear quickly and the signal curve shows a cliff-like drop, the underground range of the current measurement area is determined to be a goaf. Multiple key indicators are extracted to assist in this determination. These key indicators include the resistivity value at a fixed point, the severity of the magnetic field change, and the time difference between the peak values ​​of the signal. The key indicator data is input into the analysis model to eliminate the error interference of individual data. After cross-validation with known geological data, the range, depth, and risk level of the goaf are marked. S5. Three-dimensional data fusion and visualization modeling: integrating multi-line data and geological information, constructing a spatial distribution model of goaf, including data interpolation, multi-source data fusion and three-dimensional visualization processing, generating resistivity isosurfaces, marking goaf location, top and bottom plate burial depth and scale parameters, and outputting a risk level zoning map. The scale parameters include volume and extension direction.

[0030] This embodiment is used to combine scattered detection point data to generate a goaf map, which specifically includes the following contents: S5.1. Input the resistivity data of hundreds of measurement points throughout the survey area into a computer, fill the gaps between points, and generate a continuous three-dimensional grid model to achieve a natural transition between the boundaries of the goaf; S5.2. Overlay and calibrate the resistivity model with known geological information to eliminate false anomalies; S5.3. Generate an intuitive 3D risk map: Use a blue gradient to represent formation resistivity, render confirmed goafs as semi-transparent purple voids suspended at the corresponding depth, and automatically annotate key parameters. Generate a 2D warning map, using red, yellow, and green light areas to indicate avoidance, monitored passage, and safe zone types. S6. Improve the reliability of conclusions through field drilling verification and error source analysis. Select typical abnormal areas for drilling sampling. Compare the boundaries of the goaf interpreted by geophysical exploration with the actual exposure. Calculate positioning errors. Quantify the impact of noise interference, inversion parameter selection, and terrain simplification assumptions on the results. Simulate and evaluate the uncertainty distribution.

[0031] During the field drilling verification process, 2-3 verification holes are drilled in the high-risk areas marked in the geophysical report. The depth and size of the goaf actually revealed by the drilling are compared with the matching degree of the prediction model. If it is found that the predicted void is actually a rock fracture, it is necessary to reversely check the data processing link; use a computer to simulate the detection process, quantify the impact of different interference factors, and finally generate an error distribution map, marking the credible range of the goaf boundary; adjust subsequent work according to the verification results, and the final report will not only mark the location of the goaf, but also attach a credibility score.

[0032] It also includes the construction process of the verification hole: according to the abnormal target area of ​​the goaf delineated by the transient electromagnetic method, typical verification points are selected on the geophysical profile, and the GPS positioning device is used for on-site layout, and the plane error is controlled to be ≤0.1 meter; a light geological drilling rig with a diamond drill bit is used to drill the hole, and the hole diameter is 75-110mm. Before drilling, the verticality of the drilling tower is calibrated with a total station. During the drilling process, parameters are recorded every 1 meter. The recorded parameters include: core recovery rate, flushing fluid consumption, drilling speed; when the predicted goaf roof depth is ± At 2 meters, the drilling speed is switched to slow drilling with a rotation speed of 1m / min. The water level, gas concentration and return slag composition in the hole are monitored in real time. After the goaf is exposed: downhole television or laser scanner is used to directly observe the height of the cavity and the filling material, which includes water, soil and coal slag; filling material samples are taken in layers to test the water content and density; if it is a cavity, the actual span is measured with a telescopic probe, and a PVC casing is lowered to protect the hole wall. A water injection test is carried out, and finally the hole is sealed to the surface with cement mortar, and the core catalog, image data and depth correction value are recorded simultaneously.

[0033] The basic principles, main features and advantages of the present invention are shown and described above. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0034] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method, characterized in that: The following steps are involved: S1. Data acquisition parameter optimization and terrain correction. Small-sized transmitting coils and high-frequency transmitting currents are laid out. High-density measurement network measurement points are laid out. Small transmitting coils are laid on the ground with high-frequency transmitters. A central loop device is used to synchronously lay out multi-component receiving coils. The coordinates and elevation of each measurement point are recorded. S2, data preprocessing and noise suppression, multi-level preprocessing of the raw data, the preprocessing includes outlier removal, filtering noise reduction, zero drift correction and background field subtraction; S3, time-depth conversion and resistivity inversion, converts time-domain induced electromotive force data into depth-resistivity parameters, including time-depth conversion, inversion modeling and anomaly extraction; S4. Quantitative analysis of goaf response characteristics: establishing goaf identification indicators based on geological conditions and electromagnetic response characteristics. The identification indicators include attenuation curve analysis, multi-parameter fusion, and anisotropy analysis. S5. 3D data fusion and visualization modeling: integrating multi-line data with geological information to construct a spatial distribution model of goaf areas. This includes data interpolation, multi-source data fusion, and 3D visualization processing. This generates resistivity contour surfaces, annotates goaf locations, roof and floor depths, and scale parameters, and outputs a risk level zoning map. Scale parameters include volume and extension direction. S6. Improve the reliability of conclusions through field drilling verification and error source analysis. Select typical abnormal areas for drilling sampling. Compare the boundaries of the goaf interpreted by geophysical exploration with the actual exposure. Calculate positioning errors. Quantify the impact of noise interference, inversion parameter selection, and terrain simplification assumptions on the results. Simulate and evaluate the uncertainty distribution.

2. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 1, characterized in that: In step S1, a high-density survey network is designed and implemented based on the topographic map of the survey area. A small square transmitting coil is laid at the transmitting point and its position is adjusted so that the coil plane is flat and close to the ground. A multi-component receiving coil is synchronously laid out in conjunction with the central loop device and fixed on a special bracket to align with the center of the transmitting coil to maintain horizontality and directional consistency. During operation, the azimuth error of the point and the grounding status of the coil are checked in real time.

3. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 2, characterized in that: During the coil laying process, the flatness and tightness of the coil are checked. The inspection process includes: High-precision dual-axis tilt sensors are installed at five locations, including the four corners and the center point of the transmitting coil frame. If the horizontal tilt angle at any point is greater than 0.3°, an alarm will be triggered. Simultaneously, a laser rangefinder is used to scan the gap between the coil and the ground at a grid interval of 20 cm, marking and locating gaps exceeding 3 mm.

4. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 2, characterized in that: The laser rangefinder is used to eliminate the distortion effect of terrain on electromagnetic field signals. The process of recording surface undulation data includes: after the transient electromagnetic measurement points are arranged, a pulsed laser rangefinder is used to scan the terrain.

5. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 1, characterized in that: The outlier removal adopts a dynamic threshold, with three times the standard deviation of the signal in the moving time window as the threshold; The background field subtraction is done by setting a benchmark point 20 meters outside the measurement area to collect pure stratum response without interference from the goaf. After aligning the time domain waveforms using the cross-correlation algorithm, vector subtraction is performed from the measured data, which increases the signal-to-noise ratio of low-resistance anomalies in water-bearing goafs by 3-8 times. The zero drift correction is based on the background magnetic field recorded by the reference coil, and the baseline drift curve is fitted using the least square method to deduct the offset from the main signal point by point.

6. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 5, characterized in that: Step S3 uses the full-period apparent resistivity iteration algorithm to convert the pre-processed induced electromotive force decay data, with a time range of 0.01-10 ms, into depth-resistivity parameters: the initial apparent resistivity-depth curve is calculated through the formation model, and then constrained inversion modeling is performed to construct an unstructured three-dimensional grid containing topographic data.

7. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 1, characterized in that: In step S4, the characteristics of the electromagnetic signal are analyzed to determine whether there is a goaf underground, including the following: obtaining the attenuation law of the electromagnetic wave. If the electromagnetic wave disappears quickly and the signal curve drops sharply, it is determined that the underground range of the current measurement area is a goaf.

8. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 1, characterized in that: Step S5 is used to combine the scattered detection point data to generate a goaf map, which includes the following contents: S5.

1. Input the resistivity data of hundreds of measurement points in the entire survey area into the computer, fill the gaps between points, and generate a continuous three-dimensional grid model; S5.

2. Overlay and calibrate the resistivity model with known geological information; S5.

3. Generate intuitive three-dimensional risk maps and two-dimensional warning maps.

9. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 8, characterized in that: During the field drilling verification process, 2-3 verification holes are drilled in the high-risk areas marked in the geophysical prospecting report to compare the depth and size of the goaf actually revealed by the drilling with the degree of matching with the prediction model.

10. The method for analyzing geophysical data of extremely shallow goaf in coal mines based on transient electromagnetic method according to claim 9, characterized in that: It also includes the construction process of the verification hole: according to the abnormal target area of ​​the goaf delineated by the transient electromagnetic method, typical verification points are selected on the geophysical profile map, the GPS locator is used for on-site layout, and a light geological drilling rig with a diamond drill bit is used to open the hole. After the goaf is exposed: the height and filling of the cavity are directly observed with downhole television or laser scanner. If it is a cavity, the actual span is measured with a telescopic probe rod. The PVC casing is lowered to protect the hole wall. A water injection test is carried out. Finally, the hole is sealed to the surface with cement mortar.

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