Coal underground gasification wide area electromagnetic detection system and method

Abstract

The present application belongs to the technical field of geophysical exploration and coal underground gasification monitoring, and specifically relates to a coal underground gasification wide-area electromagnetic detection system and method, which comprises: a distributed electromagnetic emission unit for emitting a multi-frequency combined wave to the underground; a multi-dimensional data receiving unit for synchronously collecting electric field data through GPS; a data preprocessing unit for denoising, static correction and apparent resistivity calculation; a three-dimensional inversion interpretation unit for constructing a three-dimensional geological model containing burned and altered rock, coal seam and aquifer and performing constrained inversion based on drilling data to obtain a three-dimensional resistivity distribution; and a dynamic early warning unit for comparing time series data to track changes in electrical structure by a sliding window analysis method and issuing an early warning when the change rate exceeds a threshold. The present application realizes high-precision identification and dynamic tracking of combustion voids, gasification channels and blockage anomalies, with a detection depth of 1500 m, a boundary interpretation error of 5% to 10%, an identification accuracy of 80% to 90%, and reliable support for safe operation of coal underground gasification projects.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration and underground coal gasification monitoring technology, and in particular to a wide-area electromagnetic detection system and method for underground coal gasification. Background Technology

[0002] Underground coal gasification (UCG) is a clean utilization technology that involves the controlled combustion of underground coal to produce combustible gases. However, the gasification process creates sludge zones, gasification channels, and heat-affected zones, leading to significant changes in the physical properties (especially electrical properties) of the coal seam and surrounding rock. This can potentially trigger geological and environmental risks such as surface subsidence and groundwater pollution. Therefore, high-precision detection and dynamic monitoring of these anomalies are core requirements for ensuring the safe operation of UCG projects.

[0003] Wide-area electromagnetic methods are widely used in geological exploration due to their advantages such as large detection depth, strong anti-interference ability, and high resolution. However, in the monitoring application of underground coal gasification, existing technologies still have the following shortcomings: Inadequate field source deployment: Under complex terrain conditions, improper field source deployment can easily lead to distortion of the electromagnetic signal propagation path, affecting the accuracy of anomaly identification.

[0004] Weak data processing capabilities: Existing data processing methods are limited and cannot effectively suppress strong industrial interference and near-surface static effects, resulting in a low signal-to-noise ratio that affects the reliability of subsequent inversion results.

[0005] Inversion model mismatch: The inversion model failed to fully consider the special geological conditions of UCG and lacked coupling constraints on key geological bodies such as pyromorphic rocks and aquifers, resulting in prominent inversion ambiguity and inaccurate definition of anomaly boundaries.

[0006] Insufficient dynamic monitoring capabilities: There is a lack of real-time tracking capabilities for the evolution of abnormal bodies, resulting in delayed early warnings and an inability to meet the dynamic monitoring requirements for safe operation of engineering projects.

[0007] Therefore, there is an urgent need for a wide-area electromagnetic detection system and method that can accurately identify combustion zones, vaporization channels, and blockage anomalies, and achieve dynamic tracking. Summary of the Invention

[0008] This invention proposes a wide-area electromagnetic detection system and method for underground coal gasification, aiming to solve the technical problems of existing wide-area electromagnetic detection technologies in the application of underground coal gasification monitoring, such as unreasonable field source layout leading to signal distortion and a single data processing method.

[0009] In a first aspect, the present invention provides a wide-area electromagnetic detection system for underground coal gasification, comprising: Distributed electromagnetic transmission unit, used to transmit electromagnetic signals containing multi-frequency combined waves into the ground; The multidimensional data receiving unit is synchronized with the distributed electromagnetic transmitting unit via GPS to synchronously collect electric field component data of the underground medium response; A data preprocessing unit, connected to the multidimensional data receiving unit, is used to perform noise reduction, static correction, and wide-area apparent resistivity calculation on the acquired raw data. The three-dimensional inversion interpretation unit, connected to the data preprocessing unit, is used to construct a three-dimensional geological model containing sintered rock, coal seam, and aquifer, and to perform constrained inversion based on borehole data to obtain the three-dimensional distribution of underground resistivity. The dynamic early warning unit, connected to the three-dimensional inversion interpretation unit, is used to track changes in electrical structure by comparing time-series data through a sliding window analysis method, and to generate early warning information when the rate of change exceeds a preset threshold.

[0010] The technical advantages of the wide-area electromagnetic detection system for underground coal gasification disclosed in this invention are as follows: This invention constructs a complete wide-area electromagnetic detection system by setting up a distributed electromagnetic transmission unit, a multi-dimensional data receiving unit, a data preprocessing unit, a three-dimensional inversion interpretation unit, and a dynamic early warning unit, and defining the connection relationships and core functions between the five units. This system can transmit multi-frequency combined waves underground and achieve high-precision data acquisition through GPS synchronization. The data preprocessing unit improves the signal-to-noise ratio. By constructing a three-dimensional geological model including flaked rock, coal seam, and aquifer and combining borehole data to constrain inversion, the system reduces ambiguity. Finally, the dynamic early warning unit tracks anomaly evolution through sliding window time-series comparison. This solves the problems of unreasonable field source deployment, difficulty in interference suppression, prominent inversion ambiguity, and insufficient dynamic monitoring capabilities in existing technologies, and achieves high-precision definition of the boundary of the combustion zone and accurate identification of abnormal structures.

[0011] Furthermore, the distributed electromagnetic transmission unit includes a high-power broadband transmitter, a multi-frequency combined signal controller, and a grounding electrode array; the transmitter has a rated power of ≥160kW and supports broadband transmission from 0.01 to 10kHz; the signal controller is used to generate multi-frequency combined waves; the grounding electrode array is constructed by embedding 1m×1m metal plates at multiple points, with a grounding resistance ≤2kΩ.

[0012] Furthermore, the grounding electrode array of the distributed electromagnetic emission unit is arranged parallel to the survey line, the length of the AB field source is set to 1000m, the distance from the midpoint to the well site is ≥1500m, the azimuth error is ≤1°, the emission current is controlled at 120~150A, and the terminal voltage is ≤1000V.

[0013] Furthermore, the multidimensional data receiving unit consists of more than 12 distributed electromagnetic receivers, non-polarized electrodes, and a GPS synchronization module; the receiver has a 24-bit AD resolution and supports DC~10kHz wideband acquisition; the synchronization error of the GPS synchronization module is ≤50ns; the electrode spacing is set to 20m, and the spacing in special areas is increased to 10m.

[0014] Furthermore, the measurement points of the multidimensional data receiving unit are arranged with the gasification well site as the center and three main measurement lines are laid out along the geological structure direction. Each measurement line is 1500m long, and one measurement line is equipped with a 300m dynamic repeated observation section, with a total number of physical points ≥255.

[0015] Furthermore, the data preprocessing unit includes a signal denoising module, a static correction module, and a wide-area apparent resistivity calculation module; the signal denoising module is used to sequentially perform trend filtering, comb filtering with a center frequency of 50Hz and its harmonics, and high-pass filtering with a cutoff frequency of 0.01Hz to suppress interference; the static correction module is used to perform five-point moving average filtering to eliminate static displacement; the wide-area apparent resistivity calculation module is used to calculate the wide-area apparent resistivity based on the inverse interpolation method.

[0016] Secondly, the present invention provides a wide-area electromagnetic detection method for underground coal gasification, which, based on the aforementioned system, includes the following steps: S1, Pre-processing of the survey area: collecting geological data, borehole data and gasification engineering parameters of the survey area, determining the distribution of interference sources, and setting up 3 parallel survey lines and dynamic observation sections; S2, Field source and receiver system deployment: The transmitting electrode array is arranged according to the equatorial device, and a distributed receiver is deployed. GPS synchronization is used to achieve transmission-reception timing alignment. S3, multi-frequency data acquisition, generates multi-frequency combined waves through the signal controller, the transmitter transmits electromagnetic signals into the ground, and the receiver synchronously acquires electric field component data, with a sampling rate ≥120Ksps; S4, Data preprocessing: The raw data is subjected to flying point removal, trend filtering, comb filtering and high-pass filtering in sequence to suppress interference, and then five-point moving average filtering is performed to eliminate static displacement. Finally, the wide-area apparent resistivity is calculated based on the inverse interpolation method. S5, 3D inversion calculation, constructs a 3D geological model including coal seam, sintered rock and aquifer, inputs preprocessed data, uses borehole data as constraints, performs joint 1D and 2D inversion to obtain the 3D distribution of underground resistivity; S6, Anomaly Identification and Interpretation: Combine geological data to analyze resistivity anomaly characteristics, determine low resistivity anomaly areas as combustion zones or gasification channels, identify blockage anomalies by contour density, and delineate the spatial range and morphology of the anomaly body. S7, dynamic monitoring and early warning, compares the data of the dynamic observation segment over time, analyzes the trend of electrical structure change through sliding window analysis, and triggers an early warning and pushes relevant information when the change rate of the abnormal body boundary exceeds the threshold.

[0017] The technical advantages of the wide-area electromagnetic detection method for underground coal gasification disclosed in this invention are as follows: This invention forms a complete and operable wide-area electromagnetic monitoring process through seven steps: limited measurement area preprocessing, deployment of field source and receiving system, multi-frequency data acquisition, data preprocessing, three-dimensional inversion calculation, anomaly identification and interpretation, and dynamic monitoring and early warning. By deploying dynamic observation sections and using the sliding window analysis method to compare time-series data, this method can track the changing trend of underground electrical structure in real time and trigger early warning in a timely manner when the rate of change of the anomaly boundary exceeds the threshold. This enables dynamic monitoring and early warning of combustion chambers, gasification channels, and blockage anomalies, providing reliable technical support for the safe operation of underground coal gasification projects.

[0018] Furthermore, in the data acquisition process of S3, the physical point qualification rate is ≥100%, the excellent grade rate is ≥85%, the system inspection and observation workload is ≥5% of the total workload, and the mean square relative error is ≤5%.

[0019] Furthermore, the inversion calculation in S5 adopts a combination of continuous medium inversion and layered medium inversion, with an inversion depth ≥1500m, a vertical resolution ≤5m, and a horizontal resolution ≤20m.

[0020] Furthermore, the anomaly identification in S6 adopts a three-level process of "qualitative-quantitative-verification": first, the anomaly location is qualitatively identified through the simulated cross-section diagram; then, the anomaly size is quantitatively calculated through the inversion results; and finally, the identification results are verified by combining the borehole data. Attached Figure Description

[0021] Figure 1 This is a structural block diagram of a wide-area electromagnetic detection system for dynamic monitoring of the geological environment of underground coal gasification provided in an embodiment of the present invention. Figure 2 A schematic flowchart of the monitoring method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the signal denoising and static correction process of the data preprocessing unit in an embodiment of the present invention; Figure 4 This is a schematic diagram of the three-dimensional geological model of the three-dimensional inversion interpretation unit in an embodiment of the present invention; Figure 5 This is a schematic diagram of the workflow of the dynamic early warning unit in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The existing wide-area electromagnetic detection technology mentioned in the background technology has several drawbacks in its application to underground coal gasification monitoring. These include signal distortion due to unreasonable field source deployment, difficulty in effectively suppressing industrial interference and static effects due to a single data processing method, prominent multiple solutions due to the inversion model not fully considering key geological bodies such as pyromorphic rocks and aquifers, and a lack of real-time dynamic tracking capability for the evolution of anomalies such as combustion goaf. As a result, it is difficult to meet the monitoring needs of deep coal seam gasification projects under complex terrain conditions for accurate definition of combustion goaf boundaries and high-precision identification of blockage anomalies.

[0024] Example 1: System composition and deployment.

[0025] This embodiment provides a wide-area electromagnetic detection system for dynamic monitoring of the geological environment of underground coal gasification. For example... Figure 1 As shown, the system includes: a distributed electromagnetic transmission unit, a multi-dimensional data receiving unit, a data preprocessing unit, a three-dimensional inversion interpretation unit, and a dynamic early warning unit.

[0026] 1. Distributed electromagnetic transmission unit. The distributed electromagnetic transmission unit is used to transmit electromagnetic signals containing multi-frequency combined waves underground. Specifically, it includes a high-power broadband transmitter, a multi-frequency combined signal controller, and a grounding electrode array.

[0027] Transmitter: Employs the TX-200 high-power wideband transmitter with a rated power of 160kW, a maximum transmit current of 200A, and a terminal voltage controlled at ≤1000V. It supports wideband electromagnetic signal transmission from 0.01Hz to 10kHz. This design ensures strong penetration of deep-layer signals, meeting the requirements for a detection depth of 1500m.

[0028] Multi-frequency combined signal controller: This controller can generate multi-frequency combined waves, such as third-order 43-frequency and fourth-order 53-frequency. Its core advantage lies in fusing the target exploration frequencies into a set of waveforms, covering the entire frequency band with a single transmission, significantly improving acquisition efficiency.

[0029] Grounding electrode array: Use 1m×1m aluminum foil (or metal plate) as electrodes, dig a pit at a depth of more than 0.5m to bury them, with an adjacent pit distance of ≥3m, and pour sodium chloride solution to reduce the grounding resistance, ensuring that the grounding resistance is ≤2kΩ.

[0030] Preferred deployment method: The grounding electrode array is deployed parallel to the survey line, the AB field source length is set to 1000m, the distance from the midpoint to the well site is ≥1500m, the azimuth error is ≤1°, and the transmission current is controlled between 120~150A. This optimized field source deployment method can effectively reduce the influence of terrain, ensure the stable and uniform propagation of electromagnetic signals underground, and improve signal quality.

[0031] 2. Multidimensional data receiving unit. The multidimensional data receiving unit is synchronized with the distributed electromagnetic transmitting unit via GPS to synchronously acquire electric field component data of the underground medium response. Specifically, it consists of more than 12 distributed electromagnetic receivers, non-polarized electrodes, and a GPS synchronization module.

[0032] Receiver: The receiver adopts the EMR-01 distributed electromagnetic receiver. Each receiver has a 2-channel design, 24-bit AD resolution, input impedance up to 6MΩ, measurement accuracy of ±0.5%, and supports DC~10kHz wideband acquisition, ensuring accurate capture of weak signals.

[0033] Non-polarizable electrodes: These are used to ensure good contact with the soil and reduce electrode polarization potential noise. The electrode spacing is usually set at 20m, but in special anomalous areas (such as fault zones and fracture zones), it is increased to 10m to improve lateral resolution.

[0034] GPS synchronization module: achieves precise timing alignment between all receivers and transmitters, with a synchronization error of ≤50ns, ensuring the accuracy of multi-frequency signal superposition.

[0035] Preferred deployment method: The measuring points are deployed with the gasification well site as the center, along three main measuring lines, each 1500m long, and the line spacing is set according to the possible distribution range of the anomaly. One of the measuring lines (such as line L2) has a 300m dynamic repeat observation section for subsequent dynamic monitoring. The total number of physical points is no less than 255.

[0036] 3. Data preprocessing unit. For example... Figure 3 As shown, the data preprocessing unit is connected to the multidimensional data receiving unit and is used to perform denoising, static correction, and wide-area apparent resistivity calculation on the acquired raw data. Specifically, it includes a signal denoising module, a static correction module, and a wide-area apparent resistivity calculation module.

[0037] Signal denoising module: First, flying point removal is performed on the raw data, using the 3σ criterion to identify and remove obvious outlier data points. Then, trend filtering, comb filtering with a center frequency of 50Hz and its harmonics, and high-pass filtering with a cutoff frequency of 0.01Hz are executed sequentially. The trend filtering window is set to 5-10 data points to eliminate baseline drift; comb filtering is used to precisely suppress power frequency interference; and high-pass filtering is used to suppress natural electromagnetic noise and electrode polarization effects.

[0038] Static correction module: Static correction is performed using a five-point moving average filter. This algorithm effectively eliminates static displacement caused by near-surface inhomogeneities by calculating the moving average of data within a certain window, thus improving data consistency.

[0039] Wide-area apparent resistivity calculation module: Calculates wide-area apparent resistivity based on inverse interpolation. By constructing an approximate function to fit the relationship between the electric field and frequency, the wide-area apparent resistivity value reflecting the underground electrical characteristics is obtained, providing basic data for subsequent inversion.

[0040] 4. Three-dimensional inversion interpretation unit. For example... Figure 4 As shown, the three-dimensional inversion interpretation unit is connected to the data preprocessing unit to construct a three-dimensional geological model containing sintered rock, coal seam, and aquifer, and to perform constrained inversion based on borehole data to obtain the three-dimensional distribution of underground resistivity.

[0041] Model Construction: Based on the geological data of the survey area, a three-dimensional geological model was constructed, including the Jurassic Badaowan Formation coal seam, pyrophyllite, Triassic Huangshanjie Formation aquitard, and Quaternary alluvial-diluvial deposits. The vertical grid of the model adopts an unequal-spacing grid, with shallow grid spacing ≤5m and deep grid spacing ≤20m, to balance shallow resolution and deep computational efficiency.

[0042] Inversion Calculation: Preprocessed apparent resistivity data is input, and a combination of continuous medium inversion and layered medium inversion is used to perform a joint one-dimensional and two-dimensional inversion. Constraints are applied using data on formation interfaces, coal seam thickness, and lithological and electrical parameters revealed by boreholes. Model parameters (such as resistivity and layer thickness) are adjusted until the mean square relative error between the simulated response and the measured data is ≤5%. Finally, the three-dimensional resistivity distribution within a depth of 1500m is obtained, with a vertical resolution ≤5m and a horizontal resolution ≤20m.

[0043] 5. Dynamic early warning unit. For example... Figure 5 As shown, the dynamic early warning unit 500 is connected to the three-dimensional inversion interpretation unit 400, and is used to track changes in electrical structure by comparing time-series data using a sliding window analysis method, and generate early warning information when the rate of change exceeds a preset threshold. Specifically, it includes a time-series data comparison module, an anomaly threshold determination module, and an early warning push module.

[0044] Time-series data comparison module: Using the sliding window analysis method, it compares data collected at different times in the dynamic observation segment (such as line L2) to analyze the changing trend of electrical structure, such as the rate of change of the boundary of the anomalous body.

[0045] Anomaly threshold determination module: Dynamically adjusts the early warning threshold based on the geological conditions and engineering safety requirements of the survey area. For example, when the rate of change of the combustion zone boundary exceeds a preset threshold, it is determined to be a state requiring early warning.

[0046] Early warning push module: Once an early warning is triggered, the warning information will be pushed to relevant personnel via SMS, system pop-ups, etc., specifying the warning location, anomaly type (such as airspace expansion, blockage anomaly) and risk level.

[0047] Example 2: Monitoring Method This embodiment provides a wide-area electromagnetic detection method for underground coal gasification based on the above-mentioned system, such as... Figure 2 As shown, it includes the following steps: S1: Pre-processing of the survey area. Collect regional geological data, borehole data, coal seam distribution data, and underground gasification engineering parameters. Conduct on-site reconnaissance to verify well site locations, topography, and the distribution of electromagnetic interference sources (such as high-voltage lines and railway tracks). Based on the gasification well site locations, lay out three parallel survey lines along the main geological structure directions, each 1500m long, with the line spacing set according to the possible distribution range of anomalies. Set up a 300m dynamic repeated observation section on line L2, with a total of no less than 255 physical points.

[0048] S2: Deployment of the Field Source and Receiver System. The transmitting electrode array (AB field source) is deployed along the equatorial device, with a length of 1000m and a midpoint approximately 1500m from the well site. The AB field source is ensured to be parallel to the survey line, with an azimuth error ≤1°. Distributed receivers are deployed along the survey line at designed point spacing (20m, locally 10m), using a GPS synchronization module to achieve timing alignment between transmission and reception. The grounding resistance of the electrodes is measured using a grounding resistance meter to ensure it meets the requirement of ≤2kΩ. If not, optimization is achieved by increasing the number of electrodes or applying conductive liquid.

[0049] S3: Multi-frequency data acquisition. A fourth-order 53-frequency multi-frequency combination wave is generated by the signal controller, and the transmitter emits electromagnetic signals into the ground, with the transmission current controlled between 120 and 150 A. The receiver synchronously acquires the electric field Ex component data, with a sampling rate set to 120 Ksps, and full-band data is acquired at each measuring point. Signal quality is monitored in real time during the acquisition process. After acquisition, a quality inspection is performed according to specifications, requiring a 100% pass rate for physical points, an excellent / good rate ≥ 85%, a system inspection and observation workload ≥ 5% of the total workload, and a mean square relative error ≤ 5%.

[0050] S4: Data Preprocessing. First, the raw data undergoes flypoint removal, using the 3σ criterion to identify and remove obvious outliers to ensure data integrity. Then, trend filtering, comb filtering with a center frequency of 50Hz and its harmonics, and high-pass filtering with a cutoff frequency of 0.01Hz are performed sequentially to suppress power frequency interference, natural electromagnetic noise, and electrode polarization effects. The trend filtering window is set to 5-10 data points, comb filtering is used to accurately suppress 50Hz power frequency and its harmonic interference, and high-pass filtering is used to suppress low-frequency drift. A five-point moving average filter is then performed to eliminate static displacement and improve data consistency. Finally, wide-area apparent resistivity is calculated using inverse interpolation, and a frequency-apparent resistivity pseudo-section plot is constructed.

[0051] S5: 3D Inversion Calculation. Based on the geological data of the survey area, a 3D geological model was constructed, including the Jurassic Badaowan Formation coal seam, pyrophyllite, Triassic Huangshanjie Formation aquitard, and Quaternary alluvial-diluvial deposits. The model's vertical grid was divided using unequal-spacing grids, with shallow grid spacing ≤5m and deep grid spacing ≤20m, balancing resolution and computational efficiency. Preprocessed apparent resistivity data was input, and a combination of continuous medium inversion and layered medium inversion was used to perform a joint 1D and 2D inversion. Using data such as stratigraphic interfaces, coal seam thickness, and lithological and electrical parameters revealed by boreholes as constraints, the model parameters were adjusted until the mean square relative error between the simulation results and measured data was ≤5%, obtaining the 3D resistivity distribution within a depth of 1500m underground, with a vertical resolution ≤5m and a horizontal resolution ≤20m.

[0052] S6: Anomaly Identification and Interpretation. Combining geological data, gasification project operation data, and resistivity distribution characteristics, anomaly identification criteria are established: Gas flow zones, due to fracture development and the presence of fluid media (groundwater, gasification products), exhibit low resistivity anomalies; gasification channels show strip-shaped low resistivity characteristics, extending in the same direction as the gasification well trajectory; blockage anomalies are characterized by interruptions or contractions in the low-resistivity zone, with a sudden change in resistivity amplitude ≥30%. Anomaly identification employs a three-level process: qualitative-quantitative-verification. Qualitative identification: First, the location and direction of the anomaly are qualitatively identified by using frequency-apparent resistivity pseudo-section diagrams.

[0053] Quantitative calculation: The spatial range, morphology, burial depth, and resistivity amplitude of the anomaly are then quantitatively calculated using the inversion results. The combustion zone is delineated using 60% of the background resistivity as the lower limit of the anomaly, and the gasification channel is delineated using 70% of the background resistivity as the lower limit of the anomaly.

[0054] Verification: Finally, the identification results were verified by combining borehole data and gasification engineering operation data to ensure that the interpretation error of the combustion zone boundary was controlled within 5%~10% and the accuracy of abnormal structure identification reached 80%~90%.

[0055] S7: Dynamic Monitoring and Early Warning. Data is collected monthly on the 300m dynamic observation section of line L2 (collection frequency is set according to the gasification project progress). The changing trend of underground electrical structure is analyzed through time-series comparison. The sliding window analysis method is used to calculate the rate of change of the boundary of the anomaly body at different times. When the rate of change exceeds the set threshold, the system automatically triggers an early warning. The early warning threshold is dynamically adjusted according to the geological conditions of the survey area and the project requirements. Early warning information is pushed to relevant personnel through SMS, system pop-ups, etc., clearly specifying the warning location, anomaly type (such as gas flow expansion, blockage anomaly), and risk level, providing a basis for project adjustments.

[0056] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A wide-area electromagnetic detection system for underground coal gasification, characterized in that, include: Distributed electromagnetic transmission unit, used to transmit electromagnetic signals containing multi-frequency combined waves into the ground; The multidimensional data receiving unit is synchronized with the distributed electromagnetic transmitting unit via GPS to synchronously collect electric field component data of the underground medium response; A data preprocessing unit, connected to the multidimensional data receiving unit, is used to perform noise reduction, static correction, and wide-area apparent resistivity calculation on the acquired raw data. The three-dimensional inversion interpretation unit, connected to the data preprocessing unit, is used to construct a three-dimensional geological model containing sintered rock, coal seam, and aquifer, and to perform constrained inversion based on borehole data to obtain the three-dimensional distribution of underground resistivity. The dynamic early warning unit, connected to the three-dimensional inversion interpretation unit, is used to track changes in electrical structure by comparing time-series data through a sliding window analysis method, and to generate early warning information when the rate of change exceeds a preset threshold.

2. The system according to claim 1, characterized in that, The distributed electromagnetic transmission unit includes a high-power broadband transmitter, a multi-frequency combined signal controller, and a grounding electrode array; the transmitter has a rated power of ≥160kW and supports broadband transmission from 0.01 to 10kHz; the signal controller is used to generate multi-frequency combined waves; the grounding electrode array is constructed by embedding 1m×1m metal plates at multiple points, with a grounding resistance ≤2kΩ.

3. The system according to claim 2, characterized in that, The grounding electrode array of the distributed electromagnetic emission unit is arranged parallel to the survey line. The length of the AB field source is set to 1000m, the distance from the midpoint to the well site is ≥1500m, the azimuth error is ≤1°, the emission current is controlled at 120~150A, and the terminal voltage is ≤1000V.

4. The system according to claim 1, characterized in that, The multidimensional data receiving unit consists of more than 12 distributed electromagnetic receivers, non-polarized electrodes, and a GPS synchronization module; the receiver has a 24-bit AD resolution and supports DC~10kHz wideband acquisition; the synchronization error of the GPS synchronization module is ≤50ns; the electrode spacing is set to 20m, and the spacing is increased to 10m in special areas.

5. The system according to claim 4, characterized in that, The multidimensional data receiving unit is set up with the gasification well site as the center and three main measuring lines are laid out along the geological structure direction. Each measuring line is 1500m long, and one measuring line is set with a 300m dynamic repeated observation section, with a total number of physical points ≥255.

6. The system according to claim 1, characterized in that, The data preprocessing unit includes a signal denoising module, a static correction module, and a wide-area apparent resistivity calculation module. The signal denoising module is used to sequentially perform trend filtering, comb filtering with a center frequency of 50Hz and its harmonics, and high-pass filtering with a cutoff frequency of 0.01Hz to suppress interference. The static correction module is used to perform five-point moving average filtering to eliminate static displacement. The wide-area apparent resistivity calculation module is used to calculate the wide-area apparent resistivity based on the inverse interpolation method.

7. A wide-area electromagnetic detection method for underground coal gasification based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: S1, Pre-processing of the survey area: collecting geological data, borehole data and gasification engineering parameters of the survey area, determining the distribution of interference sources, and setting up 3 parallel survey lines and dynamic observation sections; S2, Field source and receiver system deployment: The transmitting electrode array is arranged according to the equatorial device, and a distributed receiver is deployed. GPS synchronization is used to achieve transmission-reception timing alignment. S3, multi-frequency data acquisition, generates multi-frequency combined waves through the signal controller, the transmitter transmits electromagnetic signals into the ground, and the receiver synchronously acquires electric field component data, with a sampling rate ≥120Ksps; S4, Data preprocessing: The raw data is subjected to flying point removal, trend filtering, comb filtering and high-pass filtering in sequence to suppress interference, and then five-point moving average filtering is performed to eliminate static displacement. Finally, the wide-area apparent resistivity is calculated based on the inverse interpolation method. S5, 3D inversion calculation, constructs a 3D geological model including coal seam, sintered rock and aquifer, inputs preprocessed data, uses borehole data as constraints, performs joint 1D and 2D inversion to obtain the 3D distribution of underground resistivity; S6, Anomaly Identification and Interpretation: Combine geological data to analyze resistivity anomaly characteristics, determine low resistivity anomaly areas as combustion zones or gasification channels, identify blockage anomalies by contour density, and delineate the spatial range and morphology of the anomaly body. S7, dynamic monitoring and early warning, compares the data of the dynamic observation segment over time, analyzes the trend of electrical structure change through sliding window analysis, and triggers an early warning and pushes relevant information when the change rate of the abnormal body boundary exceeds the threshold.

8. The method according to claim 7, characterized in that, During the data acquisition process in S3, the physical point qualification rate is ≥100%, the excellent grade rate is ≥85%, the system inspection and observation workload is ≥5% of the total workload, and the mean square relative error is ≤5%.

9. The method according to claim 7, characterized in that, The inversion calculation in S5 adopts a combination of continuous medium inversion and layered medium inversion, with an inversion depth ≥1500m, a vertical resolution ≤5m, and a horizontal resolution ≤20m.

10. The method according to claim 7, characterized in that, The anomaly identification in S6 adopts a three-level process of "qualitative-quantitative-verification": first, the anomaly location is qualitatively identified through the simulated cross-section diagram; then, the anomaly size is quantitatively calculated through the inversion results; and finally, the identification results are verified by combining the borehole data.

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