A device and method for detecting deep targets using multiple cross-electromagnetic component modes

Abstract

This invention discloses a method for detecting deep targets using multiple cross-electromagnetic component modes, belonging to the field of geophysical exploration. It includes several exploration vehicles equipped with multi-dimensionally adjustable sensors. The area to be explored is gridded, with the transmitting vehicle fixed to one side and multiple receiving vehicles automatically addressing to each detection node. At each node, the orientation of the detection sensors is adjusted to form six orthogonal transmission-reception combination modes: TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy, acquiring multi-mode electromagnetic data. Based on the principle of common depth, horizontal distance is converted into vertical detection depth. The rate of change of magnetic field strength with polarization direction for each mode is calculated as a weight for data fusion, and then a least-squares inversion algorithm is used to image the underground target. This invention achieves multi-mode, multi-node, and automated detection, significantly improving the detection accuracy and imaging effect of deep targets such as coal mine fire boundaries and cavities.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration, specifically to a deep target detection device and method using a multi-intersecting electromagnetic component mode, which is particularly suitable for fields such as boundary detection of coal mine fire zones, detection of cavities and subsidence areas, investigation of potential hazards in water conservancy dams, and development of urban underground space. Background Technology

[0002] After spontaneous combustion, the heat diffuses outwards, baking or melting the surrounding rocks, which then metamorphose into scorched rocks. These rocks collapse, creating numerous cracks and cavities, posing a significant safety hazard to coal mining in the vicinity of the fire zone. It is urgent to determine the boundaries, depth, and spatial distribution characteristics of the fire zone. However, due to the complexity of geological conditions and combustion processes, the combustion boundary of the fire zone in the direction of the coal seam extinguishing zone is extremely irregular, making the exploration of the fire zone boundary very difficult.

[0003] Currently, electromagnetic detection methods for naturally burned coal mine areas include: magnetic detection, transient electromagnetic method, high-density resistivity detection method, and ground-penetrating radar method.

[0004] Magnetic detection utilizes the characteristic that the magnetism of coal seams and surrounding ferromagnetic materials changes significantly with temperature variations. However, the formation is heterogeneous, and the presence of magnetic materials in the surrounding rocks can affect the detection results. Furthermore, the magnetic changes in rocks are greatly affected by high temperatures, leading to poor detection accuracy.

[0005] Transient electromagnetic methods include ground-towed and airborne transient electromagnetic detection modes. By emitting brief electromagnetic pulses into the ground, the resistivity distribution is retrieved by observing the attenuated secondary field signal. However, due to the pulse turn-off delay and coil ringing effect, there is a large detection blind zone, making it impossible to detect coal mine fire zones tens of meters away, and the boundaries of the fire zone cannot be accurately identified.

[0006] The high-density resistivity method constructs a DC field by manually deploying dozens to hundreds of electrodes, but it has shortcomings such as long deployment time, low efficiency, and great influence from electrode grounding resistance. Moreover, it is a DC field and cannot achieve precise identification of the boundary of the fire zone.

[0007] Ground penetrating radar (GPR) emits high-frequency electromagnetic pulses and receives reflected signals, which has the advantages of high resolution and strong effectiveness. However, due to the large attenuation of high-frequency electromagnetic waves, it has the disadvantage of shallow detection depth.

[0008] For example, a method for continuous exploration of the boundary of a coal mine burning area (patent publication number: CN118033775A) uses the drilling method, which determines the boundary by arranging multiple vertical drilling groups, but it has problems such as long time consumption, low efficiency, high cost, and discontinuous information.

[0009] In the prior art, CN115343768B discloses a vehicle-mounted transient electromagnetic underground space detection system based on weak coupling of a primary field. It employs a circular transmitting coil and a three-component receiving coil, achieving weak coupling of the primary field through differential and orthogonal arrangement. However, its transmission direction is fixed, and it only receives different components, failing to form multiple transmit-receive combination modes. Furthermore, the coils are fixedly installed, lacking automated attitude adjustment capabilities, and data processing is limited to conventional inversion. CN108732519B discloses a three-dimensional magnetic measurement method for wireless charging electromagnetic fields, measuring the maximum magnetic field strength through the rotation of a three-axis orthogonal coil. However, this method is used for electromagnetic environment assessment, not for underground target detection, and lacks data fusion and inversion capabilities.

[0010] Therefore, there is an urgent need for a detection device and method that can achieve highly effective, multi-dimensional, and automated detection, and can accurately identify and image deep underground targets. Summary of the Invention

[0011] To address the needs of existing technologies, this invention provides a deep target detection device and method with multiple cross-electromagnetic component modes. The aim is to overcome the problems of single detection mode, low automation, and insufficient imaging accuracy in existing technologies, and to achieve high-precision, multi-dimensional detection of deep targets such as coal mine fire zone boundaries and cavities.

[0012] A method for detecting deep targets using a multi-cross electromagnetic component mode includes several detection vehicles equipped with detection sensors. The detection sensors are used to transmit electromagnetic signals into the ground and / or receive secondary field signals from the ground. It also includes the following steps: Step 1: Divide the area to be detected into a grid of multiple detection nodes; Step 2: Set up a detection vehicle on one side of the area to be detected to transmit electromagnetic signals into the ground, denoted as the transmitting vehicle; Step 3: Set up a detection vehicle in the area to be detected to receive signals from the underground secondary field, denoted as the receiving vehicle; Step 4: Instruct the receiving vehicle to automatically locate each detection node sequentially, and perform the following operations at each detection node: Step 4.1: Adjust the orientation of the detection sensor so that it is transmitted along the three mutually orthogonal x, y, and z directions, and adjust the receiving direction accordingly to form multiple transmission-reception orthogonal combination modes; Step 4.2: In each combination mode, the transmitter vehicle transmits electromagnetic signals into the ground, the receiver vehicle receives the secondary field signals from the ground, and records the corresponding magnetic field strength data; Step 4.3: Associate and store the collected magnetic field strength data with the detection nodes and combination modes to obtain detection data; Step 5: Process the detection data to obtain the spatial distribution of underground targets in the area to be detected.

[0013] Furthermore, the processing procedure in step 5 is as follows: Step 5.1: Normalize the magnetic field strength data collected by each detection node for each combination mode; Step 5.2: Based on the horizontal distance between the transmitting and receiving points, convert the horizontal distance into the longitudinal detection depth using the common depth method. Step 5.3: For the normalized data of different modes of the same detection node, calculate the rate of change of magnetic field strength with polarization direction in each mode, and use the rate of change as a weight to perform weighted summation of the normalized data of each mode to obtain the fused data of the detection node. Step 5.4: Based on the fused data from all detection nodes, the electrical parameter distribution of the underground medium is obtained using an inversion algorithm, and then the underground target is imaged.

[0014] Furthermore, the multiple transmit-receive orthogonal combination modes include: TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy, where Tx represents transmit in the x-direction, Rx represents receive in the x-direction, Ty represents transmit in the y-direction, Ry represents receive in the y-direction, Tz represents transmit in the z-direction, and Rz represents receive in the z-direction.

[0015] Further, the calculation of the rate of change of magnetic field strength with polarization direction under each mode specifically involves calculating the rate of change of magnetic field strength between adjacent nodes or adjacent sampling points under the six modes TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy respectively. The weighted summation is specifically performed according to the formula: + + + Computational fusion of data, in which , , , , , These are the normalized magnetic field strength data for the six modes.

[0016] Furthermore, the inversion algorithm is a least squares inversion algorithm, including: 5.4.1: Constructing the objective function in, ( ) is the Lagrange multiplier; It is a measurement data matrix With the construction of forward model prediction values The difference, It is a priori estimate of the noise; It is the covariance matrix of the prior model, which measures the parameters of the inverted model. With specified parameters Deviation; It is the covariance matrix of the measured data; 5.4.2: The objective function is solved iteratively using the Newton minimization method, and the model parameters are updated until convergence is obtained to obtain the electrical parameters of the underground target; Step 5.4.3: Draw a spatial distribution map of the underground targets based on their electrical parameters.

[0017] Furthermore, the detection sensor is mounted on the detection vehicle via a multi-dimensional adjustment mechanism. This multi-dimensional detection mechanism includes a base fixedly mounted on the detection vehicle, a bracket mounted on the base rotating around the z-axis, and the detection sensor mounted on the bracket rotating around the x-axis or y-axis. Both the bracket and the detection sensor are driven to rotate independently by a drive mechanism.

[0018] Furthermore, the specific process by which the receiving vehicle automatically addresses each detection node sequentially is as follows: Several receiving vehicles are located on a single column or row of detector nodes in the grid, and simultaneously receive secondary field signals from the ground; then they synchronously move to the next column or row of detector nodes, and simultaneously receive secondary field signals from the ground again; this cycle continues until all detector nodes in the grid have been addressed.

[0019] The beneficial effects of this invention are as follows: By employing multi-detection node and multi-cross electromagnetic component detection technology, and utilizing multiple detection combination modes such as TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy, the effectiveness and accuracy of detecting the boundaries and geometry of deep coal mine cavities can be greatly improved. Attached Figure Description

[0020] Figure 1 This is a flowchart of the present invention; Figure 2 For grid-based detection construction drawings; Figure 3 This is a schematic diagram of the probe vehicle's structure; Figure 4 This is a schematic diagram of various transmit-receive orthogonal combination modes in this invention; Figure 5 This is a spatial distribution map of underground targets in the area to be explored. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings. Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The directional terms such as left, center, right, top, and bottom in the embodiments of the present invention are only relative concepts or referenced to the normal use state of the product, and should not be considered restrictive.

[0022] A method for detecting deep targets using a multi-intersecting electromagnetic component mode includes several detection vehicles equipped with detection sensors. These sensors transmit electromagnetic signals into the ground and / or receive secondary field signals from underground. Figure 1 and Figure 2 As shown, it also includes the following steps: Step 1: Divide the area to be detected into a grid of multiple detection nodes; Step 2: Set up a detection vehicle on one side of the area to be detected to transmit electromagnetic signals into the ground, denoted as the transmitting vehicle; Step 3: Set up a detection vehicle in the area to be detected to receive signals from the underground secondary field, denoted as the receiving vehicle; Step 4: Instruct the receiving vehicle to automatically locate each detection node sequentially, and perform the following operations at each detection node: Step 4.1: Adjust the orientation of the detection sensor so that it is orthogonal in the x, y, and z directions as the transmission direction, and adjust the receiving direction accordingly to form multiple orthogonal transmission-reception combination modes; such as... Figure 4 As shown, the various transmit-receive orthogonal combination modes include: TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy, where Tx represents transmission in the x-direction, Rx represents reception in the x-direction, Ty represents transmission in the y-direction, Ry represents reception in the y-direction, Tz represents transmission in the z-direction, and Rz represents reception in the z-direction; wherein, as... Figure 3As shown, the detection sensor 4 is mounted on the detection vehicle 1 via a multi-dimensional adjustment mechanism. This multi-dimensional detection mechanism includes a base 2 fixedly mounted on the detection vehicle, and a bracket 7 rotatably mounted on the base 2 about the z-axis. Specifically, the bracket 7 is a C-shaped structure with its opening facing upwards. The middle part of the bracket 7 is mounted on the base 2 via a vertical first rotating shaft 3. The detection sensor 4 is rotatably mounted on the bracket 7 about the x-axis or y-axis. Specifically, the two sides of the detection sensor 4 are rotatably mounted at both ends of the bracket 7 via a horizontal second rotating shaft 5. Both the bracket 7 and the detection sensor 4 are driven to rotate independently by a drive mechanism, which includes a first servo motor 9 and a second servo motor 6. The first servo motor 9 drives the bracket 7 to rotate via a gear set 8, and the second servo motor 6 drives the detection sensor 4 to rotate via a coupling and a second rotating shaft 5. By cooperating with the first servo motor 9 and the second servo motor 6, the plane of the detection sensor 4 can be controlled to be parallel to the x-axis-y-axis plane, the x-axis-z-axis plane, and the y-axis-z-axis plane, respectively. An attitude detection sensor (ADI dual-axis high-precision MEMS accelerometer ADXL203) is integrated on the detection sensor 4. Step 4.2: In each combination mode, the transmitting vehicle transmits electromagnetic signals into the ground, and the receiving vehicle receives the secondary field signals from the ground and records the corresponding magnetic field strength data. The specific process of the receiving vehicle automatically addressing each detection node in sequence is as follows: several receiving vehicles are located on a single column or row of detection nodes in the grid and simultaneously receive secondary field signals from the ground; then they synchronously move to the next column or row of detection nodes and simultaneously receive secondary field signals from the ground again; this cycle continues until all detection nodes in the grid have been addressed. Step 4.3: Associate and store the collected magnetic field strength data with the detection nodes and combination modes to obtain detection data; Step 5: Process the detected data to obtain the spatial distribution of underground targets in the area to be detected; wherein, the processing procedure is as follows: Step 5.1: Normalize the magnetic field strength data collected by each detection node for each combination mode; Step 5.2: Calculate the distance between two points using the formula: The horizontal distance between each receiving vehicle and the transmitting vehicle is calculated. Based on the horizontal distance between the transmitting and receiving points, the horizontal distance is converted into the longitudinal detection depth using the common depth method. Step 5.3: For the normalized data of different modes of the same detection node, calculate the rate of change of magnetic field strength with polarization direction in each mode, and use the rate of change as a weight to perform a weighted summation of the normalized data of each mode to obtain the fused data of the detection node; wherein, the calculation of the rate of change of magnetic field strength with polarization direction in each mode specifically involves: calculating the rate of change of magnetic field strength between adjacent nodes or adjacent sampling points in the six modes TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy respectively. The weighted summation is specifically performed according to the formula: Computational fusion of data, in which , , , , , These are the normalized magnetic field strength data under six different modes (i.e., the normalized data). Construct a measurement data matrix for n*m receiving points. , Step 5.4: Based on the fused data from all detection nodes, an inversion algorithm is used to obtain the electrical parameter distribution of the subsurface medium, thereby imaging the subsurface target; wherein, the inversion algorithm is a least squares inversion algorithm, including: 5.4.1: Constructing the objective function in, ( ) is the Lagrange multiplier; It is a measurement data matrix With the construction of forward model prediction values The difference, It is a priori estimate of the noise; It is the covariance matrix of the prior model, which measures the parameters of the inverted model. With specified parameters Deviation, model parameters To obtain the formation conductivity parameters through inversion, specify the parameters. This is a constraint value for the formation conductivity; It is the covariance matrix of the measured data; Its purpose is to prevent non-uniqueness in inversion when there is too much measurement data or the model parameters are not sensitive; at the same time, it suppresses errors caused by noise, which can lead to instability in inversion. 5.4.2: Using the Newton-Raphson minimization method to iteratively solve the objective function, the k-th iteration is: in It is the kth iteration Orientation The step size in the minimum direction; It is the gradient vector of the objective function, which can be obtained from the following formula: in It is the model parameter vector The nth unit, It is an n×m Jacobian matrix, obtained by the following formula: Among them It is the Hessian matrix of the objective function, and it is a real symmetric N×m matrix; in, It is the second-order information of the Taylor expansion of the objective function; Update the model parameters until convergence to obtain the electrical parameters of the underground target; Step 5.4.3: Based on the electrical parameters of the underground targets, draw a spatial distribution map of the underground targets, such as... Figure 5 As shown.

[0023] Experimental test: The cavity and subsidence area formed by the spontaneous combustion of coal seams in a coalfield fire area gradually expanded, affecting the safe production of coal mines and the ecological environment. Through detailed exploration of the burial depth, geometric dimensions, and boundaries of the deep underground cavity and subsidence area of ​​the coal mine, technical support was provided for the treatment of deep hidden dangers caused by spontaneous combustion of coal mines.

[0024] Depend on Figure 5 It can be seen that three potential cavity areas with high resistivity have been clearly identified and delineated: Location 1: at coordinates (6m, -41m), i.e., 4m along the survey line at a depth of 41m, there is a cavity target with a diameter of approximately 5m. Location 2: at coordinates (4m, -54m) - (4m, -63m), a continuous depth from 54m to 63m, there is an underground cavity target with a depth of approximately 17m and a diameter of approximately 5m along the survey line. Location 3: at coordinates (36m, -70m), i.e., 36m along the survey line, a continuous depth from 62m to 73m, and a diameter of approximately 4m along the survey line.

[0025] In summary, the measurement method disclosed in this embodiment innovatively adopts a novel multi-node, multi-intersection electromagnetic component detection technology. By utilizing multiple detection combination modes such as TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy, it can significantly improve the effectiveness and accuracy of detecting the boundaries and geometry of deep coal mine cavities.

[0026] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting deep targets using multiple cross-mode electromagnetic components, characterized in that: It includes several exploration vehicles, each equipped with a detection sensor. The detection sensor is used to transmit electromagnetic signals into the ground and / or receive secondary field signals from the ground. It also includes the following steps: Step 1: Divide the area to be detected into a grid of multiple detection nodes; Step 2: Set up a detection vehicle on one side of the area to be detected to transmit electromagnetic signals into the ground, denoted as the transmitting vehicle; Step 3: Set up a detection vehicle in the area to be detected to receive signals from the underground secondary field, denoted as the receiving vehicle; Step 4: Instruct the receiving vehicle to automatically locate each detection node sequentially, and perform the following operations at each detection node: Step 4.1: Adjust the orientation of the detection sensor so that it is transmitted along the three mutually orthogonal x, y, and z directions, and adjust the receiving direction accordingly to form multiple transmission-reception orthogonal combination modes; Step 4.2: In each combination mode, the transmitter vehicle transmits electromagnetic signals into the ground, the receiver vehicle receives the secondary field signals from the ground, and records the corresponding magnetic field strength data; Step 4.3: Associate and store the collected magnetic field strength data with the detection nodes and combination modes to obtain detection data; Step 5: Process the detection data to obtain the spatial distribution of underground targets in the area to be detected.

2. The method for detecting deep targets using multiple intersecting electromagnetic component modes according to claim 1, characterized in that: The processing procedure in step 5 is as follows: Step 5.1: Normalize the magnetic field strength data collected by each detection node for each combination mode; Step 5.2: Based on the horizontal distance between the transmitting and receiving points, convert the horizontal distance into the longitudinal detection depth using the common depth method. Step 5.3: For the normalized data of different modes of the same detection node, calculate the rate of change of magnetic field strength with polarization direction in each mode, and use the rate of change as a weight to perform weighted summation of the normalized data of each mode to obtain the fused data of the detection node. Step 5.4: Based on the fused data from all detection nodes, the electrical parameter distribution of the underground medium is obtained using an inversion algorithm, and then the underground target is imaged.

3. The deep target detection method with multiple intersecting electromagnetic component modes according to claim 2, characterized in that: The various transmit-receive orthogonal combination modes include: TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy, where Tx represents transmit in the x-direction, Rx represents receive in the x-direction, Ty represents transmit in the y-direction, Ry represents receive in the y-direction, Tz represents transmit in the z-direction, and Rz represents receive in the z-direction.

4. The deep target detection method with multiple cross-electromagnetic component modes according to claim 3, characterized in that: The calculation of the rate of change of magnetic field strength with polarization direction under each mode specifically involves calculating the rate of change of magnetic field strength between adjacent nodes or adjacent sampling points under the six modes TxRz, TxRy, TyRx, TyRz, TzRx, and TzRy respectively. The weighted summation is specifically performed according to the formula: + + + Computational fusion of data, in which , , , , , These are the normalized magnetic field strength data for the six modes.

5. The deep target detection method with multiple cross-electromagnetic component modes according to claim 4, characterized in that: The inversion algorithm is a least squares inversion algorithm, including: 5.4.1: Constructing the objective function in, ( ) is the Lagrange multiplier; It is a measurement data matrix With the construction of forward model prediction values The difference, It is a priori estimate of the noise; It is the covariance matrix of the prior model, which measures the parameters of the inverted model. With specified parameters Deviation; It is the covariance matrix of the measured data; 5.4.2: The objective function is solved iteratively using the Newton minimization method, and the model parameters are updated until convergence is obtained to obtain the electrical parameters of the underground target; Step 5.4.3: Draw a spatial distribution map of the underground targets based on their electrical parameters.

6. The method for detecting deep targets using multiple intersecting electromagnetic component modes according to claim 1, characterized in that: The detection sensor is mounted on the detection vehicle via a multi-dimensional adjustment mechanism. The multi-dimensional detection mechanism includes a base fixedly mounted on the detection vehicle, a bracket mounted on the base rotating around the z-axis, and the detection sensor mounted on the bracket rotating around the x-axis or y-axis. Both the bracket and the detection sensor are driven to rotate independently by a drive mechanism.

7. The method for detecting deep targets using multiple intersecting electromagnetic component modes according to claim 1, characterized in that: The specific process by which the receiving vehicle automatically addresses each detection node in sequence is as follows: Several receiving vehicles are located on the detection nodes in a single column or row of the grid, and simultaneously receive secondary field signals from the ground; then they synchronously move to the next column or row of detection nodes, and then simultaneously receive secondary field signals from the ground again. This process is repeated until all probe nodes within the grid have been addressed.

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