A helicopter-borne airborne electromagnetic detection system

Abstract

This invention discloses a helicopter-borne airborne electromagnetic detection system, belonging to the field of geophysical exploration technology. It includes a historical detection statistics unit (unit one) that extracts first historical detection data from historical electromagnetic detection data under the condition of helicopter flight vibration and the development trend of the underground target being parallel to the helicopter's flight path, and extracts second historical detection data under the condition of helicopter stability and the development trend of the underground target being non-horizontally related to the helicopter's flight path. An error influence factor one for the three-component electromagnetic signals generated in the first historical detection data is extracted. A second historical detection statistics unit extracts an error influence factor two for the three-component electromagnetic signals generated in the second historical detection data. A current geological exploration processing unit transmits electromagnetic signals to the target area. Based on error influence factors one and two, the geological three-dimensional image detection results are obtained. This invention can improve the accuracy of geological three-dimensional image detection.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a helicopter-borne airborne electromagnetic exploration system. Background Technology

[0002] Airborne magnetotelluric surveying is an advanced geophysical exploration technology that uses highly sensitive magnetic field sensors mounted on aircraft such as helicopters to capture underground electromagnetic response signals generated by the Earth's natural magnetic field. These signals contain rich geological information. Through specialized data inversion techniques, the collected signals are processed and analyzed to obtain information such as underground faults, fissures, groundwater distribution, and mineral resource reserves. This process is similar to performing a "CT scan" on the Earth, clearly presenting a three-dimensional image of the underground structure.

[0003] In traditional techniques, the three-component magnetic field signals are directly used to infer three-dimensional images of underground structures. However, no further verification is performed to check for errors caused by the interrelationship between the three-component magnetic field signals. This can easily lead to errors in the final three-dimensional image construction of underground structures, such as incorrect reconstruction of the location distribution of underground structures, which reduces the efficiency of the detection work and wastes resources. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this application provides a helicopter-borne airborne electromagnetic detection system.

[0005] This application provides a helicopter-borne airborne electromagnetic detection system, which adopts the following technical solution:

[0006] Historical detection statistics unit one is used to extract the first historical detection data from historical electromagnetic detection data where helicopter flight vibration exists and the development trend of the underground target is parallel to the helicopter flight path, and to extract the second historical detection data where helicopter stability exists and the development trend of the underground target is not horizontal to the helicopter flight path. The error influence coefficients generated by the three components of electromagnetic signals in the ground static flight test condition data and the error influence coefficients generated by the three components of electromagnetic signals in the ground dynamic flight test condition data are integrated by weighted average method to obtain error influence factor one.

[0007] Historical detection statistics unit two is used to integrate the error influence coefficients of the three components of electromagnetic signals generated in the ground static flight test condition data and the error influence coefficients of the three components of electromagnetic signals generated in the ground dynamic flight test condition data using a weighted average method to obtain error influence factor two.

[0008] The current geological exploration and processing unit is used to plan the flight route of the helicopter according to the target area where underground structure exploration is needed, and to transmit electromagnetic signals to the target area, obtain component received electromagnetic signals, and obtain geological three-dimensional image exploration results based on error influence factor one, error influence factor two and component received electromagnetic signals.

[0009] Preferably, historical electromagnetic detection data is acquired. If the historical electromagnetic detection data contains electromagnetic data that detects helicopter flight vibration and the development trend of the underground target is parallel to the helicopter flight path, then the first historical detection data is output.

[0010] If the historical electromagnetic detection data contains electromagnetic data detected when helicopters are stable and the development trend of underground targets is not horizontally related to the helicopter's flight path, then the second historical detection data will be output.

[0011] Preferably, the first historical detection data is classified into ground static flight detection condition data and dynamic flight detection condition data, and the first static test data and the first dynamic test data are output.

[0012] Environmental data features and helicopter vibration amplitude are extracted from the first static test data, and the first training features are output.

[0013] The received three-component electromagnetic signal is extracted from the first static test data, and the first historical test component signal is output. The three-component electromagnetic signal refers to the magnetic field signal in the x, y, and z directions of the plane normal direction measured by the x, y, and z component magnetic field sensors.

[0014] Obtain the simulated signal under ideal conditions, compare the first historical test component signal with the simulated signal, and obtain the error of the first component signal.

[0015] Establish a correlation trend feature map between the first component signal error and the first training feature, and extract the first correlation influence coefficient between the first component signal error and the first training feature.

[0016] Preferably, the helicopter's flight speed, flight direction, and tilt angle between the wind direction are extracted from the first dynamic test data, and flight data feature one is output. The first training feature and the flight data feature are combined to form a second training feature.

[0017] Based on the first component signal error one and the first dynamic test data, the first component signal error two is obtained. The first component signal error two and the second training feature are used to establish a correlation influence trend feature map, and the second correlation influence coefficient between the first component signal error two and the second training feature is extracted.

[0018] The first training feature and the second training feature are combined to form reference detection condition feature one. The first correlation influence coefficient and the second correlation influence coefficient are integrated by weighted average method to obtain error influence factor one.

[0019] Preferably, the second historical detection data is classified into ground static flight detection condition data and dynamic flight detection condition data, and the second static test data and the second dynamic test data are output.

[0020] The environmental data features and the tilt angle formed between the development trend of the underground target and the helicopter flight path are extracted from the second static test data to output the third training features.

[0021] The error of the second component signal is obtained based on the error of the first component signal and the second static test data.

[0022] Establish a correlation influence trend feature map between the second component signal error and the third training feature, and extract the third correlation influence coefficient between the second component signal error and the third training feature.

[0023] Preferably, the helicopter's flight speed, the tilt angle between the flight direction and the wind direction are extracted from the second dynamic test data, and the second flight data feature is output. The third training feature and the second flight data feature are combined to form the fourth training feature.

[0024] Based on the error of the first component signal and the second dynamic test data, the error of the second component signal is obtained.

[0025] Establish a correlation influence trend feature map between the second component signal error and the fourth training feature, and extract the fourth correlation influence coefficient between the second component signal error and the fourth training feature;

[0026] The third and fourth training features are combined to form the second reference detection condition feature. The third and fourth correlation influence coefficients are then integrated using a weighted average method to obtain the second error influence factor.

[0027] Preferably, based on the target area where underground structure detection is needed, a helicopter flight path is planned, and electromagnetic signals are transmitted to the target area according to the flight path to obtain component electromagnetic signals.

[0028] The current detection condition characteristics of the helicopter are detected, and the current detection condition characteristics are matched with reference detection condition characteristic one and reference detection condition characteristic two to obtain one of the matching results. The preprocessing influence factors are extracted from error influence factor one and error influence factor two accordingly.

[0029] Based on the preprocessing influence factor, error compensation is performed on the received component electromagnetic signal to obtain the actual received component signal. Based on the actual received component signal, three-dimensional inversion processing is performed to obtain the geological three-dimensional image detection result.

[0030] Compared with the prior art, the present invention has the following characteristics and beneficial effects:

[0031] This study analyzes the electromagnetic signals received from helicopter-transmitted electromagnetic signals using historical electromagnetic survey data from geological exploration. The analysis distinguishes between different flight conditions, specifically extracting first and second historical survey data. The first historical survey data refers to data detected under conditions where helicopter vibration exists and the underground target's development trend is parallel to the helicopter's flight path. The second historical survey data refers to data detected under conditions where the helicopter is stable and the underground target's development trend is not horizontal to the helicopter's flight path. These two conditions are highly likely to cause correlational errors in the received three-component electromagnetic signals, and the correlational errors differ between these two conditions, necessitating a differential analysis. To reduce the probability of errors in subsequent data feature analysis of the first and second historical exploration data, the historical exploration data under these two conditions are classified into ground static exploration data and dynamic flight exploration data. Exploration under ground static conditions can separate fundamental error terms, while exploration under dynamic flight conditions can expose residual errors caused by time-varying factors, thereby enhancing the accuracy of subsequent correlation coefficient extraction, achieving a calibration effect. By statistically analyzing the electromagnetic signal error influence factors received under these two conditions, error compensation can be performed on the component electromagnetic signals obtained from current underground structure exploration, and three-dimensional inversion processing can be performed to obtain geological three-dimensional image exploration results. This processing method fully considers the correlation between diverse factors, further improving the accuracy of received electromagnetic signals and increasing resource utilization. Attached Figure Description

[0032] Figure 1 This is a structural block diagram of a helicopter-borne airborne electromagnetic detection method, which is the main feature of this embodiment. Detailed Implementation

[0033] The present invention will be further described in detail below with reference to the following embodiments.

[0034] Reference Figure 1 A helicopter-borne airborne electromagnetic detection system, comprising:

[0035] Historical detection statistics unit one is used to extract the first historical detection data from historical electromagnetic detection data where helicopter flight vibration exists and the development trend of the underground target is parallel to the helicopter flight path, and to extract the second historical detection data where helicopter stability exists and the development trend of the underground target is not horizontal to the helicopter flight path. The error influence coefficients generated by the three components of electromagnetic signals in the ground static flight test condition data and the error influence coefficients generated by the three components of electromagnetic signals in the ground dynamic flight test condition data are integrated by weighted average method to obtain error influence factor one.

[0036] The second historical exploration statistics unit is used to integrate the error influence coefficients of the three components of electromagnetic signals generated in the ground static flight test condition data and the error influence coefficients of the three components of electromagnetic signals generated in the ground dynamic flight test condition data using a weighted average method to obtain the second error influence factor.

[0037] The current geological exploration and processing unit is used to plan the flight route of the helicopter according to the target area where underground structure exploration is needed, and to transmit electromagnetic signals to the target area, obtain component received electromagnetic signals, and obtain geological three-dimensional image exploration results based on error influence factor one, error influence factor two and component received electromagnetic signals.

[0038] Specifically, this study analyzes the electromagnetic signals received from helicopter-transmitted electromagnetic signals using historical electromagnetic detection data from geological surveys. The analysis involves distinguishing between different flight conditions experienced by the helicopter. Specifically, it extracts first and second historical detection data from the historical electromagnetic detection data. The first historical detection data refers to electromagnetic data detected when helicopter vibration exists and the development trend of the underground target is parallel to the helicopter's flight path. The second historical detection data refers to electromagnetic data detected when the helicopter is stable and the development trend of the underground target is not horizontal to the helicopter's flight path. These two scenarios are highly likely to have a correlation effect on the received three-component electromagnetic signals, and the correlation effect errors differ between these two scenarios, thus requiring a differential analysis. To reduce the probability of errors in subsequent data feature analysis of the first and second historical exploration data, the historical exploration data under these two conditions are classified into ground static exploration data and dynamic flight exploration data. Exploration under ground static conditions can separate fundamental error terms, while exploration under dynamic flight conditions can expose residual errors caused by time-varying factors, thereby enhancing the accuracy of subsequent correlation influence coefficient extraction, achieving a calibration effect. By statistically analyzing the electromagnetic signal error influence factors received under these two conditions, error compensation can be performed on the component electromagnetic signals obtained from current underground structure exploration, and three-dimensional inversion processing can be performed to obtain geological three-dimensional image exploration results. Through the above processing method, the correlation between diverse factors is fully considered, which can further improve the accuracy of received electromagnetic signals and increase resource utilization.

[0039] Among them, historical exploration statistics unit one includes:

[0040] Acquire historical electromagnetic detection data. If the historical electromagnetic detection data contains electromagnetic data showing helicopter flight vibrations and the development trend of the underground target is parallel to the helicopter flight path, then output the first historical detection data.

[0041] If the historical electromagnetic detection data contains electromagnetic data detected when helicopters are stable and the development trend of underground targets is not horizontally related to the helicopter's flight path, then the second historical detection data will be output.

[0042] The first historical detection data is classified into ground static flight detection condition data and dynamic flight detection condition data, and the first static test data and the first dynamic test data are output.

[0043] Environmental data features and helicopter vibration amplitude are extracted from the first static test data, and the first training features are output.

[0044] The received three-component electromagnetic signal is extracted from the first static test data, and the first historical test component signal is output. The three-component electromagnetic signal refers to the magnetic field signal in the x, y, and z directions of the plane normal direction measured by the x, y, and z component magnetic field sensors.

[0045] The simulated signal under ideal conditions is obtained, and the first historical component signal to be measured is compared with the simulated signal to obtain the error of the first component signal.

[0046] Establish a correlation trend feature map between the first component signal error and the first training feature, and extract the first correlation influence coefficient between the first component signal error and the first training feature.

[0047] Extract the helicopter's flight speed, flight direction, and tilt angle between the wind direction from the first dynamic test data, and output the first flight data feature. The first training feature and the flight data feature are combined to form the second training feature.

[0048] Based on the first component signal error one and the first dynamic test data, the second component signal error is obtained. The first component signal error two is then used to establish a correlation influence trend feature map with the second training feature, and the second correlation influence coefficient between the first component signal error two and the second training feature is extracted.

[0049] The first training feature and the second training feature are combined to form reference detection condition feature one. The first correlation influence coefficient and the second correlation influence coefficient are integrated by weighted average method to obtain error influence factor one.

[0050] Specifically, historical electromagnetic detection data (including historical data on transmitted and received electromagnetic signals, environmental characteristics of the helicopter, flight speed, flight direction, and the bedding structure or fracture development direction of underground targets obtained from historical detection), and first historical detection data (when the helicopter vibrates, it will cause signal errors in the received three-component electromagnetic signals. For example, due to the diverse vibration sources of helicopters (such as forced vibrations caused by rotor, engine, and other components), their periodic oscillations will cause slight shifts or wobbling in the position of the onboard electromagnetic sensors. This mechanical displacement will change the relative attitude between the sensor and the target area, This affects the directionality and amplitude accuracy of magnetic field measurements, leading to misjudgments in the subsequent 3D geological structure images derived from the three-component electromagnetic signals, thus reducing the efficiency of the detection work. Similarly, if the development trend of the underground target (the direction of bedding structure or fracture development) has an angular relationship with the flight path, its abnormal response may be projected and superimposed on multiple components, resulting in signal errors in the received three-component electromagnetic signals. If the development trend of the underground target (the direction of bedding structure or fracture development) does not have an angular relationship with the flight path (i.e., parallel relationship), the signal error caused by helicopter vibration can be mainly considered. Second, historical detection data (as above) As stated: If the development trend of the underground target (the direction of bedding structure or fracture development) has an angular relationship with the flight path, its abnormal response may be projected and superimposed on multiple components, resulting in signal errors in the received three-component electromagnetic signal. If the helicopter maintains a stable flight state, the signal error caused by the tilt angle value of the angular relationship between the development trend of the underground target (the direction of bedding structure or fracture development) and the flight path should be mainly considered. The electromagnetic signal is transmitted by the transmitting coil and received by the receiving sensor. The transmitting coil, receiving sensor, historical detection statistics unit one, historical detection statistics unit two, and current geological detection processing are all considered. All units (including the data acquisition unit) are integrated onto the helicopter platform. Helicopter flight is used for signal excitation (e.g., electromagnetic field transmission): a pulsed primary field (magnetic field) is sent underground via a transmission loop on the helicopter. This field induces eddy currents in underground conductors. To achieve deep-sea exploration, the peak value of the transmission current, coil area, or number of turns needs to be increased to enhance the magnetic moment; simultaneously, rapid shutdown is required to broaden the bandwidth and enhance shallow-level resolution. Secondary field acquisition: After the primary field disappears, the decaying eddy currents generate a secondary electromagnetic field. The receiver uses a low-noise sensor to capture this response signal and reduces primary field interference through reverse flux compensation coils or "zero-position" deployment.Some systems also employ large dynamic range receivers combined with adaptive filtering algorithms to suppress the aircraft's own electromagnetic noise. The first static test data (referring to electromagnetic signals collected by electromagnetic detection when the helicopter is relatively stationary with respect to the ground) and the first dynamic test data (referring to electromagnetic signals collected by electromagnetic detection when the helicopter is in relative motion with respect to the ground) are used. Detection under static ground conditions can separate the fundamental error term, while detection under dynamic flight conditions can expose residual errors caused by time-varying factors, thereby enhancing the accuracy of subsequent correlation coefficient extraction, achieving a calibration effect. The first training features (environmental data features including wind speed, wind direction, ambient temperature and humidity, air density, etc., helicopter vibration...) Dynamic amplitude: While helicopters typically possess a dynamic balance adjustment system to regulate their attitude, signal errors are still highly likely. For example, even with dynamic adjustment, the helicopter's movement can still cause changes in the relative position and angle between the pod and the ground. These changes alter the coupling relationship of the electromagnetic field, introducing signal errors. Therefore, further detection of helicopter vibration amplitude is necessary, such as using a three-axis MEMS accelerometer (which collects vibration signals via a multi-axis accelerometer, combined with an A / D conversion module and a wireless transmission module to transmit data to a computer for processing in real time). For instance, testing devices based on this technology have been successfully applied to rotor vibration characteristic analysis, obtaining key parameters such as amplitude and frequency. Parameters), simulation data (the ideal conditions here usually refer to a series of assumptions or optimized states that can maximize system performance, reduce interference and ensure data accuracy, such as a uniform and stable flight platform, an ideal electromagnetic environment, etc., and the flight conditions are ground static flight detection conditions), first component signal error (by comparing the first historical measured component signal and the simulation signal, the signal error value generated by the three-component electromagnetic signal received under the condition of helicopter flight vibration and the development trend of the underground target being parallel to the helicopter flight path can be known, which is used for subsequent characteristic statistics of the correlation influence relationship between related error influencing factors), correlation influence trend feature map (the first component signal is compared with the simulation data to determine the signal error value generated by the simulation data under the condition of helicopter flight vibration and the development trend of the underground target being parallel to the helicopter flight path, which is used for subsequent characteristic statistics of the correlation influence relationship between related error influencing factors), and correlation influence trend feature map (the first component signal is compared with the simulation data to determine the signal error value generated by the simulation data). Using the component signal error as the y-axis value and the first training feature as the x-axis value, a correlation trend feature map of the first component signal error and the first training feature is constructed. First, the environmental data features in the first training feature are subjected to unified data fusion processing (such as eliminating dimensional differences through mathematical transformations to make each indicator fall within the same scale range. Common methods include: Z-score standardization (based on mean and standard deviation), Min-Max normalization (mapped to the [0,1] interval); simple and efficient, suitable for multivariate analysis of linear relationships (such as preprocessing of machine learning model inputs). For example, ℃, %RH, and kg / m³ can be converted into dimensionless values ​​and then directly concatenated for use.If there is a linear relationship between the first component signal error and the first training feature, the correlation coefficient can be directly extracted (e.g., the average slope, i.e., the average slope between the first component signal error and the environmental data feature in the first training feature, and the average slope between the first component signal error and the helicopter vibration amplitude in the first training feature; these two are combined to form the first correlation influence coefficient). If there is a nonlinear relationship between the first component signal error and the first training feature, traditional linear correlation analysis is no longer applicable. It is recommended to use a nonlinear regression model based on machine learning or statistical learning, such as support vector regression (SVR), random forest regression, or Gaussian process regression. These methods can capture complex nonlinear mapping relationships and quantify the dependence between inputs (vibration amplitude, environmental parameters) and outputs (signal errors), i.e., obtain the first correlation influence coefficient. Flight data feature one (similarly, the tilt angle values ​​between helicopter flight speed, flight direction, and wind direction are uniformly fused (e.g., the combination of an inertial navigation system (INS) and an atmospheric data system (ADCS): acquiring helicopter attitude, acceleration, and other information through an inertial measurement unit (IMU), then combining it with a global positioning system (GPS) to correct position errors; simultaneously using an airspeed tube, angle-of-attack sensor, etc., to collect real-time airflow parameters (including wind direction and speed). The data from both can be fused using a Kalman filter algorithm.) Unified to the same coordinate system), and so on), First component signal error two (the explanation is the same as the first component signal error one, the difference is that the flight condition of the simulation signal is a dynamic flight detection condition), second correlation influence coefficient (and so on), error influence factor one (the weighting basis is determined according to the importance, frequency of occurrence or criticality of each motion state in the actual application scenario, etc., to determine their respective weights (w1 and w2). For example, if the ground static flight detection condition is more common and has a greater impact on the system performance, then it is given a higher weight; and vice versa; calculate the comprehensive coefficient: let the first correlation influence coefficient and the second correlation influence coefficient be a and b respectively, then the comprehensive influence coefficient Q = w1*a +w2*b, where w1+w2=1. This method is simple and intuitive, but the weights need to be reasonably selected to ensure accuracy), reference detection condition feature one (as a database for subsequent feature information matching judgment).

[0051] Among them, historical exploration statistics unit two includes:

[0052] The second historical detection data is classified into ground static flight detection condition data and dynamic flight detection condition data, and the second static test data and the second dynamic test data are output.

[0053] The environmental data features and the tilt angle formed between the development trend of the underground target and the helicopter flight path are extracted from the second static test data to output the third training features.

[0054] The second component signal error is obtained based on the first component signal error and the second static test data.

[0055] A correlation trend feature map is established between the error of the second component signal and the third training feature, and the third correlation influence coefficient between the error of the second component signal and the third training feature is extracted.

[0056] Extract the helicopter's flight speed, flight direction, and tilt angle between the wind direction from the second dynamic test data, and output the second flight data feature. The third training feature and the second flight data feature are combined to form the fourth training feature.

[0057] Based on the error of the first component signal and the second dynamic test data, the error of the second component signal is obtained.

[0058] A correlation trend feature map is established between the second component signal error and the fourth training feature, and the fourth correlation influence coefficient between the second component signal error and the fourth training feature is extracted.

[0059] The third and fourth training features are combined to form reference detection condition feature two. The third and fourth correlation influence coefficients are then integrated using a weighted average method to obtain error influence factor two.

[0060] Specifically, the second static test data and the second dynamic test data (ground static flight detection condition data refers to detection data when the helicopter is stationary relative to the ground, and dynamic flight detection condition data refers to detection data when the helicopter is in motion relative to the ground; the interpretation of this detection data is the same as that of historical electromagnetic detection data, except that the former is included by the latter), and the tilt angle formed between the development trend of the underground target and the helicopter flight path: if the bedding structure or fracture development direction of the underground target has an angular relationship with the flight path, its anomalous response may be projected and superimposed on multiple components. Here, the development trend of the underground target refers to... The layered structure or fracture development direction of the underground target body, the second component signal error one (based on the first component signal error one and so on, by comparing the second historical measured component signal (the three-component electromagnetic signal received from the first dynamic test data is extracted to obtain the second historical measured component signal) with the simulation signal, can be known as the signal error value generated by the three-component electromagnetic signal received under the condition that there is stable helicopter flight and the development trend of the underground target body is not parallel to the helicopter flight path), the third correlation influence coefficient (based on the first correlation influence coefficient and the second correlation influence coefficient, and so on), and the error influence factor two (based on the error influence factor one, and so on).

[0061] The current geological exploration and processing unit includes:

[0062] Based on the target area where underground structure detection is needed, a helicopter flight path is planned, and electromagnetic signals are transmitted to the target area according to the flight path, and component electromagnetic signals are received.

[0063] The current detection condition characteristics of the helicopter are detected, and the current detection condition characteristics are matched with reference detection condition characteristic one and reference detection condition characteristic two to obtain one of the matching results. The preprocessing influence factors are extracted from error influence factor one and error influence factor two accordingly.

[0064] Based on the preprocessing influence factor, error compensation is performed on the received component electromagnetic signal to obtain the actual received component signal. Based on the actual received component signal, three-dimensional inversion processing is performed to obtain the geological three-dimensional image detection result.

[0065] Specifically, for example, planning the flight path of a helicopter (e.g., through a single-rotor helicopter flight status intelligent monitoring system: this system includes a central control unit, an environmental monitoring module, a flight data analysis module, and a flight mode intelligent selection module, etc. Among them, the intelligent navigation system is integrated into the central control unit, which can perform real-time analysis of terrain and obstacles, thereby automatically planning the optimal flight path in complex terrain, effectively avoiding possible obstacles and conflicts. This function can be directly applied to underground structure detection missions, ensuring that the helicopter performs detection operations efficiently and safely according to the optimized route), obtaining one of the matching results (e.g., if the environmental data characteristics and helicopter vibration amplitude in the current detection conditions match the conditions of the first historical detection data, then the error influence factor is selected as the preprocessing influence factor), and the actual component received signal (actual component received signal = component The received electromagnetic signal is calculated as follows: * Preprocessing Influence Factor + Component Received Electromagnetic Signal. It should be noted that if the current detection conditions match those of the first and second historical detection data, then both Error Influence Factor 1 and Error Influence Factor 2 are used as preprocessing influence factors (Error Influence Factor 1 and Error Influence Factor 2 are summed). Therefore, the actual component received signal = component received electromagnetic signal * preprocessing influence factor + component received electromagnetic signal. The geological 3D image detection results (Helicopter high-power time-domain airborne electromagnetic system (TEM): based on Faraday's law of electromagnetic induction, a helicopter carries a transmitter and receiver. An alternating electromagnetic field is generated by an excitation signal, causing the conductors inside the ore body to discharge and radiate secondary electromagnetic signals after inducing current. The receiver transmits the signal back to the ground for 3D electromagnetic theory simulation and inversion calculation, which can generate two-dimensional or three-dimensional imaging images of the conductors within the ore layer).

[0066] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A helicopter-borne airborne electromagnetic survey system, characterized in that Comprise: The historical detection statistical unit one is used for extracting the first historical detection data from the historical electromagnetic detection data, and the development trend of the underground target body is parallel to the helicopter flight route, and the second historical detection data is extracted under the condition that the helicopter is stable, and the development trend of the underground target body is not horizontal to the helicopter flight route. The error influence coefficient of the three-component electromagnetic signal generated in the ground static flight test condition data and the error influence coefficient of the three-component electromagnetic signal generated in the ground dynamic flight test condition data are weighted and averaged to obtain the error influence factor one; The historical detection statistical unit two is used for weighting and averaging the error influence coefficient of the three-component electromagnetic signal generated in the ground static flight test condition data and the error influence coefficient of the three-component electromagnetic signal generated in the ground dynamic flight test condition data to obtain the error influence factor two; The current geological detection processing unit is used for planning the flight route of the helicopter according to the target area which needs to detect the underground structure, and emitting electromagnetic signals to the target area to obtain component received electromagnetic signals. The error influence factor one, the error influence factor two and the component received electromagnetic signals are used to obtain the geological three-dimensional image detection result.

2. A helicopter-borne airborne electromagnetic survey system according to claim 1, wherein, The historical detection statistical unit one comprises: Obtain historical electromagnetic detection data, if the helicopter flight vibration exists in the historical electromagnetic detection data, and the development trend of the underground target body is parallel to the helicopter flight route, the electromagnetic data detected is output as the first historical detection data; If the helicopter is stable in the historical electromagnetic detection data, and the development trend of the underground target body is not horizontal to the helicopter flight route, the electromagnetic data detected is output as the second historical detection data.

3. A helicopter-borne airborne electromagnetic survey system according to claim 2, wherein, The historical detection statistical unit one further comprises: Classify the first historical detection data into ground static flight detection condition data and dynamic flight detection condition data, and output the first static test data and the first dynamic test data; Extract the environmental data features and the helicopter vibration amplitude from the first static test data, and output the first training features; Extract the three-component electromagnetic signals received from the first static test data, and output the first historical component signal to be measured. The three-component electromagnetic signals refer to the magnetic field signals in the x, y and z directions measured by the x, y and z component magnetic field sensors. Obtain the simulation signal under ideal conditions, compare the first historical component signal to be measured with the simulation signal, and obtain the first component signal error one; Establish the correlation influence trend feature map between the first component signal error one and the first training features, and extract the first correlation influence coefficient between the first component signal error one and the first training features.

4. A helicopter-borne airborne electromagnetic survey system according to claim 3, wherein The historical detection statistical unit one further comprises: Extract the inclination value between the helicopter flight speed, flight direction and wind direction from the first dynamic test data, and output the flight data features one. The first training features and the flight data features one are combined to form the second training features; According to the first component signal error one and the first dynamic test data, a first component signal error two is obtained, the first component signal error two and the second training feature are associated to establish an influence trend characteristic map, and a second associated influence coefficient between the first component signal error two and the second training feature is extracted; The first training feature and the second training feature are combined into a reference detection condition feature one, the first associated influence coefficient and the second associated influence coefficient are integrated by a weighted average method, and an error influence factor one is obtained.

5. A helicopter-borne airborne electromagnetic survey system according to claim 4, wherein, The historical detection statistical unit two comprises: The second historical detection data are classified into ground static flight detection condition data and dynamic flight detection condition data, and second static test data and second dynamic test data are output; Environment data features and an inclination angle between a development trend of a subsurface target body and a helicopter flight route are extracted from the second static test data, and third training features are output; According to the first component signal error one and the second static test data, a second component signal error one is obtained; The second component signal error one and the third training features are associated to establish an influence trend characteristic map, and a third associated influence coefficient between the second component signal error one and the third training features is extracted.

6. A helicopter-borne airborne electromagnetic survey system according to claim 5, wherein, The historical detection statistical unit two further comprises: An inclination angle between a helicopter flight speed, a flight direction and a wind direction is extracted from the second dynamic test data, and flight data features two are output, the third training features and the flight data features two are combined into fourth training features; According to the first component signal error one and the second dynamic test data, a second component signal error two is obtained; The second component signal error two and the fourth training features are associated to establish an influence trend characteristic map, and a fourth associated influence coefficient between the second component signal error two and the fourth training features is extracted. The third training features and the fourth training features are combined into a reference detection condition feature two, the third associated influence coefficient and the fourth associated influence coefficient are integrated by a weighted average method, and an error influence factor two is obtained.

7. A helicopter-borne airborne electromagnetic survey system according to claim 6, wherein, The current geological detection processing unit comprises: According to a target area requiring a subsurface structure detection, a flight route of a helicopter is planned, an electromagnetic signal is emitted to the target area according to the flight route, and a component received electromagnetic signal is obtained; A current detection condition feature of the current helicopter is detected, the current detection condition feature is matched with the reference detection condition feature one and the reference detection condition feature two, one kind of matching result is obtained, and a pretreatment influence factor is extracted from the error influence factor one and the error influence factor two accordingly; According to the pretreatment influence factor, the component received electromagnetic signal is error compensated to obtain an actual component received signal, and a geological three-dimensional image detection result is obtained by three-dimensional inversion processing according to the actual component received signal.

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