Method for calculating the effective depth of detection of airborne transient electromagnetic

Abstract

The application provides a method for calculating an effective detection depth of airborne transient electromagnetic, comprising: obtaining original airborne transient electromagnetic measurement data measured by an airborne transient electromagnetic measurement system; calculating a system background noise level of each measuring point according to original time series of each measuring point of the airborne transient electromagnetic observation; calculating an average resistivity of an overburden layer of each measuring point according to the original airborne transient electromagnetic measurement data; and calculating the effective detection depth of each measuring point according to the system background noise level and the average resistivity of the overburden layer of each measuring point. The application calculates the system background noise level and the average resistivity of the overburden layer of the airborne transient electromagnetic, realizes the rapid calculation of the effective detection depth of the airborne transient electromagnetic, and solves the technical problem of insufficient accuracy caused by the fact that the effective detection depth of the measured airborne transient electromagnetic measurement is obtained through the empirical formula in the prior art.

Description

Technical Field

[0001] This application belongs to the field of electromagnetic exploration technology, specifically relating to a method for calculating the effective detection depth of airborne transient electromagnetic fields. Background Technology

[0002] Airborne transient electromagnetic method (AEEM) uses a helicopter as a platform, equipped with a high-power, high-sensitivity electromagnetic measurement system. It transmits an induced electromagnetic field into the ground via a transmitting coil, generating a secondary induced field within the underground geological body. A receiving coil receives the signal of this secondary induced field, and analysis of the received signal allows for the detection of underground geological formations. AEEM offers advantages such as high measurement speed, high efficiency, large exploration depth, minimal impact from terrain, and strong detection capability for good conductors. It is an ideal detection method for high-altitude, complex terrain areas and has already achieved good application results in the exploration and engineering surveying of polymetallic deposits such as copper, nickel, gold, and uranium.

[0003] The effective detection depth of airborne transient electromagnetic systems has always been a focus of attention. In 2019, Han Xue, Yin Changchun, and others conducted a study on the detection depth of time-domain airborne electromagnetic systems using the VTEM system from Geotech Corporation of Canada as an example. Through theoretical numerical simulation, they showed that the detection depth of airborne transient electromagnetic systems is closely related to the emitted magnetic moment, flight altitude, and the thickness of the overburden layer (Han Xue, Yin Changchun, Ren Xiuyan, et al. Study on the detection depth of time-domain airborne electromagnetic systems. Journal of Jilin University (Earth Science Edition), 2019, 49(5): 1448-1456). This study provides some guidance for setting exploration parameters of airborne transient electromagnetic systems through theoretical numerical simulation, but it does not provide a method for calculating the effective detection depth of measured airborne electromagnetic data. Currently, the effective detection depth of measured airborne transient electromagnetic measurements is mostly determined by empirical formulas. Calculations show that, due to The coefficient is an empirical factor (generally taken as 0.4~1.5), which is closely related to geological conditions, noise levels, etc. Therefore, the detection depth calculated by this formula is only an estimate, which to some extent affects the accuracy of data interpretation. How to quickly obtain the effective detection depth of each measuring point through actual measured airborne transient electromagnetic measurement data is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, this application provides a method for calculating the effective detection depth of airborne transient electromagnetic systems. By calculating the system background noise level of the airborne transient electromagnetic system and the average resistivity of the overlying layer, the effective detection depth of the airborne transient electromagnetic system is calculated rapidly, thereby solving the technical problem that the effective detection depth of the measured airborne transient electromagnetic system in the prior art is not accurate enough because it is mostly calculated by empirical formulas.

[0005] The first aspect of this application provides a method for calculating the effective detection depth of airborne transient electromagnetic fields, which includes the following steps.

[0006] Step S1: Obtain the raw airborne transient electromagnetic measurement data from the airborne transient electromagnetic measurement system. The raw airborne transient electromagnetic measurement data includes the raw time series of each measurement point of the airborne transient electromagnetic observation.

[0007] Step S2: Based on the original time series of each measuring point from the airborne transient electromagnetic observation, calculate the system background noise level η at each measuring point using Formula 1. v .

[0008] Formula 1 In Formula 1, η v For noise level, This is the original time series of measurement point i from airborne transient electromagnetic observations. This is the average value of the original time series. N The total number of time series samples for a single measurement point. i For the first i One sampling point.

[0009] Step S3: Based on the original airborne transient electromagnetic measurement data, the apparent resistivity depth imaging algorithm is used to calculate the late apparent resistivity corresponding to each measurement point point by point and time channel. And imaging depth, then based on the late apparent resistivity corresponding to each measurement point. Based on the imaging depth and Formula 2, the average resistivity of the overlying layer at each measuring point is calculated. .

[0010] Formula 2 Step S4: Based on the system background noise level η at each measuring point v and the average resistivity of the overcoat layer The effective detection depth of each measuring point is calculated using Formula 3.

[0011] Formula 3 In Formula 3, I is the transmitting current (unit: A), and A is the area of ​​the transmitting coil (unit: m²). 2 ), The average resistivity of the overlying layer (unit: ), η v System background noise level (unit: ).

[0012] In one specific embodiment of this application, step S2 includes: Step S2a: Calculate the average decay curve for the original time series data of airborne transient electromagnetic events to obtain the average value of the original time series. Step S2b: Subtract the average value from the original time series data, and calculate the root mean square error of the difference using Formula 1 to obtain the system background noise level η at each measuring point. v .

[0013] In one specific embodiment of this application, the multiple observation time channels include channels 0-44, and the multiple time windows are 45 time windows.

[0014] In one specific embodiment of this application, the method further includes the following after step S2: Step S21: Perform data preprocessing on the raw airborne transient electromagnetic measurement data to obtain multi-channel dB / dt data for multiple observation time channels. Data preprocessing includes filtering, stacking, channel extraction, and background field correction; Step S3 includes: Step S3a: Using multi-channel dB / dt data from multiple observation time channels, employ the apparent resistivity depth imaging algorithm to calculate the late apparent resistivity corresponding to each measurement point point by point and time channel. And the imaging depth, and then calculate the average resistivity of the overlying layer at each measuring point. .

[0015] In one specific embodiment of this application, step S21 includes: Step S211: Perform high-pass and low-pass filtering on the original airborne transient electromagnetic measurement data to obtain the filtered data; Step S212: Weight the filtered data and superimpose them to obtain the superimposed aero-electromagnetic response; Step S213: Perform logarithmic equal-interval sampling on the superimposed aero-electromagnetic response to obtain aero-electromagnetic response data for multiple time windows; Step S214: Perform background field correction on the airborne electromagnetic response data of multiple time windows to obtain multi-channel dB / dt data of multiple observation time channels after data preprocessing.

[0016] In one specific embodiment of this application, step S3 includes: Step S3b: Based on the original airborne transient electromagnetic measurement data, calculate the late apparent resistivity corresponding to each measurement point using Formula 4. .

[0017] Formula 4 In Formula 4, SF The result of normalizing the induced electromotive force of the receiving coil with respect to the transmitting magnetic moment (unit: pV / Am) 4 ), The observation time is expressed in milliseconds (ms).

[0018] Step S3c: Calculate the imaging depth corresponding to each measuring point using Formula 5.

[0019] Formula 5 In Formula 5, For depth coefficient, For observation time Apparent resistivity at late stage (unit: ), The observation time is expressed in milliseconds (ms).

[0020] Step S3d: Based on the late apparent resistivity corresponding to each measuring point Based on the imaging depth and formula 2, the average resistivity of the overlying layer at each measuring point is calculated.

[0021] In one specific embodiment of this application, the depth coefficient With observation time The corresponding number of channels has the following relationship: Formula Six In Formula Six, For observation time The corresponding number of channels.

[0022] A second aspect of this application provides a computer apparatus including a processor and a memory. The processor is used to execute a method for calculating the effective detection depth of airborne transient electromagnetic fields according to the first aspect of this application. The memory is used to store executable instructions of the processor.

[0023] A third aspect of this application provides a computer-readable storage medium having executable instructions stored thereon. When executed by a processor, the executable instructions implement a method for calculating the effective depth of airborne transient electromagnetic detection according to the first aspect of this application.

[0024] The fourth aspect of this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements a method for calculating the effective detection depth of airborne transient electromagnetic fields according to the first aspect of this application.

[0025] The beneficial effects of the technical solution of this application are as follows: by calculating the system background noise level of airborne transient electromagnetic and the average resistivity of the overlying layer, the effective detection depth of airborne transient electromagnetic is rapidly calculated. Compared with the currently commonly used empirical values, the calculation of the effective detection depth of this technology is more accurate, which improves the accuracy of airborne transient electromagnetic inversion interpretation to a certain extent. Attached Figure Description

[0026] Figure 1 The diagram shown is a flowchart illustrating a method for calculating the effective detection depth of airborne transient electromagnetic fields according to an embodiment of this application.

[0027] Figure 2 The diagram shown is a schematic diagram of data acquisition for an airborne transient electromagnetic measurement system according to an embodiment of this application.

[0028] Figure 3 The diagram shown is a data preprocessing flowchart for measured airborne transient electromagnetic data provided in an embodiment of this application.

[0029] Figure 4 The figure shown is a calculation result of the effective detection depth of airborne transient electromagnetic field in an example. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] At least one embodiment of this application provides a method for calculating the effective detection depth of airborne transient electromagnetic fields. The method is executed by a processor or server, etc., and the following example uses a processor as the execution subject. (Reference) Figure 1 The method for calculating the effective detection depth of airborne transient electromagnetic fields includes the following steps.

[0032] Step S1: Obtain the raw airborne transient electromagnetic measurement data from the airborne transient electromagnetic measurement system. The raw airborne transient electromagnetic measurement data includes the raw time series of each measurement point of the airborne transient electromagnetic observation.

[0033] In some embodiments, an airborne transient electromagnetic measurement system is used to conduct airborne transient electromagnetic measurements within the work area according to a pre-arranged survey line, and data acquisition is performed to obtain raw airborne transient electromagnetic measurement data. The processor can directly acquire the raw airborne transient electromagnetic measurement data from the airborne transient electromagnetic measurement system, or the raw airborne transient electromagnetic measurement data acquired by the system can be manually stored in memory, and the processor can retrieve the raw airborne transient electromagnetic measurement data from memory.

[0034] For example, refer to Figure 1 , Figure 1 This is a schematic diagram of data acquisition for an airborne transient electromagnetic measurement system. Figure 1It is known that the airborne transient electromagnetic measurement system transmits an induced electromagnetic field into the ground through a transmitting coil, generating an induced secondary field in the underground geological body. The receiving coil receives the induced secondary field signal, and by analyzing the received induced signal, the system aims to detect the underground geological body and obtain the original airborne transient electromagnetic measurement data.

[0035] Step S2: Based on the original time series of each measuring point from the airborne transient electromagnetic observation, calculate the system background noise level η at each measuring point using Formula 1. v .

[0036] Formula 1 In Formula 1, η v For noise level, This is the original time series of measurement point i from airborne transient electromagnetic observations. This is the average value of the original time series. N The total number of time series samples for a single measurement point. i For the first i One sampling point.

[0037] It should be noted that airborne transient electromagnetic measurement data can also be referred to as airborne transient electromagnetic measured data. The raw time series of airborne transient electromagnetic observations can also be referred to as the raw time series of observations or the raw time series data of airborne transient electromagnetic observations.

[0038] Step S3: Based on the original airborne transient electromagnetic measurement data, the apparent resistivity depth imaging algorithm is used to calculate the late apparent resistivity corresponding to each measurement point point by point and time channel. And imaging depth, then based on the late apparent resistivity corresponding to each measurement point. Based on the imaging depth and Formula 2, the average resistivity of the overlying layer at each measuring point is calculated. .

[0039] Formula 2 In Formula 2, n is the number of channels for observation time, which can be equal to 45 in the embodiment, unlike N in Formula 1. The apparent resistivity is calculated for the i-th time channel. Let be the imaging depth corresponding to the i-th time channel.

[0040] Late apparent resistivity corresponding to each measuring point They are all based on apparent resistivity. and Both represent imaging depth, but one is represented by time channel i, and the other by observation time t. Time channel i and observation time t are in one-to-one correspondence. Observation time t is also the decay time.

[0041] It should be noted that the late apparent resistivity It can also be referred to as the resistivity of the late trace or the apparent resistivity value at observation time t. Methods for calculating late apparent resistivity include, but are not limited to, inverse spline interpolation, continued fraction definition, binary search algorithm, and translation algorithm.

[0042] Step S4: Based on the system background noise level η at each measuring point v and the average resistivity of the overcoat layer The effective detection depth of each measuring point is calculated using Formula 3.

[0043] Formula 3 In Formula 3, I is the transmitting current (unit: A), and A is the area of ​​the transmitting coil (unit: m²). 2 ), The average resistivity of the overlying layer (unit: ), η v System background noise level (unit: ).

[0044] It should be noted that Formula 3 is also the formula or expression for calculating the effective detection depth.

[0045] For example, in airborne transient electromagnetic measurements, when At that time, diffusion depth Exactly equal to the thickness of the overlay d At this point, the measured signal begins to deviate from the half-space decay curve. For the "detectable" moment, d For effective depth detection. Among them: In the late stage, the induced electromotive force received by the aircraft transient electromagnetic receiving coil can be expressed as: In the formula, I is the emission current (unit: A). The average electrical conductivity of the overlying layer (unit: S / m). Vacuum permeability (unit: H / m). a The radius of the transmitting coil (unit: m). decay time (unit: s ).

[0046] Equating the two equations together, we can eliminate... t ,have: Substitute into A = πa 2 (Area of ​​the transmitting coil), after simplification, we get: By combining the numerical constant terms, the induced voltage of the late-channel receiving coil is... Equal to the system background noise level using average conductivity Average conductivity of the replacement overcoat Then we get Formula 3.

[0047] In this embodiment, the effective detection depth of airborne transient electromagnetic systems is rapidly calculated by calculating the system background noise level and the average resistivity of the overlying layer. Compared with the currently used empirical values, this technique calculates the effective detection depth more accurately, thereby improving the accuracy of airborne transient electromagnetic inversion interpretation to a certain extent.

[0048] In at least one embodiment of this application, steps S2a and S2b are specific implementations of step S2.

[0049] Step S2a: Calculate the average decay curve for the original time series data of the airborne transient electromagnetic system to obtain the average value of the original time series.

[0050] Step S2b: Subtract the average value from the original time series data, and calculate the root mean square error of the difference using Formula 1 to obtain the system background noise level η at each measuring point. v .

[0051] In at least one embodiment of this application, step S21 is included after step S2. Step S3a is a specific implementation of step S3.

[0052] Step S21: Perform data preprocessing on the raw airborne transient electromagnetic measurement data to obtain multi-channel dB / dt data for multiple observation time channels. Data preprocessing includes filtering, stacking, channel extraction, and background field correction.

[0053] Step S3a: Using multi-channel dB / dt data from multiple observation time channels, employ the apparent resistivity depth imaging algorithm to calculate the late apparent resistivity corresponding to each measurement point point by point and time channel. And the imaging depth, and then calculate the average resistivity of the overlying layer at each measuring point. .

[0054] It should be noted that multi-channel dB / dt data from multiple observation time channels can also be referred to as dB / dt data corresponding to multiple time windows (or time windows). Multi-channel dB / dt data from multiple observation time channels primarily utilizes the dB values ​​in the vertical Z-direction. z / dt data; SF in Formulas 4 and 5 is the multi-channel dBz / dt data.

[0055] In at least one embodiment of this application, combined with Figure 3 Steps S211 to S214 are a specific implementation of step S21.

[0056] Step S211: Perform high-pass and low-pass filtering on the original airborne transient electromagnetic measurement data to obtain the filtered data.

[0057] It should be noted that the filtered data can also be the data after removing atmospheric motion noise and other noise.

[0058] Step S212: Weight the filtered data and superimpose them to obtain the superimposed aero-electromagnetic response.

[0059] Step S213: Perform logarithmic equal-interval sampling on the superimposed aero-electromagnetic response to obtain aero-electromagnetic response data for multiple time windows.

[0060] Step S214: Perform background field correction on the airborne electromagnetic response data of multiple time windows to obtain multi-channel dB / dt data of multiple observation time channels after data preprocessing.

[0061] In at least one embodiment of this application, the multiple observation time channels include channels 0-44, and the multiple time windows are 45 time windows.

[0062] In at least one embodiment of this application, steps S3b to S3d are a specific implementation of step S3.

[0063] Step S3b: Based on the original airborne transient electromagnetic measurement data, calculate the late apparent resistivity corresponding to each measurement point using Formula 4. .

[0064] Formula 4 In Formula 4, SF The result of normalizing the induced electromotive force of the receiving coil with respect to the transmitting magnetic moment (unit: pV / Am) 4 ), The observation time is expressed in milliseconds (ms).

[0065] For example, for an airborne transient electromagnetic measurement system with a center loop, the late apparent resistivity at each measurement point is calculated using the following formula: In the formula, Vacuum permeability (unit: H / m). I is the transmitting magnetic moment, I is the transmitting current (unit: A), and A is the effective area of ​​the transmitting coil (unit: m²). 2 ), The decay time is expressed in seconds. This is the time derivative of the magnetic flux density of the receiving coil (unit: nT / s).

[0066] The induced electromotive force received by the receiving coil V Normalizing the emitted magnetic moment and extracting the constant term from the above equation, we have: .

[0067] Step S3c: Calculate the imaging depth corresponding to each measuring point using Formula 5. .

[0068] Formula 5 In Formula 5, For depth coefficient, For observation time Apparent resistivity at late stage (unit: ), The observation time is expressed in milliseconds (ms).

[0069] Step S3d: Based on the late apparent resistivity corresponding to each measuring point Based on the imaging depth and formula 2, the average resistivity of the overlying layer at each measuring point is calculated.

[0070] In at least one embodiment of this application, the depth coefficient With observation time The corresponding number of channels has the following relationship: In the formula, For observation time Corresponding channel number. Observation time. The corresponding number of channels can also be simply referred to as the observation time channel.

[0071] Figure 4 This figure shows the calculated effective detection depth for an example of airborne transient electromagnetic (TEM). The black curve in the figure represents the calculated effective detection depth of the measured TEM. Inversion results exceeding the depth range of this curve are unreliable, which improves the accuracy of data interpretation to some extent.

[0072] At least one embodiment of this application also provides a computer device including a processor and a memory. The processor is used to execute a method for calculating the effective detection depth of airborne transient electromagnetic fields provided in any of the above embodiments of this application. The memory is used to store executable instructions of the processor, such as application programs. There may be one or more processors. The application programs stored in the memory may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor is configured to execute instructions to perform the above-described method for calculating the effective detection depth of airborne transient electromagnetic fields.

[0073] The computer device may also include a power supply component configured for power management, a wired or wireless network interface configured to connect the computer device to a network, and an input / output (I / O) interface. The computer device can operate on an operating system stored in memory, such as Windows Server. TM Mac OSX TM Unix TM Linux TM FreeBSD TM Or similar.

[0074] At least one embodiment of this application also provides a computer-readable storage medium storing executable instructions for a computer thereon. When executed by a processor, the executable instructions implement a method for calculating the effective detection depth of airborne transient electromagnetic fields provided in any of the above embodiments of this application.

[0075] A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the computer device, enables the computer device to perform the method for calculating the effective detection depth of airborne transient electromagnetic fields. The method for calculating the effective detection depth of airborne transient electromagnetic fields is executed by an agent program.

[0076] Those skilled in the art will recognize that the algorithmic steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0077] At least one embodiment of this application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements a method for calculating the effective detection depth of airborne transient electromagnetic fields provided in any of the above embodiments of this application.

[0078] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a computer program product. This computer program product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method for calculating the effective detection depth of airborne transient electromagnetic fields according to various embodiments of this application. The aforementioned storage medium includes various media capable of storing program verification codes, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] It should be noted that the combination of the technical features in the embodiments of this application is not limited to the combination methods described in the embodiments of this application or the combination methods described in specific embodiments. All technical features described in this application can be freely combined or combined in any way, unless they contradict each other.

[0080] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the term "comprising" only indicates that it includes the explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0081] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for calculating the effective detection depth of airborne transient electromagnetic fields, characterized in that, include: Step S1: Obtain the raw airborne transient electromagnetic measurement data measured by the airborne transient electromagnetic measurement system. The raw airborne transient electromagnetic measurement data includes the raw time series of each measurement point of the airborne transient electromagnetic observation. Step S2: Based on the original time series of each measuring point from the airborne transient electromagnetic observation, calculate the system background noise level η at each measuring point using Formula 1. v , Formula 1 In Formula 1, η v For noise level, This is the original time series of measurement point i from airborne transient electromagnetic observations. This is the average value of the original time series. N The total number of time series samples for a single measurement point. i For the first i One sampling point; Step S3: Based on the original airborne transient electromagnetic measurement data, the apparent resistivity depth imaging algorithm is used to calculate the late apparent resistivity corresponding to each measurement point point by point and time channel. And imaging depth, then based on the late apparent resistivity corresponding to each measurement point. Based on the imaging depth and Formula 2, the average resistivity of the overlying layer at each measuring point is calculated. ; Formula 2 Step S4: Based on the system background noise level η at each measuring point v and the average resistivity of the overcoat layer The effective detection depth of each measuring point is calculated using Formula 3. Formula 3 In Formula 3, I is the transmitting current (unit: A), and A is the area of ​​the transmitting coil (unit: m²). 2 ), The average resistivity of the overlying layer (unit: ), η v System background noise level (unit: ).

2. The method according to claim 1, characterized in that, Step S2 includes: Step S2a: Calculate the average decay curve for the original time series data of airborne transient electromagnetic events to obtain the average value of the original time series. Step S2b: Subtract the average value from the original time series data, and calculate the root mean square error of the difference using Formula 1 to obtain the system background noise level η at each measuring point. v .

3. The method according to claim 1, characterized in that, The process after step S2 also includes: Step S21: Perform data preprocessing on the raw airborne transient electromagnetic measurement data to obtain multi-channel dB / dt data for multiple observation time channels. Data preprocessing includes filtering, stacking, channel extraction, and background field correction. Step S3 includes: Step S3a: Using multi-channel dB / dt data from multiple observation time channels, employ the apparent resistivity depth imaging algorithm to calculate the late apparent resistivity corresponding to each measurement point point by point and time channel. And the imaging depth, and then calculate the average resistivity of the overlying layer at each measuring point. .

4. The method according to claim 1, characterized in that, Step S21 includes: Step S211: Perform high-pass and low-pass filtering on the original airborne transient electromagnetic measurement data to obtain the filtered data; Step S212: Weight the filtered data and superimpose them to obtain the superimposed aero-electromagnetic response; Step S213: Perform logarithmic equal-interval sampling on the superimposed aero-electromagnetic response to obtain aero-electromagnetic response data for multiple time windows; Step S214: Perform background field correction on the airborne electromagnetic response data of multiple time windows to obtain multi-channel dB / dt data of multiple observation time channels after data preprocessing.

5. The method according to claim 4, characterized in that, Multiple observation channels, including channels 0-44, and multiple time windows, totaling 45 time windows.

6. The method according to claim 1, characterized in that, Step S3 includes: Step S3b: Based on the original airborne transient electromagnetic measurement data, calculate the late apparent resistivity corresponding to each measurement point using Formula 4. , Formula 4 In Formula 4, SF The result of normalizing the induced electromotive force of the receiving coil with respect to the transmitting magnetic moment (unit: pV / Am) 4 ), Observation time (unit: ms); Step S3c: Calculate the imaging depth corresponding to each measuring point using Formula 5. Formula 5 In Formula 5, For depth coefficient, For observation time Apparent resistivity at late stage (unit: ), Observation time (unit: ms); Step S3d: Based on the late apparent resistivity corresponding to each measuring point Based on the imaging depth and formula 2, the average resistivity of the overlying layer at each measuring point is calculated.

7. The method according to claim 6, characterized in that, Depth coefficient With observation time The corresponding number of channels has the following relationship: Formula Six In Formula Six, For observation time The corresponding number of channels.

8. A computer device, characterized in that, include: A processor for executing a method for calculating the effective depth of airborne transient electromagnetic detection as described in any one of claims 1 to 7; as well as Memory for storing the executable instructions of the processor.

9. A computer-readable storage medium having executable instructions stored thereon, characterized in that, When the executable instructions are executed by the processor, they implement a method for calculating the effective detection depth of airborne transient electromagnetic fields according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement a method for calculating the effective detection depth of airborne transient electromagnetic fields as described in any one of claims 1 to 7.

Images