Self-adaptive time-frequency electrical method detection array and complex geologic body detection method

Abstract

The invention relates to the field of geological exploration, and discloses a self-adaptive time-frequency electrical method detection array and a complex geologic body detection method.The self-adaptive time-frequency electrical method detection array comprises a plurality of detection units, the detection units are arranged on a target area according to a preset array, each detection unit is provided with an emitter and a receiver, and the emitter is connected with the receiver; the transmitter is used for transmitting an electric signal to a target area, and the receiver is used for receiving an echo signal reflected or transmitted back by a geologic body of the target area; the time-frequency analysis module is used for carrying out time-frequency analysis on the received echo signal; the self-adaptive adjusting module is in two-way communication connection with the time-frequency analysis module and each detection unit and is used for dynamically adjusting transmitting parameters and receiving parameters of the detection units according to the characteristic information output by the time-frequency analysis module; and the data fusion and processing module is electrically connected with the plurality of detection units and the time-frequency analysis module and is used for integrating data from each detection unit.

Description

Technical Field

[0001] The present invention relates to the field of geological detection, and in particular to an adaptive time-frequency electrical detection array and a complex geological body detection method. Background Art

[0002] With the continuous increase in the demand for geological exploration, accurate detection of complex geological bodies has become increasingly critical. Traditional electrical detection technology has exposed many limitations when dealing with today's complex geological conditions: on the one hand, traditional electrical methods often use fixed electrode arrays and single-frequency signal transmission modes. Fixed electrode spacing is difficult to adapt to the detection needs of geological bodies of different depths and scales. When the scale of the detection object varies greatly, either the signal resolution is insufficient or the detection range is limited. On the other hand, a single-frequency signal can only reflect the electrical characteristics of a geological body in a specific frequency band. When faced with geological bodies with complex components and multi-layer structures, the information obtained is extremely limited, and it is impossible to fully outline the internal electrical distribution of the geological body, which can easily lead to misjudgment.

[0003] At the same time, in different geological environments, such as high-humidity coastal areas and highly corrosive mining areas, traditional electrode materials are susceptible to corrosion, resulting in poor signal transmission stability and greatly reduced detection accuracy. In addition, traditional detection methods lack a real-time adaptive adjustment mechanism during data collection. Once the initial parameter settings are unreasonable, the entire detection process often needs to be reworked, which is inefficient. In order to overcome these difficulties, it is urgent to develop an adaptive time-frequency electrical detection array and corresponding detection methods, aiming to flexibly respond to complex geological environments and improve detection accuracy and efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide an adaptive time-frequency electrical detection array and a complex geological body detection method to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An adaptive time-frequency electrical detection array, comprising:

[0007] A detection unit, wherein the detection unit is provided in plurality and arranged in a preset array on the target area, each detection unit is configured with a transmitter and a receiver, the transmitter is used to transmit an electrical signal to the target area, and the receiver is used to receive an echo signal reflected or transmitted back by a geological body in the target area;

[0008] The time-frequency analysis module is connected to the receiver of each detection unit for performing time-frequency analysis on the received echo signal; the process of performing time-frequency analysis on the received echo signal is as follows:

[0009] The time domain echo signal collected by the receiver is converted into the time-frequency domain through the time-frequency analysis algorithm;

[0010] Extracting characteristic information of frequency components, energy distribution and phase changes in the signal, wherein the time-frequency analysis algorithm includes but is not limited to short-time Fourier transform and wavelet transform;

[0011] Output the above characteristic information to the adaptive adjustment module;

[0012] An adaptive adjustment module, which is bidirectionally connected to the time-frequency analysis module and each of the detection units, and is used to dynamically adjust the transmission parameters and receiving parameters of the detection unit according to the characteristic information output by the time-frequency analysis module; wherein the transmission parameters include frequency, waveform, and power; and the receiving parameters include gain and sensitivity;

[0013] The data fusion and processing module is electrically connected to the plurality of detection units and the time-frequency analysis module, and is used to integrate the data from each detection unit.

[0014] As a further technical solution, the process of integrating the data from each detection unit is as follows:

[0015] First, an adaptive filtering algorithm is used to eliminate noise interference data, and then a wavelet transform enhancement algorithm is used to highlight key signal features. Finally, with the help of a three-dimensional inversion imaging algorithm, the processed data is converted into an intuitive three-dimensional geological structure of the target area.

[0016] As a further technical solution, the process for dynamically adjusting the transmission parameters and receiving parameters of the detection unit according to the characteristic information output by the time-frequency analysis module is as follows:

[0017] Firstly, each transmission parameter and receiving parameter is processed to obtain the frequency coefficient, power coefficient, waveform transformation judgment function, gain coefficient and sensitivity coefficient;

[0018] Then through the formula:

[0019] A=F a *P b *F w c

[0020] Calculate and obtain the adjustment evaluation index A of the transmission parameters;

[0021] B=G d *S n s

[0022] Calculate and obtain the adjustment evaluation index B of the receiving parameters;

[0023] Among them, F is the frequency coefficient, P is the power coefficient, F w is the waveform transformation judgment function, G is the gain coefficient, S nis the sensitivity coefficient, a, b, c, d, s are the preset weight coefficients;

[0024] Compare the adjustment evaluation index A of the transmission parameter with the adjustment evaluation threshold A0. If A≥A0, the transmission parameter is judged to be abnormal. Then compare each transmission parameter with its corresponding threshold to determine which parameter needs to be adjusted.

[0025] The adjustment evaluation index B of the receiving parameter is compared with the adjustment evaluation threshold B0. If B≥B0, the receiving parameter is judged to be abnormal. Then each receiving parameter is compared with its corresponding threshold to determine which parameter needs to be adjusted.

[0026] As a further technical solution, the process of determining whether the current frequency parameter needs to be adjusted is:

[0027] By formula: Calculate the detection depth estimate D cu , where Δt is the time difference between the receiver capturing the echo, and v is the propagation speed of the electrical signal in the geological body;

[0028] By formula Calculate the vertical resolution R vcu , where f is the signal frequency, k is a preset coefficient and is a constant;

[0029] Then D cu , R vcu Substituting into the formula:

[0030]

[0031] The frequency coefficient F is calculated;

[0032] Wherein, D0 is the preset expected detection depth value, R0 is the preset expected vertical resolution value, β1 and β2 are the preset proportional coefficients;

[0033] The calculated frequency coefficient f is compared with the preset frequency coefficient threshold f0. If f≥f0, it is determined that the current frequency needs to be increased; otherwise, the current frequency is maintained.

[0034] As a further technical solution, the process of determining whether the current power needs to be adjusted is:

[0035] By formula: The energy KE of the echo signal in the frequency band f0-f1 is calculated; E(f) is the curve of energy changing with frequency;

[0036] Substituting into the formula:

[0037]

[0038] The power coefficient P is calculated;

[0039] in, is the judgment function about the detection depth, when hour, when hour, KE th is the energy threshold, γ1 is the reference coefficient;

[0040] The calculated power coefficient P is compared with the power threshold P0. If P≥P0, it is determined that the current power needs to be increased; otherwise, the current power is maintained.

[0041] As a further technical solution, the process of determining whether the current waveform needs to be adjusted is:

[0042] Input the waveform data into the pre-trained convolutional neural network model and output the square wave waveform fitness F waqu And the sine wave waveform adaptability F wsin ;

[0043] If |F wsin -F wsqu |>ΔF w And F wsin >F wsqu , then the current waveform theory is judged to be a sine wave;

[0044] If |F wsin -F wsqu |>ΔF w And F wsin ≤F wsqu , then the current waveform is theoretically a square wave; ΔF w is the preset threshold;

[0045] If the current waveform is consistent with the theoretical waveform, it is determined that the current waveform does not need to be adjusted, and the waveform transformation judgment function F w =1, if not, it is determined that the current waveform needs to be adjusted, then the waveform transformation judgment function F w =2.

[0046] As a further technical solution, the process of determining whether the current gain parameter needs to be adjusted is:

[0047] First, measure the high-frequency noise power spectrum density N and signal power spectrum density S, and use the formula: Calculate the noise gain ratio r, and then obtain the signal strength I;

[0048] Substitute the noise gain ratio r and the signal strength I into the formula:

[0049]

[0050] The gain coefficient G is calculated;

[0051] Among them, I min is the lower limit of signal strength, K(rr max ) is the judgment function about the noise gain ratio; when rr max ≥0, then K(rr max )=1, when rr max <0, then K(rr max )=1 / 2,r max is the upper limit of noise gain ratio;

[0052] Compare the calculated gain coefficient G with the preset gain threshold G0;

[0053] If G≥G0, it is determined that the current receiving gain needs to be increased; otherwise, the current receiving gain is maintained.

[0054] As a further technical solution, the process of determining whether the current sensitivity parameter needs to be adjusted is:

[0055] By formula:

[0056]

[0057] Calculate the sensitivity coefficient S n ;

[0058] Where B is the bandwidth, NF is the noise figure, k is the preset fixed coefficient, p is the bandwidth fluctuation coefficient, B i is the observed value of the i-th sampling point, is the bandwidth mean, n is the total number of sampling times; q is the preset proportional coefficient;

[0059] The calculated sensitivity coefficient S n The sensitivity coefficient S is the preset sensitivity threshold n 0 for comparison;

[0060] If S n ≥S n 0, it is determined that the current receiving sensitivity needs to be increased, otherwise, the current receiving sensitivity is maintained.

[0061] A method for detecting complex geological bodies, which utilizes the above-mentioned adaptive time-frequency electrical detection array.

[0062] Beneficial effects of the present invention:

[0063] The present invention firstly uses multiple detection units in a preset array layout to achieve a wide coverage range, which can fully capture the echo signals of the target area without missing any key details. Then, the time-frequency analysis module uses short-time Fourier, wavelet transform and other algorithms to accurately extract frequency, energy and phase characteristics, so that the identification and analysis of geological bodies are more accurate, whether it is a complex stratum or a hidden structure, it can be clear. Subsequently, based on the results of time-frequency analysis, the transmission and receiving parameters are flexibly adjusted. In the face of different geological conditions, the frequency and power are quickly optimized to ensure the adaptation of signal transmission and reception, thereby improving both detection efficiency and quality. Finally, in data processing, adaptive filtering is used to remove noise and purify data; wavelet transform enhances key features to highlight weak useful signals; and the three-dimensional inversion imaging algorithm visualizes abstract data into a three-dimensional geological body structure for intuitive presentation, which greatly shortens the interpretation time, assists in making decisions more timely and accurately, and reduces exploration costs and difficulties. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention will be further described below in conjunction with the accompanying drawings.

[0065] Figure 1 This is a system structure block diagram of the adaptive time-frequency electrical detection array of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1 As shown, the present invention is an adaptive time-frequency electrical detection array, comprising:

[0068] A detection unit, wherein the detection unit is provided in plurality and arranged in a preset array on the target area, each detection unit is configured with a transmitter and a receiver, the transmitter is used to transmit an electrical signal to the target area, and the receiver is used to receive an echo signal reflected or transmitted back by a geological body in the target area;

[0069] The time-frequency analysis module is connected to the receiver of each detection unit for performing time-frequency analysis on the received echo signal; the process of performing time-frequency analysis on the received echo signal is as follows:

[0070] The time domain echo signal collected by the receiver is converted into the time-frequency domain through the time-frequency analysis algorithm;

[0071] Extracting characteristic information of frequency components, energy distribution and phase changes in the signal, wherein the time-frequency analysis algorithm includes but is not limited to short-time Fourier transform and wavelet transform;

[0072] Output the above characteristic information to the adaptive adjustment module;

[0073] An adaptive adjustment module is bidirectionally connected to the time-frequency analysis module and each of the detection units, and is used to dynamically adjust the transmission parameters and receiving parameters of the detection unit according to the characteristic information output by the time-frequency analysis module; wherein the transmission parameters include frequency, waveform, and power; and the receiving parameters include gain and sensitivity;

[0074] The data fusion and processing module is electrically connected to the plurality of detection units and the time-frequency analysis module, and is used to integrate the data from each detection unit.

[0075] The process of integrating data from various detection units is as follows:

[0076] First, an adaptive filtering algorithm is used to eliminate noise interference data, and then a wavelet transform enhancement algorithm is used to highlight key signal features. Finally, with the help of a three-dimensional inversion imaging algorithm, the processed data is converted into an intuitive three-dimensional geological structure of the target area.

[0077] In this embodiment, the frequency components, energy distribution and phase change characteristics are accurately extracted through advanced algorithms such as short-time Fourier transform and wavelet transform. Different geological body structures correspond to unique frequency, energy and phase characteristics. Accurately extracting this information makes the identification of geological bodies more accurate. Whether it is distinguishing different lithologies or locating complex faults and fold structures, higher accuracy can be achieved, significantly reducing the misjudgment rate. Then, the adaptive adjustment module dynamically adjusts the transmission parameters (frequency, waveform, power) and receiving parameters (gain, sensitivity) based on the characteristic information obtained by time-frequency analysis; for example, when detecting deep geological bodies, the transmission power is increased and the frequency is reduced in time to ensure that the signal can effectively penetrate and reflect back; in the face of shallow fine structures, fine-tuning is performed to improve the resolution of high-frequency signals, so that the detection results fit the actual geological conditions;

[0078] At the same time, facing the complex field detection environment, various types of electromagnetic interference emerge in an endless stream. The adaptive filtering algorithm can track the noise characteristics in real time, intelligently remove noise interference data, avoid the useful signal from being "drowned", ensure the purity of the original data, and lay a solid foundation for subsequent precise analysis; the wavelet transform enhancement algorithm further improves the signal quality, by highlighting the key signal characteristics, amplifying the weak but valuable signal characteristics, so that the subsequent processing flow can firmly "grasp" the key information, reduce the repeated detection work caused by noise, and greatly improve the overall detection efficiency. Finally, with the help of the three-dimensional inversion imaging algorithm, the processed data is transformed into an intuitive three-dimensional geological structure of the target area; the previously complex and abstract data can now be displayed in a visual three-dimensional model. Geological researchers and engineering exploration personnel can directly observe the spatial distribution, direction, thickness and other information of the geological body without the need to laboriously interpret obscure data reports, greatly shortening the analysis cycle, accelerating decision-making, and providing clear guidance for subsequent geological research and engineering construction.

[0079] The process for dynamically adjusting the transmission parameters and receiving parameters of the detection unit according to the characteristic information output by the time-frequency analysis module is as follows:

[0080] Firstly, each transmission parameter and receiving parameter is processed to obtain the frequency coefficient, power coefficient, waveform transformation judgment function, gain coefficient and sensitivity coefficient;

[0081] Then through the formula:

[0082] A=F a *P b *F w c

[0083] Calculate and obtain the adjustment evaluation index A of the transmission parameters;

[0084] B=G d *S n s

[0085] Calculate and obtain the adjustment evaluation index B of the receiving parameters;

[0086] Among them, F is the frequency coefficient, P is the power coefficient, F w is the waveform transformation judgment function, G is the gain coefficient, S n is the sensitivity coefficient, a, b, c, d, s are the preset weight coefficients;

[0087] Compare the adjustment evaluation index A of the transmission parameter with the adjustment evaluation threshold A0. If A≥A0, the transmission parameter is judged to be abnormal. Then compare each transmission parameter with its corresponding threshold to determine which parameter needs to be adjusted.

[0088] The adjustment evaluation index B of the receiving parameter is compared with the adjustment evaluation threshold B0. If B≥B0, the receiving parameter is judged to be abnormal. Then each receiving parameter is compared with its corresponding threshold to determine which parameter needs to be adjusted.

[0089] In this embodiment, a method for determining whether a transmission parameter and a receiving parameter need to be adjusted is provided. Specifically, the transmission parameter and the receiving parameter are respectively calculated with different weights by formulas to obtain an adjustment evaluation index of the transmission parameter and an adjustment evaluation index of the receiving parameter. Then, the adjustment evaluation index A of the transmission parameter is compared with the adjustment evaluation threshold A0, and the adjustment evaluation index B of the receiving parameter is compared with the adjustment evaluation threshold B0, so as to quickly determine whether there is an abnormality in the overall transmission parameter and the receiving parameter. Then, on the premise of determining whether there is an abnormality, each parameter of the transmission parameter or each parameter of the receiving parameter is separately compared, so as to finally determine the single parameter that needs to be adjusted. By first judging the whole and then comparing separately, it can be quickly determined whether the transmission parameter needs to be adjusted or the receiving parameter needs to be adjusted, so as to reduce the time consumption caused by paying attention to and comparing each parameter. Only after it is determined that adjustment is required, each parameter is compared, which can effectively save time.

[0090] The process of judging whether the current frequency parameters need to be adjusted is as follows:

[0091] By formula: Calculate the detection depth estimate D cu , where Δt is the time difference between the receiver capturing the echo, and v is the propagation speed of the electrical signal in the geological body;

[0092] By formula Calculate the vertical resolution R vcu , where f is the signal frequency, k is a preset coefficient and is a constant;

[0093] Then D cu , R vcu Substituting into the formula:

[0094]

[0095] The frequency coefficient F is calculated;

[0096] Wherein, D0 is the preset expected detection depth value, R0 is the preset expected vertical resolution value, β1 and β2 are the preset proportional coefficients;

[0097] The calculated frequency coefficient f is compared with the preset frequency coefficient threshold f0. If f≥f0, it is determined that the current frequency needs to be increased; otherwise, the current frequency is maintained.

[0098] In this embodiment, a method for determining whether the current frequency parameter needs to be adjusted is provided. Specifically, the formula The frequency coefficient F is calculated. Obviously, the smaller the current detection depth value is than the expected detection depth value, the The larger the frequency coefficient F is, the greater the current detection depth is. Therefore, it is necessary to increase the current transmission frequency to enhance the signal detection depth. At the same time, the current vertical resolution is smaller than the expected vertical resolution value. The larger the value, the larger the frequency coefficient F is, which means that the current vertical resolution is insufficient, so it is necessary to increase the current transmission frequency.

[0099] The process of determining whether the current power needs to be adjusted is as follows:

[0100] By formula: The energy KE of the echo signal in the frequency band f0-f1 is calculated; E(f) is the curve of energy changing with frequency;

[0101] Substituting into the formula:

[0102]

[0103] The power coefficient P is calculated;

[0104] in, is the judgment function about the detection depth, when When when hour, KE th is the energy threshold, γ1 is the reference coefficient;

[0105] The calculated power coefficient P is compared with the power threshold P0. If P≥P0, it is determined that the current power needs to be increased; otherwise, the current power is maintained.

[0106] In this embodiment, a method for determining whether the current power needs to be adjusted is provided. Specifically, the energy KE of the echo signal in the f0-f1 frequency band and the judgment function about the detection depth are adjusted. Substitute into the formula The power coefficient P is calculated. Obviously, if It means that the detection depth of the current signal meets the requirements, otherwise it does not meet the requirements, so Obviously, the larger the value of the power coefficient P, the more it is necessary to increase the current power. The larger the value is, the smaller the energy KE in the f0-f1 frequency band is than the energy threshold. Therefore, the current power needs to be increased to compensate for the signal energy and enhance the detection depth or penetration capability.

[0107] The process of judging whether the current waveform needs to be adjusted is:

[0108] Input the waveform data into the pre-trained convolutional neural network model and output the square wave waveform fitness F wsqu And the sine wave waveform adaptability F wsin ;

[0109] If |F wsin -F wsqu |>ΔF w And F wsin >F wsqu , then the current waveform theory is judged to be a sine wave;

[0110] If |F wsin -F wsqu |>ΔF w And F wsin ≤F wsqu , then the current waveform is theoretically a square wave; ΔF w is the preset threshold;

[0111] If the current waveform is consistent with the theoretical waveform, it is determined that the current waveform does not need to be adjusted, and the waveform transformation judgment function F w =1, if not, it is determined that the current waveform needs to be adjusted, then the waveform transformation judgment function F w =2.

[0112] In this embodiment, a method for determining whether the current waveform needs to be adjusted is provided, wherein the waveform data is processed by a convolutional neural network model to obtain a square wave waveform adaptation degree F wsqu And the sine wave waveform adaptability F wsin , and then by if |F wsin -F wsqu |>ΔF w And F wsin >F wsqu , then the current waveform theory is judged to be a sine wave; if |F wsin -F wsqu |>ΔF w And F wsin ≤F wsqu , then the current waveform is judged to be a square wave in theory, and the theoretical waveform is obtained. Then, whether the current waveform is consistent with the theoretical waveform is determined. If they are consistent, it meets the requirements. If they are inconsistent, it does not meet the requirements and needs to be adjusted.

[0113] The process of judging whether the current gain parameter needs to be adjusted is as follows:

[0114] First, measure the high-frequency noise power spectrum density N and signal power spectrum density S, and use the formula: Calculate the noise gain ratio r, and then obtain the signal strength I;

[0115] Substitute the noise gain ratio r and the signal strength I into the formula:

[0116]

[0117] The gain coefficient G is calculated;

[0118] Among them, I min is the lower limit of signal strength, K(rr max ) is the judgment function about the noise gain ratio; when rr max ≥0, then K(rr max )=1, when rr max <0, then K(rr max )=1 / 2,r max is the upper limit of noise gain ratio;

[0119] Compare the calculated gain coefficient G with the preset gain threshold G0;

[0120] If G≥G0, it is determined that the current receiving gain needs to be increased; otherwise, the current receiving gain is maintained.

[0121] In this embodiment, a method for determining whether the current gain parameter needs to be adjusted is provided. Specifically, the noise gain ratio r and the signal strength I are substituted into the formula: The gain coefficient G is calculated. Obviously, if the signal strength is smaller, it means that the current signal strength is less than the preset signal strength lower limit, which cannot meet the demand, so the larger the gain coefficient is, the more necessary it is to increase the receiving gain. As for the judgment function of the noise gain ratio, it is obviously affected by the value. If the noise gain ratio exceeds the preset upper limit, it means that the greater the interference of the noise on the signal, the larger the gain coefficient is, and the more necessary it is to increase the receiving gain. Then compare the calculated gain coefficient G with the preset gain threshold G0. If G≥G0, it is determined that the current receiving gain needs to be increased, otherwise, the current receiving gain is maintained.

[0122] The process of judging whether the current sensitivity parameter needs to be adjusted is:

[0123] By formula:

[0124]

[0125] Calculate the sensitivity coefficient S n ;

[0126] Where B is the bandwidth, NF is the noise figure, k is the preset fixed coefficient, p is the bandwidth fluctuation coefficient, B i is the observed value of the i-th sampling point, is the bandwidth mean, n is the total number of sampling times; q is the preset proportional coefficient;

[0127] The calculated sensitivity coefficient S n The sensitivity coefficient S is the preset sensitivity threshold n 0 for comparison;

[0128] If S n ≥S n 0, it is determined that the current receiving sensitivity needs to be increased, otherwise, the current receiving sensitivity is maintained.

[0129] In this embodiment, a method for determining whether the receiving sensitivity needs to be adjusted is provided. First, the formula Calculate the sensitivity coefficient S n Obviously, if the bandwidth is smaller, the sensitivity is greater, and the smaller the noise factor is, the greater the sensitivity coefficient is. Then the calculated sensitivity coefficient S n The sensitivity coefficient S is the preset sensitivity threshold n 0 for comparison, if S n ≥S n 0, then when the noise coefficient remains unchanged, it is obvious that the bandwidth is too large, so it is judged that the current receiving sensitivity needs to be increased, otherwise, the current receiving sensitivity is maintained; by considering the fluctuation of the bandwidth in the historical period, it is obvious that the greater the discreteness of the bandwidth, the smaller the q / p, which means the more unstable the bandwidth, so the sensitivity is less affected by the bandwidth change, so there is no need to adjust the sensitivity.

[0130] A method for detecting complex geological bodies, which utilizes the above-mentioned adaptive time-frequency electrical detection array.

[0131] It should be noted that the calculation formulas and various parameters involved in the calculation in the present invention have been dimensionally processed in advance, and the process of dimensionless processing is well known in the industry and will not be described here.

[0132] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An adaptive time-frequency electrical detection array, characterized in that: include: A detection unit, wherein the detection unit is provided in plurality and arranged in a preset array on the target area, each detection unit is configured with a transmitter and a receiver, the transmitter is used to transmit an electrical signal to the target area, and the receiver is used to receive an echo signal reflected or transmitted back by a geological body in the target area; A time-frequency analysis module, which is in communication with the receivers of each of the detection units and is used to perform time-frequency analysis on the received echo signals; An adaptive adjustment module, which is bidirectionally connected to the time-frequency analysis module and each of the detection units, and is used to dynamically adjust the transmission parameters and receiving parameters of the detection unit according to the characteristic information output by the time-frequency analysis module; wherein the transmission parameters include frequency, waveform, and power; and the receiving parameters include gain and sensitivity; The data fusion and processing module is electrically connected to the plurality of detection units and the time-frequency analysis module, and is used to integrate the data from each detection unit.

2. The adaptive time-frequency electrical detection array according to claim 1, characterized in that: The process of integrating data from various detection units is as follows: First, an adaptive filtering algorithm is used to eliminate noise interference data, and then a wavelet transform enhancement algorithm is used to highlight key signal features. Finally, with the help of a three-dimensional inversion imaging algorithm, the processed data is converted into an intuitive three-dimensional geological structure of the target area.

3. The adaptive time-frequency electrical detection array according to claim 1 or 2, characterized in that: The process for dynamically adjusting the transmission parameters and receiving parameters of the detection unit according to the characteristic information output by the time-frequency analysis module is as follows: Firstly, each transmission parameter and receiving parameter is processed to obtain the frequency coefficient, power coefficient, waveform transformation judgment function, gain coefficient and sensitivity coefficient; Then through the formula: A=F a *P b *F w c Calculate and obtain the adjustment evaluation index A of the transmission parameters; B=G d *S n s Calculate and obtain the adjustment evaluation index B of the receiving parameters; Among them, F is the frequency coefficient, P is the power coefficient, F w is the waveform transformation judgment function, G is the gain coefficient, S n is the sensitivity coefficient, a, b, c, d, s are the preset weight coefficients; Compare the adjustment evaluation index A of the transmission parameter with the adjustment evaluation threshold A0. If A≥A0, the transmission parameter is judged to be abnormal. Then compare each transmission parameter with its corresponding threshold to determine which parameter needs to be adjusted. The adjustment evaluation index B of the receiving parameter is compared with the adjustment evaluation threshold B0. If B≥B0, the receiving parameter is judged to be abnormal. Then each receiving parameter is compared with its corresponding threshold to determine which parameter needs to be adjusted.

4. The adaptive time-frequency electrical detection array according to claim 3, characterized in that: The process of judging whether the current frequency parameters need to be adjusted is as follows: By formula: Calculate the detection depth estimate D cu , where Δt is the time difference between the receiver capturing the echo, and v is the propagation speed of the electrical signal in the geological body; By formula Calculate the vertical resolution R vcu , where f is the signal frequency, k is a preset coefficient and is a constant; Then D cu , R vcu Substituting into the formula: The frequency coefficient F is calculated; Wherein, D0 is the preset expected detection depth value, R0 is the preset expected vertical resolution value, β1 and β2 are the preset proportional coefficients; The calculated frequency coefficient f is compared with the preset frequency coefficient threshold f0. If f≥f0, it is determined that the current frequency needs to be increased; otherwise, the current frequency is maintained.

5. The adaptive time-frequency electrical detection array according to claim 4, characterized in that: The process of determining whether the current power needs to be adjusted is as follows: By formula: The energy KE of the echo signal in the frequency band f0-f1 is calculated; E(f) is the curve of energy changing with frequency; Substituting into the formula: The power coefficient P is calculated; in, is the judgment function about the detection depth, when hour, when hour, KE th is the energy threshold, γ1 is the reference coefficient; The calculated power coefficient P is compared with the power threshold P0. If P≥P0, it is determined that the current power needs to be increased; otherwise, the current power is maintained.

6. The adaptive time-frequency electrical detection array according to claim 5, characterized in that: The process of judging whether the current waveform needs to be adjusted is: Input the waveform data into the pre-trained convolutional neural network model and output the square wave waveform fitness F wsqu And the sine wave waveform adaptability F wsin ; If |F wsin -F wsqu |>ΔF w And F wsin >F wsqu , then the current waveform theory is judged to be a sine wave; If |F wsin -F wsqu |>ΔF w And F wsin ≤F wsqu , then the current waveform is theoretically a square wave; ΔF w is the preset threshold; If the current waveform is consistent with the theoretical waveform, it is determined that the current waveform does not need to be adjusted, and the waveform transformation judgment function F w =1, if not, it is determined that the current waveform needs to be adjusted, then the waveform transformation judgment function F w =2.

7. The adaptive time-frequency electrical detection array according to claim 5, characterized in that: The process of judging whether the current gain parameter needs to be adjusted is as follows: First, measure the high-frequency noise power spectrum density N and signal power spectrum density S, and use the formula: Calculate the noise gain ratio r, and then obtain the signal strength I; Substitute the noise gain ratio r and the signal strength I into the formula: The gain coefficient G is calculated; Among them, I min is the lower limit of signal strength, K(rr max ) is the judgment function about the noise gain ratio; when rr max ≥0, then K(rr max )=1, when rr max <0, then K(rr max )=1 / 2,r max is the upper limit of noise gain ratio; Compare the calculated gain coefficient G with the preset gain threshold G0; If G≥G0, it is determined that the current receiving gain needs to be increased; otherwise, the current receiving gain is maintained.

8. The adaptive time-frequency electrical detection array according to claim 5, characterized in that: The process of judging whether the current sensitivity parameter needs to be adjusted is: By formula: Calculate the sensitivity coefficient S n ; Where B is the bandwidth, NF is the noise figure, k is the preset fixed coefficient, p is the bandwidth fluctuation coefficient, B i is the observed value of the i-th sampling point, is the bandwidth mean, n is the total number of sampling times; q is the preset proportional coefficient; The calculated sensitivity coefficient S n The sensitivity coefficient S is the preset sensitivity threshold n 0 for comparison; If S n ≥S n 0, it is determined that the current receiving sensitivity needs to be increased, otherwise, the current receiving sensitivity is maintained.

9. A method for detecting a complex geological body, characterized in that: The method is carried out using the adaptive time-frequency electrical detection array described in any one of 1-8 above.

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