Method, system and device for suppressing static effects of high-frequency magnetotelluric sounding data

Abstract

The present invention relates to the field of geophysical exploration technology, and discloses a static effect suppression system for high-frequency magnetotelluric sounding data, comprising: a data processing unit, used for real-time online suppression processing of the static effect of magnetotelluric sounding data collected by a host; a control unit, the control unit is electrically connected to the data processing unit, the control unit is used to control the state of the data processing unit, and is also used to receive the processing result of the data processing unit, and control the display unit to display the processing result; the display unit is used to display the detection result; an access unit is used to access the host interface to enable the control unit to communicate with the host; wherein, after receiving the processing result, the control unit controls the display unit to display; when the control unit establishes communication with the host, the host collects data. The present invention can quickly understand the cause of the abnormality and improve the quality of the collected data by acquiring high signal-to-noise ratio data online in real time and comparing it with the actual exploration situation.

Description

Technical Field

[0001] The present invention relates to the field of geophysical detection technology, and in particular to a method, system and device for suppressing static effects of high-frequency magnetotelluric sounding data. Background Art

[0002] The EH4 electromagnetic imaging system is a magnetotelluric sounding system that combines some controllable sources with natural sources. Deep structures are imaged by natural background field sources (MT), and the information source is 10Hz to 100kHz. Shallow structures are imaged by a new type of portable low-power transmitter that emits 1 to 100kHz artificial electromagnetic signals to compensate for the lack of natural signals, thereby obtaining high-resolution imaging.

[0003] At present, the EH4 system is widely used in my country, and its application areas include water exploration in arid and water-scarce areas, engineering geological surveys, mineral resource exploration, etc. However, in field operations, it is easily affected by terrain, environmental noise, human noise, etc. The collected data contains a lot of noise, and the signal-to-noise ratio needs to be improved.

[0004] At the same time, in actual field measurements, it is particularly susceptible to the influence of small geological bodies on the shallow surface, resulting in static effects. The existence of static effects will cause vertical elongation, resulting in false anomalies, and the impact on apparent resistivity will lead to orders of magnitude differences, seriously affecting the accuracy of forward and inversion. Although the EH4 system is equipped with static effect suppression software, some of the algorithm parameters are themselves affected by static effects. For example, the Hanning window width is calculated based on the bostick depth of the measuring point, and the calculation of the apparent resistivity at this depth is affected. At the same time, when choosing the window width multiple, too large or too small will easily lead to poor suppression effect.

[0005] When using a laptop to export data for processing at the actual data collection site, time cost and power consumption are the primary issues that need to be considered. Therefore, it is necessary to develop a new static effect suppression hardware and software system to obtain high signal-to-noise ratio data online in real time, effectively improve the quality of collected data, and lay the foundation for improving the inversion quality. At the same time, it must have the characteristics of small size, easy to carry, and low power consumption. Summary of the invention

[0006] The present invention aims to provide a static effect suppression system for high-frequency magnetotelluric sounding data. By acquiring high signal-to-noise ratio data online in real time and comparing it with the actual exploration situation, the cause of the anomaly can be quickly understood and the quality of the collected data can be improved.

[0007] The technical solution provided by the present invention is: a static effect suppression system for high-frequency magnetotelluric sounding data, comprising:

[0008] A data processing unit, used for real-time online suppression processing of the static effect of the magnetotelluric sounding data collected by the host;

[0009] A control unit, the control unit is electrically connected to the data processing unit, the control unit is used to control the state of the data processing unit, and is also used to receive the processing result of the data processing unit and control the display unit to display the processing result;

[0010] A display unit, used for displaying the test result;

[0011] An access unit, used to access a host interface so that the control unit communicates with the host;

[0012] After receiving the processing result, the control unit controls the display unit to display it; when the control unit establishes communication with the host, it extracts the host collection data.

[0013] The working principle and advantages of the present invention are: when static effect suppression is performed, the access unit is connected to the host, the control unit communicates with the host, and the collected data is extracted. Afterwards, the data processing unit performs static effect suppression processing on the EH4 data, and the processing results are sent to the control unit, and the control unit controls the display unit to display the processing results. By observing the display unit, the static effect suppression of the data can be intuitively understood. If the data is far from the actual survey results, it is recommended to re-collect the geoelectric information of the measuring point. The display unit is set up because the display screen of the current EH4 instrument is relatively small. The addition of a display screen in this system allows the staff to clearly and intuitively observe the processing results. By acquiring high signal-to-noise ratio data online in real time and comparing it with the actual survey situation, when the processing results do not match the actual survey situation, the cause of the abnormality can be quickly understood, thereby improving the quality of the collected data.

[0014] Furthermore, the data processing unit performs wavelet packet decomposition and reconstruction on the EH4 data through a wavelet packet algorithm to obtain data suppressing static effects.

[0015] A wavelet packet algorithm with strong ability to process nonlinear data is selected. The unique frequency parameter index in the wavelet packet algorithm can overcome the defect that the frequency resolution of wavelet analysis decreases with the increase of frequency, thereby subdividing high-frequency details and retaining the details of the signal as much as possible during the data processing process, so as to achieve reasonable suppression of static effects and improve over-suppression.

[0016] Furthermore, the control unit is also used to initialize the data processing unit when establishing communication with the host.

[0017] Before each test, the control unit will initialize the data processing unit to prevent detection errors caused by non-initialization and ensure the accuracy of the processing results.

[0018] Furthermore, the access unit is a CY7C68001 chip.

[0019] The USB chip CY7C68001 integrates two functions: USB transceiver and USB serial interface engine, which can ensure data communication between the detection system and the detection interface. In addition, since CY7C68001 integrates a serial interface, two-way communication can be achieved with only one pair of transmission lines, making the overall structure of the detection system more concise.

[0020] Furthermore, the control unit is an embedded system based on ARM11.

[0021] Embedded systems are only for a specific task, so designers can optimize them to reduce size and cost. In addition, the ARM chip itself has the characteristics of small size, low power consumption, low cost and high performance. Using the ARM11 embedded system as the control unit can reduce the overall size of the detection system.

[0022] Furthermore, it also includes a shell, the control unit and the data processing unit are integrated in the shell, and the access unit and the display unit are embedded in the shell.

[0023] The housing is used to integrate the access unit, the data processing unit, the control unit and the display unit, wherein the control unit and the data processing unit are integrated in the housing, and the access unit and the display unit are embedded in the housing. In this way, the overall structure of the system is more neat and convenient to carry and use.

[0024] The present invention also provides a static effect suppression method for high-frequency magnetotelluric sounding data, which is applied to the static effect suppression system for high-frequency magnetotelluric sounding data, and comprises:

[0025] S1: The control unit extracts the EH4 data collected by the host and initializes the data processing unit;

[0026] S2: The data processing unit performs static effect suppression processing on the EH4 data through the wavelet packet algorithm embedded in the ARM, and feeds back the processing results to the control unit;

[0027] S3: After receiving the processing result from the data processing unit, the control unit controls the display unit to display the detection result.

[0028] Further, the S2 includes:

[0029] S2-1: The data processing unit identifies the static effects of EH4 data through the Lipschitz index;

[0030] S2-2: The EH4 data is decomposed and reconstructed by wavelet packet algorithm to obtain data that suppresses static effects;

[0031] S2-3: Feedback the processing result to the control unit.

[0032] Combined with the Lipschitz index, the static effect is effectively identified. When the Lipschitz index is less than zero, the abnormality of the geoelectric signal is judged to be caused by the static effect. When it is greater than zero, the abnormality is caused by the target body. The static effect is identified by calculating the Lipschitz index, which makes up for the defect of no static effect identification in the EMAP algorithm embedded in the EH4 host.

[0033] Further, the S2-2 includes:

[0034] S2-2-1: Calculate the noise elimination error factor of EH4 data by wavelet packet algorithm, perform wavelet packet decomposition according to the noise elimination error factor, and determine the basis function and decomposition level of wavelet packet decomposition;

[0035] S2-2-2: Perform threshold function analysis and threshold calculation based on basis functions and decomposition levels;

[0036] S2-2-3: Perform wavelet packet reconstruction based on the results of threshold function analysis and threshold calculation to obtain data that suppresses static effects.

[0037] The basis function and decomposition layer of wavelet packet decomposition are determined by calculating the noise elimination error factor, and the quality of data acquisition can be intuitively and quickly understood through the display unit in a visual way. Threshold function analysis and threshold calculation are performed, and the advantages and disadvantages of soft and hard threshold functions are combined to make the threshold function continuous and high-order differentiable. In order to retain the original signal to the greatest extent, the threshold on the corresponding scale is obtained according to the decomposition scale of the signal. Therefore, as the decomposition scale changes, the threshold also changes, achieving effective separation of static effects and geological anomaly information, retaining anomaly information to the greatest extent, and suppressing static effects.

[0038] The present invention also provides a static effect suppression device for high-frequency magnetotelluric sounding data, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to implement the above-mentioned static effect suppression method for high-frequency magnetotelluric sounding data when executing the computer program. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a module block diagram of a static effect suppression system for high-frequency magnetotelluric sounding data according to an embodiment of the present invention;

[0040] Figure 2 It is a logic block diagram of a static effect suppression method of high-frequency magnetotelluric sounding data according to an embodiment of the present invention;

[0041] Figure 3 A fault model diagram with a two-dimensional low-resistance body according to an embodiment of the present invention;

[0042] Figure 4 It is a pseudo-section diagram of apparent resistivity of the model of an embodiment of the present invention under TM polarization mode;

[0043] Figure 5 It is a pseudo-section diagram of apparent resistivity of the model of an embodiment of the present invention under TE polarization mode;

[0044] Figure 6 It is a pseudo-section diagram of apparent resistivity of a model using a wavelet packet soft threshold method according to an embodiment of the present invention;

[0045] Figure 7 A pseudo-section diagram of model apparent resistivity using an improved wavelet packet threshold method according to an embodiment of the present invention;

[0046] Figure 8 The original cross-section diagram of the Yimen mining area according to an embodiment of the present invention;

[0047] Fig. 9 This is a cross-sectional view after wavelet packet improved threshold processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0049] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0050] Example:

[0051] like Figure 1 As shown, this embodiment discloses a static effect suppression system for high-frequency magnetotelluric sounding data, including an access unit, a data processing unit, a control unit, a display unit and a shell.

[0052] The access unit is used to access the host interface, so that the control unit communicates with the host and connects the host to the static effect suppression system. In this embodiment, the access unit is a CY7C68001 chip. Since the CY7C68001 USB chip integrates two major functions, a USB transceiver and a USB serial interface engine, it can ensure data communication between the detection system and the detection interface. In addition, since the CY7C68001 chip integrates a serial interface, bidirectional communication can be achieved with only one pair of transmission lines, which can make the overall structure of the static effect suppression system more concise.

[0053] The data processing unit is used to suppress the static effect of the magnetotelluric sounding data collected by the host in real time online. Specifically, the data processing unit is an ARM11 processor. Through the data processing unit, the EH4 data can be decomposed and reconstructed by wavelet packet algorithm to obtain data suppressing the static effect.

[0054] The control unit implements initialization, interruption, communication and related control between modules based on C language. In this embodiment, the control unit is an embedded system based on ARM11. Specifically, when the control unit is connected to the host for communication, it extracts the data collected by the host and initializes the data processing unit; when receiving the processing result, the control unit controls the display unit to display it.

[0055] The display unit is used to display the detection results of the data processing unit. Specifically, the display unit uses a touch screen to display the data processing algorithm interface, and then selects static effect suppression parameters according to the interface prompts, such as wavelet packet basis function, decomposition layer number, threshold criterion and threshold calculation.

[0056] The housing is used to integrate the access unit, the data processing unit, the control unit and the display unit, wherein the control unit and the data processing unit are integrated in the housing, and the access unit and the display unit are embedded in the housing. In this way, the overall structure of the system is more neat and convenient to carry and use.

[0057] At the same time, since CY7C68001 integrates a serial interface, bidirectional communication can be achieved with just one pair of transmission lines, making the overall structure of the system more concise; using the ARM11 embedded system as the control unit can further reduce the overall size of the detection system.

[0058] The specific implementation process of this embodiment is as follows:

[0059] When the static effect suppression system of high-frequency magnetotelluric sounding data is operated, the access unit, namely the USB interface, is connected to the host, the control unit establishes communication with the host, and the data collected by the host is extracted. At the same time, the control unit also initializes the data processing unit.

[0060] Afterwards, the wavelet packet algorithm embedded in the ARM is used to suppress the static effect, and the processing result is fed back to the control unit.

[0061] After receiving the processing result from the data processing unit, the control unit controls the display unit to display the detection result.

[0062] When the processing result is consistent with the observed geological conditions, the geoelectric information collection work of the next survey line will continue. In this way, the quality of data collection can be effectively improved.

[0063] Compared with the existing technology, the use of this system can effectively improve the quality of field work and provide good original data.

[0064] like Figure 2 As shown, this embodiment also discloses a static effect suppression method for high-frequency magnetotelluric sounding data, which is applied to the static effect suppression system for high-frequency magnetotelluric sounding data, and specifically includes the following steps (the numbering of each step in this scheme is only used to distinguish the steps, and does not limit the specific execution order of each step, and each step can also be performed simultaneously):

[0065] S1: The control unit extracts the EH4 data collected by the host and initializes the data processing unit.

[0066] S2-1: The data processing unit identifies the static effect of EH4 data through the Lipschitz index. The static effect is effectively identified by combining the Lipschitz index. When the Lipschitz index is less than zero, it is judged that the abnormality of the geoelectric signal is caused by the static effect. When it is greater than zero, the abnormality is caused by the target body. The static effect is identified by calculating the Lipschitz index, which makes up for the defect of no static effect identification in the EMAP algorithm embedded in the EH4 host.

[0067] S2-2-1: Calculate the noise elimination error factor of EH4 data through the wavelet packet algorithm, perform wavelet packet decomposition based on the noise elimination error factor, and determine the basis function and decomposition layer of the wavelet packet decomposition. After determining that the abnormality of the geoelectric signal is caused by static effects, establish a geoelectric model of the survey area based on the survey data, and calculate the model apparent resistivity data. At the same time, use the calculation of the noise elimination error factor to determine the basis function and decomposition layer of the wavelet packet decomposition, and use the display unit to intuitively and quickly understand the quality of data acquisition in a visual way. Among them, the formula of the noise elimination error factor is as follows:

[0068]

[0069] Where f(t) is the model calculation data, f n (t) is the real-time data collected by the instrument, f 1 (t) is the data after wavelet packet decomposition, N is the data length, α 1 , α 2 are the overall deviation factor and the extreme deviation factor, respectively, which are non-negative constants here, and α is agreed to be 1 +α 2 =1. When calculating, since the norm is divided by the data length, α can usually be taken 1 =α 2 = 0.5, ||f(t)|| is the Euclidean norm of the data, which is defined by the sum of squares after the signal is discretized, that is:

[0070]

[0071] At this time, the noise reduction factor ε reflects the relative error in signal reconstruction, and the filtering scalar It reflects the degree to which the signal approximates the original signal. By defining the filter scalar, it can be ensured that the overall deviation and local deviation between the denoised signal and the original signal can be fully reflected. The larger the calculated filter scalar, the better the effect of signal-noise separation using this type of wavelet packet basis. The same method is used to determine the number of decomposition layers.

[0072] S2-2-2: Perform threshold function analysis and threshold calculation based on basis functions and decomposition levels. Perform threshold function analysis and threshold calculation, combine the advantages and disadvantages of soft and hard threshold functions, make the threshold function continuous and high-order differentiable, and use the improved threshold function:

[0073]

[0074] Where n is the adjustment parameter, and n>2, ω j,k is the noisy wavelet coefficient, and λ is the threshold. j,k When |→±λ, That is, the improved threshold function is continuous at the threshold ±λ, and there is no step phenomenon. j,k When |≥λ, The improved threshold function is between the soft and hard threshold functions. j,k When |≥λ, is a nonlinear function, and as the wavelet coefficient ω j,k As increases, the deviation between the reconstructed wavelet coefficients and the original wavelet coefficients gradually decreases, which not only improves the discontinuity problem of the hard threshold function at the threshold point, but also solves the problem of fixed deviation of the soft threshold function.

[0075] S2-2-3: According to the results of threshold function analysis and threshold calculation, wavelet packet reconstruction is performed to obtain data that suppresses static effects. In order to retain the original signal to the greatest extent, the threshold on the corresponding scale is obtained according to the decomposition scale of the signal:

[0076]

[0077] In the formula, j is the decomposition scale, λ j is the threshold value at the corresponding scale. Therefore, as the decomposition scale changes, the threshold value also changes, achieving the goal of effectively separating the static effect and geological anomaly information, retaining the anomaly information to the greatest extent, and suppressing the static effect.

[0078] S2-3: Feedback the processing result to the control unit.

[0079] S3: After receiving the processing result from the data processing unit, the control unit controls the display unit to display the detection result. The data of the processing result is plotted, and then it is determined whether it is consistent with the actual geological survey data. If it is consistent, the survey line data collection is completed. If it is not consistent, the instrument measurement parameter setting is modified to re-collect data.

[0080] The technical effect of the static effect suppression method of the high-frequency magnetotelluric sounding data is reflected through model data, such as Figure 3 As shown, Figure 3 This is a fault model diagram with a two-dimensional low-resistance body. The resistivity value of the model is 100Ω·m at 1000 meters from the surface and on the left side of measuring point 21. The rock mass on the right is relatively high-resistance, with a resistivity value of 500Ω·m. There is a low-resistance body of 50m×50m at 50 meters from the shallow surface, with a resistivity value of 0.1Ω·m. 41 measuring points are set with a point spacing of 50 meters. The model response frequency range is 0.001-320Hz, with 56 frequency points, and the apparent resistivity is simulated and calculated.

[0081] Figure 4 and Figure 5 These are the apparent resistivity pseudo-sections of the model in TM and TE polarization modes. Comparison shows that both are affected by the static effect. Relatively speaking, the apparent resistivity value of the TM mode is more seriously affected. The shape of the apparent resistivity curve on the left is basically consistent with the geoelectric model. The survey line at the center of the static body is distorted, showing dense contours with a small lateral range, extending downward to the bottom, covering up the resistivity anomaly of the fault part in the geoelectric model, and the characteristics of the fault model cannot be accurately identified from the pseudo-section.

[0082] Figure 6 and Figure 7 The figures are respectively the effect of correcting the apparent resistivity data of the fault model in TM mode using the existing wavelet packet soft threshold method and the improved wavelet packet threshold method. It can be seen from the figure that the static effect still exists in the pseudo-section diagram after the wavelet packet soft threshold method is processed, and the correction effect is relatively unobvious. The wavelet packet improved threshold method suppresses the static effect better, and basically restores the apparent resistivity contour lines before the static body of the fault model does not exist, which maximizes the authenticity of the geological situation and makes the correction effect more ideal.

[0083] Analysis of static effect suppression method of high-frequency magnetotelluric sounding data in field measured data:

[0084] The Yimen copper ore belt is located in central Yunnan and the western edge of the Yangtze block. It is one of the famous copper mineralization areas in my country. The folds and fault structures in the survey area are well developed, the terrain conditions are complex, and there is a lot of human interference and noise. The observed data contains serious static effects. The high-frequency magnetotelluric sounding data of the Xinzhuang section is particularly seriously affected by the static effect. Figure 8This is the original cross-section of the Yimen mining area, that is, the measured pseudo-section of the resistivity of Kania. In the figure, the contour lines are distorted, and the abnormal zone extends vertically downward, which is easy to be misjudged as the existence of steep faults or dykes, affecting the accuracy of geological interpretation.

[0085] The corrected data is processed and reconstructed using this method, and the pseudo-section diagram is as follows: Fig. 9 As shown in the figure, it can be seen that the vertical elongation phenomenon of the shallow surface electrical inhomogeneity in the profile has been improved after wavelet packet improved threshold processing, and the vertical contour distortion anomaly belt has been suppressed well. The correction result is consistent with the actual geological data inference, which verifies the effectiveness and practicality of the algorithm.

[0086] Finally, according to the characteristics of the survey area, appropriate static effect suppression algorithms can be selected and written into the ARM11 system to improve the quality of high-frequency magnetotelluric sounding data acquisition and lay a solid foundation for geological interpretation.

[0087] This embodiment also discloses a static effect suppression device for high-frequency magnetotelluric sounding data, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to implement the above-mentioned static effect suppression method for high-frequency magnetotelluric sounding data when executing the computer program.

[0088] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field know all the common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment obtained by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. Static effect suppression system for high-frequency magnetotelluric sounding data, It is characterized in that include: A data processing unit, used for real-time online suppression processing of the static effect of the magnetotelluric sounding data collected by the host; A control unit, the control unit is electrically connected to the data processing unit, the control unit is used to control the state of the data processing unit, and is also used to receive the processing result of the data processing unit and control the display unit to display the processing result; A display unit, used for displaying the test result; An access unit, used to access a host interface so that the control unit communicates with the host; After receiving the processing result, the control unit controls the display unit to display it; when the control unit establishes communication with the host, it extracts the host collection data; The data processing unit performs wavelet packet decomposition and reconstruction on the EH4 data by wavelet packet algorithm to obtain data for suppressing static effects, specifically: The denoising error factor of EH4 data is calculated by wavelet packet algorithm, and wavelet packet decomposition is performed according to the denoising error factor to determine the basis function and decomposition layer number of wavelet packet decomposition. The threshold function analysis and threshold calculation are performed according to the basis function and the number of decomposition layers; the threshold function expression is: Where n is the adjustment parameter, and n>2, ω j,k is the noisy wavelet coefficient, λ is the threshold; when |ω j,k When |→±λ, That is, the improved threshold function is continuous at the threshold ±λ, and there is no step phenomenon; when |ω j,k When |≥λ, The improved threshold function is between the soft and hard threshold functions; when |ω j,k When |≥λ, is a nonlinear function, and as the wavelet coefficient ω j,k As increases, the deviation between the reconstructed wavelet coefficients and the original wavelet coefficients gradually decreases; Wavelet packet reconstruction is performed based on the results of threshold function analysis and threshold calculation to obtain data that suppresses static effects.

2. The static effect suppression system for high-frequency magnetotelluric sounding data according to claim 1, Features: The control unit is also used to initialize the data processing unit when establishing communication with the host.

3. The static effect suppression system for high-frequency magnetotelluric sounding data according to claim 1, Features: The access unit is a CY7C68001 chip.

4. The static effect suppression system for high-frequency magnetotelluric sounding data according to claim 1, Features: The control unit is an embedded system based on ARM11.

5. The static effect suppression system for high frequency magnetotelluric sounding data according to claim 1, Features: It also includes a shell, the control unit and the data processing unit are integrated in the shell, and the access unit and the display unit are embedded in the shell.

6. A method for suppressing static effects of high-frequency magnetotelluric sounding data, applied to a system for suppressing static effects of high-frequency magnetotelluric sounding data as claimed in any one of claims 1 to 5, It is characterized in that include: S1: The control unit extracts the EH4 data collected by the host and initializes the data processing unit; S2: The data processing unit performs static effect suppression processing on the EH4 data through the wavelet packet algorithm embedded in the ARM, and feeds back the processing results to the control unit; S3: After receiving the processing result from the data processing unit, the control unit controls the display unit to display the detection result; The S2 includes: S2-1: The data processing unit identifies the static effects of EH4 data through the Lipschitz index; S2-2: The EH4 data is decomposed and reconstructed by wavelet packet algorithm to obtain data that suppresses static effects; S2-3: Feedback the processing result to the control unit; The S2-2 includes: S2-2-1: Calculate the noise elimination error factor of EH4 data by wavelet packet algorithm, perform wavelet packet decomposition according to the noise elimination error factor, and determine the basis function and decomposition level of wavelet packet decomposition; S2-2-2: Perform threshold function analysis and threshold calculation based on basis functions and decomposition levels; S2-2-3: Perform wavelet packet reconstruction based on the results of threshold function analysis and threshold calculation to obtain data that suppresses static effects.

7. Static effect suppression device for high-frequency magnetotelluric sounding data, It is characterized in that It comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to implement the static effect suppression method of high-frequency magnetotelluric sounding data as claimed in claim 6 when executing the computer program.

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