CN106932828A - Earth prospecting method and device - Google Patents

Abstract

The invention discloses a method and a device for geodetic exploration, and belongs to the field of electromagnetic exploration. The method comprises the following steps: collecting data signals reflecting the earth electromagnetic field; denoising the data signal by adopting a wavelet analysis mode to obtain a target signal; based on the target signal, the earth formation is determined. The method solves the problem of poor accuracy of the land exploration, improves the accuracy of the land exploration, and is used for the land exploration.

Description

Earth prospecting method and device

technical field

The invention relates to the field of electromagnetic prospecting, in particular to a method and device for earth prospecting.

Background technique

Before oil exploration, in order to understand the structure of the earth, it is often necessary to conduct earth exploration.

In related technologies, when performing geodesic exploration, it is first necessary to collect data signals reflecting the magnetotelluric field, and then determine the geodetic structure according to the collected data signals. Specifically, the resistivity and depth of each position in the earth, the thickness of each formation in the earth, and the distribution of oil in each formation can be determined according to the collected data signals.

In the related art, the collected data signal contains noise, and the noise has a great influence on the data signal in the low frequency band, therefore, there are errors in the determined geodetic structure, and the accuracy of the geodetic exploration is poor.

Contents of the invention

In order to solve the problem of poor accuracy of geophysical prospecting, the present invention provides a geophysical prospecting method and device. Described technical scheme is as follows:

In one aspect, a method of geodesic exploration is provided, the method comprising:

Collect data signals reflecting the magnetotelluric field;

Denoising the data signal by means of wavelet analysis to obtain a target signal;

Based on the target signal, the ground formation is determined.

Optionally, the wavelet analysis method is used to perform denoising processing on the data signal to obtain the target signal, including:

determining a sampling frequency corresponding to the data signal and an interference frequency corresponding to noise in the data signal;

Determine the number of decomposition layers according to the sampling frequency and the interference frequency;

performing multi-scale decomposition in the time domain on the data signal according to the number of decomposition levels, decomposing the data signal into an approximate part and at least one detail part, the number of the detail parts being equal to the number of decomposition levels;

Denoising processing is performed on the approximation part and the at least one detail part to obtain the target signal.

Optionally, the performing denoising processing on the approximate part and the at least one detail part to obtain the target signal includes:

determining a frequency band corresponding to the approximation part and a frequency band corresponding to each detail part in the at least one detail part;

In the frequency band corresponding to the approximate part and the frequency band corresponding to each detailed part, determine the interference frequency band where the interference frequency is located;

Use the heuristic threshold selection method to determine the filtering threshold;

According to the filtering threshold, a soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part;

Reconstructing the target part and parts corresponding to other frequency bands in the data signal to obtain the target signal, the other frequency bands being the frequency band corresponding to the approximate part and the frequency band corresponding to the at least one detail part, Frequency bands other than the interference frequency band are removed.

Optionally, according to the filtering threshold, the soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part, including:

determining a set of wavelet coefficients of a portion corresponding to the interference frequency band in the data signal;

Determining that a wavelet coefficient less than 3 times the filtering threshold in the group of wavelet coefficients is the first wavelet coefficient;

Determining the wavelet coefficients in the group of wavelet coefficients that are greater than or equal to 3 times the filtering threshold as the second wavelet coefficients;

Setting the first wavelet coefficient to zero to obtain the filtered first wavelet coefficient;

Subtracting 3 times the filtering threshold from the second wavelet coefficient to obtain the filtered second wavelet coefficient;

The target part is obtained according to the filtered first wavelet coefficient and the filtered second wavelet coefficient.

Optionally, the number of decomposition layers is 5, and the frequency band corresponding to the data signal is 0.00055 Hz-6 Hz.

In another aspect, a geodetic exploration device is provided, and the geodetic exploration device includes:

The acquisition module is used to collect data signals reflecting the magnetotelluric field;

A denoising module, configured to denoise the data signal by means of wavelet analysis to obtain a target signal;

The determination module is configured to determine the ground structure according to the target signal.

Optionally, the denoising module includes:

A first determining unit, configured to determine a sampling frequency corresponding to the data signal and an interference frequency corresponding to noise in the data signal;

A second determining unit, configured to determine the number of decomposition layers according to the sampling frequency and the interference frequency;

a decomposition unit, configured to perform multi-scale decomposition in the time domain on the data signal according to the number of decomposition layers, and decompose the data signal into an approximate part and at least one detail part, and the number of the detail parts is equal to the Decomposition layers;

A denoising unit, configured to perform denoising processing on the approximate part and the at least one detail part to obtain the target signal.

Optionally, the denoising unit is also used for:

determining a frequency band corresponding to the approximation part and a frequency band corresponding to each detail part in the at least one detail part;

In the frequency band corresponding to the approximate part and the frequency band corresponding to each detailed part, determine the interference frequency band where the interference frequency is located;

Use the heuristic threshold selection method to determine the filtering threshold;

According to the filtering threshold, a soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part;

Reconstructing the target part and parts corresponding to other frequency bands in the data signal to obtain the target signal, the other frequency bands being the frequency band corresponding to the approximate part and the frequency band corresponding to the at least one detail part, Frequency bands other than the interference frequency band are removed.

Optionally, the denoising unit is also used for:

determining a set of wavelet coefficients of a portion corresponding to the interference frequency band in the data signal;

Determining that a wavelet coefficient less than 3 times the filtering threshold in the group of wavelet coefficients is the first wavelet coefficient;

Determining the wavelet coefficients in the group of wavelet coefficients that are greater than or equal to 3 times the filtering threshold as the second wavelet coefficients;

Setting the first wavelet coefficient to zero to obtain the filtered first wavelet coefficient;

Subtracting 3 times the filtering threshold from the second wavelet coefficient to obtain the filtered second wavelet coefficient;

The target part is obtained according to the filtered first wavelet coefficient and the filtered second wavelet coefficient.

Optionally, the number of decomposition layers is 5, and the frequency band corresponding to the data signal is 0.00055 Hz-6 Hz.

The invention provides a geodetic exploration method and device. After collecting the data signal reflecting the magnetotelluric field, the data signal is denoised by means of wavelet analysis, and the target signal is obtained, and the geodetic structure is determined according to the target signal . Since the data signal is de-noised by means of wavelet analysis, the obtained target signal has less noise, and the noise in the low-frequency target signal is reduced, so the error of the determined terrestrial structure is reduced. Improved accuracy of geodetic exploration.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained based on these drawings without creative effort.

Fig. 1 is a method flowchart of a geodesic exploration method provided by an embodiment of the present invention;

Fig. 2 is a method flowchart of another earth prospecting method provided by an embodiment of the present invention;

Fig. 3 is a schematic structural diagram of a ground exploration device provided by an embodiment of the present invention;

Fig. 4 is a schematic structural diagram of a noise cancellation module provided by an embodiment of the present invention.

detailed description

In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, an embodiment of the present invention provides a method for geodesic exploration, which may include:

Step 101, collecting data signals reflecting the magnetotelluric field.

Step 102, performing denoising processing on the data signal by means of wavelet analysis to obtain a target signal.

Step 103, determine the ground structure according to the target signal.

To sum up, in the geodesic prospecting method provided by the embodiment of the present invention, after the data signal reflecting the magnetotelluric field is collected, the data signal is de-noised by means of wavelet analysis to obtain the target signal, and according to the target signal , to determine the tectonics. Since the data signal is de-noised by means of wavelet analysis, the obtained target signal has less noise, and the noise in the low-frequency target signal is reduced, so the error of the determined terrestrial structure is reduced. Improved accuracy of geodetic exploration.

Optionally, step 102 may include:

Determine the sampling frequency corresponding to the data signal and the interference frequency corresponding to the noise in the data signal;

Determine the number of decomposition layers according to the sampling frequency and interference frequency;

Decomposing the data signal into multiple scales in the time domain according to the number of decomposition layers, decomposing the data signal into an approximate part and at least one detail part, the number of detail parts is equal to the number of decomposition layers;

Denoising is performed on the approximation part and at least one detail part to obtain the target signal.

Optionally, denoising the approximate part and at least one detail part to obtain the target signal may include:

determining a frequency band corresponding to the approximation part and a frequency band corresponding to each detail part in at least one detail part;

In the frequency band corresponding to the approximate part and the frequency band corresponding to each detailed part, determine the interference frequency band where the interference frequency is located;

Use the heuristic threshold selection method to determine the filtering threshold;

According to the filtering threshold, the soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part;

The target part and the part corresponding to other frequency bands in the data signal are reconstructed to obtain the target signal, and the other frequency bands are the frequency bands corresponding to the approximate part and the frequency bands corresponding to at least one detail part, excluding the frequency bands other than the interference frequency band.

Optionally, according to the filtering threshold, the soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part, including:

determining a set of wavelet coefficients of a portion corresponding to the interference frequency band in the data signal;

Determining a wavelet coefficient less than 3 times the filtering threshold in a group of wavelet coefficients as the first wavelet coefficient;

Determining a wavelet coefficient greater than or equal to 3 times the filtering threshold in a group of wavelet coefficients as the second wavelet coefficient;

Set the first wavelet coefficient to zero to obtain the filtered first wavelet coefficient;

Subtracting 3 times the filtering threshold from the second wavelet coefficient to obtain the filtered second wavelet coefficient;

The target part is obtained according to the filtered first wavelet coefficient and the filtered second wavelet coefficient.

Optionally, the number of decomposition layers may be 5, and the frequency band corresponding to the data signal is 0.00055 Hz-6 Hz.

To sum up, in the geodesic prospecting method provided by the embodiment of the present invention, after the data signal reflecting the magnetotelluric field is collected, the data signal is de-noised by means of wavelet analysis to obtain the target signal, and according to the target signal , to determine the tectonics. Since the data signal is de-noised by means of wavelet analysis, the obtained target signal has less noise, and the noise in the low-frequency target signal is reduced, so the error of the determined terrestrial structure is reduced. Improved accuracy of geodetic exploration.

As shown in Figure 2, the embodiment of the present invention provides another geodesic exploration method, which may include:

Step 201, collecting data signals reflecting the magnetotelluric field.

As an example, a magnetotelluric sounding instrument can be used to measure the electromagnetic field of the earth, and the electromagnetic field data measured by the magnetotelluric sounding instrument is stored in binary form, and the electromagnetic field data can be stored in header files (*.TBL), High-frequency electromagnetic field recording files (*.TSH) and low-frequency electromagnetic field recording files (*.TSL). For example, the low-frequency electromagnetic field recording file may be composed of multiple records, each record may be composed of labels and multiple scan samples, each scan sample may be composed of multiple tracks, and each track may be a data signal reflecting the magnetotelluric field . The electromagnetic field data is stored in 24-bit complement code. The tag consists of 16 bytes or 32 bytes. The first 8 bytes record the time, followed by the second, minute, hour, day, month, year, week and century in Greenwich Mean Time, and the following 8 bytes 2-byte instrument serial number, the two bytes behind the instrument serial number indicate the number of scans in the record, and the 13th byte is the number of channels in each scan. The 14th byte is the tag length code, the 15th byte is the status code, and the 16th byte is the saturation status identifier.

When collecting data signals reflecting the magnetotelluric field, Ex (the electric field component in the x direction), Ey (the electric field component in the y direction), Hx (the magnetic field component in the x direction), Hy (the Magnetic field component) and Hz (magnetic field component in the z direction), these five data signals reflect the electromagnetic field of the earth.

Step 202. Determine the sampling frequency corresponding to the data signal and the interference frequency corresponding to the noise in the data signal.

Specifically, the specific method for determining the sampling frequency corresponding to the data signal and the interference frequency corresponding to the noise in the data signal can refer to the specific method for determining the sampling frequency corresponding to the data signal and the interference frequency in the data signal in the related art. The embodiment of the present invention again Without going into too much detail, by way of example, software can be used to determine the interfering frequency of noise in the data signal.

Step 203: Determine the number of decomposition layers according to the sampling frequency and the interference frequency.

The number of decomposition layers in the wavelet decomposition process is related to the sampling frequency corresponding to the data signal and the interference frequency corresponding to the noise in the data signal. Therefore, after determining the sampling frequency corresponding to the data signal and the interference frequency of the noise in the data signal, the data signal can be The corresponding sampling frequency and the interference frequency of noise in the data signal determine the number of decomposition layers in the wavelet decomposition process. For example, the number of decomposition layers in the wavelet decomposition process may be five.

Step 204, perform multi-scale decomposition on the data signal in the time domain according to the number of decomposition layers, and decompose the data signal into an approximate part and at least one detail part, and the number of detail parts is equal to the number of decomposition layers.

After determining the number of decomposition layers in the wavelet decomposition process, the data signal can be decomposed into multiple scales.

In the first aspect, assuming that the number of decomposition levels is 5, the data signal Ex(t) can be decomposed into an approximate part A1Ex(t) and a detail part D1Ex(t) when performing the first level of decomposition; When decomposing, the approximate part A1Ex(t) of the data signal Ex(t) can be decomposed into the approximate part A2Ex(t) and the detail part D2Ex(t); when the third layer is decomposed, the approximate part A2Ex(t) can be decomposed Decompose into approximate part A3Ex(t) and detail part D3Ex(t); when carrying out the fourth level of decomposition, the approximate part A3Ex(t) can be decomposed into approximate part A4Ex(t) and detail part D4Ex(t); When the fifth layer is decomposed, the approximate part A4Ex(t) can be decomposed into the approximate part A5Ex(t) and the detail part D5Ex(t). At this time, the data signal Ex(t) is decomposed into: detail part D1Ex(t), detail part D2Ex(t), detail part D3Ex(t), detail part D4Ex(t), detail part D5Ex(t) and approximate Part A5Ex(t), ie Ex(t)=A5Ex(t)+D1Ex(t)+D2Ex(t)+D3Ex(t)+D4Ex(t)+D5Ex(t). That is, the data signal Ex(t) is decomposed into an approximate part and five detail parts.

In the second aspect, assuming that the number of decomposition levels is 5, the data signal Ey(t) can be decomposed into an approximate part A1Ey(t) and a detail part D1Ey(t) when performing the first level of decomposition; When decomposing, the approximate part A1Ey(t) of the data signal Ey(t) can be decomposed into the approximate part A2Ey(t) and the detail part D2Ey(t); when the third layer is decomposed, the approximate part A2Ey(t) can be decomposed into It is decomposed into approximate part A3Ey(t) and detail part D3Ey(t); when performing fourth-level decomposition, the approximate part A3Ey(t) can be decomposed into approximate part A4Ey(t) and detail part D4Ey(t); When the fifth layer is decomposed, the approximate part A4Ey(t) can be decomposed into the approximate part A5Ey(t) and the detail part D5Ey(t). At this time, the data signal Ey(t) is decomposed into: detail part D1Ey(t), detail part D2Ey(t), detail part D3Ey(t), detail part D4Ey(t), detail part D5Ey(t) and approximate Part A5Ey(t), ie Ey(t)=A5Ey(t)+D1Ey(t)+D2Ey(t)+D3Ey(t)+D4Ey(t)+D5Ey(t). That is, the data signal Ey(t) is decomposed into an approximate part and five detail parts.

In the third aspect, assuming that the number of decomposition levels is 5, the data signal Hx(t) can be decomposed into an approximate part A1Hx(t) and a detail part D1Hx(t) when performing the first level of decomposition; When decomposing, the approximate part A1Hx(t) of the data signal Hx(t) can be decomposed into the approximate part A2Hx(t) and the detail part D2Hx(t); when the third layer is decomposed, the approximate part A2Hx(t) can be decomposed into Decompose into approximate part A3Hx(t) and detail part D3Hx(t); when carrying out the fourth layer of decomposition, the approximate part A3Hx(t) can be decomposed into approximate part A4Hx(t) and detail part D4Hx(t); When the fifth layer is decomposed, the approximate part A4Hx(t) can be decomposed into the approximate part A5Hx(t) and the detail part D5Hx(t). At this time, the data signal Hx(t) is decomposed into: detail part D1Hx(t), detail part D2Hx(t), detail part D3Hx(t), detail part D4Hx(t), detail part D5Hx(t) and approximate A part of A5Hx(t), that is, Hx(t)=A5Hx(t)+D1Hx(t)+D2Hx(t)+D3Hx(t)+D4Hx(t)+D5Hx(t). That is, the data signal Hx(t) is decomposed into an approximate part and five detail parts.

In the fourth aspect, assuming that the number of decomposition levels is 5, the data signal Hy(t) can be decomposed into an approximate part A1Hy(t) and a detail part D1Hy(t) during the first level of decomposition; When decomposing, the approximate part A1Hy(t) of the data signal Hy(t) can be decomposed into the approximate part A2Hy(t) and the detail part D2Hy(t); when the third layer is decomposed, the approximate part A2Hy(t) can be decomposed into It is decomposed into approximate part A3Hy(t) and detail part D3Hy(t); when performing fourth-layer decomposition, the approximate part A3Hy(t) can be decomposed into approximate part A4Hy(t) and detail part D4Hy(t); When the fifth layer is decomposed, the approximate part A4Hy(t) can be decomposed into the approximate part A5Hy(t) and the detail part D5Hy(t). At this time, the data signal Hy(t) is decomposed into: detail part D1Hy(t), detail part D2Hy(t), detail part D3Hy(t), detail part D4Hy(t), detail part D5Hy(t) and approximate A part of A5Hy(t), that is, Hy(t)=A5Hy(t)+D1Hy(t)+D2Hy(t)+D3Hy(t)+D4Hy(t)+D5Hy(t). That is, the data signal Hy(t) is decomposed into an approximate part and five detail parts.

In the fifth aspect, assuming that the number of decomposition layers is 5, the data signal Hz(t) can be decomposed into an approximate part A1Hz(t) and a detail part D1Hz(t) when performing the first-level decomposition; When decomposing, the approximate part A1Hz(t) of the data signal Hz(t) can be decomposed into the approximate part A2Hz(t) and the detail part D2Hz(t); when the third layer is decomposed, the approximate part A2Hz(t) can be decomposed It is decomposed into approximate part A3Hz(t) and detail part D3Hz(t); when performing fourth-layer decomposition, the approximate part A3Hz(t) can be decomposed into approximate part A4Hz(t) and detail part D4Hz(t); When the fifth layer is decomposed, the approximate part A4Hz(t) can be decomposed into the approximate part A5Hz(t) and the detail part D5Hz(t). At this time, the data signal Hz(t) is decomposed into: detail part D1Hz(t), detail part D2Hz(t), detail part D3Hz(t), detail part D4Hz(t), detail part D5Hz(t) and approximate Part A5Hz(t), ie Hz(t)=A5Hz(t)+D1Hz(t)+D2Hz(t)+D3Hz(t)+D4Hz(t)+D5Hz(t). That is, the data signal Hz(t) is decomposed into an approximate part and five detail parts.

Illustratively, when wavelet analysis is performed in the embodiment of the present invention, the wavelet function used can be: haar (Hall) wavelet, wavelet in the Daubechies wavelet system, wavelet in the Biorthogonal wavelet system, wavelet in the Coifet wavelet system, Wavelets, morlet wavelets, Mexican Hat wavelets, and Meyer wavelets in the Symlets wavelet system, preferably, the coif4 wavelet in the Coifet wavelet system is used in the embodiment of the present invention for wavelet analysis. It should be noted that the above wavelet functions are common wavelet functions in the software matrix factory (English: matrix&laboratory; abbreviation: MATLAB).

Step 205, performing denoising processing on the approximation part and at least one detail part to obtain a target signal.

Specifically, after multi-scale decomposition of the data signal, the approximate part corresponding to the data signal and at least one detailed part can be determined, the frequency band corresponding to the approximate part and the frequency band corresponding to each detailed part, for example, the data signal corresponds to The frequency band can be from 0.00055 Hz to 6 Hz.

Assuming that the frequency band corresponding to the data signal is [0, f], when performing the first-level decomposition, the frequency band corresponding to the approximate part A1Ex(t) can be [0, 0.5f], and the frequency band corresponding to the detail part D1Ex(t) can be [0.5f,f]. When performing second-level decomposition, the frequency band corresponding to the approximate part A2Ex(t) may be [0, 0.25f], and the frequency band corresponding to the detail part D2Ex(t) may be [0.25f, 0.5f].

Because in step 202, the sampling frequency corresponding to the data signal and the interference frequency corresponding to the noise in the data signal are determined, therefore, in step 205, the interference frequency can be determined in the frequency band corresponding to the approximate part and the frequency band corresponding to each detail part the interference frequency band. Further, after the interference frequency band is determined, the heuristic threshold selection method can be used to determine the filtering threshold. filter threshold.

After the filtering threshold is determined, the part corresponding to the interference frequency band in the data signal may be filtered by using a soft filtering method according to the filtering threshold to obtain the target part. Specifically, a group of wavelet coefficients of the part corresponding to the interference frequency band in the data signal can be firstly determined, and a wavelet coefficient less than 3 times of the filtering threshold in a group of wavelet coefficients can be determined as the first wavelet coefficient, and a group of wavelet coefficients greater than or equal to The wavelet coefficient equal to 3 times the filtering threshold is the second wavelet coefficient. Then, set the first wavelet coefficient less than 3 times the filtering threshold to zero to obtain the filtered first wavelet coefficient; subtract 3 times the filtering threshold from the second wavelet coefficient greater than or equal to 3 times the filtering threshold to obtain the filtered After the second wavelet coefficients. Finally, the target part is obtained according to the filtered first wavelet coefficient and the filtered second wavelet coefficient. It should be noted that, after the filtering threshold is determined, a hard filtering method may also be used to filter the part corresponding to the interference frequency band in the data signal according to the filtering threshold to obtain the target part.

After the target part is obtained, the target part and the part corresponding to other frequency bands in the data signal can be reconstructed to obtain the target signal. It should be noted that the other frequency bands in the data signal are the frequency bands corresponding to the approximate part of the data signal and the data signal Among the frequency bands corresponding to at least one detailed part of , the frequency bands other than the interference frequency band are removed.

In the first aspect, if the interference frequency band in the data signal Ex(t) is the frequency band corresponding to the detail part D2Ex(t), and the heuristic threshold selection method is used to determine the filtering threshold, and the soft filtering method is used to filter the data according to the filtering threshold The part corresponding to the interference frequency band in the signal is filtered, and the obtained target part is ND2Ex(t), then the target part and the part corresponding to other frequency bands in the data signal are reconstructed, and the obtained target signal can be Ex(t)=A5Ex (t)+D1Ex(t)+ND2Ex(t)+D3Ex(t)+D4Ex(t)+D5Ex(t).

In the second aspect, if the interference frequency band in the data signal Ey(t) is the frequency band corresponding to the detail part D2Ey(t), and the heuristic threshold selection method is used to determine the filtering threshold, and the soft filtering method is used to filter the data according to the filtering threshold The part corresponding to the interference frequency band in the signal is filtered, and the obtained target part is ND2Ey(t), then the target part and the part corresponding to other frequency bands in the data signal are reconstructed, and the obtained target signal can be Ey(t)=A5Ey (t)+D1Ey(t)+ND2Ey(t)+D3Ey(t)+D4Ey(t)+D5Ey(t).

In the third aspect, if the interference frequency band in the data signal Hx(t) is the frequency band corresponding to the detail part D2Hx(t), and the heuristic threshold selection method is used to determine the filtering threshold, and the soft filtering method is used to filter the data according to the filtering threshold The part corresponding to the interference frequency band in the signal is filtered, and the obtained target part is ND2Hx(t), then the target part and the part corresponding to other frequency bands in the data signal are reconstructed, and the obtained target signal can be:

Hx(t)=A5Hx(t)+D1Hx(t)+ND2Hx(t)+D3Hx(t)+D4Hx(t)+D5Hx(t).

In the fourth aspect, if the interference frequency band in the data signal Hy(t) is the frequency band corresponding to the detail part D2Hy(t), and a heuristic threshold selection method is used to determine the filtering threshold, and according to the filtering threshold, a soft filtering method is used to filter the data The part corresponding to the interference frequency band in the signal is filtered, and the obtained target part is ND2Hy(t), then the target part and the part corresponding to other frequency bands in the data signal are reconstructed, and the obtained target signal can be:

Hy(t)=A5Hy(t)+D1Hy(t)+ND2Hy(t)+D3Hy(t)+D4Hy(t)+D5Hy(t).

In the fifth aspect, if the interference frequency band in the data signal Hz(t) is the frequency band corresponding to the detail part D2Hz(t), and the heuristic threshold selection method is used to determine the filtering threshold, and the soft filtering method is used to filter the data according to the filtering threshold. The part corresponding to the interference frequency band in the signal is filtered, and the obtained target part is ND2Hz(t), then the target part and the part corresponding to other frequency bands in the data signal are reconstructed, and the obtained target signal can be:

Hz(t)=A5Hz(t)+D1Hz(t)+ND2Hz(t)+D3Hz(t)+D4Hz(t)+D5Hz(t).

Step 206: Determine the ground structure according to the target signal.

After the target signal is obtained, the ground structure can be determined according to the target signal. Specifically, the specific method of determining the ground structure according to the target signal can refer to the specific method of determining the ground structure according to the target signal in the related art, and the embodiment of the present invention will not repeat it again.

To sum up, in the geodesic prospecting method provided by the embodiment of the present invention, after the data signal reflecting the magnetotelluric field is collected, the data signal is de-noised by means of wavelet analysis to obtain the target signal, and according to the target signal , to determine the tectonics. Since the data signal is de-noised by means of wavelet analysis, the obtained target signal has less noise, and the noise in the low-frequency target signal is reduced, so the error of the determined terrestrial structure is reduced. Improved accuracy of geodetic exploration.

As shown in Fig. 3, an embodiment of the present invention provides a geodetic exploration device 30, which may include:

The collection module 301 is used to collect data signals reflecting the magnetotelluric field.

The denoising module 302 is configured to denoise the data signal by means of wavelet analysis to obtain the target signal.

The determination module 303 is configured to determine the ground structure according to the target signal.

To sum up, in the geodesic exploration device provided by the embodiment of the present invention, after the acquisition module collects the data signal reflecting the magnetotelluric field, the denoising module uses wavelet analysis to denoise the data signal, and obtains the target signal , the determination module determines the ground structure according to the target signal. Since the data signal is de-noised by means of wavelet analysis, the obtained target signal has less noise, and the noise in the low-frequency target signal is reduced, so the error of the determined terrestrial structure is reduced. Improved accuracy of geodetic exploration.

As shown in Figure 4, the denoising module 302 may include:

The first determining unit 3021 is configured to determine a sampling frequency corresponding to the data signal and an interference frequency corresponding to noise in the data signal.

The second determination unit 3022 is configured to determine the number of decomposition layers according to the sampling frequency and the interference frequency.

The decomposition unit 3023 is configured to perform multi-scale decomposition on the data signal in the time domain according to the number of decomposition layers, and decompose the data signal into an approximate part and at least one detail part, and the number of detail parts is equal to the number of decomposition layers.

A denoising unit 3024, configured to denoise the approximate part and at least one detail part to obtain the target signal.

Optionally, the denoising unit 3024 may also be used to: determine the frequency band corresponding to the approximate part and the frequency band corresponding to each detail part in at least one detail part; in the frequency band corresponding to the approximate part and the frequency band corresponding to each detail part, Determine the interference frequency band where the interference frequency is located; use the heuristic threshold selection method to determine the filtering threshold; according to the filtering threshold, use the soft filtering method to filter the part corresponding to the interference frequency band in the data signal to obtain the target part; combine the target part and the data The parts corresponding to other frequency bands in the signal are reconstructed to obtain the target signal, and the other frequency bands are the frequency bands corresponding to the approximate part and the frequency bands corresponding to at least one detailed part, excluding the frequency bands except the interference frequency band.

Optionally, the denoising unit 3024 can also be used to: determine a set of wavelet coefficients of the part corresponding to the interference frequency band in the data signal; determine a wavelet coefficient less than 3 times the filtering threshold in the set of wavelet coefficients as the first wavelet coefficient; Determine the wavelet coefficient greater than or equal to 3 times of the filtering threshold in a group of wavelet coefficients as the second wavelet coefficient; set the first wavelet coefficient to zero to obtain the first wavelet coefficient after filtering; subtract 3 times from the second wavelet coefficient The threshold is filtered to obtain the filtered second wavelet coefficient; the target part is obtained according to the filtered first wavelet coefficient and the filtered second wavelet coefficient.

Optionally, the number of decomposition layers may be 5, and the frequency band corresponding to the data signal is 0.00055 Hz-6 Hz.

To sum up, in the geodesic exploration device provided by the embodiment of the present invention, after the acquisition module collects the data signal reflecting the magnetotelluric field, the denoising module uses wavelet analysis to denoise the data signal, and obtains the target signal , the determination module determines the ground structure according to the target signal. Since the data signal is de-noised by means of wavelet analysis, the obtained target signal has less noise, and the noise in the low-frequency target signal is reduced, so the error of the determined terrestrial structure is reduced. Improved accuracy of geodetic exploration.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described geodesic exploration device can refer to the corresponding process in the aforementioned embodiment of the geodesic exploration method, which will not be repeated here.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within range.

Claims (10)

1. A method for earth prospecting, characterized in that the method comprises: Collect data signals reflecting the magnetotelluric field; Denoising the data signal by means of wavelet analysis to obtain a target signal; Based on the target signal, the ground formation is determined. 2. The method according to claim 1, wherein the method of adopting wavelet analysis is used to carry out denoising processing on the data signal to obtain the target signal, including: determining a sampling frequency corresponding to the data signal and an interference frequency corresponding to noise in the data signal; Determine the number of decomposition layers according to the sampling frequency and the interference frequency; performing multi-scale decomposition in the time domain on the data signal according to the number of decomposition levels, decomposing the data signal into an approximate part and at least one detail part, the number of the detail parts being equal to the number of decomposition levels; Denoising processing is performed on the approximation part and the at least one detail part to obtain the target signal. 3. The method according to claim 2, wherein said performing denoising processing on said approximation part and said at least one detail part to obtain said target signal comprises: determining a frequency band corresponding to the approximation part and a frequency band corresponding to each detail part in the at least one detail part; In the frequency band corresponding to the approximate part and the frequency band corresponding to each detailed part, determine the interference frequency band where the interference frequency is located; Use the heuristic threshold selection method to determine the filtering threshold; According to the filtering threshold, a soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part; Reconstructing the target part and parts corresponding to other frequency bands in the data signal to obtain the target signal, the other frequency bands being the frequency band corresponding to the approximate part and the frequency band corresponding to the at least one detail part, Frequency bands other than the interference frequency band are removed. 4. The method according to claim 3, wherein, according to the filtering threshold, a soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part ,include: determining a set of wavelet coefficients of a portion corresponding to the interference frequency band in the data signal; Determining that a wavelet coefficient less than 3 times the filtering threshold in the group of wavelet coefficients is the first wavelet coefficient; Determining the wavelet coefficients in the group of wavelet coefficients that are greater than or equal to 3 times the filtering threshold as the second wavelet coefficients; Setting the first wavelet coefficient to zero to obtain the filtered first wavelet coefficient; Subtracting 3 times the filtering threshold from the second wavelet coefficient to obtain the filtered second wavelet coefficient; The target part is obtained according to the filtered first wavelet coefficient and the filtered second wavelet coefficient. 5. The method according to any one of claims 2 to 4, characterized in that, The number of decomposition layers is 5, and the frequency band corresponding to the data signal is 0.00055 Hz-6 Hz. 6. A ground exploration device, characterized in that the ground exploration device comprises: The acquisition module is used to collect data signals reflecting the magnetotelluric field; A denoising module, configured to denoise the data signal by means of wavelet analysis to obtain a target signal; The determination module is configured to determine the ground structure according to the target signal. 7. The earth exploration device according to claim 6, wherein the noise elimination module comprises: A first determining unit, configured to determine a sampling frequency corresponding to the data signal and an interference frequency corresponding to noise in the data signal; A second determining unit, configured to determine the number of decomposition layers according to the sampling frequency and the interference frequency; a decomposition unit, configured to perform multi-scale decomposition in the time domain on the data signal according to the number of decomposition layers, and decompose the data signal into an approximate part and at least one detail part, and the number of the detail parts is equal to the Decomposition layers; A denoising unit, configured to perform denoising processing on the approximate part and the at least one detail part to obtain the target signal. 8. The earth exploration device according to claim 7, characterized in that, the noise canceling unit is also used for: determining a frequency band corresponding to the approximation part and a frequency band corresponding to each detail part in the at least one detail part; In the frequency band corresponding to the approximate part and the frequency band corresponding to each detailed part, determine the interference frequency band where the interference frequency is located; Use the heuristic threshold selection method to determine the filtering threshold; According to the filtering threshold, a soft filtering method is used to filter the part corresponding to the interference frequency band in the data signal to obtain the target part; Reconstructing the target part and parts corresponding to other frequency bands in the data signal to obtain the target signal, the other frequency bands being the frequency band corresponding to the approximate part and the frequency band corresponding to the at least one detail part, Frequency bands other than the interference frequency band are removed. 9. The earth exploration device according to claim 8, characterized in that, the noise canceling unit is also used for: determining a set of wavelet coefficients of a portion corresponding to the interference frequency band in the data signal; Determining that a wavelet coefficient less than 3 times the filtering threshold in the group of wavelet coefficients is the first wavelet coefficient; Determining the wavelet coefficients in the group of wavelet coefficients that are greater than or equal to 3 times the filtering threshold as the second wavelet coefficients; Setting the first wavelet coefficient to zero to obtain the filtered first wavelet coefficient; Subtracting 3 times the filtering threshold from the second wavelet coefficient to obtain the filtered second wavelet coefficient; The target part is obtained according to the filtered first wavelet coefficient and the filtered second wavelet coefficient. 10. The earth exploration device according to any one of claims 7 to 9, characterized in that, The number of decomposition layers is 5, and the frequency band corresponding to the data signal is 0.00055 Hz-6 Hz.