AMT denoising method, system and computer readable storage medium

Abstract

The present invention relates to an AMT denoising method, system and computer-readable storage medium, and belongs to the field of geophysical electromagnetic exploration. By setting structural element parameters that can be changed according to actual data conditions and selecting four structural elements of triangle, parabola, disk and cosine types to construct a generalized morphological filter, the electric field components such as Ex, Ey, Hx, Hy contained in high-frequency, medium-frequency and low-frequency files are respectively subjected to combined generalized morphological filtering processing, the noise morphology of each sequence is extracted, and then deleted from the original sequence, and finally the denoised high-frequency, medium-frequency and low-frequency files are reconstructed. The above method can remove various morphological noises in time series. Through the computer program stored on the computer-readable storage medium, the denoised TS2, TS3, and TS4 files can be directly generated, which is easy to operate and can be used flexibly.

Description

Technical Field

[0001] The present invention relates to the field of geophysical electromagnetic exploration, and in particular to an AMT denoising method, system and computer-readable storage medium. Background Art

[0002] Audio magnetotelluric sounding (AMT) has been widely used in the fields of oil and gas prospecting area inference, geothermal exploration, finding water sources and conductive ore bodies, and detecting sedimentary basin structures. In the field, V8 (host) combined with AMTC30 (magnetic probe) was used to collect data, and three time series files of TS2 (high frequency), TS3 (medium frequency), and TS4 (low frequency) were obtained, with corresponding sampling rates of 24000, 2400, and 150Hz respectively. Each TS file contains five electromagnetic field component time series: Ex, Ey, Hx, Hy, Hz (E represents electric field, H represents magnetic field).

[0003] However, since the AMT method uses the weak natural magnetotelluric field as the field source to detect underground electrical structures, and with the development of the country's industry, the working area for data collection is increasingly subject to various noise interferences, resulting in the collected signals being submerged in the noise and poor data quality.

[0004] Therefore, it is urgent to develop a method, system and computer-readable storage medium that can perform denoising on different noises. This method can remove different forms of noise in AMT data, thereby obtaining accurate underground electrical structure information. Summary of the invention

[0005] The object of the present invention is to provide an AMT denoising method, system and computer-readable storage medium, so as to obtain denoised high-frequency, medium-frequency and low-frequency time series files.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] An AMT denoising method, the method comprising:

[0008] The AMT method is used to obtain high-frequency, medium-frequency, and low-frequency time series files, wherein the high-frequency, medium-frequency, and low-frequency time series files are files of underground electrical structure information;

[0009] Four structural elements, triangle, parabola, disk and cosine, are selected to construct a generalized morphological filter;

[0010] According to the data of the high-frequency time series file, the structural element parameters K2 and L2 are set, and the structural element parameters K2 and L2 are different according to the data of the high-frequency time series file, wherein K2 is an extreme value parameter and L2 is a zero point parameter;

[0011] According to the generalized morphological filter and the structural element parameters K2 and L2, the combined generalized morphological filtering processing is performed on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the high-frequency time series file, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the high-frequency time series file, wherein the Ex and Ey are the components of the electric field on the x-axis and the y-axis, respectively, and the Hx and Hy are the components of the magnetic field on the x-axis and the y-axis, respectively, and the combined generalized filtering processing includes the generalized morphological filtering processing of the negative structural element and the generalized morphological filtering processing of the positive structural element;

[0012] Respectively deleting the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the high-frequency time series file to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the high-frequency time series file after denoising;

[0013] Reconstructing a denoised high-frequency file using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised high-frequency time series file;

[0014] Setting structural element parameters K3 and L3 according to the data of the intermediate frequency time series file, wherein the structural element parameters K3 and L3 are different according to the data of the intermediate frequency time series file;

[0015] According to the generalized morphological filter and the structural element parameters K3 and L3, the combined generalized morphological filtering is performed on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file;

[0016] Respectively deleting the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the intermediate frequency time series file after denoising;

[0017] Reconstructing the denoised intermediate frequency file using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised intermediate frequency time series file;

[0018] The structural element parameters K3 and L3 are set according to the data of the low-frequency time series file, and the structural element parameters K4 and L4 are different according to the data of the low-frequency time series file;

[0019] According to the generalized morphological filter and the structural element parameters K4 and L4, the combined generalized morphological filtering is performed on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the low-frequency time series file, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy in the low-frequency time series file;

[0020] Respectively deleting the respective noise sequences from the Ex, Ey, Hx, and Hy of the low-frequency time series file to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the low-frequency time series file after denoising;

[0021] The denoised low-frequency file is reconstructed using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised low-frequency time series file.

[0022] The present invention also provides an AMT denoising system, the system comprising:

[0023] A file acquisition module, used to acquire high-frequency, medium-frequency, and low-frequency time series files using the AMT method, wherein the high-frequency, medium-frequency, and low-frequency time series files are files of underground electrical structure information;

[0024] The structural element selection module is used to select four structural elements of triangle, parabola, disk and cosine to construct a generalized morphological filter;

[0025] A high-frequency time series structural element parameter setting module, used to set structural element parameters K2 and L2 according to the data of the high-frequency time series file, wherein the structural element parameters K2 and L2 are different according to the data of the high-frequency time series file;

[0026] A high-frequency time series filtering processing module is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the high-frequency time series file according to the generalized morphological filter and the structural element parameters K2 and L2, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the high-frequency time series file, wherein the Ex and Ey are the components of the electric field on the x-axis and the y-axis, respectively, and the Hx and Hy are the components of the magnetic field on the x-axis and the y-axis, respectively, and the combined generalized filtering processing includes the generalized morphological filtering processing of the negative structural element and the generalized morphological filtering processing of the positive structural element;

[0027] A high-frequency time series noise sequence deletion module is used to delete the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the high-frequency time series file, and obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the high-frequency time series file after denoising;

[0028] A high-frequency file acquisition module, used to reconstruct the denoised high-frequency file using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised high-frequency time series file;

[0029] An intermediate frequency time series structural element parameter setting module, used to set structural element parameters K3 and L3 according to the data of the intermediate frequency time series file, wherein the structural element parameters K3 and L3 are different according to the data of the intermediate frequency time series file;

[0030] The intermediate frequency time series filtering processing module is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file according to the generalized morphological filter and the structural element parameters K3 and L3, so as to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file;

[0031] An intermediate frequency time series noise sequence deletion module is used to delete the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file, respectively, to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the intermediate frequency time series file after denoising;

[0032] An intermediate frequency file acquisition module, used for reconstructing the denoised intermediate frequency file by using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised intermediate frequency time series file;

[0033] A low-frequency time series structural element parameter setting module, used to set structural element parameters K3 and L3 according to the data of the low-frequency time series file, and the structural element parameters K4 and L4 are different according to the data of the low-frequency time series file;

[0034] A low-frequency time series filtering processing module is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the low-frequency time series file according to the generalized morphological filter and the structural element parameters K4 and L4, so as to obtain the noise sequences of the Ex, Ey, Hx, and Hy in the low-frequency time series file;

[0035] A low-frequency time series noise sequence deletion module is used to delete the respective noise sequences from the Ex, Ey, Hx, and Hy of the low-frequency time series file, and obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the low-frequency time series file after denoising;

[0036] The low-frequency file acquisition module is used to reconstruct the denoised low-frequency file by using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised low-frequency time series file.

[0037] The present invention also provides a computer-readable storage medium on which a computer program is stored, characterized in that the program implements the steps of the AMT denoising method when executed by a processor.

[0038] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0039] The denoising method provided by the present invention sets structural element parameters K2 and L2 that can be changed according to the actual data situation for high-frequency files, and selects four structural elements of triangle, parabola, disk and cosine to construct a generalized morphological filter, and performs combined generalized morphological filtering on the electromagnetic field components such as Ex, Ey, Hx, Hy contained in the high-frequency files, extracts the noise morphology of each sequence, and then deletes it from the original sequence, and finally reconstructs the denoised high-frequency file. The denoising method for medium-frequency files and low-frequency files is similar. By selecting structural elements of different types and sizes, various morphological noises in the time series are removed, and by setting structural element parameters that can be changed according to the actual data situation, the best denoising result is obtained. The computer program stored on the computer-readable storage medium can directly generate the denoised TS2, TS3, and TS4 files, which is easy to operate and can be used flexibly. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 AMT denoising method flow chart provided in Example 1 of the present invention;

[0042] Figure 2 A flowchart of extracting a noise sequence from a time series provided in Example 1 of the present invention;

[0043] Figure 3 Schematic diagram of five-point triangle, five-point parabola, five-point disk, and five-point cosine structural elements provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0044] 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.

[0045] The purpose of the present invention is to provide an AMT denoising method, system and computer-readable storage medium. By selecting structural elements of different types and sizes, various forms of noise in the time series are removed, and parameters are selected according to the actual noise interference of TS2, TS3 and TS4 data, so as to obtain the best denoising result.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Example 1

[0048] This embodiment provides an AMT denoising method, such as Figure 1 As shown, the method includes:

[0049] S1, use AMT method to obtain high-frequency, medium-frequency and low-frequency time series files respectively;

[0050] Among them, the high-frequency, medium-frequency and low-frequency time series files are files of underground electrical structure information.

[0051] S2. Select four structural elements: triangle, parabola, disk and cosine to construct a generalized morphological filter.

[0052] The selection and setting of structural elements are as follows:

[0053] The structural element is a very important part of morphological filtering, which is related to whether the noise in the time series (TSn) can be accurately extracted. Different structural elements correspond to different functions and different shapes. The size and size of each structural element are determined by the two parameters K and L (the shape refers to the shape of the function curve); structural elements of different shapes can better extract the corresponding shape of noise when filtering. In order to better remove various morphological noises in the time series, this system selects four structural elements (type and size) of s1, s2, s3, and s4 (the parameters K and L of the four structural elements are consistent. If K and L are changed, the size of the four structural elements will change at the same time) to construct a generalized morphological filter. S1 is set to triangular t(n), s2 is set to parabolic p(n), s3 is set to disk d(n), and s4 is set to cosine cos(n). The function expressions are respectively as follows (1)(2)(3)(4). Where n is the independent variable, L is an integer, and k is a real number.

[0054]

[0055] p(x)=k(L 2 -x 2 ),-L≤x≤L;x,L∈Z,k∈R (2)

[0056]

[0057]

[0058] S3. Set structural element parameters K2 and L2 according to the data of the high-frequency time series file; perform combined generalized morphological filtering on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the high-frequency time series file according to the generalized morphological filter and the structural element parameters K2 and L2 to obtain the noise sequences of Ex, Ey, Hx, and Hy respectively; delete the noise sequences of each from the Ex, Ey, Hx, and Hy respectively to obtain the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence; and reconstruct the denoised high-frequency file using the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence.

[0059] It should be noted that Ex and Ey are the components of the electric field on the x-axis and y-axis respectively, and Hx and Hy are the components of the magnetic field on the x-axis and y-axis respectively. The data of the high-frequency time series file is different, and the set structural element parameters K2 and L2 are different, that is, the settings of the structural element parameters K and L are different according to the data of the high-frequency time series file.

[0060] The parameter K determines the magnitude of the extreme value of the structure element, and the parameter L determines the zero point of the structure element; in this embodiment, the parameter K is referred to as the extreme value parameter, and the parameter L is referred to as the zero point parameter.

[0061] The K value should generally be selected to be 2 to 3 orders of magnitude smaller than the average grid value of the sequence to be processed, and the L value can be set to about ten times the K value.

[0062] The steps of the combined generalized filtering process are as follows:

[0063] First, treat the time series (including the original E x 、E y , H x , H y The generalized morphological filtering of negative structural elements and positive structural elements is performed on the electromagnetic field component time series. The flow chart (the processing of Ex series is taken as an example) and the calculation formula are as follows: Figure 2 shown.

[0064] The calculation formula of the generalized morphological filtering process of the negative structure element includes:

[0065]

[0066] The calculation formula of generalized morphological filtering processing of positive structure elements includes:

[0067]

[0068] In formulas (5) and (6), GOCOC(f(n)) and GCOCO(f(n)) are generalized morphological open-close-open-close filter and generalized morphological closed-open-close-open filter, respectively. s1 refers to the triangular structure element, s2 refers to the parabolic structure element, s3 refers to the disk structure element, and s4 refers to the cosine structure element. is the morphological opening operation, which is defined as shown in formula (7); “·” is the morphological closing operation, which is defined as shown in formula (8);

[0069]

[0070]

[0071] Among them, Θ is the corrosion operation, It is an expansion operation, and its detailed calculation formula is as follows:

[0072]

[0073]

[0074] f(n) in equations (5)(6)(7)(8)(9)(10) is the time series to be filtered.

[0075] As an optional implementation, in this denoising method, five function values ​​in the structure element are selected to participate in the filtering operation, and the horizontal coordinates of the five function values ​​are set to -2L / 3, -L / 3, 0, L / 3, and 2L / 3, respectively. The image is as follows: Figure 3 As shown, taking the structural element s1 as an example, it can be understood that: the function value corresponding to the horizontal coordinate -2L / 3 is k / 3, the function value corresponding to the horizontal coordinate -L / 3 is 2k / 3, the function value corresponding to the horizontal coordinate 0 is k, the function value corresponding to the horizontal coordinate L / 3 is 2k / 3, and the function value corresponding to the horizontal coordinate 2L / 3 is k / 3.

[0076] It can be understood that in this scheme, the structural element parameters k2, L2 are first obtained according to the time series to be processed, and four structural elements s1, s2, s3, and s4 are selected, and the function expressions of the four structural elements are obtained accordingly, and for each structural element, five function values ​​in the structural element are selected to participate in the filtering operation. During the filtering process, the values ​​of -2L / 3, -L / 3, 0, L / 3, and 2L / 3 are calculated respectively according to the value of L2 (at this time, the value of L2 is used to replace L in -2L / 3, -L / 3, 0, L / 3, and 2L / 3, so as to calculate the values ​​of -2L / 3, -L / 3, 0, L / 3, and 2L / 3. Similarly, when filtering the intermediate frequency file later, the value of L3 is used to replace L in -2L / 3, -L / 3, 0, L / 3, and 2L / 3, so as to calculate the values ​​of -2L / 3, -L / 3, 0, L / 3, and 2L / 3). According to the value of k2 and the function expression of each structural element, the function values ​​corresponding to -2L / 3, -L / 3, 0, L / 3, and 2L / 3 in each structural element are calculated. Substitute the function values ​​corresponding to -2L / 3, -L / 3, 0, L / 3, and 2L / 3 in each structural element into the generalized morphological filtering processing formula of the negative structural element and the generalized morphological filtering processing formula of the positive structural element, and replace the structural elements s1, s2, s3, and s4 in the formula respectively to obtain the calculation result, that is, the noise sequence.

[0077] When removing the noise sequence, the original sequence Ex, Ey, Hx, and Hy is used to subtract the noise sequence to obtain the denoised signal. Taking the original electric field component Ex as an example, after processing with this method, its noise sequence can be extracted and E x-noise The denoised electric field component sequence is represented by E xdn Indicates that

[0078] E xdn =Ex-E x-noise

[0079] After the denoising process, E x 、E y , H x , H y The signal is synthesized into a new TS2 (high frequency) file. Taking the synthesis of the denoised TS2 file as an example, the process is as follows:

[0080] First, the tag information of the Ex, Ey, Hx, Hy, and Hz sequences contained in the TS2 file is read out (it can be understood that the tag information is the locomotive, and Ex, Ey, Hx, Hy, and Hz are the carriages behind it). It should be noted that the storage of tag information occupies 32 bytes, and a group of tags corresponds to 2400 groups of (Ex, Ey, Hx, Hy, and Hz) data, and each component data is stored in 3 bytes. Then, the Ex, Ey, Hx, and Hy sequences are processed by combined generalized morphological filtering and noise is removed. Since Hz does not participate in the calculation of apparent resistivity, it is kept as it is without processing. Then, the denoised Ex, Ey, Hx, Hy sequences, and Hz sequences are converted into binary and merged and stored with the corresponding tag information (merged storage: still in the original order, which can be understood as the order of the original carriages, and the order of merging here is what order), so that the denoised TS2 file is resynthesized. The processing of TS3 and TS4 files is the same.

[0081] The TSn file synthesized after denoising can be used to obtain information such as spectrum, power spectrum density, apparent resistivity, phase, etc., and invert to obtain better underground electrical structure information.

[0082] S4, setting structural element parameters K3 and L3 according to the data of the intermediate frequency time series file; performing combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file according to the generalized morphological filter and the structural element parameters K3 and L3, respectively, to obtain the noise sequences of Ex, Ey, Hx, and Hy respectively; deleting the noise sequences of each from the Ex, Ey, Hx, and Hy respectively, to obtain the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence; and reconstructing the denoised intermediate frequency file using the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence;

[0083] S5. Set structural element parameters K4 and L4 according to the data of the low-frequency time series file; perform combined generalized morphological filtering on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the low-frequency time series file according to the generalized morphological filter and the structural element parameters K4 and L4 to obtain the noise sequences of Ex, Ey, Hx, and Hy respectively; delete the noise sequences of Ex, Ey, Hx, and Hy respectively to obtain the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence; and reconstruct the denoised low-frequency file using the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence.

[0084] It should be noted that the denoising method for medium-frequency files and low-frequency files is the same as that for high-frequency files, which will not be repeated here. The specific steps can refer to the denoising method for high-frequency files.

[0085] Example 2

[0086] This embodiment provides an AMT denoising system, the system comprising:

[0087] The file acquisition module M1 is used to acquire high-frequency, medium-frequency and low-frequency time series files using the AMT method;

[0088] The structural element selection module M2 is used to select four structural elements of triangle, parabola, disk and cosine to construct a generalized morphological filter;

[0089] A high-frequency time series structural element parameter setting module M3 is used to set structural element parameters K2 and L2 according to the data of the high-frequency time series file. The structural element parameters K2 and L2 are set differently when the data of the high-frequency time series file is different;

[0090] The high-frequency time series filtering processing module M4 is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the high-frequency time series file according to the generalized morphological filter and the structural element parameters K2 and L2, respectively, to obtain the noise sequences of Ex, Ey, Hx, and Hy, respectively, wherein Ex and Ey are the components of the electric field on the x-axis and the y-axis, respectively, and Hx and Hy are the components of the magnetic field on the x-axis and the y-axis, respectively, and the combined generalized filtering processing includes generalized morphological filtering processing of negative structural elements and generalized morphological filtering processing of positive structural elements;

[0091] A high-frequency time series noise sequence deletion module M5 is used to delete the respective noise sequences from the Ex, Ey, Hx, and Hy to obtain the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence;

[0092] A high-frequency file acquisition module M6 is used to reconstruct the denoised high-frequency file using the denoised Ex sequence, Ey sequence, Hx sequence and Hy sequence;

[0093] The intermediate frequency time series structural element parameter setting module M7 is used to set structural element parameters K3 and L3 according to the data of the intermediate frequency time series file. The structural element parameters K3 and L3 are set differently when the data of the intermediate frequency time series file is different;

[0094] The intermediate frequency time series filtering processing module M8 is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file according to the generalized morphological filter and the structural element parameters K3 and L3, so as to obtain the noise series of Ex, Ey, Hx, and Hy respectively;

[0095] The intermediate frequency time series noise sequence removal module M9 is used to remove the respective noise sequences from the Ex, Ey, Hx, and Hy to obtain the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence;

[0096] An intermediate frequency file acquisition module M10 is used to reconstruct the denoised intermediate frequency file using the denoised Ex sequence, Ey sequence, Hx sequence and Hy sequence;

[0097] A low-frequency time series structural element parameter setting module M11 is used to set structural element parameters K3 and L3 according to the data of the low-frequency time series file. The structural element parameters K4 and L4 set are different for different data of the low-frequency time series file.

[0098] The low-frequency time series filtering processing module M12 is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the low-frequency time series file according to the generalized morphological filter and the structural element parameters K4 and L4, so as to obtain the noise sequences of Ex, Ey, Hx, and Hy respectively;

[0099] A low-frequency time series noise sequence removal module M13 is used to remove the respective noise sequences from the Ex, Ey, Hx, and Hy to obtain the denoised Ex sequence, Ey sequence, Hx sequence, and Hy sequence;

[0100] The low-frequency file acquisition module M14 is used to reconstruct the denoised low-frequency file using the denoised Ex sequence, Ey sequence, Hx sequence and Hy sequence.

[0101] Example 3

[0102] The present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in Embodiment 1 when the program is executed by a processor;

[0103] When the computer program is used to perform the denoising method, the following steps are included:

[0104] (1) The user must first create a folder named liyubo in drive E, and then create a subfolder named 04284244 in the folder. Then, create folders named TS2, TS3, and TS4 in the subfolder.

[0105] (2) Use Tbl-Edit software to generate TXT files for TS2, TS3, and TS4 files respectively.

[0106] (3) After running, follow the prompts to enter the file path and file name of the TS2 file that needs to be denoised, as well as the file path and file name of the TXT2 file. (For example: e: / liyubo / 20190428 / 4244428A.TS2, e: / liyubo / 20190428 / 4244428a_TS2.txt).

[0107] (4) According to the actual collected TS2 data and its noise interference (the noise interference of the data and the average grid value of the data), input the values ​​of parameters L2 and K2. The K value is generally 2 to 3 orders of magnitude smaller than the average grid value of the sequence to be processed. The L value can be set to about ten times the K value. (For example: L2 = 3, K2 = 0.5).

[0108] (5) Wait for the program to run, and then the denoised TS2 file will be saved in the TS2 folder created in step (1). It will display: Conguantulations! Data processing has been finished. Denoising TS2 has been made.

[0109] (6) Follow the prompts to enter the file path and file name of the TS3 file that needs to be denoised, as well as the file path and file name of the TXT3 file.

[0110] (7) According to the TS3 data actually collected and the noise interference, the values ​​of the input parameters L3 and K3 are selected. The K value is generally selected to be 2 to 3 orders of magnitude smaller than the average grid value of the sequence to be processed. The L value can be set to about ten times the K value.

[0111] (8) Wait for the program to run, and then the denoised TS3 file will be saved in the TS3 folder created in step (1). It will display: Conguantulations! Data processing has been finished. Denoising TS3 has been made.

[0112] (9) Follow the prompts to enter the file path and file name of the TS4 file that needs to be denoised, as well as the file path and file name of the TXT4 file.

[0113] (10) According to the TS4 data actually collected and the noise interference, the values ​​of the input parameters L4 and K4 are selected. The K value is generally selected to be 2 to 3 orders of magnitude smaller than the average grid value of the sequence to be processed. The L value can be set to about ten times the K value.

[0114] (11) Wait for the program to run, and then the denoised TS4 file will be saved in the TS4 folder created in step (1). It will display: Conguantulations! Data processing has been finished. Denoising TS4 has been made.

[0115] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0116] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An AMT denoising method, characterized in that: The method comprises: The AMT method is used to obtain high-frequency, medium-frequency and low-frequency time series files, wherein the high-frequency, medium-frequency and low-frequency time series files are files of underground electrical structure information; Four structural elements, namely triangle, parabola, disk and cosine, are selected to construct a generalized morphological filter; According to the data of the high-frequency time series file, the structural element parameters K2 and L2 are set, and the structural element parameters K2 and L2 are different according to the data of the high-frequency time series file, wherein K2 is the structural element extreme value parameter of the high-frequency time series file data, and L2 is the structural element zero point parameter of the high-frequency time series file data; According to the generalized morphological filter and the structural element parameters K2 and L2, the combined generalized morphological filtering processing is performed on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the high-frequency time series file, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the high-frequency time series file, wherein the Ex and Ey are the components of the electric field on the x-axis and the y-axis, respectively, and the Hx and Hy are the components of the magnetic field on the x-axis and the y-axis, respectively, and the combined generalized morphological filtering processing includes the generalized morphological filtering processing of the negative structural element and the generalized morphological filtering processing of the positive structural element; Respectively deleting the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the high-frequency time series file to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the high-frequency time series file after denoising; Reconstructing a denoised high-frequency file using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised high-frequency time series file; According to the data of the intermediate frequency time series file, the structural element parameters K3 and L3 are set, and the structural element parameters K3 and L3 are different according to the data of the intermediate frequency time series file, wherein K3 is the structural element extreme value parameter of the intermediate frequency time series file data, and L3 is the structural element zero point parameter of the intermediate frequency time series file data; According to the generalized morphological filter and the structural element parameters K3 and L3, the combined generalized morphological filtering is performed on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file; Respectively deleting the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the intermediate frequency time series file after denoising; Reconstructing the denoised intermediate frequency file using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised intermediate frequency time series file; According to the data of the low-frequency time series file, the structural element parameters K4 and L4 are set, and the structural element parameters K4 and L4 are different according to the data of the low-frequency time series file, wherein K4 is the structural element extreme value parameter of the low-frequency time series file data, and L4 is the structural element zero point parameter of the low-frequency time series file data; According to the generalized morphological filter and the structural element parameters K4 and L4, the combined generalized morphological filtering is performed on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the low-frequency time series file, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy in the low-frequency time series file; Respectively deleting the respective noise sequences from the Ex, Ey, Hx, and Hy of the low-frequency time series file to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the low-frequency time series file after denoising; The denoised low-frequency file is reconstructed using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised low-frequency time series file.

2. The method according to claim 1, characterized in that The function expression of the triangular structure element includes: In the formula, x is the independent variable, L is an integer, and k is a real number.

3. The method according to claim 1, characterized in that The function expression of the parabolic structure element includes: p(x)=k(L 2 -x 2 ),-L≤x≤L;x,L∈Z,k∈R (2) In the formula, x is the independent variable, L is an integer, and k is a real number.

4. The method according to claim 1, characterized in that: The function expression of the disk-shaped structural element includes: In the formula, x is the independent variable, L is an integer, and k is a real number.

5. The method according to claim 1, characterized in that The function expression of the cosine type structure element includes: In the formula, x is the independent variable, L is an integer, and k is a real number.

6. The method according to claim 1, characterized in that The calculation formula of the generalized morphological filtering process of the negative structure element includes: Where, GOCOC(f(n)) and GCOCO(f(n)) are generalized morphological open-close-open-close filter and generalized morphological closed-open-close-open filter, respectively. s1 refers to the triangular structure element, s2 refers to the parabolic structure element, s3 refers to the disk structure element, and s4 refers to the cosine structure element. is the morphological opening operation, which is defined as shown in formula (7); "·" is the morphological closing operation, which is defined as shown in formula (8); Among them, Θ is the corrosion operation, It is an expansion operation, and its detailed calculation formula is as follows: f(n) in equations (5), (7), (8), (9), and (10) is the time series to be filtered.

7. The method according to claim 6, characterized in that The operation formula of the generalized morphological filtering process of the positive structure element includes:

8. An AMT denoising system, characterized in that: The system comprises: A file acquisition module, used to acquire high-frequency, medium-frequency, and low-frequency time series files using the AMT method, wherein the high-frequency, medium-frequency, and low-frequency time series files are files of underground electrical structure information; The structural element selection module is used to select four structural elements of triangle, parabola, disk and cosine to construct a generalized morphological filter; A high-frequency time series structural element parameter setting module is used to set structural element parameters K2 and L2 according to the data of the high-frequency time series file, wherein the structural element parameters K2 and L2 are different according to the data of the high-frequency time series file, wherein K2 is the structural element extreme value parameter of the high-frequency time series file data, and L2 is the structural element zero point parameter of the high-frequency time series file data; A high-frequency time series filtering processing module is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the high-frequency time series file according to the generalized morphological filter and the structural element parameters K2 and L2, respectively, to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the high-frequency time series file, wherein the Ex and Ey are the components of the electric field on the x-axis and the y-axis, respectively, and the Hx and Hy are the components of the magnetic field on the x-axis and the y-axis, respectively, and the combined generalized morphological filtering processing includes the generalized morphological filtering processing of the negative structural element and the generalized morphological filtering processing of the positive structural element; A high-frequency time series noise sequence deletion module is used to delete the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the high-frequency time series file, and obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the high-frequency time series file after denoising; A high-frequency file acquisition module, used to reconstruct the denoised high-frequency file using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised high-frequency time series file; The intermediate frequency time series structural element parameter setting module is used to set structural element parameters K3 and L3 according to the data of the intermediate frequency time series file, and the structural element parameters K3 and L3 are different according to the data of the intermediate frequency time series file, wherein K3 is the structural element extreme value parameter of the intermediate frequency time series file data, and L3 is the structural element zero point parameter of the intermediate frequency time series file data; The intermediate frequency time series filtering processing module is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file according to the generalized morphological filter and the structural element parameters K3 and L3, so as to obtain the noise sequences of the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file; An intermediate frequency time series noise sequence deletion module is used to delete the respective noise sequences from the Ex, Ey, Hx, and Hy contained in the intermediate frequency time series file, respectively, to obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the intermediate frequency time series file after denoising; An intermediate frequency file acquisition module, used for reconstructing the denoised intermediate frequency file by using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised intermediate frequency time series file; A low-frequency time series structural element parameter setting module is used to set structural element parameters K4 and L4 according to the data of the low-frequency time series file, wherein the structural element parameters K4 and L4 are different according to the data of the low-frequency time series file, wherein K4 is the structural element extreme value parameter of the low-frequency time series file data, and L4 is the structural element zero point parameter of the low-frequency time series file data; A low-frequency time series filtering processing module is used to perform combined generalized morphological filtering processing on the time series of the electromagnetic field components Ex, Ey, Hx, and Hy contained in the low-frequency time series file according to the generalized morphological filter and the structural element parameters K4 and L4, so as to obtain the noise sequences of the Ex, Ey, Hx, and Hy in the low-frequency time series file; A low-frequency time series noise sequence deletion module is used to delete the respective noise sequences from the Ex, Ey, Hx, and Hy of the low-frequency time series file, and obtain the Ex sequence, Ey sequence, Hx sequence, and Hy sequence of the low-frequency time series file after denoising; The low-frequency file acquisition module is used to reconstruct the denoised low-frequency file by using the Ex sequence, Ey sequence, Hx sequence and Hy sequence of the denoised low-frequency time series file.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images