A multi-station remote reference magnetotelluric denoising method based on heavy coherence

Abstract

The application discloses a multi-station remote reference magnetotelluric denoising method based on heavy coherence, and belongs to the technical field of electromagnetic detection. The method comprises the following steps: acquiring magnetotelluric time sequence data synchronously collected by a main measuring station and multiple remote reference stations, checking whether the time sequences of the main measuring station and the remote reference stations have the same time period and sampling rate, dividing the time sequences of the main measuring station and the remote reference stations in the same time period into independent data segments, calculating the electromagnetic field spectrum and power spectrum of each data segment, sorting and grouping the data segments based on the power spectrum, calculating the impedance tensor and superimposed power spectrum of each group, calculating the heavy coherence based on the impedance tensor and the superimposed power spectrum, screening each group of data based on the heavy coherence, and calculating the final impedance tensor with less interference degree based on the impedance tensor of each group of screened data. The application increases the data amount during impedance estimation of different frequency bands of measuring points, improves the accuracy and stability of the estimation result, and improves the impedance tensor estimation quality of the measuring points.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electromagnetic detection technology, and in particular to a multi-station far-reference magnetotelluric denoising method based on heavy coherence. BACKGROUND

[0002] Noise suppression techniques for magnetotelluric (MT) include mutual power spectrum impedance method, far-reference method (RR), robust method, Hilbert-Huang transform, wavelet transform, digital morphological filtering method, blind source separation method, etc. The most effective method at present is the method combining Robust method and far-reference method.

[0003] The far-reference method is to set a far-reference station at a certain distance from the measuring station without human interference, and to estimate the impedance by using the mutual power spectrum of the electromagnetic fields of the far-reference and the measuring station, which can effectively remove the non-correlated noise of the measuring station and the far-reference station. The specific process is as follows: the electromagnetic field data of both are collected at the same time, the time series data of the measuring station and the far-reference station at the same period are segmented, each segment of data is processed by far-reference, and finally the processed data of each segment is processed by Robust, weighted and averaged to obtain the final impedance tensor of the base station. With the increasing human interference, different noise sources have different interference frequency bands and intensities, and the denoising ability of a far-reference station is limited. In addition, the existing denoising methods have inherent limitations, and only through the data screening method of man-machine interaction can the robust impedance tensor data be obtained.

[0004] In addition, the existing technology has a method that can improve the accuracy of tensor impedance estimation, but usually uses a single far-reference processing to delete the disturbed time series data in the time domain by using the common coherence, and the common coherence is only suitable for describing one-dimensional medium. Therefore, the heavy coherence is more reasonable for describing non-one-dimensional medium.

[0005] In view of the above problems, a multi-station far-reference magnetotelluric denoising method based on heavy coherence is needed to solve the above problems existing in the traditional method. SUMMARY

[0006] The present application aims to provide a multi-station far-reference magnetotelluric denoising method based on heavy coherence, which uses multiple different far-reference stations to provide multi-reference channel data, thereby increasing the amount of data for impedance estimation of different frequency bands of the measuring point, improving the accuracy and stability of the estimation result, and further selecting the data in the frequency domain by using the heavy coherence coefficient which is more suitable for describing non-one-dimensional medium, so as to improve the quality of impedance tensor estimation of the measuring point.

[0007] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0008] A multi-station far-reference magnetotelluric denoising method based on heavy coherence, comprising:

[0009] Step 1: Obtain the time series data of the MT collected by the main station and the remote reference stations, and check whether the time series of the main station and the remote reference stations have the same time period and sampling rate;

[0010] Step 2: Divide the time series of the main station and the remote reference stations into independent data segments, and calculate the electromagnetic field spectrum of each data segment, and calculate the power spectrum based on the electromagnetic field spectrum;

[0011] Step 3: Sort and group the data segments based on the power spectrum, and calculate the impedance tensor and the superimposed power spectrum of each group;

[0012] Step 4: Calculate the re-coherence based on the impedance tensor and the superimposed power spectrum, and filter each group of data based on the re-coherence;

[0013] Step 5: Calculate the final impedance tensor with less interference based on the impedance tensor of each group of filtered data.

[0014] Further, in step 2, the length of each data segment is 256, and the data of each data segment is overlapped by 25%.

[0015] Further, in step 3, the data segments are sorted and grouped based on the power spectrum, specifically:

[0016] The remote reference station is provided with m, and the power spectrum of each data segment is sorted in natural order, and each n data segment is divided into a group, and a plurality of groups of data segments are obtained.

[0017] Further, in step 3, the impedance tensor of each group of data segments is calculated, specifically:

[0018] (1)

[0019] (2)

[0020] (3)

[0021] (4)

[0022] wherein, E , H are the electric and magnetic field components of the station, x , y indicate the direction of the component, with north as x and east as y; Rx and Ry are the observation values of the magnetic field x and y directions of the remote reference station, is the impedance tensor element, denotes the conjugate, and the form like is called power spectrum, m is the number of remote reference stations, and n is the number of data segments participating in the calculation in this group, W ij is the weight coefficient of each power spectrum in the Robust process.

[0023] Further, in step 3, the superimposed power spectrum of each group of data segments is calculated, specifically:

[0024] (5)

[0025] In the formula, denotes the superimposed power spectrum.

[0026] Further, in step 4, the re-coherence degree is calculated based on the impedance tensor and the superimposed power spectrum, specifically:

[0027] The re-coherence degree is calculated based on the impedance tensor and the superimposed power spectrum , which is:

[0028] (6).

[0029] Further, in step 4, each group of data is screened based on the re-coherence degree, specifically:

[0030] If the re-coherence degree of a group of data is greater than a preset re-coherence degree threshold, the group of data is retained and marked, otherwise the group of data is deleted, so as to realize the screening of the data.

[0031] Further, in step 5, the impedance tensor with smaller interference degree is calculated based on the impedance tensor of each group of screened data, specifically:

[0032] The impedance tensor of each group of screened data is calculated by complex average, and the impedance tensor with smaller interference degree is obtained.

[0033] In summary, the present application has the following at least one beneficial technical effect:

[0034] 1. The present application uses multiple different remote reference stations to provide multi-reference data, thereby increasing the amount of data when estimating the impedance of different frequency bands of the measuring point, and effectively improving the denoising ability of different frequency band interference.

[0035] 2. The present application screens the data in the frequency domain by using the re-coherence coefficient which is more suitable for describing the non-one-dimensional medium, improves the impedance tensor estimation quality of the measuring point, and obtains a robust impedance tensor. DETAILED DESCRIPTION

[0036] Figure 1 is a flowchart of the method of the present application;

[0037] Figure 2 Figure 2 is a schematic diagram of comparison between single-station data (ss) of station 5 and data (MRR7&8) of station 5 processed by multi-station remote reference and screened by re-coherence;

[0038] Figure 3 Figure 4 is a schematic diagram of comparison between night data (nd) of station 5 and MRR7&8;

[0039] Figure 4 Figure 6 is a schematic diagram of comparison between RR7 (data of station 5 with station 7 as remote reference) and MRR7&8;

[0040] Figure 5 Figure 8 is a schematic diagram of comparison between RR8 (data of station 5 with station 8 as remote reference) and MRR7&8. DETAILED DESCRIPTION

[0041] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0042] As shown in Figure 1 Figure 1, the present application provides a multi-station remote reference magnetotelluric denoising method based on re-coherence, comprising:

[0043] Step 1: obtaining magnetotelluric time series data synchronously collected by a main station and multiple remote reference stations, and checking whether the time series of the main station and each remote reference station have the same time period and sampling rate;

[0044] Step 2: dividing the time series of the same time period of the main station and the remote reference stations into independent data segments, and calculating the electromagnetic field spectrum of each data segment, and calculating the power spectrum based on the electromagnetic field spectrum;

[0045] Step 3: sorting and grouping the data segments based on the power spectrum, and calculating the impedance tensor and the superimposed power spectrum of each group;

[0046] Step 4: calculating the re-coherence based on the impedance tensor and the superimposed power spectrum, and screening each group of data based on the re-coherence;

[0047] Step 5: calculating the final impedance tensor with less interference degree based on the impedance tensor of each group of screened data.

[0048] In step 2, the length of each data segment is 256, and the data of each data segment is overlapped by 25%.

[0049] In step 3, the data segments are sorted and grouped based on the power spectrum, specifically:

[0050] The remote reference stations are provided with m, and the power spectrum of each data segment is sorted in natural order, and each n data segment is divided into a group, and a plurality of groups of data segments are obtained.

[0051] In step 3, the impedance tensor of each group of data segments is calculated, specifically:

[0052] (1)

[0053] (2)

[0054] (3)

[0055] (4)

[0056] In the formula, E , H are the electric and magnetic field components of the station, x , y indicate the direction of the component, north as x, east as y; Rx and Ry are the observation values of the magnetic field x and y direction of the remote reference station, is the impedance tensor element, indicates the conjugate, in the form of is called the power spectrum, m is the number of remote reference stations, n is the number of data segments participating in the calculation of this group, i indicates the i-th remote reference station, and j indicates the j-th data segment, W ij is the weight coefficient of each power spectrum in the Robust process;

[0057] In step 3, the superimposed power spectrum of each group of data segments is calculated, specifically:

[0058] (5)

[0059] In the formula, indicates the superimposed power spectrum.

[0060] In step 4, the re-coherence degree is calculated based on the impedance tensor and the superimposed power spectrum, specifically:

[0061] The re-coherence degree is calculated based on the impedance tensor and the superimposed power spectrum is:

[0062] (6).

[0063] In step 4, each group of data is screened based on the heavy coherence, specifically:

[0064] If the heavy coherence of a group of data is greater than the preset heavy coherence threshold, that is, wherein T is the heavy coherence threshold, and the value range is [0, 1], the group of data is retained and marked, otherwise the group of data is deleted, thereby realizing the screening of the data.

[0065] In step 5, the final impedance tensor with smaller interference degree is calculated based on the impedance tensor of each group of screened data, specifically:

[0066] The complex average calculation is performed on the impedance tensor of each group of screened data, and the final impedance tensor with smaller interference degree is obtained.

[0067] An embodiment of the present application is provided for verifying the effect of the present application, specifically:

[0068] (1) Test arrangement

[0069] The Beijing-Guangzhou high-speed railway (BGHSR) is selected as the interference source in this experiment. The Beijing-Guangzhou high-speed railway runs through the entire Jizhong Depression, connecting Beijing and Guangzhou, with a total length of 2298 kilometers and a design speed of 300 kilometers per hour. Nearly 200 trains run every day from 6 am to 24 pm local time, and a train passes every few minutes. Its traction power supply system uses 25kV, 50Hz single-phase alternating current (AC) to power the train. The basic principle is: the traction substation converts the power grid high voltage into 25kV alternating current in the catenary. Electric locomotives obtain current through the sliding contact of the pantograph-catenary system, flow through the high-voltage side of the on-board traction transformer into the steel rail, and then return to the traction substation through the steel rail to form a current loop. The instantaneous value of the traction current is close to 1000A, which greatly increases the current in the steel rail. Ideally, the steel rail current will return to the traction substation through the steel rail. However, steel rail aging can cause current leakage to the ground, thereby generating strong electromagnetic noise signals.

[0070] In the experiment, near the Sanlipu Village of Gaobeidian City in Hebei Province, test stations (test stations 1, 2, 3, 4, 5 and 6) were set up along the Beijing-Guangzhou high-speed railway at different distances (500 meters, 1 kilometer, 1.5 kilometers, 2 kilometers, 3 kilometers and 5 kilometers from the track) to collect data. Then, two far reference stations (test station 7 and test station 8) were set up in a place with little human interference, and synchronous data collection was carried out. The distance between test station 5 and test station 7 is 280 kilometers, and the distance between test station 5 and test station 8 is 550 kilometers.

[0071] (2) Test results

[0072] In this experiment, m is 2, n is 16 (16 power spectrum is divided into a group); in the ideal case, the base station and the remote reference station are homologous, and are not affected by human interference, and the re-coherence is 1. But in the actual situation, electromagnetic interference is everywhere, the smaller the interference, the greater the re-coherence, and vice versa, the value range is [0, 1]. In this work, the threshold When , it is considered that this group of data segments meets the requirements and is retained for subsequent calculation; otherwise, it is considered that the base station's data is severely polluted, and the multi-station remote reference cannot eliminate the interference, and is marked and does not participate in the subsequent tensor impedance calculation.

[0073] Figures 2-5 The data curves of the apparent resistivity xy and impedance phase xy of the various processing results of the station 5 are shown, wherein the red curve represents the single station data (ss) of the station 5, the blue curve represents the night data (nd) of the station 5, the magenta curve represents the data of the station 5 with the station 8 as the remote reference (RR8), the green curve represents the data of the station 8 with the station 7 as the remote reference (RR7), and the black curve represents the data of the station 8 with the multi-station remote reference processing of the remote reference 7 and the remote reference station 8 and the data screening through the re-coherence (MRR7&8).

[0074] Figures 2-5 The data processing results of the station 5 (3 kilometers away from the Beijing-Shanghai high-speed rail) based on different remote reference stations are shown. The red curve corresponds to the result of single station without remote reference processing, and the apparent resistivity and phase curves are continuous and smooth, but present two abnormal characteristics in the 1-0.01 Hz frequency band: the resistivity presents a 45-degree slope rise, and the phase tends to-180 degrees. These near-field effect characteristics obviously deviate from the actual geological conditions. The blue curve is the result of night data without remote reference processing (collected at 0-6 local time), and the near-field effect is slightly improved due to the relatively low noise environment of the train stop, but the improvement degree is limited in the 0.05-10 Hz frequency band. The magenta and green curves respectively represent the processing effect of the remote reference 8 station and the remote reference 7 station as the remote reference station: except that the near-field interference in the 0.1 Hz frequency band cannot be effectively corrected, the rest of the frequency band has good remote reference denoising effect. The black curve is the result of the multi-station remote reference processing of the joint station 7 and station 8 and the data screening through the re-coherence, and only a few frequency points exist from high frequency to low frequency. The curve has good convergence and clear trend characteristics, and the near-field interference is basically eliminated. It can be proved that the remote reference method significantly improves the data quality, and the multi-station remote reference technology based on the re-coherence has more advantages in eliminating various interferences and better effect.

[0075] Embodiments of the present application can be provided as a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer-readable program code.

[0076] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the present application. It is understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0077] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0079] The content described in the present application specification that is not described in detail is the prior art known to those skilled in the art. It is indicated herein that the above description helps those skilled in the art to understand the present application, but is not limited to the protection scope of the present application. Any equivalent replacement, modification, improvement and deletion of the above description without departing from the essential content of the present application falls within the protection scope of the present application.

Claims

1. A multi-station remote reference magnetotelluric denoising method based on heavy coherence, characterized in that, The application relates to a method for calculating an impedance tensor of a main station and a plurality of remote reference stations. The method comprises the following steps: Step 1: acquiring time series data of the main station and the remote reference stations, and checking whether the time series of the main station and the remote reference stations have the same time period and sampling rate; Step 2: dividing the time series of the main station and the remote reference stations into independent data segments, and calculating the electromagnetic field spectrum of each data segment, and calculating the power spectrum based on the electromagnetic field spectrum; Step 3: sorting and grouping the data segments based on the power spectrum, and calculating the impedance tensor and the superimposed power spectrum of each group; Step 4: calculating the re-coherence based on the impedance tensor and the superimposed power spectrum, and screening each group of data based on the re-coherence; 2. The multi-station remote reference magnetotelluric denoising method based on the degree of re-coherence according to claim 1, characterized in that, Step 5: calculating the final impedance tensor based on the impedance tensor of each group of screened data.

3. The multi-station remote reference magnetotelluric denoising method based on the degree of re-coherence according to claim 2, characterized in that, In step 2, the length of each data segment is 256, and the data of each data segment is overlapped by 25%. In step 3, the data segments are sorted and grouped based on the power spectrum, and the specific steps are as follows:

4. The multi-station remote reference magnetotelluric denoising method based on the degree of re-coherence according to claim 3, characterized in that, The remote reference stations are provided with m, and the power spectrum of each data segment is sorted in natural order, each n data segment is divided into a group, and a plurality of groups of data segments are obtained. （1） （2） （3） （4） where E , H are the electric and magnetic field components at the station, x , y denote the direction of the components, northward as x and eastward as y; In step 3, the impedance tensor of each group of data segments is calculated, and the specific steps are as follows: and Rx are the observed values of the magnetic field x and y directions at the remote reference stations, are the impedance tensor elements, denote the conjugate, in the form of The form is called power spectrum, m is the number of remote reference stations, n is the number of data segments in this group participating in the calculation, W ij is the weight coefficient of each power spectrum in the Robust process.

5. The multi-station remote reference magnetotelluric denoising method based on the degree of re-coherence according to claim 4, characterized in that, Ry （5） In the formula, represents the superimposed power spectrum.

6. The multi-station remote reference magnetotelluric denoising method based on the degree of coherence according to claim 5, characterized in that, In step 3, the superimposed power spectrum of each group of data segments is calculated, and the specific steps are as follows: Impedance tensor based and superposition power spectrum based calculation of re-coherence To: （6）。 7. The multi-station remote reference magnetotelluric denoising method based on the degree of coherence according to claim 6, characterized in that, In step 4, the re-coherence is calculated based on the impedance tensor and the superimposed power spectrum, and the specific steps are as follows: In step 4, each group of data is screened based on the re-coherence, and the specific steps are as follows:

8. The multi-station remote reference magnetotelluric denoising method based on the degree of re-coherence according to claim 7, characterized in that, If the re-coherence of a group of data is greater than a preset re-coherence threshold, the group of data is retained and marked, otherwise the group of data is deleted, so that the data is screened. In step 5, the final impedance tensor is calculated based on the impedance tensor of each group of screened data, and the specific steps are as follows: The impedance tensor of each group of screened data is calculated by complex average, and the final impedance tensor is obtained.

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