EAM data set pole shift forecasting method based on noise suppression

Abstract

The invention discloses an EAM data set polar shift forecasting method based on noise suppression, and belongs to the technical field of earth science, and the method comprises the steps: obtaining polar shift observation data in an IERS C04 data set, and obtaining geodetic measurement angular momentum data through the conversion of a Arweel equation; obtaining an effective angular momentum data set, and performing noise reduction processing on effective angular momentum observation data in the effective angular momentum data set by using the geodetic measurement angular momentum data to obtain effective angular momentum observation data after noise reduction processing; splicing the effective angular momentum observation data and the effective angular momentum forecast data, performing least square fitting on the obtained spliced effective angular momentum data, and further combining with a Kalman filtering algorithm for noise reduction to obtain effective angular momentum forecast data after noise reduction processing; and based on the effective angular momentum observation data after the noise reduction processing and the effective angular momentum forecast data after the noise reduction processing, carrying out polar shift forecast. According to the method, the polar shift forecasting precision is effectively improved.

Description

Technical Field

[0001] The invention belongs to the technical field of earth science, and in particular relates to a method for predicting polar motion of an EAM data set based on noise suppression. Background Art

[0002] Polar Motion (PM) refers to the movement of the Earth's rotation axis relative to the Earth's interior, that is, the movement of the position of the Earth's poles on the Earth's surface, reflecting the change in the position of the Earth's instantaneous rotation axis relative to the Earth's interior. Polar Motion is an important component of Earth Orientation Parameters (EOP) and plays an important role in deep space exploration, satellite navigation, and the establishment and maintenance of reference frames. Space geodetic technology can directly or indirectly obtain high-precision polar motion parameters, but due to the large amount of observation data and the complex data processing process, the acquisition of polar motion often lags behind for several days or even weeks, which cannot meet the real-time needs of users. Therefore, it is necessary to predict polar motion data for a certain period of time in the future based on the rapid solution data of geodetic measurements.

[0003] Based on historical data, technicians in this field have conducted a lot of research on polar motion prediction methods. The relevant results show that for short-term forecasts, the use of atmospheric angular momentum (AAM) data to assist forecasts is more accurate; for seasonal medium- and long-term forecasts, the use of least squares algorithms and autoregressive algorithms is more accurate. In addition, there are studies that show that the use of effective angular momentum (EAM) observation data and EAM forecast data is effective for polar motion forecasts, and the prediction accuracy of the polar motion forecast method using EAM is the highest.

[0004] Although the polar motion prediction method based on the EAM dataset makes full use of the EAM observation data and the forecast data for 1-6 days, the EAM dataset does not give its formal errors, including the original data such as AAM, Oceanic Angular Momentum (OAM), Hydrological Angular Momentum (HAM), and Sea Level Angular Momentum (SLAM). In other words, the EAM dataset has formal errors, and the above method is mixed with EAM noise in the forecast process, so the polar motion prediction accuracy is not high. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method for predicting polar motion based on EAM data set with noise suppression. The technical problem to be solved by the present invention is achieved by the following technical solutions: The present invention provides a method for predicting polar motion based on an EAM data set with noise suppression, comprising: Obtain the polar motion observation data in the International Earth Rotation Service (IERS) C04 dataset and convert it into geodetic angular momentum data using the Liouville equation; Acquire an effective angular momentum data set, and use the geodetic angular momentum data to perform denoising on effective angular momentum observation data in the effective angular momentum data set to obtain denoised effective angular momentum observation data; the effective angular momentum data set also includes effective angular momentum prediction data; Splicing the effective angular momentum observation data and the effective angular momentum prediction data, performing least square fitting on the obtained spliced ​​effective angular momentum data, and further combining the Kalman filter algorithm for noise reduction to obtain the effective angular momentum prediction data after noise reduction; Based on the effective angular momentum observation data after the noise reduction processing and the effective angular momentum prediction data after the noise reduction processing, the polar motion prediction is performed.

[0006] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for predicting a polar motion based on an EAM data set with noise suppression, and uses the Liouville equation to convert polar motion observation data into geodetic angular momentum data, thereby using the geodetic angular momentum data to perform noise reduction processing on effective angular momentum observation data; in addition, based on spliced ​​effective angular momentum data obtained by splicing effective angular momentum observation data and effective angular momentum prediction data, in combination with a Kalman filter algorithm, noise reduction is performed on the effective angular momentum prediction data; finally, polar motion prediction is performed based on the effective angular momentum observation data after noise reduction processing and the effective angular momentum prediction data after noise reduction processing; by combining the field of space geodesy with a geophysical model, the accuracy of polar motion prediction is effectively improved, and a basis for studying geophysical models is also provided.

[0007] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a flow chart of a method for predicting polar motion based on an EAM data set with noise suppression provided by an embodiment of the present invention; Figure 2a It is a frequency spectrum diagram of the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X provided by an embodiment of the present invention; Figure 2bIt is a frequency spectrum diagram of the geodetic angular momentum Y direction data GAM_Y and the effective angular momentum Y direction observation data EAM_Y provided by an embodiment of the present invention; Figure 3a is a noise reduction effect diagram of the effective angular momentum X-direction observation data EAM_X provided by an embodiment of the present invention; Figure 3b It is a noise reduction effect diagram of the effective angular momentum Y direction observation data EAM_Y provided by an embodiment of the present invention; Figure 4a This is a noise reduction effect diagram of the effective angular momentum X-direction prediction data provided by an embodiment of the present invention; Figure 4b This is a noise reduction effect diagram of the effective angular momentum Y-direction prediction data provided by an embodiment of the present invention; Figure 5a 1 is a schematic diagram of the forecast accuracy of the X-direction forecast data of the polar motion in the next 1-90 days provided by an embodiment of the present invention; Figure 5b It is a schematic diagram of the forecast accuracy of the Y-direction forecast data of the polar motion in the next 1-90 days provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0010] Figure 1 FIG. 1 is a flow chart of a method for predicting polar motion based on an EAM data set with noise suppression provided by an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a method for predicting polar motion based on an EAM data set with noise suppression, comprising: S1. Obtain the polar motion observation data in the International Earth Rotation Service (IERS) C04 dataset and convert it into geodetic angular momentum data using the Liouville equation.

[0011] S2. Acquire an effective angular momentum data set, and use geodetic angular momentum data to perform denoising on effective angular momentum observation data in the effective angular momentum data set to obtain denoised effective angular momentum observation data; the effective angular momentum data set also includes effective angular momentum prediction data.

[0012] S3. Splicing the effective angular momentum observation data and the effective angular momentum prediction data, performing least square fitting on the obtained spliced ​​effective angular momentum data, and further combining the Kalman filter algorithm for noise reduction to obtain the effective angular momentum prediction data after noise reduction.

[0013] S4. Based on the effective angular momentum observation data after noise reduction and the effective angular momentum prediction data after noise reduction, the polar motion prediction is performed.

[0014] The movement of the Earth's rotation axis relative to the Earth's surface is called polar drift. It should be understood that the forces on the Earth in space are very complex. The Earth is affected by the gravitational pull of the Sun, Moon and planets on the outside, and by the interaction of the atmosphere, ocean, crust, mantle and other spheres on the inside. These factors cause the Earth's rotation to be non-constant. On the one hand, the rotation rate changes with time, and on the other hand, the orientation of the rotation axis also changes with time. Usually, the orientation of the Earth can be described by the Earth's orientation parameters, including: nutation parameters, Universal Time (UT1) and polar drift.

[0015] Optionally, in step S1, the step of obtaining polar motion observation data in the International Earth Rotation Service IERS C04 data set and converting it into geodetic angular momentum data using the Liouville equation includes: S101, obtaining the polar motion X-direction observation data PMX and the polar motion Y-direction observation data PMY in the IERS C04 data set.

[0016] The International Earth Rotation Service (IERS) obtains various observation data, including polar motion observation data, through observation networks distributed around the world. Each polar motion observation data includes two components: polar motion X-direction observation data PMX and polar motion Y-direction observation data PMY.

[0017] S102, using Liouville equation, converting the polar motion X-direction observation data PMX into geodetic angular momentum X-direction data GAM_X, and converting the polar motion Y-direction observation data PMY into geodetic angular momentum Y-direction data GAM_Y.

[0018] Exemplarily, the Liouville equation is expressed as: ; In the formula, Indicates time, is the effective angular momentum function, represents the time increment, represents the imaginary unit, Indicated in The polar motion X-direction observation data PMX or the polar motion Y-direction observation data PMY, Indicated in The polar motion X-direction observation data PMX or the polar motion Y-direction observation data PMY, represents the Chandler complex frequency, Represents the Chandler Oscillation period.

[0019] In step S2, the effective angular momentum data set is obtained, and the effective angular momentum observation data in the effective angular momentum data set is subjected to denoising processing using the geodetic angular momentum data to obtain the effective angular momentum observation data after denoising processing, comprising: S201, obtaining effective angular momentum observation data in an effective angular momentum data set, wherein the effective angular momentum observation data includes: effective angular momentum X-direction observation data EAM_X and effective angular momentum Y-direction observation data EAM_Y.

[0020] S202, performing least square fitting on the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X respectively, reconstructing based on the obtained least square fitting term corresponding to EAM_X and the least square fitting residual term corresponding to GAM_X, to obtain the effective angular momentum X-direction observation data after noise reduction processing.

[0021] S203, perform least square fitting on the geodetic angular momentum Y direction data GAM_Y and the effective angular momentum Y direction observation data EAM_Y respectively, reconstruct based on the obtained least square fitting term corresponding to EAM_Y and the least square fitting residual term corresponding to GAM_Y, and obtain the effective angular momentum Y direction observation data after noise reduction processing.

[0022] It should be noted that after the geodetic angular momentum X-direction data GAM_X and Y-direction data GAM_Y are obtained through conversion by the Liouville equation, spectrum analysis can be performed on the effective angular momentum X-direction observation data EAM_X and the geodetic angular momentum X-direction data GAM_X, and spectrum analysis can be performed on the effective angular momentum Y-direction observation data EAM_Y and the geodetic angular momentum Y-direction data GAM_Y, and the obtained periodic terms are used as periodic parameters in the least squares fitting of step S202 and step S203, respectively.

[0023] Specifically, the steps of performing least square fitting on the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X respectively, reconstructing based on the obtained least square fitting term corresponding to EAM_X and the least square fitting residual term corresponding to GAM_X, and obtaining the effective angular momentum X-direction observation data after noise reduction processing include: The least squares fitting is performed on the geodetic angular momentum X-direction data GAM_X to obtain the least squares fitting term corresponding to GAM_X and the least squares fitting residual term corresponding to GAM_X.

[0024] The least squares fitting is performed on the effective angular momentum X-direction observation data EAM_X to obtain the least squares fitting term corresponding to EAM_X and the least squares fitting residual term corresponding to EAM_X.

[0025] The least square fitting term corresponding to EAM_X is added to the least square fitting residual term corresponding to GAM_X to obtain the effective angular momentum X-direction observation data after noise reduction.

[0026] On the other hand, the steps of performing least square fitting on the geodetic angular momentum Y direction data GAM_Y and the effective angular momentum Y direction observation data EAM_Y respectively, reconstructing based on the obtained least square fitting term corresponding to EAM_Y and the least square fitting residual term corresponding to GAM_Y, and obtaining the effective angular momentum Y direction observation data after noise reduction processing include: Perform the least square fitting on the Y-direction data of geodetic angular momentum GAM_Y to obtain the least square fitting term corresponding to GAM_Y and the least square fitting residual term corresponding to GAM_Y.

[0027] Perform the least squares fitting on the effective angular momentum Y direction observation data EAM_Y to obtain the least squares fitting term corresponding to EAM_Y and the least squares fitting residual term corresponding to EAM_Y.

[0028] The least square fitting term corresponding to EAM_Y is added to the least square fitting residual term corresponding to GAM_Y to obtain the effective angular momentum Y-direction observation data after noise reduction.

[0029] In this embodiment, the effective angular momentum data set includes effective angular momentum forecast data for the next 1-6 days, and the effective angular momentum forecast data after noise reduction processing includes effective angular momentum forecast data for the next 1-6 days after noise reduction processing.

[0030] In step S3, the effective angular momentum observation data and the effective angular momentum prediction data are spliced ​​together, the obtained spliced ​​effective angular momentum data is subjected to least square fitting, and further denoised by a Kalman filter algorithm to obtain the denoised effective angular momentum prediction data, including: S301, splicing effective angular momentum observation data and effective angular momentum prediction data to obtain spliced ​​effective angular momentum data; the spliced ​​effective angular momentum data includes spliced ​​effective angular momentum X-direction data EAM_X' and spliced ​​effective angular momentum Y-direction data EAM_Y'.

[0031] S302 , performing least square fitting on the X-direction data EAM_X′ of the spliced ​​effective angular momentum to obtain the least square fitting term corresponding to EAM_X′ and the least square fitting residual term corresponding to EAM_X′.

[0032] S303, after filtering the least square fitting residual term corresponding to EAM_X' using the Kalman filter algorithm, add it to the least square fitting term corresponding to EAM_X' to obtain the effective angular momentum X-direction prediction data after noise reduction processing.

[0033] S304 , performing least square fitting on the Y-direction data EAM_Y′ of the spliced ​​effective angular momentum to obtain the least square fitting term corresponding to EAM_Y′ and the least square fitting residual term corresponding to EAM_Y′.

[0034] S305 , after performing Kalman filtering on the least square fitting residual term corresponding to EAM_Y′, the residual term is added to the least square fitting term corresponding to EAM_Y′ to obtain the effective angular momentum Y direction prediction data after noise reduction processing.

[0035] Optionally, in step S4, the step of performing polar motion prediction based on the effective angular momentum observation data after noise reduction processing and the effective angular momentum prediction data after noise reduction processing includes: Based on the effective angular momentum observation data after noise reduction and the effective angular momentum forecast data after noise reduction, short-term polar motion forecast or medium- to long-term polar motion forecast is carried out.

[0036] For example, the short-term polar motion forecast is performed according to the following steps: The geodetic angular momentum X-direction data GAM_X is subtracted from the effective angular momentum X-direction observation data after noise reduction to obtain first residual data.

[0037] Based on the least squares algorithm and autoregression algorithm, the first residual data is extrapolated to the next 6 days.

[0038] The extrapolated first residual data is added to the effective angular momentum X-direction forecast data after noise reduction, and converted by the Liouville equation to obtain the polar motion X-direction forecast data for the next 1-6 days.

[0039] The second residual data is obtained by subtracting the geodetic angular momentum Y direction data GAM_Y from the effective angular momentum Y direction observation data after noise reduction processing.

[0040] Based on the least squares algorithm and autoregression algorithm, the second residual data is extrapolated to the next 6 days.

[0041] The extrapolated second residual data is added to the effective angular momentum Y-direction forecast data after noise reduction, and converted through the Liouville equation to obtain the polar motion Y-direction forecast data for the next 1-6 days.

[0042] In some other embodiments of the present application, medium- and long-term polar motion prediction may also be performed according to the following steps: The polar motion X-direction forecast data for the next 1-6 days are spliced ​​with the polar motion X-direction observation data PMX, and then the least squares algorithm and autoregression algorithm are used to make the medium- and long-term forecast of the polar motion X-direction.

[0043] The polar motion Y direction forecast data for the next 1-6 days are spliced ​​with the polar motion Y direction observation data PMY, and then the least squares algorithm and autoregression algorithm are used to make the medium- and long-term forecast of the polar motion in the Y direction.

[0044] Optionally, the medium to long term here refers to the next 6-90 days.

[0045] Furthermore, in order to evaluate the results of the polar motion prediction, the IERS C04 dataset can be used as a standard, and the mean absolute error (MAE) can be used to evaluate the accuracy of the polar motion prediction results, which is expressed as follows: ; In the formula, Indicates the number of forecasts, Indicates the forecast start date. Indicates the forecast start date The future The daily polar motion X-direction observation data PMX or the polar motion Y-direction observation data PMY, , Indicates the number of days for the forecast. For example, if a short-term polar motion forecast is made for 1-6 days, then The value of is 6. Indicates the forecast start date The future Daily polar motion X-direction forecast data or polar motion Y-direction forecast data.

[0046] Next, the method for predicting the polar motion of the EAM data set based on noise suppression provided by the present invention is further illustrated by simulation experiments.

[0047] First, the data set is prepared. The polar motion observation data from 2017 to 2023 in the IERS C04 data set are selected; the observation data and forecast data of AAM, OAM, HAM, and SLAM released by the German Geoscience Center from 2017 to 2023 are selected respectively, and these data are combined into EAM observation data and EAM forecast data with daily intervals.

[0048] Next, the polar motion observation data are converted into geodetic angular momentum data through the Liouville equation, including the X-direction data GAM_X of geodetic angular momentum obtained by converting the X-direction polar motion observation data PMX, and the Y-direction data GAM_Y of geodetic angular momentum obtained by converting the Y-direction polar motion observation data PMY; then, the Fourier transform is used to perform spectral analysis on the geodetic angular momentum data and the effective angular momentum observation data from 2017 to 2023 to obtain the periodic terms as the periodic parameters for the subsequent least squares fitting.

[0049] like Figure 2a~2b As shown, Figure 2a is a spectrum diagram of the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X provided by an embodiment of the present invention, Figure 2b is a spectrum diagram of the geodetic angular momentum Y direction data GAM_Y and the effective angular momentum Y direction observation data EAM_Y provided by an embodiment of the present invention, Figure 2a and 2b In the figure, the horizontal axis represents the period in days, the vertical axis represents the amplitude, and GAM_X-EAM_X represents the difference between the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X. Figure 2a-2b It can be seen that the periodic terms and amplitudes of the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X are basically consistent, and the periodic terms and amplitudes of the geodetic angular momentum Y-direction data GAM_Y and the effective angular momentum Y-direction observation data EAM_Y are basically consistent. In other words, the periodic terms and amplitudes of the geodetic angular momentum data and the effective angular momentum observation data are basically consistent, and the two have a strong correlation and relatively consistent periodic terms.

[0050] Further, to reduce the noise of the effective angular momentum observation data, see Figure 3a~3b , Figure 3a is a diagram showing the denoising effect of the effective angular momentum X-direction observation data EAM_X provided by an embodiment of the present invention, Figure 3b : It is a denoising effect diagram of the effective angular momentum Y direction observation data EAM_Y provided by an embodiment of the present invention, wherein the horizontal axis represents the date: 2017~2023, the vertical axis represents the numerical value of the corresponding data, New EAM_X represents the effective angular momentum X direction observation data after denoising processing, New EAM_Y represents the effective angular momentum Y direction observation data after denoising processing, and New EAM_X-GAM_X represents the difference between the effective angular momentum X direction observation data after denoising processing and the geodetic angular momentum X direction data GAM_X.

[0051] The Kalman filter algorithm is used to reduce the noise of the effective angular momentum prediction data, such as Figure 4a-4b As shown, Figure 4a is a noise reduction effect diagram of the effective angular momentum X-direction prediction data provided by an embodiment of the present invention, Figure 4b : is a denoising effect diagram of the effective angular momentum Y direction forecast data provided by an embodiment of the present invention, wherein the horizontal axis represents the date: 2017~2023, the vertical axis represents the corresponding numerical value, KEAM_X represents the effective angular momentum X direction forecast data after denoising processing, KEAM_Y represents the effective angular momentum Y direction forecast data after denoising processing, and KEAM_X-GAM_X represents the difference between the effective angular momentum X direction forecast data after denoising processing and the geodetic angular momentum X direction data GAM_X.

[0052] Finally, based on the effective angular momentum observations after denoising and the effective angular momentum forecast data after denoising, the least squares fitting and autoregression algorithm are used to predict the polar motion in the next 1-90 days.

[0053] In this simulation, 365 polar motion forecasts for the next 1-90 days are conducted within the time span of 2022-2023, and the 365 polar motion forecast data are compared with the polar motion observation data in the IERS C04 dataset using the MAE (mean absolute error) criterion to calculate the polar motion forecast accuracy. Here, the prediction accuracy of the polar motion prediction method based on noise suppression of the EAM dataset provided by the present invention is compared horizontally with the prediction accuracy of other polar motion prediction methods in the same period. The results are as follows: Figure 5a~5b shown.

[0054] Specifically, Figure 5a : is a schematic diagram of the forecast accuracy of the X-direction forecast data of the polar motion in the next 1-90 days provided by an embodiment of the present invention, Figure 5b : This is a schematic diagram of the forecast accuracy of the Y-direction forecast data of the future 1-90 days of polar motion provided by an embodiment of the present invention, wherein the horizontal axis represents the forecast days, the vertical axis represents the average forecast error, the black line represents the forecast accuracy of the polar motion forecast method based on the noise suppression EAM data set provided by the present invention, and the green line represents the forecast accuracy of the existing IERS forecast product in the same period. It can be seen that the polar motion forecast accuracy of the present invention is better than the forecast accuracy of the existing IERS forecast product in the forecast span of 1-90 days in the future.

[0055] It can be seen from the above embodiments that the beneficial effects of the present invention are: The present invention provides a method for predicting a polar motion based on an EAM data set with noise suppression, and uses the Liouville equation to convert polar motion observation data into geodetic angular momentum data, thereby using the geodetic angular momentum data to perform noise reduction processing on effective angular momentum observation data; in addition, based on spliced ​​effective angular momentum data obtained by splicing effective angular momentum observation data and effective angular momentum prediction data, in combination with a Kalman filter algorithm, noise reduction is performed on the effective angular momentum prediction data; finally, polar motion prediction is performed based on the effective angular momentum observation data after noise reduction processing and the effective angular momentum prediction data after noise reduction processing; by combining the field of space geodesy with a geophysical model, the accuracy of polar motion prediction is effectively improved, and a basis for studying geophysical models is also provided.

[0056] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0057] The description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification.

[0058] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the present application for which protection is sought, a person skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims.

[0059] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for predicting polar motion based on EAM data set with noise suppression, characterized in that: include: Obtain the polar motion observation data in the International Earth Rotation Service (IERS) C04 dataset and convert it into geodetic angular momentum data using the Liouville equation; Acquire an effective angular momentum data set, and use the geodetic angular momentum data to perform denoising on effective angular momentum observation data in the effective angular momentum data set to obtain denoised effective angular momentum observation data; the effective angular momentum data set also includes effective angular momentum prediction data; Splicing the effective angular momentum observation data and the effective angular momentum prediction data, performing least square fitting on the obtained spliced ​​effective angular momentum data, and further combining the Kalman filter algorithm for noise reduction to obtain the effective angular momentum prediction data after noise reduction; Based on the effective angular momentum observation data after the noise reduction processing and the effective angular momentum prediction data after the noise reduction processing, the polar motion prediction is performed.

2. The method for predicting polar motion based on noise-suppressed EAM data set according to claim 1, characterized in that: The steps of obtaining the polar motion observation data in the International Earth Rotation Service (IERS) C04 data set and converting it into geodetic angular momentum data using the Liouville equation include: Obtain the polar motion X-direction observation data PMX and the polar motion Y-direction observation data PMY in the IERS C04 data set; By using the Liouville equation, the polar motion X-direction observation data PMX is converted into the geodetic angular momentum X-direction data GAM_X, and the polar motion Y-direction observation data PMY is converted into the geodetic angular momentum Y-direction data GAM_Y.

3. The method for predicting the polar motion based on the noise-suppressed EAM data set according to claim 2, characterized in that: The Liouville equation is expressed as: ； In the formula, Indicates time, is the effective angular momentum function, represents the time increment, represents the imaginary unit, Indicated in The polar motion X-direction observation data PMX or the polar motion Y-direction observation data PMY, Indicated in The polar motion X-direction observation data PMX or the polar motion Y-direction observation data PMY, represents the Chandler complex frequency, Represents the Chandler Oscillation period.

4. The method for predicting the polar motion based on the noise-suppressed EAM data set according to claim 2, characterized in that: The step of obtaining an effective angular momentum data set, and performing denoising processing on effective angular momentum observation data in the effective angular momentum data set using the geodetic angular momentum data to obtain the denoised effective angular momentum observation data comprises: Acquire effective angular momentum observation data in the effective angular momentum data set, wherein the effective angular momentum observation data includes: effective angular momentum X-direction observation data EAM_X and effective angular momentum Y-direction observation data EAM_Y; Performing least square fitting on the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X respectively, reconstructing based on the obtained least square fitting term corresponding to EAM_X and the least square fitting residual term corresponding to GAM_X, to obtain the effective angular momentum X-direction observation data after noise reduction processing; The geodetic angular momentum Y-direction data GAM_Y and the effective angular momentum Y-direction observation data EAM_Y are respectively subjected to least square fitting, and reconstruction is performed based on the obtained least square fitting term corresponding to EAM_Y and the least square fitting residual term corresponding to GAM_Y to obtain the effective angular momentum Y-direction observation data after denoising.

5. The method for predicting the polar motion based on the noise-suppressed EAM data set according to claim 4, characterized in that: The steps of performing least square fitting on the geodetic angular momentum X-direction data GAM_X and the effective angular momentum X-direction observation data EAM_X respectively, reconstructing based on the obtained least square fitting term corresponding to EAM_X and the least square fitting residual term corresponding to GAM_X, and obtaining the effective angular momentum X-direction observation data after noise reduction processing include: Performing least square fitting on the geodetic angular momentum X-direction data GAM_X to obtain a least square fitting term corresponding to GAM_X and a least square fitting residual term corresponding to GAM_X; Performing least square fitting on the effective angular momentum X-direction observation data EAM_X to obtain a least square fitting term corresponding to EAM_X and a least square fitting residual term corresponding to EAM_X; The least square fitting term corresponding to the EAM_X and the least square fitting residual term corresponding to the GAM_X are added to obtain the effective angular momentum X-direction observation data after noise reduction processing.

6. The method for predicting the polar motion based on the noise-suppressed EAM data set according to claim 4, characterized in that: The steps of performing least square fitting on the geodetic angular momentum Y direction data GAM_Y and the effective angular momentum Y direction observation data EAM_Y respectively, reconstructing based on the obtained least square fitting term corresponding to EAM_Y and the least square fitting residual term corresponding to GAM_Y, and obtaining the effective angular momentum Y direction observation data after noise reduction processing include: Performing least square fitting on the geodetic angular momentum Y-direction data GAM_Y to obtain a least square fitting term corresponding to GAM_Y and a least square fitting residual term corresponding to GAM_Y; Performing least square fitting on the effective angular momentum Y-direction observation data EAM_Y to obtain a least square fitting term corresponding to EAM_Y and a least square fitting residual term corresponding to EAM_Y; The least square fitting term corresponding to the EAM_Y is added to the least square fitting residual term corresponding to the GAM_Y to obtain the effective angular momentum Y direction observation data after noise reduction processing.

7. The method for predicting the polar motion based on the noise-suppressed EAM data set according to claim 4, characterized in that: The effective angular momentum data set includes effective angular momentum forecast data for the next 1-6 days, and the effective angular momentum forecast data after noise reduction processing includes effective angular momentum forecast data for the next 1-6 days after noise reduction processing; The steps of splicing the effective angular momentum observation data and the effective angular momentum prediction data, performing least square fitting on the obtained spliced ​​effective angular momentum data, and further combining the Kalman filter algorithm for noise reduction to obtain the effective angular momentum prediction data after noise reduction processing include: splicing the effective angular momentum observation data and the effective angular momentum prediction data to obtain spliced ​​effective angular momentum data; The spliced ​​effective angular momentum data includes spliced ​​effective angular momentum X direction data EAM_X' and spliced ​​effective angular momentum Y direction data EAM_Y'; Performing least square fitting on the X-direction data EAM_X' of the spliced ​​effective angular momentum to obtain a least square fitting term corresponding to EAM_X' and a least square fitting residual term corresponding to EAM_X'; After filtering the least square fitting residual term corresponding to the EAM_X' by using the Kalman filter algorithm, the residual term is added to the least square fitting term corresponding to the EAM_X' to obtain the effective angular momentum X-direction prediction data after noise reduction processing; Performing least square fitting on the Y-direction data EAM_Y' of the spliced ​​effective angular momentum to obtain a least square fitting term corresponding to EAM_Y' and a least square fitting residual term corresponding to EAM_Y'; After the least square fitting residual term corresponding to the EAM_Y' is subjected to Kalman filtering, it is added to the least square fitting term corresponding to the EAM_Y' to obtain the effective angular momentum Y direction prediction data after noise reduction processing.

8. The method for predicting the polar motion based on the noise-suppressed EAM data set according to claim 4, characterized in that: The step of performing polar motion prediction based on the effective angular momentum observation data after the noise reduction processing and the effective angular momentum prediction data after the noise reduction processing comprises: Based on the effective angular momentum observation data after the noise reduction processing and the effective angular momentum prediction data after the noise reduction processing, a short-term polar motion prediction or a medium- to long-term polar motion prediction is performed.

9. The method for predicting the polar motion based on the noise-suppressed EAM data set according to claim 8, characterized in that: Follow the steps below to make a short-term polar motion forecast: Subtract the geodetic angular momentum X-direction data GAM_X from the effective angular momentum X-direction observation data after the noise reduction process to obtain first residual data; Based on the least squares algorithm and the autoregressive algorithm, the first residual data is extrapolated to the next 6 days; The extrapolated first residual data is added to the effective angular momentum X-direction forecast data after the noise reduction process, and converted by the Liouville equation to obtain the polar motion X-direction forecast data for the next 1-6 days; Subtract the geodetic angular momentum Y direction data GAM_Y from the effective angular momentum Y direction observation data after the noise reduction process to obtain second residual data; Based on the least squares algorithm and the autoregressive algorithm, the second residual data is extrapolated to the next 6 days; The extrapolated second residual data is added to the effective angular momentum Y-direction forecast data after the noise reduction process, and converted by the Liouville equation to obtain the polar motion Y-direction forecast data for the next 1-6 days.

10. The method for predicting the pole motion based on the noise-suppressed EAM data set according to claim 9, characterized in that: Follow the steps below to make a medium- to long-term polar motion forecast: The polar motion X-direction forecast data for the next 1-6 days are spliced ​​with the polar motion X-direction observation data PMX, and then the medium- and long-term forecast of the polar motion X-direction is performed using the least squares algorithm and the autoregressive algorithm; The polar motion Y direction forecast data for the next 1-6 days are spliced ​​with the polar motion Y direction observation data PMY, and then the least squares algorithm and autoregression algorithm are used to make a medium- and long-term forecast of the polar motion Y direction.

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