本文選題：極移預(yù)報 + 最小二乘外推��； 參考：《山東科技大學(xué)》2017年碩士論文

【摘要】：高精度的極移參數(shù)具有重要的理論研究意義和實(shí)用價值。但是,測量數(shù)據(jù)需要通過復(fù)雜的解算過程,才能得到最終的極移參數(shù),因此極移是不能實(shí)時獲取的。為了滿足極移的實(shí)時需要,對極移的預(yù)報的研究尤為必要。但是,由于極移的激發(fā)機(jī)制相對復(fù)雜,導(dǎo)致其預(yù)報結(jié)果并不是很理想。因此,極移的預(yù)報仍然需要進(jìn)行進(jìn)一步的深入研究。隨著極移序列觀測精度的提高,以及極移預(yù)報方法的優(yōu)化,極移的預(yù)報精度正在逐步提高。但是關(guān)于極移預(yù)報的方法還有待進(jìn)一步改進(jìn)�；诖�,本文做了如下工作:(1)為了進(jìn)一步提高極移的短期預(yù)報精度,本文進(jìn)一步研究了 ARMA模型族在極移預(yù)報中的應(yīng)用,提出了用LS+ARMA對極移進(jìn)行1-5天的短期預(yù)報,通過與其他方法的預(yù)報結(jié)果對比發(fā)現(xiàn),本文精度均好于其他學(xué)者的預(yù)報結(jié)果,驗(yàn)證了該模型的可行性。(2)從數(shù)據(jù)平穩(wěn)性出發(fā),提出了基于雙差分LS+ARMA模型的預(yù)報方法,用于極移1-100天跨度上的預(yù)報。并與直接用LS+ARMA模型進(jìn)行預(yù)報的結(jié)果進(jìn)行比較,結(jié)果顯示,雙差分LS+ARMA模型的預(yù)報結(jié)果在100天跨度上,在1-100天跨度上的預(yù)報結(jié)果均優(yōu)于直接用LS+ARMA模型進(jìn)行預(yù)報。(3)考慮到存在高頻信號會對極移預(yù)報精度產(chǎn)生一定的影響,提出了采用小波分析將極移序列分解成一個低頻信號與多個高頻信號,通過不同的頻率組合方案進(jìn)行極移預(yù)報,來研究高頻信號對極移預(yù)報精度的影響。從統(tǒng)計結(jié)果可以看出,高頻信號會在一定程度上降低極移的短期預(yù)報精度,但是對極移的中長期預(yù)報精度的影響幾乎可以忽略不計,總來看,低頻信號與相對應(yīng)高頻信號的組合預(yù)報結(jié)果最優(yōu)。此外,用該方法對極移進(jìn)行1-365天跨度上的預(yù)報,并與IERS發(fā)布的預(yù)報結(jié)果進(jìn)行對比,結(jié)果表明,該方法優(yōu)于IERS的預(yù)報結(jié)果,驗(yàn)證了該方法的可行性與有效性。
[Abstract]:High precision polar motion parameters have important theoretical significance and practical value. However, the measured data can only be obtained by complex calculation process, so the pole shift can not be obtained in real time. In order to meet the real-time needs of the pole shift, it is necessary to study the prediction of the pole shift. However, due to the complexity of the excitation mechanism of the pole shift, the prediction results are not very satisfactory. Therefore, the prediction of the pole shift still needs to be further studied. With the improvement of the observation accuracy of the polar shift sequence and the optimization of the polar motion prediction method, the prediction accuracy of the pole shift is gradually improved. However, the method of pole shift prediction needs further improvement. Based on this, this paper has done the following work: (1) in order to further improve the accuracy of short-term polar motion prediction, this paper further studies the application of ARMA model family in polar shift prediction, and puts forward a short-term forecast of polar shift using LS ARMA for 1-5 days. By comparing the prediction results with other methods, it is found that the accuracy of this paper is better than that of other scholars, and the feasibility of the model is verified. (2) based on the data smoothness, a prediction method based on double difference LS ARMA model is proposed. Used for the prediction of the polar shift over the span of 1-100 days. The results are compared with the results obtained by using the LS ARMA model directly. The results show that the prediction results of the double difference LS ARMA model are on a 100-day span. The prediction results on 1-100 days span are better than those from LS ARMA model directly. (3) considering the existence of high frequency signals, the accuracy of polar motion prediction is affected to a certain extent. The polar shift sequence is decomposed into one low frequency signal and several high frequency signals by wavelet analysis. The influence of high frequency signal on the precision of pole shift prediction is studied by different frequency combination schemes. It can be seen from the statistical results that the high frequency signal will reduce the short-term prediction accuracy of the pole shift to some extent, but the effect on the long-term prediction accuracy of the pole shift can be almost ignored. The combination prediction results of low frequency signal and corresponding high frequency signal are optimal. In addition, the method is used to predict the polar shift over 1-365 days span, and the results are compared with the predicted results released by IERS. The results show that this method is superior to the prediction results of IERS, and the feasibility and effectiveness of the method are verified.
【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P183.31;P228

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