| 米兰科维奇旋回时间序列分析法研究进展 |
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| 引用本文： | 宋翠玉,吕大炜.米兰科维奇旋回时间序列分析法研究进展[J].沉积学报,2022,40(2):380-395. |
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| 作者姓名： | 宋翠玉 吕大炜 |
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| 作者单位： | 山东科技大学地球科学与工程学院，山东 青岛 266590 |
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| 基金项目： | 国家自然科学基金（41772096,41972170）~~； |
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| 摘 要： | 时间序列分析法是米兰科维奇旋回（米氏旋回）研究的主要方法。它利用数学变换对地层数据序列进行定量分析，从而识别地质记录中天文驱动形成的旋回信号，为年代校准、地层划分和古气候研究等提供重要依据。针对米氏旋回研究中时间序列分析法的主要步骤，分别从地层序列的数据类型、天文检验及天文调谐等三方面，阐述了现有方法的基本原理，并总结了其优势和局限性。在基于时间序列分析的米氏旋回研究中，不同地层序列数据对天文轨道驱动和非天文噪声的响应具有差异性，综合利用多种数据指标中蕴含的地质信息有助于减小旋回分析中的不确定性；近年来涌现的新型天文检验和调谐方法促进了米氏旋回分析向定量化方向发展，但这些新方法仍需要进一步验证及改进。未来的工作中，地层数据序列的定量评价、多数据类型的信息优化等方面仍值得深入研究；面对多种时间序列分析方法，有效整合现有方法的同时，也需要引入新方法和新思路。
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| 关 键 词： | 天文驱动检验 天文调谐 沉积序列 沉积速率 频谱分析 |
| 收稿时间： | 2020-09-07 |
Advances in Time Series Analysis Methods for Milankovitch Cycles |
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| SONG CuiYu,Lü DaWei.Advances in Time Series Analysis Methods for Milankovitch Cycles[J].Acta Sedimentologica Sinica,2022,40(2):380-395. |
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| Authors: | SONG CuiYu Lü DaWei |
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| Institution: | College of Earth Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong 266590, China |
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| Abstract: | Time series analysis has been widely employed in the study of Milankovitch cycles in recent years. It uses mathematical transformation to quantitatively analyze the stratigraphic data series， so as to identify the astronomically forcing cycle signals in geological records， which can provide an important basis for time calibration， stratigraphic division， paleoclimate research， etc. According to the main steps of the time series analysis method in the study of Milankovitch cycles， the basic principles of the existing methods were expounded upon from three aspects of the data types of stratigraphic sequence， astronomical testing， and astronomical tuning， and their advantages and limitations were summarized. In the study of Milankovitch cycles based on time series analysis， responses of different stratigraphic sequences to astronomical forcing and non-astronomical noise are different. The comprehensive utilization of geological information contained in various data proxies can help reduce the uncertainty in cyclostratigraphic analysis； various newly emerged approaches for astronomical testing and tuning have greatly promoted the quantitative development of Milankovitch cycle analysis， but further verification and improvement of these new methods are still of great concern. In the future work， quantitative evaluation of different stratigraphic data series and the information optimization of multiple data types are still worthy of in-depth study； more attention could be paid to the effective integration of existing time series analysis methods， as well as the introduction of novel methods and ideas. |
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