Inference of abrupt changes in noisy geochemical records using transdimensional changepoint models
Inference of abrupt changes in noisy geochemical records using transdimensional changepoint models
复制标题
DOI：
10.1016/j.epsl.2011.09.015
复制
发表时间：
2011-11-01
影响因子：
5.3
通讯作者：
Large, David
中科院分区：
文献类型：
--
作者：
Gallagher, Kerry;Bodin, Thomas;Large, David
我们提出了一种方法来量化数据序列中的突变（或变点），表示为深度或时间的函数。这些变化通常是气候或环境变化的结果，可以在多个数据集中表现为不同的响应，但所有数据集都可以具有相同的变点位置/时间。我们提出的方法使用transdimensional马尔可夫链蒙特卡罗推断概率分布的数量和位置（在深度或时间）的变化点，变化点之间的平均值，如果需要的话，与每个数据集相关的噪声方差被认为。后一点很重要，因为我们通常对噪声的信息有限，例如仅对测量不确定性的估计，并且在大多数情况下，进行重复采样/测量以评估对数据变化的其他贡献是不切实际的。我们描述了该方法的主要特点（并在补充材料中描述了数学公式），并使用合成数据集证明了其有效性，已知的变点结构（变点的数量和位置）和每个数据集的噪声方差分布。我们表明，当使用多个数据时，我们希望比单独使用每个数据集时实现更好的变点结构分辨率。这取决于不同数据集之间共同变化点假设的有效性。然后，我们将该方法应用于两套真实的地球化学数据，无论是从泥炭芯，从澳大利亚东北部和西藏东部。在所有数据集同时发生变化的假设下，我们恢复的解决方案与先前从独立数据和解释中定性推断的解决方案一致。然而，我们的方法提供了一个定量估计的相对概率推断的变化点，允许客观评估的意义，每一个变化。(C)2011 Elsevier B.V.保留所有权利。
We present a method to quantify abrupt changes (or changepoints) in data series, represented as a function of depth or time. These changes are often the result of climatic or environmental variations and can be manifested in multiple datasets as different responses, but all datasets can have the same changepoint locations/timings. The method we present uses transdimensional Markov chain Monte Carlo to infer probability distributions on the number and locations (in depth or time) of changepoints, the mean values between changepoints and, if required, the noise variance associated with each dataset being considered. This latter point is important as we generally will have limited information on the noise, such as estimates only of measurement uncertainty, and in most cases it is not practical to make repeat sampling/measurement to assess other contributions to the variation in the data. We describe the main features of the approach (and describe the mathematical formulation in supplementary material), and demonstrate its validity using synthetic datasets, with known changepoint structure (number and locations of changepoints) and distribution of noise variance for each dataset We show that when using multiple data, we expect to achieve better resolution of the changepoint structure than when we use each dataset individually. This is conditional on the validity of the assumption of common changepoints between different datasets. We then apply the method to two sets of real geochemical data, both from peat cores, taken from NE Australia and eastem Tibet. Under the assumption that changes occur at the same time for all datasets, we recover solutions consistent with those previously inferred qualitatively from independent data and interpretations. However, our approach provides a quantitative estimate of the relative probability of the inferred changepoints, allowing an objective assessment of the significance of each change. (C) 2011 Elsevier B.V. All rights reserved.