A high-resolution δ&lt;sup&gt;18&lt;/sup&gt;O record and Mediterranean climate variability

Abstract. A~high-resolution, well-dated foraminiferal δ18O record from a shallow-water core drilled from the Gallipoli Terrace in the Gulf of Taranto (Ionian Sea), previously measured over the last two millennia, has been extended to cover 707 BC–1979 AD. Spectral analysis of this series, performed by Singular Spectrum Analysis (SSA) and other classical and advanced methods, strengthens the results obtained analysing the shorter δ18O profile, detecting the same highly significant oscillations of about 600 yr, 380 yr, 170 yr, 130 yr, and 11 yr, respectively explaining about 12%, 7%, 5%, 2% and 2% of the time series total variance, plus a millennial trend (18% of the variance). The comparison with the results of Multi-channel Singular Spectrum Analysis (MSSA) applied to a data set of 26 Northern Hemisphere (NH) temperature-proxy records shows that NH temperature anomalies share with our local record a long-term trend and a bicentennial cycle. These two variability modes, previously identified as temperature-driven, are the most powerful modes in the NH temperature data set. Both the long-term trends and the bicentennial oscillations, when reconstructed locally and hemispherically, show coherent phases. Also the corresponding local and hemispheric amplitudes are comparable, if changes in the precipitation-evaporation balance of the Ionian sea, presumably associated with temperature changes, are taken into account.

Download Full-text

A high-resolution δ<sup>18</sup>O record and Mediterranean climate variability

Climate of the Past ◽

10.5194/cp-11-509-2015 ◽

2015 ◽

Vol 11 (3) ◽

pp. 509-522 ◽

Cited By ~ 8

Author(s):

C. Taricco ◽

G. Vivaldo ◽

S. Alessio ◽

S. Rubinetti ◽

S. Mancuso

Keyword(s):

High Resolution ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Ionian Sea ◽

Data Set ◽

Singular Spectrum ◽

Proxy Records ◽

Long Term Trends ◽

Temperature Proxy

Abstract. A high-resolution, well-dated foraminiferal δ18O record from a shallow-water core drilled from the Gallipoli Terrace in the Gulf of Taranto (Ionian Sea), previously measured over the last two millennia, has been extended to cover 707 BC–AD 1979. Spectral analysis of this series, performed using singular-spectrum analysis (SSA) and other classical and advanced methods, strengthens the results obtained analysing the shorter δ18O profile, detecting the same highly significant oscillations of about 600, 380, 170, 130 and 11 years, respectively explaining about 12, 7, 5, 2 and 2% of the time series total variance, plus a millennial trend (18% of the variance). The comparison with the results of multi-channel singular-spectrum analysis (MSSA) applied to a data set of 26 Northern Hemisphere (NH) temperature-proxy records shows that NH temperature anomalies share with our local record a~long-term trend and a bicentennial (170-year period) cycle. These two variability modes, previously identified as temperature-driven, are the most powerful modes in the NH temperature data set. Both the long-term trends and the bicentennial oscillations, when reconstructed locally and hemispherically, show coherent phases. Furthermore, the corresponding local and hemispheric amplitudes are comparable if changes in the precipitation–evaporation balance of the Ionian sea, presumably associated with temperature changes, are taken into account.

Download Full-text

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

Journal of Applied Geodesy ◽

10.1515/jag-2020-0005 ◽

2020 ◽

Vol 14 (3) ◽

pp. 295-302

Author(s):

Chuandong Zhu ◽

Wei Zhan ◽

Jinzhao Liu ◽

Ming Chen

Keyword(s):

Time Series ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Mixture Effect ◽

Singular Spectrum ◽

Grace Data ◽

Terrestrial Water ◽

Annual Signal ◽

Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

Download Full-text

Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam

Structural Health Monitoring ◽

10.1177/1475921710395807 ◽

2011 ◽

Vol 10 (6) ◽

pp. 587-601 ◽

Cited By ~ 36

Author(s):

Chin-Hsiung Loh ◽

Chia-Hui Chen ◽

Ting-Yu Hsu

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Threshold Level ◽

Arch Dam ◽

Static Deformation ◽

Monitoring Data ◽

Singular Spectrum

The objective of this article is to develop methods for extracting trends from long-term structural health monitoring data and try to set an early warning threshold level based on the results of analyses. The long-term monitoring data in this study is the continuous monitoring of the dam static deformation. Two different approaches were applied to extract features of the long-term structural health monitoring data of the static deformation of the Fei-Tsui Arch Dam (Taiwan). The methods include the singular spectrum analysis with auto regressive model (SSA-AR) and the nonlinear principal component analysis (NPCA) using auto-associative neural network method (AANN). The singular spectrum analysis is a novel nonparametric technique based on principles of multi-variance statistics. An AR model is optimized for each of the principal components obtained from SSA, and the multi step predicted values are recombined to make the time series. Different from SSA method the NPCA-AANN method is also used to extract the underlying features of static deformation of the dam. By using these two different methods, the residual deformation between the estimated and the recorded data was generated, through statistical analysis, the threshold level of the dam static deformation can be determined. Discussion on the two proposed methods to the static deformation monitoring data of Fei-Tsui Arch Dam (Taiwan) is discussed.

Download Full-text

Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis

Geophysics ◽

10.1190/1.3552706 ◽

2011 ◽

Vol 76 (3) ◽

pp. V25-V32 ◽

Cited By ~ 302

Author(s):

Vicente Oropeza ◽

Mauricio Sacchi

Keyword(s):

Spectrum Analysis ◽

Seismic Data ◽

Field Data ◽

Convex Sets ◽

Singular Spectrum Analysis ◽

Hankel Matrix ◽

Reconstruction Method ◽

Rank Reduction ◽

Data Set ◽

Singular Spectrum

We present a rank reduction algorithm that permits simultaneous reconstruction and random noise attenuation of seismic records. We based our technique on multichannel singular spectrum analysis (MSSA). The technique entails organizing spatial data at a given temporal frequency into a block Hankel matrix that in ideal conditions is a matrix of rank [Formula: see text], where [Formula: see text] is the number of plane waves in the window of analysis. Additive noise and missing samples will increase the rank of the block Hankel matrix of the data. Consequently, rank reduction is proposed as a means to attenuate noise and recover missing traces. We present an iterative algorithm that resembles seismic data reconstruction with the method of projection onto convex sets. In addition, we propose to adopt a randomized singular value decomposition to accelerate the rank reduction stage of the algorithm. We apply MSSA reconstruction to synthetic examples and a field data set. Synthetic examples were used to assess the performance of the method in two reconstruction scenarios: a noise-free case and data contaminated with noise. In both cases, we found extremely low reconstructions errors that are indicative of an optimal recovery. The field data example consists of a 2D prestack volume that depends on common midpoint and offset. We use the MSSA reconstruction method to complete missing offsets and, at the same time, increase the signal-to-noise ratio of the seismic volume.

Download Full-text