Treffer: Enhancing in search of Milankovitch cycles from stratigraphic record using convex optimization algorithm.
Title:
Enhancing in search of Milankovitch cycles from stratigraphic record using convex optimization algorithm.
Authors:
Alam S; Faculty of Geological Engineering, Universitas Padjadjaran, Sumedang, 45363, Indonesia. syaiful.alam@unpad.ac.id., Hadian MSD; Faculty of Geological Engineering, Universitas Padjadjaran, Sumedang, 45363, Indonesia., Hamdani AH; Faculty of Geological Engineering, Universitas Padjadjaran, Sumedang, 45363, Indonesia., Sulaiman N; Department of Geoscience, Faculty of Earth Science, Universiti Malaysia Kelantan, Campus Jeli, 17600 Jeli, Kelantan, Malaysia.
Source:
Scientific reports [Sci Rep] 2025 Jan 07; Vol. 15 (1), pp. 1099. Date of Electronic Publication: 2025 Jan 07.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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Meyers, S. R. Seeing red in cyclic stratigraphy: Spectral noise estimation for astrochronology. Paleoceanography https://doi.org/10.1029/2012PA002307 (2012). (PMID: 10.1029/2012PA002307)
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Weedon, G. P. Problems with the current practice of spectral analysis in cyclostratigraphy: Avoiding false detection of regular cyclicity. Earth Sci Rev 235, 104261 (2022). (PMID: 10.1016/j.earscirev.2022.104261)
Hajek, E. A. & Straub, K. M. Autogenic sedimentation in clastic stratigraphy. Annu Rev Earth Planet Sci 45, 681–709 (2017). (PMID: 10.1146/annurev-earth-063016-015935)
Charkhgard, H., Savelsbergh, M. & Talebian, M. A linear programming based algorithm to solve a class of optimization problems with a multi-linear objective function and affine constraints. Comput Oper Res 89, 17–30 (2018). (PMID: 10.1016/j.cor.2017.07.015)
Salman, A. M., Alridha, A. & Hussain, A. H. Some topics on convex optimization. J Phys Conf Ser 1818, 12171 (2021). (PMID: 10.1088/1742-6596/1818/1/012171)
Acer, S., Selvitopi, O. & Aykanat, C. Improving performance of sparse matrix dense matrix multiplication on large-scale parallel systems. Parallel Comput 59, 71–96 (2016). (PMID: 10.1016/j.parco.2016.10.001)
Yadav, V. & Michalak, A. M. Technical Note: Improving the computational efficiency of sparse matrix multiplication in linear atmospheric inverse problems. Geosci. Model Dev. Discuss. 2016, 1–15 (2016).
Tuan, P. D. Maximum likelihood estimation of the autoregressive model by relaxation on the reflection coefficients. IEEE Trans. Acoust. 36, 1363–1367 (1988). (PMID: 10.1109/29.1667)
Zhang, R.-M. & Chan, N. H. Maximum likelihood estimation for nearly non-stationary stable autoregressive processes. J. Time Ser. Anal. 33, 542–553 (2012). (PMID: 10.1111/j.1467-9892.2011.00762.x)
Kallas, M., Honeine, P., Richard, C., Francis, C. & Amoud, H. Prediction of time series using Yule-Walker equations with kernels. in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2185–2188 (2012). https://doi.org/10.1109/ICASSP.2012.6288346 .
Ward, M. P., Iglesias, R. M. & Brookes, V. J. Autoregressive models applied to time-series data in veterinary science. Front. Vet. Sci. https://doi.org/10.3389/fvets.2020.00604 (2020). (PMID: 10.3389/fvets.2020.00604335752757527444)
Dimitriou-Fakalou, C. Yule-Walker estimation for the moving-average model. Int. J. Stochastic Anal. 2011, 151823 (2011).
Vaughan, S., Bailey, R. J. & Smith, D. Detecting cycles in stratigraphic data: spectral analysis in the presence of red noise. Paleoceanography 26, 1–15 (2011). (PMID: 10.1029/2011PA002195)
Aswan, A. & Ozawa, T. Milankovitch 41000-year cycles in lithofacies and molluscan content in the tropical Middle Miocene Nyalindung Formation, Jawa Indonesia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 235, 382–405 (2006). (PMID: 10.1016/j.palaeo.2005.11.004)
Frigola, A., Prange, M. & Schulz, M. Boundary conditions for the Middle Miocene Climate Transition (MMCT v1.0). Geosci. Model Dev. 11, 1607–1626 (2018). (PMID: 10.5194/gmd-11-1607-2018)
Methner, K. et al. Middle Miocene long-term continental temperature change in and out of pace with marine climate records. Sci. Rep. 10, 7989 (2020). (PMID: 32409728722429510.1038/s41598-020-64743-5)
Song, Y. et al. Mid-Miocene climatic optimum: Clay mineral evidence from the red clay succession, Longzhong Basin Northern China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 512, 46–55 (2018). (PMID: 10.1016/j.palaeo.2017.10.001)
Kürschner, W. M., Kvaček, Z. & Dilcher, D. L. The impact of Miocene atmospheric carbon dioxide fluctuations on climate and the evolution of terrestrial ecosystems. Proc. Nat. Acad. Sci. 105, 449–453 (2008). (PMID: 18174330220655610.1073/pnas.0708588105)
Steinthorsdottir, M. et al. The Miocene: The Future of the Past. Paleoceanogr Paleoclimatol 36, (2021).
Westerhold, T., Bickert, T. & Röhl, U. Middle to late Miocene oxygen isotope stratigraphy of ODP site 1085 (SE Atlantic): New constrains on Miocene climate variability and sea-level fluctuations. Preprint at https://doi.org/10.1594/PANGAEA.694041 , (2005).
Miller, K. G. et al. Cenozoic sea-level and cryospheric evolution from deep-sea geochemical and continental margin records. Sci Adv 6, eaaz1346 (2020). (PMID: 32440543722874910.1126/sciadv.aaz1346)
Foster, G. L., Lear, C. H. & Rae, J. W. B. The evolution of pCO2, ice volume and climate during the middle Miocene. Earth Planet Sci. Lett. 341–344, 243–254 (2012). (PMID: 10.1016/j.epsl.2012.06.007)
Lewis, A. R. et al. Mid-Miocene cooling and the extinction of tundra in continental Antarctica. Proc. Nat. Acad. Sci. 105, 10676–10680 (2008). (PMID: 18678903249501110.1073/pnas.0802501105)
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Contributed Indexing:
Keywords: Convex optimization; Milankovitch cycles; Stratigraphic record
Entry Date(s):
Date Created: 20250108 Latest Revision: 20250108
Update Code:
20250114
DOI:
10.1038/s41598-024-82720-0
PMID:
39775130
Database:
MEDLINE
Weitere Informationen
Accurately identifying Milankovitch cycles has been a significant challenge in cyclostratigraphic studies, as it is essential for improving geochronology. This manuscript focuses on developing a method that distinguishes Milankovitch cycles from sedimentary noise to enhance stratigraphic precision. Despite their often-conspicuous magnitude, these periodicities frequently intertwine with noise, posing a challenge for conventional spectral analysis. Therefore, to address this issue, we have developed an algorithm that enhances the resolution of the Milankovitch signal by employing convex optimization in spectral analysis. To evaluate the effectiveness of this new algorithm, we applied it to four distinct types of local stratigraphy where the Milankovitch signal has been confirmed. These include the stratigraphic sections in the middle Miocene molluscan beds of Java and the Mahakam Delta, Pleistocene sediments of Hominin Flores, and the Towuti Lake in Sulawesi Island, Indonesia. Our findings demonstrate the preservation of all targeted signals, with a confidence level surpassing 99%. By setting the significance level to 1%, we can reject the null hypothesis, which assumes noise or the absence of a Milankovitch signal in the stratigraphic data being tested. The absence of deviations from the identified periodicities further strengthens the Milankovitch signal, underscoring the robustness of our algorithm. However, we acknowledge that achieving optimal results still hinges on the accurate selection of the initial parameters z and λ.
(© 2025. The Author(s).)
Competing interests: The authors declare no conflicts of interest in financial or personal relationships that could influence the work in this manuscript.