Alam Syaiful, Hadian Mohamad Sapari Dwi, Hamdani Ahmad Helman, Sulaiman Noorzamzarina
Faculty of Geological Engineering, Universitas Padjadjaran, Sumedang, 45363, Indonesia.
Department of Geoscience, Faculty of Earth Science, Universiti Malaysia Kelantan, Campus Jeli, 17600 Jeli, Kelantan, Malaysia.
Sci Rep. 2025 Jan 7;15(1):1099. doi: 10.1038/s41598-024-82720-0.
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.
准确识别米兰科维奇旋回一直是旋回地层学研究中的一项重大挑战，因为这对于改进地质年代学至关重要。本手稿着重于开发一种方法，该方法能将米兰科维奇旋回与沉积噪声区分开来，以提高地层精度。尽管这些周期性信号的幅度通常很显著，但它们常常与噪声交织在一起，给传统的频谱分析带来了挑战。因此，为了解决这个问题，我们开发了一种算法，通过在频谱分析中采用凸优化来提高米兰科维奇信号的分辨率。为了评估这种新算法的有效性，我们将其应用于四种不同类型的局部地层，这些地层中的米兰科维奇信号已得到证实。其中包括爪哇和马哈坎三角洲中新世中期软体动物层的地层剖面、弗洛勒斯人更新世沉积物，以及印度尼西亚苏拉威西岛的托乌蒂湖。我们的研究结果表明，所有目标信号均得以保留，置信水平超过99%。通过将显著性水平设定为1%，我们可以拒绝原假设，原假设假定在被测试的地层数据中存在噪声或不存在米兰科维奇信号。所识别出的周期性没有偏差，这进一步强化了米兰科维奇信号，凸显了我们算法的稳健性。然而，我们承认，要获得最佳结果仍取决于初始参数z和λ的准确选择。
