Analysis Method and Indicator Optimization of Astronomical Cycles in Lacustrine Fine-Grained Sedimentary Strata- A case study of Es4scs in Well Niuye 1, Dongying Sag, Jiyang Depression, Bohai Bay Basin

Analysis Method and Indicator Optimization of Astronomical Cycles in Lacustrine Fine-Grained Sedimentary Strata- A case study of Es4scs in Well Niuye 1, Dongying Sag, Jiyang Depression, Bohai Bay Basin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis Method and Indicator Optimization of Astronomical Cycles in Lacustrine Fine-Grained Sedimentary Strata- A case study of Es 4 scs in Well Niuye 1, Dongying Sag, Jiyang Depression, Bohai Bay Basin Ledan Yu, Jiao Wang, Jun Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6651998/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Apr, 2026 Read the published version in Geomechanics and Geophysics for Geo-Energy and Geo-Resources → Version 1 posted 13 You are reading this latest preprint version Abstract In recent years, great progress has been made in fine-grain sedimentary oil and gas exploration. However, the rotary division of fine grain sedimentary strata is an important foundation of fine grain deposition oil and gas exploration. With the development of research, the complexity and differences of indicators and methods in the rotary division have led to the increasing contradiction of research results. The lacustrine shale from the upper interval of the upper sub-member of the 4th member of the Paleogene Shahejie Formation（Es 4 scs ）in Well Niuye 1，Dongying Sag，Jiyang Depression，Bohai Bay Basin was used as the study object. This study was conducted by combining testing methods and techniques such as X-ray diffraction whole-rock analysis, X-ray fluorescence spectroscopy, TOC analysis, and magnetic susceptibility testing. Based on the data of geophysical, geochemical, environmental magnetic parameters magnetic susceptibility, mineral content and core grayscale, and using TSAM 1 and TSAM 2 methods to optimize the rotary analysis method and sedimentation series substitution indexes. The research results are shown as follows. First, The TSAM 2 method has the best results for GR, AC, RL, RN, log series principal component analysis and log series factor analysis. Second, The TSAM 2 method works the best for the magnetic susceptibility data. Third, TSAM 2 was best for Fe, Mn, Ti, Ca, Al, Si, CaO / MgO, (CaO + K 2 O + Na 2 O) / MgO Al 2 O 3 , (Fe + Al) / (Ca + Mg), principal component analysis and factor analysis of each element data. Fourth, In the core grayscale data, the TSAM 2 method has the best effect. The data identifies smaller solar cycles than the transition cycle scale. Fifth, The optimal deposited sequence substitutability index is the natural gamma logging data, and the optimal time series analysis method is TSAM 1. Sixth, The study strata identified 5 long eccentricity cycle of 405 kyr , 22 short eccentricity cycle of 95.24kyr , 48 obliquity cycle of 39.76kyr, 53 obliquity cycle of 38.54kyr , 87 precession cycle of 23.28kyr, 98 precession cycle of 22kyr and 107 precession cycle of 18.82kyr. The study concluded that the deposition time was roughly 2.038Ma, and the average deposition rate is estimated to be 0.074m / kyr. This research is helpful to promote the research of scientific problems such as the establishment of rotary geological age and the determination of terrestrial rotation level. Meanwhile, it has a good application value and research prospect for the development of fine-grain sedimentary spiral stratigraphy. lacustrine mud shale astronomical orbital period substitution index of deposition sequence time series analysis method preferred Dongying Sag Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1 Introduction With the global rise of unconventional shale oil and gas exploration, high-frequency sequence division of fine-grained sedimentary rocks has become a pressing issue in current stratigraphic research. High-frequency sea-level fluctuations driven by Milankovitch astronomical cycles are considered the driving factors for the formation of high-frequency sequences, and their temporal significance provides an effective means for conducting high-frequency cycle division and comparison. Research on cyclical stratigraphy abroad has mainly focused on marine strata, with a primary emphasis on Mesozoic and Cenozoic to Early Cambrian strata (Batenburg et al., 2012 ; Marshall et al., 2017 ). Most studies have relied on field outcrops, ocean drilling cores, rock cores, and well log data to conduct cyclical stratigraphy research (Nadia et al., 2018 ;Radzevicius et al., 2017 ). Minguez et al. (2017) conducted a cyclical stratigraphy study on the Wonoka Formation in Australia, using magnetic susceptibility data as an alternative indicator, and identified the orbital parameter cycles recorded in the studied stratigraphic section (Minguez et al., 2017). Fang et al. ( 2019 ) applied magnetic susceptibility data to study the cyclical stratigraphy of the Dawan Gou section in Keping, Xinjiang, and concluded that Earth’s orbital parameters controlled the cooling events during the Darriwilian period (Fang et al., 2019 ). Wang et al. ( 2020 ) performed a cyclical analysis on the clastic and carbonate strata of the GFD-1 well in the Pingle Depression, South China, and established a high-resolution astronomical timescale and sedimentation rate curve for the Luoping Oilfield in the GFD-1 well (Wang et al., 2020 ). Chen et al. ( 2020 ) conducted a spectral analysis, fast Fourier transform, and correlation analysis of the gamma ray logs from four wells, including Liu 8 well in the Lower Cretaceous Lower Gouwu Formation of the Jiuquan Basin, revealing the presence of orbital cycles such as long eccentricity, short eccentricity, obliquity, and precession within the study interval (Chen et al., 2020 ). Domestic scholars have conducted extensive research on both marine and continental cyclical stratigraphy. Hu et al. ( 2018 ) used typical outcrops, drilling, well logging, and seismic data, and employed natural gamma-ray logs to create Fischer diagrams, identifying high-frequency cycles with Milankovitch cycle characteristics (Hu et al., 2018 ). Zong ( 2018 ) applied wavelet transform and fast Fourier transform methods for time-frequency analysis of natural gamma-ray log data, identifying orbital cycles in sedimentary strata (Zong, 2018 ). Shi et al. ( 2019 ) used magnetic susceptibility as an alternative indicator to identify Milankovitch cycle signals, and ultimately used long eccentricity, short eccentricity, and obliquity cycle curves as reference curves for the fourth, fifth, and sixth-level sequence divisions, achieving a quantitative division of high-frequency sequences in lacustrine fine-grained sediments (Shi et al., 2019 ). Liu ( 2020 ) applied the theory of cyclical stratigraphy along with time series analysis and spectral analysis techniques to conduct astronomical cycle analysis on target wells. By utilizing time anchor points from neighboring wells, an effective astronomical timescale was established (Liu, 2020 ). Ma et al. ( 2021 ) conducted a cyclical stratigraphy study on the Upper and Middle Ordovician Salgan and Kanling formations in the Keping region of Xinjiang using high-resolution magnetic susceptibility data, identifying astronomical orbital cycles, with the obliquity signal being the most prominent (Ma et al., 2021 ). Yang et al. ( 2021 ) performed spectral analysis of magnetic susceptibility data sequences and found that certain stratigraphic intervals in the Jianza profile of the Qiangtang Basin on the northeastern Tibetan Plateau exhibited stable long eccentricity cycles, while other orbital cycles were unstable (Yang et al., 2021 ). Zhao et al. ( 2022 ) used spectral analysis and wavelet transform to reveal the presence of Milankovitch astronomical cycles in the III oil layer group of the Sartu oil reservoir. They speculated that the strata were influenced by the periodic variations in Milankovitch astronomical orbital cycles during deposition (Zhao et al., 2022 ). In summary, both domestic and international scholars have commonly used well logging, magnetic susceptibility, and geochemical testing analysis data, along with time-frequency analysis methods, to study the cyclicity of marine and continental sedimentary strata. These methods are then used to calculate the duration of cycles and the average sedimentation rate. However, previous studies on the cyclical division of the Dongying Depression have yielded different results, as various scholars have employed different methods (Peng et al., 2021 , 2022 ; Yu et al., 2021 ; Peng et al., 2021 ; Shi et al., 2019 , 2022). The discrepancies in the division results can lead to significant errors in geological dating, and the outcomes may not effectively serve oil and gas exploration. In order to better explore the astronomical cycle division of lacustrine fine-grained sedimentary strata, this study takes the Es 4 scs in well Niuye 1 as an example. Based on astronomical stratigraphy theory, the study attempts to comprehensively apply various sedimentary sequence surrogate indicators, including geophysical, geochemical, mineral content, environmental magnetic parameters (magnetic susceptibility data), and core grayscale data, along with multiple time-series analysis methods to identify astronomical cycles. Through comparison of the results, the study aims to optimize the methods and indicators, ultimately combining Re-Os isotopic dating data to establish an absolute high-precision astronomical timescale. This research will provide a reference for the division of high-frequency cycles and precise geological age estimation, as well as offer insights for selecting astronomical cycle indicators and methods in similar stratigraphic studies. 2 Geological Settings The Bohai Bay Basin is located in eastern China and developed as a Cenozoic rift basin based on the Paleozoic sedimentation of the Sino-Korean Platform and subsequent movements during the Indosinian and Yanshanian orogenies (Fig. 1 (a) and Fig. 1 (b)) (Yu et al., 2021 ). The Jiyang Depression is located in the southeastern part of the Bohai Bay Basin and is a secondary tectonic unit within the basin. The depression is composed of four secondary sub-depressions: the Chezheng Depression, Zanhua Depression, Huimin Depression, and Dongying Depression (Fig. 1 b). The Dongying Sag in the study area is a typical asymmetric, inverted, graben-type depression characterized by steep northern slopes and gentle southern slopes. A number of uplifts surround the Sag: to the north is the Chenjiazhuang Uplift, to the south is the Luxi Uplift, to the east is the Qingtuozi Uplift, and to the west are the Binxian and Qingcheng Uplifts. The exploration area covers approximately 5,800 km² (Fig. 1 (c)) (Yu et al., 2023). Within the Sag, a series of syn-sedimentary normal faults further divide the area into four main petroleum-rich sub-depressions (Boxing, Lijin, Niuzhuang, and Minfeng), as well as several secondary structural units such as the northern steep slope zone, central anticline zone, southern gentle slope zone, and multiple fault structural belts (Fig. 1 (d)) (Li et al., 2019 ; Yu et al., 2023). The stratigraphy of the Dongying Sag develops in the following order from bottom to top: the Paleozoic (Pz), Mesozoic (Mz), Paleogene (E), Neogene (N), and Quaternary (Q). Among these, the Paleogene strata are widely distributed with a significant sedimentary thickness, exceeding seven thousand meters at its thickest. The thickness gradually decreases and tapers off from the basin sedimentary center to the marginal areas. The Paleogene strata can be divided into the Kongdian Formation, Shahejie Formation, and Dongying Formation from bottom to top. The Shahejie Formation can be further subdivided into four segments from bottom to top: the Sha 4 Member, Sha 3 Member, Sha 2 Member, and Sha 1 Member. Notably, the upper sub-member of the Sha 4 Member in the Shahejie Formation represents semi-deep lake to deep lake deposition, which formed a large set of fine-grained sediments, primarily consisting of mudstone, calcareous mudstone, and limestone, among others (Fig. 2 ) (Yu et al., 2021 ; Li et al., 2019 ). 3 Data and Experimental Methods 3.1 XRF analysis of the core samples The content of various elements in the samples was tested using the NITON XL3t-950 Handheld Ore Elemental Analyzer (an instrument based on XRF spectrometry) developed by Thermo Scientific (Analytical Methods Committee and Royal Society of Chemistry, 2008). During the testing process to ensure the validity of the collected data, the testing density was about 2.5 cm to test 1 point, and the testing time of each point was 20 s according to the Nyquist sampling theorem (Li et al., 2019 ). The test was conducted in two modes of measurement, using the mineral mode for major elements and the soil mode for trace elements with a content of less than 1%. In order to carry out the analysis, this study mainly tested the elements Ti, Si, S, K, Ca, Fe, V, Ba, Zr, Mn and Zn, and categorized the elemental indicators characterizing the paleoclimate, paleobathymetry, paleosalinity, paleoredox and paleoproductivity in the test data. 3.2 X-ray diffraction (XRD) analysis of bulk rock X-diffraction whole rock analysis is a common method for determining the mineralogical composition of rocks. The rock samples are ground until the average particle size reaches about 5µm or less than 200 mesh, and then the samples are pressed and molded and tested on the machine to get the mass percentage of minerals. This experimental analysis was performed by XRD powder crystal scattering test on the samples using a TTR-type X-ray diffractometer manufactured by Rigaku, Japan, and the test conditions were performed with reference to the industry standard SY/T 5163 − 2010 (Li et al., 2019 ; Zhao et al., 2019 ). 3.3 Organic carbon analysis The method involves grinding the dried sample to a particle size of less than 0.2 mm, and removing the inorganic carbon component of the sample with dilute hydrochloric acid (analytically pure hydrochloric acid prepared with water at a volume ratio of 1:7). It is then burned in a high-temperature oxygen stream (oxygen purity 99.9%) to convert the total organic carbon into carbon dioxide, and then, based on the correspondence of carbon content between carbon dioxide and total organic carbon, the carbon dioxide content is finally detected by an infrared detector/thermal conductivity detector, and the total organic carbon content is calculated. This analytical test was performed using a LECOCS-230 carbon and sulfur analyzer, and the test conditions were performed with reference to the national standard GB/T 19145 − 2003 (Li et al., 2019 ; Zhao et al., 2019 ). 3.4 Magnetic susceptibility testing The magnetization rate was tested using a KM-7 portable magnetization rate meter as well as a KM-10 portable magnetization rate meter and a core synthesizer. KM-7 type portable magnetization rate meter: High-density magnetization rate testing was conducted for the coring well section, i.e., the magnetization rate data were tested by a combination of testing with a portable magnetization rate meter type KM-7 produced by SatisGeo during the core observation and collecting samples for in-house testing in the international standard unit of SI, with the detection points spaced at an interval of 2.5 cm and an accuracy of 10 -6 SI, and the magnetization rate of each point took about 30s of The magnetization rate of each point takes about 30s to test and record. One person performed petrographic descriptions and two persons performed magnetization rate testing and rock sample collection, which resulted in a total of 342 magnetization rate data. KM-10 portable magnetization rate meter and core comprehensive tester: A total of 1,389 magnetization data were tested, and only two depth sections of 3300.72-3326.15m and 3386.96-3403.93m of Es 3 x to Es 4 scs stratum in well Niuye 1 were tested, with the interval of detection points being 2cm, and the number of test data was 877 and 512, respectively. 3.5 Logging data The logging data were mainly obtained through Sinopec Logging Engineering Company's tests, generally at sampling intervals of 0.1m and 0.0381m to obtain a data point, and the tests obtained logging data such as natural gamma, natural gamma energy spectrum, acoustic time difference and resistivity, as well as imaging logging images. 4 Results and Discussion 4.1 Astronomical cyclic analysis methods and index optimization Astronomical stratigraphy plays a key role in addressing major geological events, exploring the evolution of climate and life, and analyzing the global carbon cycle. In other words, astronomical orbital forces are an important driving force in solving these issues (Liebrand et al., 2018 ; Crampton et al., 2018 ; Lauretano et al., 2018 ; Kocken et al., 2019 ; Bahr et al., 2020 ; Wang et al., 2021 ). The research methods in astronomical stratigraphy can be broadly divided into two categories: lithology-based visual identification and time series analysis. The former primarily relies on outcrop profiles and drilling core data, which are limited by the discontinuity of such data and the sampling density. As a result, its application is challenging and prone to missing cyclical information. Additionally, this method requires researchers to have strong geological expertise and extensive research experience. The latter, on the other hand, is a digital processing technique that has been widely applied. This method is based on widely used digital signal processing technologies, and its basic steps include data preprocessing, spectral analysis, time-frequency analysis, and correlation studies (Li et al., 2018a , 2018b , c ). The premise of these studies is to obtain continuous data sequences for astronomical cyclic research by utilizing advanced testing techniques, which serve as surrogate parameters for sedimentary stratigraphic sequences (hereinafter referred to as "surrogate parameters"). These parameters are selected to reflect paleoclimate changes and mainly include well log parameters such as natural gamma, spontaneous potential, sonic travel time, resistivity, as well as carbon-oxygen isotopes, magnetic susceptibility, various geochemical analysis data, and high-resolution continuous elemental logging data (Ait-itto et al., 2018 ; Husinec and Read, 2018 ; Yang et al., 2020 ; Xia et al., 2020 ). Astronomical cyclic theory is then applied to study the orbital cycles reflected in these data. By combining radiometric isotope ages, biostratigraphy, and magnetostratigraphy, a more refined astronomical geochronological scale can be established. This allows for a better understanding of the geological significance of sedimentary cycles on a fine timescale, with the goal of achieving precise dating (Pas et al., 2018 ; Boulila et al., 2018 ; Liu et al., 2018 ; Wu et al., 2019). 4.1.1 Time series analysis method Time series analysis is a widely used digital signal processing technique. This method primarily involves the selection of data from stratigraphic sequences, data preprocessing, spectral analysis, time-frequency analysis, and correlation studies, enabling multi-scale and multi-resolution signal decomposition. Constraints from various stratigraphy disciplines (such as radiometric isotope geochronology, biostratigraphy, and magnetostratigraphy) serve as important foundations for astronomical cyclic analysis and reliability verification (Peng et al., 2022 ). In the field of astronomical stratigraphy, time series analysis has been widely applied. Whether in field outcrop sections or core observations, the key first step in applying time series analysis is to obtain continuous data sequences for research, which then serve as the basis for conducting time series analysis. In other words, the key to applying this method for cyclic identification is to convert depth-domain data into the frequency domain, and then transform it back from the frequency domain to the time domain. Through a series of time-frequency analysis techniques, spectra that can be compared with theoretical values of astronomical orbital parameters are obtained, which allows for the delineation of cycles. The research in astronomical stratigraphy should follow a process that includes selecting time series analysis methods based on sedimentary stratigraphic sequence surrogate parameters, followed by optimizing the choice of methods, and finally, validating the cyclic results. The method of using time series analysis for studying Milankovitch cycles involves a series of research processes, including data selection, preprocessing, spectral analysis, time-frequency analysis, and astronomical tuning (Peng Jun et al., 2022). Commonly used time series analysis methods include spectral analysis, fast Fourier transform (FFT), wavelet transform, and sliding window spectral analysis (Li et al., 2022a , 2022b , c ). These methods are employed to examine whether the periodic signals in sedimentary records are influenced by astronomical orbital forcing. If orbital forcing is detected, the signals need to be tuned to align with theoretical orbital parameter curves, thereby establishing a high-precision chronostratigraphic framework. In this study, based on the methods of astronomical cycle research, two processing workflows are defined. The first workflow, referred to as TSAM1, consists of the following steps: Matlab data preprocessing to Past3.0 spectral analysis to Matlab one-dimensional continuous wavelet transform to Power spectrum estimation to Filtering analysis and extract cyclical periods. The second workflow, referred to as TSAM2, involves: Acycle data preprocessing to Acycle spectral analysis to FFT sliding window spectral analysis to Acycle wavelet transform to COCO and ECOCO estimation of sedimentation rates. The main purpose of this study is to explore the optimal time series processing methods using these two processing workflows. 4.1.2 Substitute indicators of sedimentary sequences Astronomical stratigraphy has been widely applied and has developed rapidly since its inception. However, the selection of substitute parameters for sedimentary stratigraphic sequences and the application of data processing methods have always been key challenges in cyclic stratigraphy research. Substitute parameters of sedimentary stratigraphic sequences refer to various paleoecological, geophysical, and geochemical parameters that reflect past changes in sedimentary conditions, particularly climate changes. Since there is a close relationship between climate change and orbital parameters, in theory, any indicator associated with climate change can be used as a substitute parameter in astronomical stratigraphy research (Li et al., 2019 a, 2019b ; Olsen et al., 2019 ; Ikeda and Tada, 2020 ; Ma et al., 2020 ). Through a review of previous research, it is known that substitute parameters used for astronomical cyclic analysis include paleontological parameters, environmental magnetic parameters, geophysical parameters, and geochemical parameters. By conducting a comprehensive analysis of these various parameters, a time series containing information on stratigraphic environmental changes can be constructed. In this study, various substitute indicators of sedimentary sequences are used, and astronomical cyclic division is carried out using the TSAM1 and TSAM2 processes. The optimal indicators and methods are selected through a comparison of the results. 4.1.3 Research Approach for the Selection of Indicators and Methods The natural gamma ray logging data, along with other logging data, magnetic susceptibility, elemental geochemical data, mineral content, and core grayscale data, are used as substitute indicators for cyclic division. Principal Component Analysis (PCA) (Origin) and Factor Analysis (SPSS) methods are employed to simplify and reduce the dimensionality of multiple logging data and sedimentary environmental element analysis indicator data. By comprehensively applying the TSAM1 and TSAM2 methods, spectral analysis, wavelet transform, power spectral estimation, sliding window spectral analysis, and short-time Fourier transform are conducted on various substitute indicator data. This allows for the cyclic stratigraphic division of the studied strata. Ultimately, through a comparative analysis of the results obtained from different methods and substitute indicators, the most suitable time series analysis methods and substitute indicators for lacustrine fine-grained sediment are selected. These methods are used to identify and classify the multi-scale astronomical orbital cycles in the research strata. The selected data and specific processing workflow are shown in Fig. 3 . 4.2 Cyclic Identification Results and Comparison Based on Different Substitute Indicators and Methods Due to the large amount of data being processed, this paper uses the natural gamma ray logging data and core grayscale data from the Es 4 scs in well Niuye 1 as an example to introduce the cyclic analysis process and results of TSAM1 and TSAM2. The processing steps for other substitute indicators of the sedimentary sequence will not be elaborated here. 4.2.1 Astronomical Cycle Analysis of Natural Gamma Ray Logging Data (1) TSAM1 The natural gamma ray logging data is processed using the first workflow: Matlab data preprocessing to Past3.0 spectral analysis to one-dimensional continuous wavelet transform of Matlab to power spectral estimation to filtering analysis and extract cyclic periods. ① Spectral Analysis The Redfit spectral analysis results of the natural gamma ray logging data from the Es 4 scs in well Niuye 1 indicate that during the Es 4 scs sedimentary period, the Earth's orbital elements exhibited frequent periodic variations, with multiple dominant precession and obliquity cycles. Based on the dominant frequencies, the dominant cycle thicknesses can be obtained (Fig. 4 ), and the ratio between these dominant cycle thicknesses is 30.191: 6.862: 3.145: 2.848: 1.735: 1.540: 1.411 = 21.40: 4.86: 2.23: 2.02: 1.23: 1.09: 1. This ratio is very close to the theoretical orbital period ratio of 405kyr: 95.24kyr: 39.76kyr: 38.54kyr: 23.28kyr: 22kyr: 18.82kyr. That is, the cycle thicknesses of 30.191m and 6.862m correspond to the long and short eccentricity periods of 405kyr and 95.24kyr, respectively; the cycle thicknesses of 3.145m and 2.848m correspond to the obliquity periods of 39.76kyr and 38.54kyr; and the cycle thicknesses of 1.735m, 1.54m, and 1.411m correspond to the precession periods of 23.28kyr, 22kyr, and 18.82kyr, respectively. Therefore, the Es 4 scs sedimentary period in well Niuye 1 is very likely to be controlled by Milankovitch cycles. ②Wavelet transform The Morlet wavelet from the one-dimensional continuous wavelet toolbox provided by Matlab was used as the mother wavelet to perform wavelet transform analysis on the natural gamma logging data of the Es 4 scs in well Niuye 1. Figure 5 is a schematic of the one-dimensional continuous wavelet transform result of the logging signal at scale of a = 512 of the Es 4 scs in well Niuye 1. From the figure, it can be seen that the frequencies of different components correspond to certain specific scale values, indicating that the frequency components are relatively simple and stable. ③Power spectral estimation In order to further determine whether these relatively simple and stable frequency components are controlled by a specific orbital period, power spectral estimation was conducted based on the wavelet analysis. Figure 6 shows the morlet wavelet extrema diagram of the Niuye 1 well (Es 4 scs ) obtained by matrix calculations of the energy spectrum at different scales. The appropriate extrema scales are selected for analyzing the wavelet period of the signal. From the extrema diagram, several obvious local maxima are observed, with corresponding wavelet scale values of 29, 60, 116, 332, and 512 (Fig. 6 ). Among these, the ratio of the scale values 512:116:60:29 closely matches the ratios of the astronomical orbital eccentricity period of 405 kyr, obliquity period of 95.24 kyr, precession period of 39.76 kyr, and the axial precession period of 18.82 kyr. Based on the results of the spectral analysis, it can be determined that the stratigraphic sequence is significantly controlled by the long eccentricity cycle, short eccentricity cycle, obliquity cycle, and precession cycle. Therefore, it can be inferred that the dominant frequencies corresponding to the scale values of 512, 116, 60, and 29 are driven by the long eccentricity cycle, short eccentricity cycle, obliquity cycle, and precession cycle, respectively. Thus, the wavelet curves corresponding to these scale values can be understood as the cyclical curves of the long eccentricity cycle, short eccentricity cycle, obliquity cycle, and precession cycle. Based on the wavelet analysis, the wavelet coefficient curves corresponding to these scale values can be extracted to represent the periodic cyclical curves of stratigraphy of the Es 4 scs in well Niuye 1 . Based on the filtering results and combined with the cyclic curves, a comprehensive columnar diagram of the cyclical stratigraphic division for the Es 4 scs in well Niuye 1 is created, incorporating natural gamma logging data, depth, lithology, cyclic curves with scale values of 512, 116, 60, and 29, as well as the wavelet energy spectrum (Fig. 7 ). The study concludes that the Es 4 scs in well Niuye 1 exhibits approximately 6 long eccentricity cycles of 405 kyr, 24 short eccentricity cycles of 95.24 kyr, 53 obliquity cycles of 39.76 kyr, and 110 precession cycles of 18.82 kyr. (2) TSAM2 The natural gamma logging data is processed using the following steps: Acycle data preprocessing to Acycle spectral analysis to FFT sliding window spectral analysis to Acycle wavelet transform to COCO and ECOCO methods to estimate sedimentation rates and extract cyclic periods. ①Spectral analysis and FFT sliding window spectral analysis Perform spectral analysis and Fast Fourier Transform (FFT) sliding window spectral analysis using Acycle software. The spectral analysis results of the preprocessed natural gamma logging data series show distinct sedimentary cycles. The spectrum exhibits significant peaks at frequencies of 0.033, 0.146, 0.318, 0.351, 0.576, 0.649, and 0.709. Based on the dominant frequencies, the dominant cycle thicknesses can be determined (Fig. 8 a), corresponding to clear peaks at wavelengths of 30.198m, 6.863m, 3.145m, 2.848m, 1.735m, 1.540m, and 1.411m, all exceeding the 90% confidence level. Therefore, these thicknesses represent the dominant cycle thicknesses in the stratigraphic sedimentary record. By using the cycle wavelengths, the dominant cycle thicknesses are obtained, and the ratio between the cycle thicknesses is derived as 30.198: 6.863: 3.145: 2.848: 1.735: 1.540: 1.411 = 21.40: 4.86: 2.23: 2.02: 1.23: 1.09: 1. This ratio closely matches the theoretical orbital periods of 405kyr: 95.24kyr: 39.76kyr: 38.54kyr: 23.28kyr: 22kyr: 18.82kyr. The comparison of these cycle wavelength ratios with the theoretical orbital period ratios indicates that the wavelength ratios conform to the theoretical orbital cycles of the Paleogene period. Different wavelength cycles correspond to orbital periods of different scales. Specifically, the wavelengths of 30.198m and 6.863m correspond to the long and short eccentricity cycles of 405kyr and 95.24kyr, respectively. The wavelengths of 3.146m and 2.849m correspond to the obliquity cycles of 39.76kyr and 38.54kyr, while the cycle thicknesses of 1.736m, 1.541m, and 1.411m correspond to the precession cycles of 23.28kyr, 22kyr, and 18.82kyr, respectively. The orbital periods obtained through the Spectral Analysis method in Acycle software are consistent with the results analyzed using Past 3.0 software. This further indicates that the sedimentary period of the Es 4 scs in well Niuye 1 is controlled by the Milankovitch cycles of Earth's orbital parameters. Moreover, the cycle analysis results obtained from both methods are highly consistent and reliable. Using the FFT method, a static 2π MTM analysis was conducted on the natural gamma logging depth-domain data. The resulting sliding window spectral plots were used to analyze the frequency evolution characteristics of the entire natural gamma logging sequence and identify the dominant signal wavelengths. In this sliding window analysis, a 40m running window with a step size of 0.2m was applied. From the sliding window spectral plots (Fig. 8 b), it is evident that the natural gamma logging series exhibits distinct periodic variations in the power spectrum. These variations correspond to the 405kyr long eccentricity cycle, the 95.24kyr short eccentricity cycle, the 39.76kyr and 38.54kyr obliquity cycles, as well as the 23.28kyr, 22kyr, and 18.82kyr precession cycles, with varying degrees of frequency. Based on the spectral analysis results, it is observed that the 30.198m, 3.146m, and 2.849m wavelengths, which correspond to the 405kyr long eccentricity cycle, the 39.76kyr obliquity cycle, and the 38.54kyr obliquity cycle, respectively, are stable dominant cycles in terms of frequency (period). ②Wavelet Transform The wavelet transform analysis in Acycle software was used to study the cyclical characteristics of the natural gamma logging data series. As can be seen from Fig. 8 d, during the Es 4 scs stratigraphic deposition period in well Niuye 1, distinct orbital cycles are present, including short eccentricity, obliquity, and precession cycles. The analysis results are consistent with those obtained from the spectral analysis and sliding window spectral analysis. By integrating the cycle thickness and the sedimentary strata thickness, it is observed that there are 5 long eccentricity cycles of 405kyr, 22 short eccentricity cycles of 95.24kyr, 48 obliquity cycles of 39.76kyr and 53 obliquity cycles of 38.54kyr, as well as 87 precession cycles of 23.82kyr, 98 precession cycles of 22kyr, and 107 precession cycles of 18.82kyr. In addition, this method also identified distinct sub-meter scale cycles, with cycle periods ranging from 1000 to 8000 years. Solar radiation, as the energy source for the Earth system, is the fundamental driving force for climate formation and evolution. Over long time scales of thousands of years, variations in solar radiation reaching the Earth are closely related to Earth's climate (Xiao, 2021 ). According to previous research (Ma et al., 2021 ), solar radiation exhibits different periodicities, including the 11-year Schwabe cycle, the 80–100-year Gleissberg cycle, the 210-year de Vries-Suess cycle, the 1000-year Eddy cycle, and the 2400-year Hallstatt cycle. Studies on these periodic variations have primarily focused on Holocene records, such as tree rings, stalagmites, polar ice sheets, lacustrine varves, and marine sediments. The 1000–8000 year periodicity identified by the wavelet transform in this study is likely associated with the Eddy and Hallstatt cycles. The 11-year cycle in solar radiation corresponds to the solar sunspot cycle, which is an important indicator of solar radiation variations. Within a single sunspot cycle, the periodic changes in solar irradiance influence climate variations, which in turn affect the periodic changes in sediment deposition within sedimentary strata. This is reflected as periodicity in the rhythmic layers of lacustrine deposits. This is consistent with the later-estimated average sedimentation rate of 0.0661 m/kyr for the Es 4 scs formation in well niuye 1. Under the assumption of no compaction effects, the sedimentary thickness over the 1000-8000-year period would be approximately 6.61–52.88 cm, corresponding to the scale of stratigraphic layers. In the presence of an 11-year sunspot cycle, the sedimentary thickness would be about 0.727 mm, corresponding to the scale of varve-like laminae. At the same time, some researchers have also discovered a correlation between the variations in varve-like laminae and sunspot activity (Shi et al., 2021 ; Zhao et al., 2019 ). The scale of sedimentary rhythms in lacustrine strata varies, ranging from fine laminae formed on seasonal or even shorter time scales, to larger-scale cycles corresponding to interannual, centennial, or even millennial orbital periods. The study of periodic cyclic records in layers or varve-like laminae is of great significance for the cyclostratigraphy of fine-grained lacustrine sediments. Therefore, subsequent chapters will further explore the periodic cyclic records of layers or varve-like laminae. ③Estimation of sedimentation rates using COCO and ECOCO In order to find the optimal sedimentation rate for the study interval, sedimentation rate assessments using COCO and ECOCO were conducted on the natural gamma logging depth-domain data, resulting in the optimal sedimentation rate. In this study, a 2000-run Monte Carlo simulation was used to perform COCO analysis on the natural gamma logging data series from the Es 4 scs in well niuye 1. The tested sedimentation rate range was 1–15 cm/kyr. The results show several peaks at sedimentation rates of 2.3 cm/kyr, 3.1 cm/kyr, 4 cm/kyr, 7.9 cm/kyr, 9.7 cm/kyr, and 13.4 cm/kyr (Fig. 9a to Fig. 9c). Among these peaks, the most significant cluster occurs between 3.1 cm/kyr and 7.9 cm/kyr, which may indicate that the average sedimentation rate lies between 3.1 cm/kyr and 7.9 cm/kyr. The average sedimentation rate of 3.1 cm/kyr exceeds the critical significance level. At this rate, all six astronomical orbital cycle components are included, and the null hypothesis of orbital signal is not rejected at a significance level below 0.1% (Fig. 9a to Fig. 9c). The astronomical timescale established through astronomical tuning indicates that the average sedimentation rate for this section is 6.61 cm/kyr. The corresponding correlation coefficient in the COCO analysis is greater than 0.15, suggesting that the sedimentation rate calculated using the timescale is reliable. Furthermore, the sliding correlation analysis of the correlation coefficient, H 0 , and orbital parameters (ECOCO) for this well ((Fig. 9d to Fig. 9f)) shows that all these sedimentation rates have an insignificant hypothesis significance level below 0.01 (Fig. 9). 4.2.2 Cycle analysis of grayscale values Spectrum analysis and fast Fourier transform sliding window spectrum analysis of the core grayscale data from well Niuye 1, depth range 3396.45m to 3403.94m, were conducted using Acycle software. The analysis results show higher peak values at frequencies of 0.1405, 0.3204, 0.3417, 0.5705, 0.6105, and 0.721 (Fig. 10b to Fig. 10c). Based on the dominant frequencies, the dominant cycle thicknesses can be determined, which correspond to clear peaks in the wavelength bands of 7.119m, 3.121m, 2.881m, 1.753m, 1.638m, and 1.387m. These wavelengths exceed the 99% confidence level, indicating that these thicknesses represent the dominant cycle thicknesses in the sedimentary records of the formation. By using the cycle wavelengths, the dominant cycle thicknesses are obtained, and the ratio between the cycle thicknesses is calculated as 7.119: 3.121: 2.881: 1.753: 1.638: 1.387 = 5.1327: 2.2502: 2.0771: 1.2639: 1.181: 1. This ratio closely matches the theoretical orbital periods of 95.24kyr: 39.76kyr: 38.54kyr: 23.28kyr: 22kyr: 18.82kyr. Therefore, it can be interpreted as follows: 7.119m corresponds to the short eccentricity cycle of 95.24kyr, 3.121m and 2.881m correspond to the obliquity cycles of 39.76kyr and 38.54kyr, while 1.753m, 1.638m, and 1.387m correspond to the precession cycles of 23.28kyr, 22kyr, and 18.82kyr, respectively. The Acycle spectral analysis and sliding window spectral analysis of the core grayscale data indicate that the studied stratigraphic section is clearly controlled by eccentricity, obliquity, and precession cycles, with a distinct cyclicity observed. Previous studies have shown that solar activity exhibits cycles such as the Little Ice Age cycle of 1200–1800 years, a long cycle of around 400 years, a long cycle of around 250 years, a solar activity double-century cycle of around 200 years, the Gleissberg cycle of 70–90 years (the solar activity century cycle of 80–90 years), a 60-year cycle, the 22-year Hale cycle (solar magnetic cycle), and the 11-year Schwabe sunspot cycle (sunspot cycle) (Shi et al., 2021 ; Yi et al., 2010 ). To further investigate smaller-scale cycles, Acycle software was used to perform spectral analysis and fast Fourier transform (FFT) sliding window spectral analysis on the grayscale data, identifying annual-scale cycles. The analysis results (Fig. 11 ) show that several high peaks appear at frequencies with wavelengths above the 99% confidence level. Based on the dominant frequencies, the dominant cycle thickness can be determined, which corresponds to the dominant cycle thickness in the stratigraphic sedimentary record. Therefore, the cycle thickness corresponding to the Little Ice Age cycle of 1200–1800 years in the studied section is 0.0609–0.1204 m, with approximately 62.2–123 cycles; the cycle thickness corresponding to the long cycle of around 400 years is 0.0226–0.0376 m, with approximately 199.2-331.4 cycles; the cycle thickness corresponding to the long cycle of around 250 years is 0.0151–0.0178 m, with approximately 420.8–496 cycles; the cycle thickness corresponding to the solar activity double-century cycle of around 200 years is 0.0103–0.0145 m, with approximately 516.6-727.2 cycles; and the cycle thickness corresponding to the 70–90 year Gleissberg cycle is 0.0058–0.0096 m, with approximately 780.2-1291.4 cycles. Based on subsequent research, the sedimentation rate of the Es 4 scs formation in well Niuye 1 is approximately 0.069 m/kyr. Therefore, at this sedimentation rate, the sedimentary thickness for cycles of 1200–1800 years, 400 years, 250 years, 200 years, and 70–90 years should be 0.0828–0.1242 m, 0.0276 m, 0.01725 m, 0.0138 m, and 0.00483–0.00621 m, respectively. The cycle thicknesses corresponding to each period obtained from the Acycle spectral analysis and sliding window spectral analysis align with the cycle thicknesses calculated based on the sedimentation rate. Thus, it can be confirmed that the identified cycles in the studied section are accurate and reliable on an annual scale. 4.2.3 Comparison of cycle classification results based on various alternative parameters and different analysis methods Indicators that are sensitive to climate and environmental changes are ideal data for Milankovitch cycle analysis. That is, various substitute parameters reflecting changes in sedimentary conditions, such as sedimentary stratigraphic sequences, can all be used in astronomical cycle studies. Different types of sedimentary sequence substitute parameters respond differently to astronomical orbital cycles, and astronomical timescales based on a single indicator may have uncertainties. To reduce these uncertainties, it is necessary to use a combination of various sedimentary sequence substitute parameters or apply mathematical transformations to the indicators before use. Based on this, the present study primarily involves five major types of parameters, including geophysical parameters, geochemical parameters, environmental magnetism parameters, mineral content parameters, and core grayscale parameters. The time series analysis methods mainly involve two sets of processing methods and workflows: First, Matlab data preprocessing to Past3.0 spectral analysis to Matlab one-dimensional continuous wavelet transform to power spectrum estimation to filtering analysis and extract cycle periods. This workflow is referred to as TSAM1. Second, Acycle data preprocessing to Acycle spectral analysis to FFT sliding window spectral analysis to Acycle wavelet transform to COCO and ECOCO estimation of sedimentation rates. This workflow is referred to as TSAM2. In the study, it was found that the cycle results obtained by applying different time series analysis methods to various substitute parameters differed. To ensure that the various parameters and analysis methods better serve cycle research, a comparison and optimization of the sedimentary stratigraphic sequence substitute parameters and time series analysis methods were conducted, based on the cycle analysis results (Table 1 ), as shown in Table 2 . The comparison of cycle research results (Table 1 ) shows that, among geophysical parameters, the TSAM2 method yields the best results for GR, AC, RL, RN, principal component analysis of various logging series, and factor analysis of various logging series. It is able to identify orbital parameters at different scales. On the other hand, the use of single logging data with the TSAM1 method also provides relatively good cycle recognition results, although it is slightly less effective than the results obtained by the TSAM2 method. Furthermore, principal component analysis and factor analysis of various logging series data are not well-suited for cycle analysis using the TSAM1 method. For environmental magnetism parameters, the magnetic susceptibility data analyzed using the TSAM2 method yields the best results, while the cycle recognition results from the TSAM1 method are less effective than those from TSAM2. Among geochemical parameters, TSAM2 is the most effective for Fe, Mn, Ti, Ca, Al, Si, CaO/MgO, (CaO + K 2 O + Na 2 O)/Al 2 O 3 , (Fe + Al)/(Ca + Mg), as well as principal component analysis and factor analysis of elemental data. On the other hand, the cycle recognition results obtained using the TSAM1 method are generally less effective, with some cases only able to identify the eccentricity cycle. In the analysis of cycle recognition using mineral content parameters, the TSAM2 method is not applicable, while the TSAM1 method can only identify the eccentricity and obliquity cycles, but fails to recognize the precession cycle. When using core grayscale data for cycle research, the TSAM2 method yields the best results and can identify solar activity cycles at a scale smaller than the precession cycle, down to the annual level. However, the TSAM1 method is not suitable for magnetic susceptibility cycle studies. The study shows that various substitute parameters for sedimentary stratigraphic sequences and time series analysis methods used for cycle analysis each have their own advantages and disadvantages (Table 2 ). Only by thoroughly understanding these strengths and weaknesses can the optimal substitute parameters and analysis methods be selected for cycle research, ensuring the reliability and accuracy of the cycle analysis results. For substitute parameters in cycle analysis, geophysical parameters are easily accessible, have good continuity, and high resolution, making them an important choice for cycle research. Environmental magnetism parameters, such as magnetic susceptibility data, are also crucial in cycle studies. Since astronomical cycle research requires high continuity of substitute parameter data and demands a large number of evenly spaced samples for testing and analysis, the testing methods need to be simple, cost-effective, and easy to interpret. Environmental magnetism measurements are convenient and low-cost, making them well-suited for astronomical stratigraphic analysis. Therefore, this parameter, like logging data, has a wide range of applications. However, a drawback of this method is that it may be subject to human interference during testing. Thus, it is important to create a good testing environment to minimize such influences. Geochemical parameters and mineral content parameters are rarely used in cycle recognition, primarily due to limitations in testing technology, cost, and sample availability. The acquisition of these two types of data is restricted, making it difficult to obtain high-resolution and continuous data series for astronomical cycle analysis. On the other hand, core grayscale data has high resolution and is a good indicator for cycle analysis. However, the process of obtaining, processing, and extracting grayscale values is complicated and challenging to operate. For time series analysis methods in cycle analysis, the TSAM1 time series analysis method uses spectral analysis to transform periodic signals from the time domain to the frequency domain. However, it cannot observe and analyze periodic signals in both the time-frequency domain, nor can it reflect the periodicity of depth positions. Additionally, it cannot divide the different levels of sedimentary cycle interfaces as a whole (Yan et al., 2017 ). Wavelet analysis, as a time-frequency domain analysis method, can observe and process signals at different scales and frequencies. Specifically, wavelet transform can decompose complex signals into cyclical curves of different frequencies or periods, breaking them down into independent sedimentary cycles with distinct periods, which are displayed at different scales. This allows for the examination of the variation of local energy clusters in the wavelet time-frequency energy map and the periodic oscillation characteristics at various scaling levels (Gambacorta et al., 2018 ). Although wavelet transform has the advantage of multi-resolution capability in identifying sedimentary cycles of different levels and overcomes the limitation of spectral analysis, which cannot reflect the local features of the time and frequency domains, it can more accurately display the changes of different frequency (period) components in the time (depth) domain. This, in turn, facilitates better identification of sedimentary discontinuities and reflects changes in sedimentation rates. However, when using wavelet time-frequency energy spectra to delineate sedimentary cycle interfaces, certain errors may arise, leading to inaccurate or imprecise interface readings. Power spectral estimation, based on wavelet transform analysis, uses the Morlet wavelet basis to perform multi-scale wavelet decomposition of logging curves. This method applies a wavelet transform-based power spectral estimation to extract cycles of different levels, aiming to reflect the energy changes of stratigraphic sedimentary units from small scales to large scales (Yan et al., 2017 ). In the TSAM2 time series analysis method, spectral analysis is used to assess the distribution of time series as a function of frequency. The main purpose of spectral analysis is to identify periodic or quasi-periodic components in the data sequence, ultimately producing an MTM spectral analysis plot with a noise model. The sliding window spectral analysis method provides a large number of "windows" and can accurately determine the variations of different frequencies in the depth (time) domain. This helps in identifying changes in sedimentation rates and potential sedimentary discontinuities. Additionally, this method uses the same program under the same operating system, eliminating the need to switch between different operating systems or software, making the operation convenient and straightforward. Table 1 The cycle division results of various alternative parameters and different analytical methods of Es 4 scs in well Niuye 1. Alternative Parameters of Sedimentary Sequences 处理流程 旋回划分结果 GR TSAM1 5.7个E 1 ；24个E 2 ；53个O 1 ；110.5个P 3 AC TSAM1 5个E 1 ；P2个E 2 ；52个O 1 ；108个P 3 RL TSAM1 5.5个E1；23个E2；52个O 1 RN TSAM1 5个E 1 ；25个E 2 ；51个O 1 ；108个P 3 AC TSAM2 5个E 1 ；P2个E 2 ；48个O 1 ；53个O 2 ；87个P 1 ；98个P 2 ；107个P 3 RL TSAM2 RN TSAM2 GR TSAM2 Electrical Logging Series 1 Principal Component Analysis of PC2 TSAM1+TSAM2 Radioactive Logging Series Principal Component Analysis Data TSAM1+TSAM2 Imaging Array Induction Logging Series Principal Component Analysis Data of PC2 and PC3 TSAM1+TSAM2 Fe、Mn、Ti、Ca、Al TSAM2 CaO/MgO、(CaO+K 2 O+Na 2 O)/Al 2 O 3 、(Fe+Al)/(Ca+Mg) TSAM1 Factor Analysis Data of Fe、Mn、Ti、Ca、Al、Si、(CaO+K 2 O+Na 2 O)/Al 2 O 3 、CaO/MgO TSAM2 Electrical Logging Series 2 Principal Component Analysis Data TSAM1+TSAM2 PC1：5个E 1 ；22个E 2 ；48个O 1 ；53个O 2 ；87个P 1 ；98个P 2 PC2：5个E 1 ；22个E 2 ；48个O 1 ；53个O 2 Imaging Array Induction Logging Series Principal Component Analysis Data TSAM1+TSAM2 PC1：5个E 1 ；22个E 2 ；48个O 1 ；53个O 2 ；87个P 1 ；98个P 2 Factor Analysis Data of Various Logging Series TSAM1+TSAM2 F1和F4：5个E 1 ；22个E 2 ；48个O 1 ；53个O 2 ；87个P 1 ；98个P 2 F2：5个E 1 ；P2个E 2 F3：5个E 1 ；22个E 2 ；48个O 1 ；53个O 2 ；87个P 1 ；98个P 2 Note: E1 - 405 kyr eccentricity long cycle; E2 - 95.24 kyr eccentricity short cycle; O1 - 39.76 kyr obliquity cycle; O2 - 35.54 kyr obliquity cycle; P1 - 23.28 kyr precession cycle; P2 - 22 kyr precession cycle; P3 - 18.82 kyr precession cycle. Table 2 Comparison of cyclic analysis results between alternative parameters of sedimentary stratigraphic sequences and time series analysis methods of Es 4 scs in well Niuye 1. Sedimentary Stratigraphic Sequence Substitution Parameters Time Series Analysis Methods Cycle Analysis Results Geophysical Parameters GR、AC、RL、RN TSAM1 Good performance GR、AC、RL、RN TSAM2 Good performance Principal Component Analysis of R25, R4, RL, RN Logging Series TSAM1 + TSAM2 PC2 cycle analysis shows good performance, while PC1 analysis shows poor results Principal Component Analysis of SP, Rt, Rxo, CON Logging Series TSAM2 PC1 cycle analysis performs slightly better than PC2 Principal Component Analysis of Radioactive Logging Series TSAM1 + TSAM2 PC1 cycle analysis is good, while PC2, PC3, and PC4 cycle analyses show poor results Principal Component Analysis of Imaging Array Induction Logging Series TSAM1 + TSAM2 PC2 and PC3 cycle analyses slightly outperform PC1 Factor Analysis of Various Logging Data TSAM2 F1 and F4 cycle analysis results show similar cyclic patterns, F3 shows a similar pattern to F1 and F4, but F2 cycle analysis results are relatively poor Environmental Magnetism Parameters Magnetic Susceptibility TSAM1 Good performance in cycle analysis Geochemical Parameters Fe, Mn, Ti, Ca, Al, Si, CaO/MgO, (CaO + K 2 O + Na 2 O)/Al 2 O 3 , (Fe + Al)/(Ca + Mg), Principal Component Analysis and Factor Analysis of Elemental Data TSAM1 Average performance with no precession period identified TSAM2 Excellent cycle analysis performance with consistent results Mineral Content Parameters Clay Minerals, Carbonate Minerals, Pyrite TSAM1 Average performance with no precession period identified, but the TSAM2 method cannot be used to detect the cycles Core gray scale data Gray Scale Values TSAM2 Excellent cycle analysis performance, identifying smaller scale periods, but the TSAM1 method cannot be used to detect the cycles 4.3 Optimization of multiple alternative indicators and methods In summary, by comparing the cycle research results of sedimentary stratigraphic sequence alternative parameters and time series analysis methods, as well as analyzing their advantages and disadvantages, an optimal ranking of the selection order for various indicators and analysis methods has been made. This is to ensure that the cycle research can avoid unnecessary detours while guaranteeing the selection of the most optimal alternative parameters and analysis methods to obtain the best and most accurate cycle analysis results. First, the optimal alternative indicators are geophysical parameter data, especially single well log data such as GR, AC, RL, and RN, which can be used for cycle analysis with the TSAM1 method. Additionally, cycle research can be conducted through principal component analysis (PCA) and factor analysis of various element data obtained by mathematical processing methods. Secondly, the TSAM2 method can also be used for cycle analysis, although the processing steps may be slightly more complex than those of TSAM1. Second, in cases where well log data cannot be used for cycle analysis, environmental magnetism parameters, such as magnetic susceptibility data, can be selected, and the TSAM2 method can be applied for cycle analysis. Third, core grayscale data can be used to identify cycles by applying the TSAM2 time series analysis method, and high-resolution data can help identify smaller-scale cycles. Fourth, when other data cannot reliably support cycle analysis, single-element geochemical data such as Fe, Mn, Ti, Ca, Al, Si, or element combination data like CaO/MgO, (CaO + K 2 O + Na 2 O)/Al 2 O 3 , and (Fe + Al)/(Ca + Mg) can serve as excellent alternative parameters for cycle analysis, using the TSAM2 time series analysis method to identify cycles. At the same time, data obtained through mathematical processing methods such as principal component analysis (PCA) and factor analysis of element data can also serve as alternative parameters for cycle analysis. These can be used to calibrate and verify the reliability of cycle analysis results based on single-element data or element combination data, making the cycle analysis results more convincing. In conclusion, the optimal alternative indicator for the sedimentary sequence selected in this study is geophysical parameter data. The optimal time series analysis method is as follows: Matlab data preprocessing to Past3.0 spectral analysis to Matlab one-dimensional continuous wavelet transform to power spectrum estimation to filtering analysis and extract cycle periods. 4.4 Establishment of the Astronomical Time Scale and Estimation of Sedimentation Rate A comparison and analysis of the results from cycle research using sedimentary stratigraphic sequence proxy parameters and time series analysis methods were conducted, highlighting their advantages and disadvantages. An optimal selection sequence for various indicators and analysis methods was made, choosing the most suitable proxy parameters and analytical methods to obtain the best and most accurate cyclic analysis results. This approach allows for the establishment of a chronological scale and the estimation of sedimentation rates. In the Es 4 scs strata of well Niuye 1, approximately 6 long eccentricity cycles of 405 kyr, 24 short eccentricity cycles of 95.24 kyr, 53 obliquity cycles of 39.76 kyr, and 110 precession cycles of 18.82 kyr were identified (Fig. 7 ). Using the research results of Laskar on Earth's orbital parameter solutions, theoretical curves for Earth's orbital elements were generated (Laskar et al., 2004 ). The filtered analysis results of the main peak values were compared with the theoretical curves, revealing that the filtered short eccentricity cycles closely match the theoretical eccentricity curve in frequency, and there is a strong correspondence between the two. Therefore, in this study, a "floating" astronomical timescale for the Es4scs of the Niuye 1 well in the Dongying Depression was established based on the prominent 95.24 kyr short eccentricity cycle. This was combined with the absolute age of 42 Ma for the top of the Es 4 s , as determined by previous studies using paleomagnetic and volcanic rock isotope dating (Yao et al., 2002 ; Jin et al., 2017 ). Using the theoretical eccentricity cycle curve as the target curve and the short eccentricity cycle filter curve extracted from the natural gamma log curve as the tuning curve, the short eccentricity cycles between every two tuning lines were used to divide the strata into 24 cycles. Under the control of the time point of 42 Ma, starting from the age of the top of Es 4 s , the age values of each cycle interface were calculated. Based on these calculations, the sedimentation time for the Es 4 scs in well Niuye 1 was estimated to be approximately 2.286 Myr. Therefore, the absolute geological age at the bottom of the Es 4 scs is 44.286 Ma. The results of this study show that the absolute age at the bottom of Es 4 scs in well Niuye 1, estimated to be 44.286 Ma, is in relative agreement with the 3 Ma duration of Es 4 s (including both Es 4 scs and Es 4 scx ) determined through paleomagnetic, volcanic rock isotope dating, and stratigraphic correlation. Specifically, the volcanic rock isotope age at the base of Es 4 s is 45 Ma. This indicates that the Milankovitch cycles identified in this study are accurate and reliable, thereby establishing a geological timescale for the Es 4 scs in well Niuye 1 (Fig. 7 ). This has significant implications for determining the geological age of the strata. To make the establishment of the geological timescale for Es 4 scs in Niuye 1 well more reliable and convincing, this study selected five core samples near the top boundary of Es 4 scs for Re-Os isotopic dating at the National Geological Laboratory. The key aspect of this research lies in the precise dating of the mudstone strata. Based on the current status of isotopic dating research, Re-Os isotopic dating is considered the most suitable method for mudstone strata due to its high precision and applicability to fine-grained sediments like mudstones. This approach strengthens the overall reliability of the geological timescale for Es 4 scs . By using precise Re-Os isotopic dating, combined with the latest Paleogene astronomical timescale, existing stratigraphic absolute age data from previous studies in the region, and the orbital cycle stratigraphy results from the project's earlier work, a high-precision isotopic age stratigraphic profile for the study interval can be established. On this basis, the sedimentation rates for astronomical cycles at different scales can be further calculated, converting the astronomical orbital cycles from the depth domain to the time domain. The Re-Os isotopic dating results for this study are shown in Figure (Fig. 12 ). The age value at the top boundary of Es 4 scs is 45.9 ± 4.1 Ma. Although the error margin is ± 4.1 Ma, meaning the age could range from 41.8 to 50 Ma, the result still demonstrates consistency with previous paleomagnetic and volcanic rock isotope dating data, which also provided an age for the top of Es4s in the Dongying Depression. Therefore, using 42 Ma as the absolute geological age for the top of Es 4 scs in well Niuye 1 is reliable. To study the vertical variation of sedimentation rates, the sediment thickness between adjacent peaks of the 95.24 kyr eccentricity short cycle and the sedimentation duration, based on the established timescale, were used to calculate the average sedimentation rate for Es 4 scs in well Niuye 1, which is 0.0661 m/kyr (Fig. 7 ). The sedimentation rate ranges from 0.0486 to 0.0984 m/kyr. As shown in the figure, the rate increases in the middle to upper sections of Es 4 scs , between depths of 3337.07 to 3394.09 m, suggesting a more pronounced event-driven sedimentation. 5 Conclusion (1) By integrating geophysical, geochemical, and environmental magnetism parameters such as magnetic susceptibility data and other sedimentary sequence proxies, the TSAM1 and TSAM2 methods are used to optimize the cyclic analysis methods and proxy indicators. Among the geophysical parameters, the TSAM2 method shows the best performance when applied to data such as GR, AC, RL, RN, principal component analysis of various logging series, and factor analysis of various logging series. The TSAM1 method also performs well for cyclic identification using single well log data, but its processing effect is slightly inferior to that of the TSAM2 method. Additionally, principal component analysis and factor analysis data of various logging series are not well-suited for cyclic analysis using the TSAM1 method. The TSAM2 method produces the best results for magnetic susceptibility data, while the cyclic identification effect of the TSAM1 method is inferior to that of the TSAM2 method. Among the geochemical parameters, the TSAM2 method produces the best results for data such as Fe, Mn, Ti, Ca, Al, Si, CaO/MgO, (CaO+K 2 O+Na 2 O)/Al 2 O 3 , (Fe+Al)/(Ca+Mg), as well as principal component analysis and factor analysis data of various elements. The cyclic identification effect using the TSAM1 method is generally less effective, and in some cases, it can only identify the eccentricity cycle. For mineral content parameters, the TSAM2 method is not applicable, while the TSAM1 method can only identify the eccentricity and obliquity cycles, and is unable to identify the precession cycle. When core grayscale data is used for cyclic research, the TSAM2 method provides the best results. It is capable of identifying solar activity cycles at a smaller scale than the precession cycle, down to an annual level. However, the TSAM1 method is not suitable for use in magnetic susceptibility cyclic studies. This study indicates that the optimal substitute index for sedimentary sequences is natural gamma logging data. The best time series analysis method is as follows: Matlab data preprocessing to Past3.0 spectral analysis to One-dimensional continuous wavelet transform of Matlab to power spectrum estimation to filtering analysis and extract cyclic periods. (2) By combining the two sets of time series analysis processes, five long eccentricity cycles of 405 kyr, 22 short eccentricity cycles of 95.24 kyr, 48 obliquity cycles of 39.76 kyr, 53 obliquity cycles of 38.54 kyr, 87 precession cycles of 23.28 kyr, 98 precession cycles of 22 kyr, and 107 precession cycles of 18.82 kyr were identified in the Es 4 scs section of the Niuye1 well. The sedimentary time is approximately 2.038 Myr, and the estimated average sedimentation rate is 0.074 m/kyr. In addition to the aforementioned cycles, the use of magnetic susceptibility data also identified sedimentary cycles of 1200-1800 years, 400 years, 250 years, 200 years, and 70-90 years. The study found that for the same set of strata and data, different analysis methods can yield varying cycle analysis results. Furthermore, the periods of Earth's orbital parameters are variable and not absolutely fixed. 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Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6651998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489822936,"identity":"70282645-9d88-444c-99a4-2a2eccd50f57","order_by":0,"name":"Ledan Yu","email":"","orcid":"","institution":"Shandong Institute of Petroleum and Chemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Ledan","middleName":"","lastName":"Yu","suffix":""},{"id":489822940,"identity":"373ab67a-495d-4121-b9e7-86aca642b1c3","order_by":1,"name":"Jiao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACfvnjhx98qJCQsz/efIA4LZIzeNIMZ5yxMGY4cyyBOC0GNxgMpHnbKhIbbvgYEOmy2w0JhjPYJBIbZ/B8vPGGwU5Ot4GADsY5Bw88+MAjYdws3bvZcg5DsrHZAQJamBkSgLZISMi2yZzdJs3DcCBxGyEtbAwJBtI8BhKMPRI5z4jTwiMB0pIgoThDIoeNOC0SPGeAgXxAwtiA55ix5RwDIvxif7z98IOP/+rkDNibH954U2EnR1ALmpXERg2SFlJ1jIJRMApGwYgAAEVERKgQFBT6AAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Institute of Petroleum and Chemical Technology","correspondingAuthor":true,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Wang","suffix":""},{"id":489822941,"identity":"aaaa64b8-efe0-4bf8-9b0c-4b739c897e76","order_by":2,"name":"Jun Peng","email":"","orcid":"","institution":"School of Geoscience and Technology，Southwest Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-05-13 06:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6651998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6651998/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40948-026-01154-2","type":"published","date":"2026-04-16T15:58:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87714573,"identity":"c56266cb-907e-4fb0-9202-d9459d60fea0","added_by":"auto","created_at":"2025-07-28 09:02:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)Location of the Bohaiwan Basin in the northeastern China; (b) Schematic diagram of the regional location and structural zone of Jiyang Depression; (c)Schematic diagram and well position diagram of Dongying depression; (d) Stratified developmental brief (Modified according to Yu et al., 2021, 2024; Li et al., 2019)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/584e108659f953a3379b91fc.jpg"},{"id":87714571,"identity":"66329d88-bf5a-493a-b5c2-19c61a621d3a","added_by":"auto","created_at":"2025-07-28 09:02:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacteristics of stratum development in Dongying sag, Jiyang depression (revised by Yu et al., 2021; Li et al., 2019)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/78aa957a695407cdec1bc46d.jpg"},{"id":87714596,"identity":"b19cb554-4a5a-4109-bb18-9a3f3fe36ca3","added_by":"auto","created_at":"2025-07-28 09:02:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":89936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch approach and workflow for the selection of astronomical cycle analysis methods and substitute indicators\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/f8add755382aa101154a8dd1.jpg"},{"id":87714574,"identity":"32a4e31c-65b9-45d1-a6ec-31d94d729584","added_by":"auto","created_at":"2025-07-28 09:02:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpectrum analysis plots of natural gamma-mapping data Spectrum analysis chart of natural gamma ray logging data of Es\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e4scs\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in well Niuye 1 of Dongying Sag.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/6709eec64895bcf17647d70c.jpg"},{"id":87714659,"identity":"53a73165-d87a-4472-bdaf-c517c1da4220","added_by":"auto","created_at":"2025-07-28 09:02:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram of the one-dimensional continuous wavelet transform result of the natural gamma data of Es\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e4scs\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in well Niuye 1 of Dongying Sag.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/357202c5b84d10a005c59983.jpg"},{"id":87716176,"identity":"e769d708-22e6-44a1-b448-bab652d8b88e","added_by":"auto","created_at":"2025-07-28 09:10:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePower spectral analysis modulus extreme diagram of the natural gamma logging data of Es\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e4scs\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in well niuye 1 of Dongying Sag.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/7f5246f5596cbb3bcb9ee6ba.jpg"},{"id":87716179,"identity":"d698110c-ca6c-428b-ae9f-6df00f488286","added_by":"auto","created_at":"2025-07-28 09:10:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":213669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMilankovitch cycle analysis results of the Es\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e4scs\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in well Niuye 1 of Dongying Sag.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/2829b1fef63bd19c28c186d3.jpg"},{"id":87714656,"identity":"575b770e-d9ad-40bf-8738-c4118054f5dc","added_by":"auto","created_at":"2025-07-28 09:02:29","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":242840,"visible":true,"origin":"","legend":"\u003cp\u003eSpectrum analysis of natural gamma data, fast Fourier transform sliding window spectrum analysis, and wavelet transform results of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 of Dongying Sag.\u003c/p\u003e\n\u003cp\u003e(a) Natural gamma logging data series; (b) Spectrum analysis results, showing the 2π MTM power spectrum of the depth-domain GR series with an average confidence level of 90%, with significant peaks at depths of 30.198m, 6.863m, 3.146m, 2.849m, 1.736m, 1.541m, and 1.411m; (c) 2π MTM fast Fourier transform sliding power spectrum analysis results of the natural gamma logging data series with a 40m sliding window and a 0.2m step size; (d) Wavelet transform analysis results.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/50404d034f7c08f47c93cf2f.jpg"},{"id":87716177,"identity":"745608d1-d7e6-4c61-a5d8-cc115f99bc5d","added_by":"auto","created_at":"2025-07-28 09:10:29","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":154024,"visible":true,"origin":"","legend":"\u003cp\u003eCOCO and eCOCO analysis of natural gamma logging data of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 of Dongying Sag.\u003c/p\u003e\n\u003cp\u003e(a) Sedimentation rate correlation coefficient spectrum; (b) Null hypothesis test; (c) Contribution of astronomical orbital parameters; (d) Evolutionary correlation coefficient with sedimentation rate changes achieved by astronomical calibration of the astronomical timescale; (e) Null hypothesis H0 significance level with sedimentation rate changes achieved by astronomical calibration of the astronomical timescale; (f) Evolution of the number of contributing astronomical frequencies. For the COCO and eCOCO analyses, the tested sedimentation rate range was 1 to 20 cm/kyr, with a step size of 0.1 cm/kyr, and 2000 Monte Carlo simulations were conducted.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/10b7817257f7c491d8315ae5.jpg"},{"id":87714662,"identity":"573a829b-3c59-473f-8120-08d4a46b0631","added_by":"auto","created_at":"2025-07-28 09:02:30","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":158795,"visible":true,"origin":"","legend":"\u003cp\u003eAcycle spectral analysis and sliding window spectral analysis of core grayscale data from the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in Well Niuye 1, at depths of 3396.45 to 3403.94 meters.\u003c/p\u003e\n\u003cp\u003e(a) Preprocessed grayscale value data sequence; (b) Spectrum analysis results, showing the 2π MTM power spectrum of the depth-domain grayscale value data series, with an average confidence level of 99%, and significant peaks at 7.119m, 3.121m, 2.881m, 1.753m, 1.638m, and 1.387m; (c) 2π MTM fast Fourier transform sliding power spectrum analysis results of the grayscale value data sequence with a 40m sliding window and a 0.2m step size; (d) Wavelet transform analysis results of the grayscale value data.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/771d99bfa9edd49fab8ccd92.jpg"},{"id":87717115,"identity":"63fffbef-2981-45b6-b91f-92afd4768a1d","added_by":"auto","created_at":"2025-07-28 09:18:29","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":263942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSliding window spectral analysis and wavelet transform analysis of grayscale data of Es\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003escs\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in well Niuye 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContinued from Fig. 11 Sliding window spectral analysis and wavelet transform analysis of grayscale data of Es\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003escs\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in well Niuye 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Spectral analysis of the Little Ice Age cycle (1200-1800 years); (b) Sliding window spectral analysis of the Little Ice Age cycle (1200-1800 years); (c) Spectral analysis of long cycles around 400 and 250 years; (d) Sliding window spectral analysis of long cycles around 400 and 250 years;(e) Spectral analysis of the solar activity double-century cycle (around 200 years); (f) Sliding window spectral analysis of the solar activity double-century cycle (around 200 years); (g) Spectral analysis of the 70-90 year Gleissberg cycle; (h) Sliding window spectral analysis of the 70-90 year Gleissberg cycle; (i) Wavelet transform analysis of solar activity cycles at various scales.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/59844e779aca04c250574920.jpg"},{"id":87714671,"identity":"23e2eeb1-0000-409e-a433-e2f2d6e01855","added_by":"auto","created_at":"2025-07-28 09:02:30","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":26067,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRe-Os isotopic dating analysis of the mudstone layer at the top boundary of Es\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003escs\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in well Niuye 1 of Dongying Sag.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/a19c09bcae5d682b97ed5826.jpg"},{"id":107351020,"identity":"22ef7e22-3b0c-4a38-b7f9-dde2859823ed","added_by":"auto","created_at":"2026-04-20 16:07:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2660662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6651998/v1/b76fc7e9-0e58-4b4e-ab14-548f7b6f972e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAnalysis Method and Indicator Optimization of Astronomical Cycles in Lacustrine Fine-Grained Sedimentary Strata- A case study of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in Well Niuye 1, Dongying Sag, Jiyang Depression, Bohai Bay Basin\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the global rise of unconventional shale oil and gas exploration, high-frequency sequence division of fine-grained sedimentary rocks has become a pressing issue in current stratigraphic research. High-frequency sea-level fluctuations driven by Milankovitch astronomical cycles are considered the driving factors for the formation of high-frequency sequences, and their temporal significance provides an effective means for conducting high-frequency cycle division and comparison. Research on cyclical stratigraphy abroad has mainly focused on marine strata, with a primary emphasis on Mesozoic and Cenozoic to Early Cambrian strata (Batenburg et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Marshall et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Most studies have relied on field outcrops, ocean drilling cores, rock cores, and well log data to conduct cyclical stratigraphy research (Nadia et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;Radzevicius et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Minguez et al. (2017) conducted a cyclical stratigraphy study on the Wonoka Formation in Australia, using magnetic susceptibility data as an alternative indicator, and identified the orbital parameter cycles recorded in the studied stratigraphic section (Minguez et al., 2017). Fang et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) applied magnetic susceptibility data to study the cyclical stratigraphy of the Dawan Gou section in Keping, Xinjiang, and concluded that Earth\u0026rsquo;s orbital parameters controlled the cooling events during the Darriwilian period (Fang et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Wang et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) performed a cyclical analysis on the clastic and carbonate strata of the GFD-1 well in the Pingle Depression, South China, and established a high-resolution astronomical timescale and sedimentation rate curve for the Luoping Oilfield in the GFD-1 well (Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Chen et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a spectral analysis, fast Fourier transform, and correlation analysis of the gamma ray logs from four wells, including Liu 8 well in the Lower Cretaceous Lower Gouwu Formation of the Jiuquan Basin, revealing the presence of orbital cycles such as long eccentricity, short eccentricity, obliquity, and precession within the study interval (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDomestic scholars have conducted extensive research on both marine and continental cyclical stratigraphy. Hu et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) used typical outcrops, drilling, well logging, and seismic data, and employed natural gamma-ray logs to create Fischer diagrams, identifying high-frequency cycles with Milankovitch cycle characteristics (Hu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Zong (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) applied wavelet transform and fast Fourier transform methods for time-frequency analysis of natural gamma-ray log data, identifying orbital cycles in sedimentary strata (Zong, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Shi et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) used magnetic susceptibility as an alternative indicator to identify Milankovitch cycle signals, and ultimately used long eccentricity, short eccentricity, and obliquity cycle curves as reference curves for the fourth, fifth, and sixth-level sequence divisions, achieving a quantitative division of high-frequency sequences in lacustrine fine-grained sediments (Shi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Liu (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) applied the theory of cyclical stratigraphy along with time series analysis and spectral analysis techniques to conduct astronomical cycle analysis on target wells. By utilizing time anchor points from neighboring wells, an effective astronomical timescale was established (Liu, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ma et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a cyclical stratigraphy study on the Upper and Middle Ordovician Salgan and Kanling formations in the Keping region of Xinjiang using high-resolution magnetic susceptibility data, identifying astronomical orbital cycles, with the obliquity signal being the most prominent (Ma et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Yang et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) performed spectral analysis of magnetic susceptibility data sequences and found that certain stratigraphic intervals in the Jianza profile of the Qiangtang Basin on the northeastern Tibetan Plateau exhibited stable long eccentricity cycles, while other orbital cycles were unstable (Yang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Zhao et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) used spectral analysis and wavelet transform to reveal the presence of Milankovitch astronomical cycles in the III oil layer group of the Sartu oil reservoir. They speculated that the strata were influenced by the periodic variations in Milankovitch astronomical orbital cycles during deposition (Zhao et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In summary, both domestic and international scholars have commonly used well logging, magnetic susceptibility, and geochemical testing analysis data, along with time-frequency analysis methods, to study the cyclicity of marine and continental sedimentary strata. These methods are then used to calculate the duration of cycles and the average sedimentation rate. However, previous studies on the cyclical division of the Dongying Depression have yielded different results, as various scholars have employed different methods (Peng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, 2022). The discrepancies in the division results can lead to significant errors in geological dating, and the outcomes may not effectively serve oil and gas exploration.\u003c/p\u003e\u003cp\u003eIn order to better explore the astronomical cycle division of lacustrine fine-grained sedimentary strata, this study takes the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 as an example. Based on astronomical stratigraphy theory, the study attempts to comprehensively apply various sedimentary sequence surrogate indicators, including geophysical, geochemical, mineral content, environmental magnetic parameters (magnetic susceptibility data), and core grayscale data, along with multiple time-series analysis methods to identify astronomical cycles. Through comparison of the results, the study aims to optimize the methods and indicators, ultimately combining Re-Os isotopic dating data to establish an absolute high-precision astronomical timescale. This research will provide a reference for the division of high-frequency cycles and precise geological age estimation, as well as offer insights for selecting astronomical cycle indicators and methods in similar stratigraphic studies.\u003c/p\u003e"},{"header":"2 Geological Settings","content":"\u003cp\u003eThe Bohai Bay Basin is located in eastern China and developed as a Cenozoic rift basin based on the Paleozoic sedimentation of the Sino-Korean Platform and subsequent movements during the Indosinian and Yanshanian orogenies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(a) and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b)) (Yu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Jiyang Depression is located in the southeastern part of the Bohai Bay Basin and is a secondary tectonic unit within the basin. The depression is composed of four secondary sub-depressions: the Chezheng Depression, Zanhua Depression, Huimin Depression, and Dongying Depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The Dongying Sag in the study area is a typical asymmetric, inverted, graben-type depression characterized by steep northern slopes and gentle southern slopes. A number of uplifts surround the Sag: to the north is the Chenjiazhuang Uplift, to the south is the Luxi Uplift, to the east is the Qingtuozi Uplift, and to the west are the Binxian and Qingcheng Uplifts. The exploration area covers approximately 5,800 km\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(c)) (Yu et al., 2023). Within the Sag, a series of syn-sedimentary normal faults further divide the area into four main petroleum-rich sub-depressions (Boxing, Lijin, Niuzhuang, and Minfeng), as well as several secondary structural units such as the northern steep slope zone, central anticline zone, southern gentle slope zone, and multiple fault structural belts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(d)) (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu et al., 2023).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe stratigraphy of the Dongying Sag develops in the following order from bottom to top: the Paleozoic (Pz), Mesozoic (Mz), Paleogene (E), Neogene (N), and Quaternary (Q). Among these, the Paleogene strata are widely distributed with a significant sedimentary thickness, exceeding seven thousand meters at its thickest. The thickness gradually decreases and tapers off from the basin sedimentary center to the marginal areas. The Paleogene strata can be divided into the Kongdian Formation, Shahejie Formation, and Dongying Formation from bottom to top. The Shahejie Formation can be further subdivided into four segments from bottom to top: the Sha 4 Member, Sha 3 Member, Sha 2 Member, and Sha 1 Member. Notably, the upper sub-member of the Sha 4 Member in the Shahejie Formation represents semi-deep lake to deep lake deposition, which formed a large set of fine-grained sediments, primarily consisting of mudstone, calcareous mudstone, and limestone, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Yu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"3 Data and Experimental Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 XRF analysis of the core samples\u003c/h2\u003e\u003cp\u003eThe content of various elements in the samples was tested using the NITON XL3t-950 Handheld Ore Elemental Analyzer (an instrument based on XRF spectrometry) developed by Thermo Scientific (Analytical Methods Committee and Royal Society of Chemistry, 2008). During the testing process to ensure the validity of the collected data, the testing density was about 2.5 cm to test 1 point, and the testing time of each point was 20 s according to the Nyquist sampling theorem (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The test was conducted in two modes of measurement, using the mineral mode for major elements and the soil mode for trace elements with a content of less than 1%. In order to carry out the analysis, this study mainly tested the elements Ti, Si, S, K, Ca, Fe, V, Ba, Zr, Mn and Zn, and categorized the elemental indicators characterizing the paleoclimate, paleobathymetry, paleosalinity, paleoredox and paleoproductivity in the test data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 X-ray diffraction (XRD) analysis of bulk rock\u003c/h2\u003e\u003cp\u003eX-diffraction whole rock analysis is a common method for determining the mineralogical composition of rocks. The rock samples are ground until the average particle size reaches about 5\u0026micro;m or less than 200 mesh, and then the samples are pressed and molded and tested on the machine to get the mass percentage of minerals. This experimental analysis was performed by XRD powder crystal scattering test on the samples using a TTR-type X-ray diffractometer manufactured by Rigaku, Japan, and the test conditions were performed with reference to the industry standard SY/T 5163\u0026thinsp;\u0026minus;\u0026thinsp;2010 (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Organic carbon analysis\u003c/h2\u003e\u003cp\u003eThe method involves grinding the dried sample to a particle size of less than 0.2 mm, and removing the inorganic carbon component of the sample with dilute hydrochloric acid (analytically pure hydrochloric acid prepared with water at a volume ratio of 1:7). It is then burned in a high-temperature oxygen stream (oxygen purity 99.9%) to convert the total organic carbon into carbon dioxide, and then, based on the correspondence of carbon content between carbon dioxide and total organic carbon, the carbon dioxide content is finally detected by an infrared detector/thermal conductivity detector, and the total organic carbon content is calculated. This analytical test was performed using a LECOCS-230 carbon and sulfur analyzer, and the test conditions were performed with reference to the national standard GB/T 19145\u0026thinsp;\u0026minus;\u0026thinsp;2003 (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Magnetic susceptibility testing\u003c/h2\u003e\u003cp\u003eThe magnetization rate was tested using a KM-7 portable magnetization rate meter as well as a KM-10 portable magnetization rate meter and a core synthesizer.\u003c/p\u003e\u003cp\u003eKM-7 type portable magnetization rate meter: High-density magnetization rate testing was conducted for the coring well section, i.e., the magnetization rate data were tested by a combination of testing with a portable magnetization rate meter type KM-7 produced by SatisGeo during the core observation and collecting samples for in-house testing in the international standard unit of SI, with the detection points spaced at an interval of 2.5 cm and an accuracy of 10\u003csup\u003e-6\u003c/sup\u003eSI, and the magnetization rate of each point took about 30s of The magnetization rate of each point takes about 30s to test and record. One person performed petrographic descriptions and two persons performed magnetization rate testing and rock sample collection, which resulted in a total of 342 magnetization rate data.\u003c/p\u003e\u003cp\u003eKM-10 portable magnetization rate meter and core comprehensive tester: A total of 1,389 magnetization data were tested, and only two depth sections of 3300.72-3326.15m and 3386.96-3403.93m of Es\u003csub\u003e3\u003c/sub\u003e\u003csup\u003ex\u003c/sup\u003e to Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e stratum in well Niuye 1 were tested, with the interval of detection points being 2cm, and the number of test data was 877 and 512, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Logging data\u003c/h2\u003e\u003cp\u003eThe logging data were mainly obtained through Sinopec Logging Engineering Company's tests, generally at sampling intervals of 0.1m and 0.0381m to obtain a data point, and the tests obtained logging data such as natural gamma, natural gamma energy spectrum, acoustic time difference and resistivity, as well as imaging logging images.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Astronomical cyclic analysis methods and index optimization\u003c/h2\u003e\n \u003cp\u003eAstronomical stratigraphy plays a key role in addressing major geological events, exploring the evolution of climate and life, and analyzing the global carbon cycle. In other words, astronomical orbital forces are an important driving force in solving these issues (Liebrand et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Crampton et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lauretano et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kocken et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bahr et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The research methods in astronomical stratigraphy can be broadly divided into two categories: lithology-based visual identification and time series analysis. The former primarily relies on outcrop profiles and drilling core data, which are limited by the discontinuity of such data and the sampling density. As a result, its application is challenging and prone to missing cyclical information. Additionally, this method requires researchers to have strong geological expertise and extensive research experience. The latter, on the other hand, is a digital processing technique that has been widely applied. This method is based on widely used digital signal processing technologies, and its basic steps include data preprocessing, spectral analysis, time-frequency analysis, and correlation studies (Li et al., \u003cspan class=\"CitationRef\"\u003e2018a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2018b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003ec\u003c/span\u003e). The premise of these studies is to obtain continuous data sequences for astronomical cyclic research by utilizing advanced testing techniques, which serve as surrogate parameters for sedimentary stratigraphic sequences (hereinafter referred to as \u0026quot;surrogate parameters\u0026quot;). These parameters are selected to reflect paleoclimate changes and mainly include well log parameters such as natural gamma, spontaneous potential, sonic travel time, resistivity, as well as carbon-oxygen isotopes, magnetic susceptibility, various geochemical analysis data, and high-resolution continuous elemental logging data (Ait-itto et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Husinec and Read, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xia et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Astronomical cyclic theory is then applied to study the orbital cycles reflected in these data. By combining radiometric isotope ages, biostratigraphy, and magnetostratigraphy, a more refined astronomical geochronological scale can be established. This allows for a better understanding of the geological significance of sedimentary cycles on a fine timescale, with the goal of achieving precise dating (Pas et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Boulila et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wu et al., 2019).\u003c/p\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.1 Time series analysis method\u003c/h2\u003e\n \u003cp\u003eTime series analysis is a widely used digital signal processing technique. This method primarily involves the selection of data from stratigraphic sequences, data preprocessing, spectral analysis, time-frequency analysis, and correlation studies, enabling multi-scale and multi-resolution signal decomposition. Constraints from various stratigraphy disciplines (such as radiometric isotope geochronology, biostratigraphy, and magnetostratigraphy) serve as important foundations for astronomical cyclic analysis and reliability verification (Peng et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the field of astronomical stratigraphy, time series analysis has been widely applied. Whether in field outcrop sections or core observations, the key first step in applying time series analysis is to obtain continuous data sequences for research, which then serve as the basis for conducting time series analysis. In other words, the key to applying this method for cyclic identification is to convert depth-domain data into the frequency domain, and then transform it back from the frequency domain to the time domain. Through a series of time-frequency analysis techniques, spectra that can be compared with theoretical values of astronomical orbital parameters are obtained, which allows for the delineation of cycles.\u003c/p\u003e\n \u003cp\u003eThe research in astronomical stratigraphy should follow a process that includes selecting time series analysis methods based on sedimentary stratigraphic sequence surrogate parameters, followed by optimizing the choice of methods, and finally, validating the cyclic results. The method of using time series analysis for studying Milankovitch cycles involves a series of research processes, including data selection, preprocessing, spectral analysis, time-frequency analysis, and astronomical tuning (Peng Jun et al., 2022). Commonly used time series analysis methods include spectral analysis, fast Fourier transform (FFT), wavelet transform, and sliding window spectral analysis (Li et al., \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2022b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003ec\u003c/span\u003e). These methods are employed to examine whether the periodic signals in sedimentary records are influenced by astronomical orbital forcing. If orbital forcing is detected, the signals need to be tuned to align with theoretical orbital parameter curves, thereby establishing a high-precision chronostratigraphic framework.\u003c/p\u003e\n \u003cp\u003eIn this study, based on the methods of astronomical cycle research, two processing workflows are defined. The first workflow, referred to as TSAM1, consists of the following steps: Matlab data preprocessing to Past3.0 spectral analysis to Matlab one-dimensional continuous wavelet transform to Power spectrum estimation to Filtering analysis and extract cyclical periods. The second workflow, referred to as TSAM2, involves: Acycle data preprocessing to Acycle spectral analysis to FFT sliding window spectral analysis to Acycle wavelet transform to COCO and ECOCO estimation of sedimentation rates. The main purpose of this study is to explore the optimal time series processing methods using these two processing workflows.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.2 Substitute indicators of sedimentary sequences\u003c/h2\u003e\n \u003cp\u003eAstronomical stratigraphy has been widely applied and has developed rapidly since its inception. However, the selection of substitute parameters for sedimentary stratigraphic sequences and the application of data processing methods have always been key challenges in cyclic stratigraphy research. Substitute parameters of sedimentary stratigraphic sequences refer to various paleoecological, geophysical, and geochemical parameters that reflect past changes in sedimentary conditions, particularly climate changes. Since there is a close relationship between climate change and orbital parameters, in theory, any indicator associated with climate change can be used as a substitute parameter in astronomical stratigraphy research (Li et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003ea, \u003cspan class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Olsen et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ikeda and Tada, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ma et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Through a review of previous research, it is known that substitute parameters used for astronomical cyclic analysis include paleontological parameters, environmental magnetic parameters, geophysical parameters, and geochemical parameters. By conducting a comprehensive analysis of these various parameters, a time series containing information on stratigraphic environmental changes can be constructed.\u003c/p\u003e\n \u003cp\u003eIn this study, various substitute indicators of sedimentary sequences are used, and astronomical cyclic division is carried out using the TSAM1 and TSAM2 processes. The optimal indicators and methods are selected through a comparison of the results.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e4.1.3 Research Approach for the Selection of Indicators and Methods\u003c/h2\u003e\n \u003cp\u003eThe natural gamma ray logging data, along with other logging data, magnetic susceptibility, elemental geochemical data, mineral content, and core grayscale data, are used as substitute indicators for cyclic division. Principal Component Analysis (PCA) (Origin) and Factor Analysis (SPSS) methods are employed to simplify and reduce the dimensionality of multiple logging data and sedimentary environmental element analysis indicator data. By comprehensively applying the TSAM1 and TSAM2 methods, spectral analysis, wavelet transform, power spectral estimation, sliding window spectral analysis, and short-time Fourier transform are conducted on various substitute indicator data. This allows for the cyclic stratigraphic division of the studied strata. Ultimately, through a comparative analysis of the results obtained from different methods and substitute indicators, the most suitable time series analysis methods and substitute indicators for lacustrine fine-grained sediment are selected. These methods are used to identify and classify the multi-scale astronomical orbital cycles in the research strata. The selected data and specific processing workflow are shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Cyclic Identification Results and Comparison Based on Different Substitute Indicators and Methods\u003c/h2\u003e\n \u003cp\u003eDue to the large amount of data being processed, this paper uses the natural gamma ray logging data and core grayscale data from the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 as an example to introduce the cyclic analysis process and results of TSAM1 and TSAM2. The processing steps for other substitute indicators of the sedimentary sequence will not be elaborated here.\u003c/p\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.1 Astronomical Cycle Analysis of Natural Gamma Ray Logging Data\u003c/h2\u003e\n \u003cp\u003e(1) TSAM1\u003c/p\u003e\n \u003cp\u003eThe natural gamma ray logging data is processed using the first workflow: Matlab data preprocessing to Past3.0 spectral analysis to one-dimensional continuous wavelet transform of Matlab to power spectral estimation to filtering analysis and extract cyclic periods.\u003c/p\u003e\n \u003cp\u003e① Spectral Analysis\u003c/p\u003e\n \u003cp\u003eThe Redfit spectral analysis results of the natural gamma ray logging data from the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 indicate that during the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e sedimentary period, the Earth\u0026apos;s orbital elements exhibited frequent periodic variations, with multiple dominant precession and obliquity cycles. Based on the dominant frequencies, the dominant cycle thicknesses can be obtained (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and the ratio between these dominant cycle thicknesses is 30.191: 6.862: 3.145: 2.848: 1.735: 1.540: 1.411\u0026thinsp;=\u0026thinsp;21.40: 4.86: 2.23: 2.02: 1.23: 1.09: 1. This ratio is very close to the theoretical orbital period ratio of 405kyr: 95.24kyr: 39.76kyr: 38.54kyr: 23.28kyr: 22kyr: 18.82kyr. That is, the cycle thicknesses of 30.191m and 6.862m correspond to the long and short eccentricity periods of 405kyr and 95.24kyr, respectively; the cycle thicknesses of 3.145m and 2.848m correspond to the obliquity periods of 39.76kyr and 38.54kyr; and the cycle thicknesses of 1.735m, 1.54m, and 1.411m correspond to the precession periods of 23.28kyr, 22kyr, and 18.82kyr, respectively. Therefore, the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e sedimentary period in well Niuye 1 is very likely to be controlled by Milankovitch cycles.\u003c/p\u003e\n \u003cp\u003e②Wavelet transform\u003c/p\u003e\n \u003cp\u003eThe Morlet wavelet from the one-dimensional continuous wavelet toolbox provided by Matlab was used as the mother wavelet to perform wavelet transform analysis on the natural gamma logging data of the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e is a schematic of the one-dimensional continuous wavelet transform result of the logging signal at scale of a\u0026thinsp;=\u0026thinsp;512 of the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1. From the figure, it can be seen that the frequencies of different components correspond to certain specific scale values, indicating that the frequency components are relatively simple and stable.\u003c/p\u003e\n \u003cp\u003e③Power spectral estimation\u003c/p\u003e\n \u003cp\u003eIn order to further determine whether these relatively simple and stable frequency components are controlled by a specific orbital period, power spectral estimation was conducted based on the wavelet analysis. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the morlet wavelet extrema diagram of the Niuye 1 well (Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e) obtained by matrix calculations of the energy spectrum at different scales. The appropriate extrema scales are selected for analyzing the wavelet period of the signal. From the extrema diagram, several obvious local maxima are observed, with corresponding wavelet scale values of 29, 60, 116, 332, and 512 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Among these, the ratio of the scale values 512:116:60:29 closely matches the ratios of the astronomical orbital eccentricity period of 405 kyr, obliquity period of 95.24 kyr, precession period of 39.76 kyr, and the axial precession period of 18.82 kyr. Based on the results of the spectral analysis, it can be determined that the stratigraphic sequence is significantly controlled by the long eccentricity cycle, short eccentricity cycle, obliquity cycle, and precession cycle. Therefore, it can be inferred that the dominant frequencies corresponding to the scale values of 512, 116, 60, and 29 are driven by the long eccentricity cycle, short eccentricity cycle, obliquity cycle, and precession cycle, respectively. Thus, the wavelet curves corresponding to these scale values can be understood as the cyclical curves of the long eccentricity cycle, short eccentricity cycle, obliquity cycle, and precession cycle. Based on the wavelet analysis, the wavelet coefficient curves corresponding to these scale values can be extracted to represent the periodic cyclical curves of stratigraphy of the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 .\u003c/p\u003e\n \u003cp\u003eBased on the filtering results and combined with the cyclic curves, a comprehensive columnar diagram of the cyclical stratigraphic division for the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 is created, incorporating natural gamma logging data, depth, lithology, cyclic curves with scale values of 512, 116, 60, and 29, as well as the wavelet energy spectrum (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The study concludes that the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 exhibits approximately 6 long eccentricity cycles of 405 kyr, 24 short eccentricity cycles of 95.24 kyr, 53 obliquity cycles of 39.76 kyr, and 110 precession cycles of 18.82 kyr.\u003c/p\u003e\n \u003cp\u003e(2) TSAM2\u003c/p\u003e\n \u003cp\u003eThe natural gamma logging data is processed using the following steps: Acycle data preprocessing to Acycle spectral analysis to FFT sliding window spectral analysis to Acycle wavelet transform to COCO and ECOCO methods to estimate sedimentation rates and extract cyclic periods.\u003c/p\u003e\n \u003cp\u003e①Spectral analysis and FFT sliding window spectral analysis\u003c/p\u003e\n \u003cp\u003ePerform spectral analysis and Fast Fourier Transform (FFT) sliding window spectral analysis using Acycle software. The spectral analysis results of the preprocessed natural gamma logging data series show distinct sedimentary cycles. The spectrum exhibits significant peaks at frequencies of 0.033, 0.146, 0.318, 0.351, 0.576, 0.649, and 0.709. Based on the dominant frequencies, the dominant cycle thicknesses can be determined (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea), corresponding to clear peaks at wavelengths of 30.198m, 6.863m, 3.145m, 2.848m, 1.735m, 1.540m, and 1.411m, all exceeding the 90% confidence level. Therefore, these thicknesses represent the dominant cycle thicknesses in the stratigraphic sedimentary record. By using the cycle wavelengths, the dominant cycle thicknesses are obtained, and the ratio between the cycle thicknesses is derived as 30.198: 6.863: 3.145: 2.848: 1.735: 1.540: 1.411\u0026thinsp;=\u0026thinsp;21.40: 4.86: 2.23: 2.02: 1.23: 1.09: 1. This ratio closely matches the theoretical orbital periods of 405kyr: 95.24kyr: 39.76kyr: 38.54kyr: 23.28kyr: 22kyr: 18.82kyr. The comparison of these cycle wavelength ratios with the theoretical orbital period ratios indicates that the wavelength ratios conform to the theoretical orbital cycles of the Paleogene period. Different wavelength cycles correspond to orbital periods of different scales. Specifically, the wavelengths of 30.198m and 6.863m correspond to the long and short eccentricity cycles of 405kyr and 95.24kyr, respectively. The wavelengths of 3.146m and 2.849m correspond to the obliquity cycles of 39.76kyr and 38.54kyr, while the cycle thicknesses of 1.736m, 1.541m, and 1.411m correspond to the precession cycles of 23.28kyr, 22kyr, and 18.82kyr, respectively. The orbital periods obtained through the Spectral Analysis method in Acycle software are consistent with the results analyzed using Past 3.0 software. This further indicates that the sedimentary period of the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 is controlled by the Milankovitch cycles of Earth\u0026apos;s orbital parameters. Moreover, the cycle analysis results obtained from both methods are highly consistent and reliable.\u003c/p\u003e\n \u003cp\u003eUsing the FFT method, a static 2\u0026pi; MTM analysis was conducted on the natural gamma logging depth-domain data. The resulting sliding window spectral plots were used to analyze the frequency evolution characteristics of the entire natural gamma logging sequence and identify the dominant signal wavelengths. In this sliding window analysis, a 40m running window with a step size of 0.2m was applied. From the sliding window spectral plots (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eb), it is evident that the natural gamma logging series exhibits distinct periodic variations in the power spectrum. These variations correspond to the 405kyr long eccentricity cycle, the 95.24kyr short eccentricity cycle, the 39.76kyr and 38.54kyr obliquity cycles, as well as the 23.28kyr, 22kyr, and 18.82kyr precession cycles, with varying degrees of frequency. Based on the spectral analysis results, it is observed that the 30.198m, 3.146m, and 2.849m wavelengths, which correspond to the 405kyr long eccentricity cycle, the 39.76kyr obliquity cycle, and the 38.54kyr obliquity cycle, respectively, are stable dominant cycles in terms of frequency (period).\u003c/p\u003e\n \u003cp\u003e②Wavelet Transform\u003c/p\u003e\n \u003cp\u003eThe wavelet transform analysis in Acycle software was used to study the cyclical characteristics of the natural gamma logging data series. As can be seen from Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ed, during the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e stratigraphic deposition period in well Niuye 1, distinct orbital cycles are present, including short eccentricity, obliquity, and precession cycles. The analysis results are consistent with those obtained from the spectral analysis and sliding window spectral analysis. By integrating the cycle thickness and the sedimentary strata thickness, it is observed that there are 5 long eccentricity cycles of 405kyr, 22 short eccentricity cycles of 95.24kyr, 48 obliquity cycles of 39.76kyr and 53 obliquity cycles of 38.54kyr, as well as 87 precession cycles of 23.82kyr, 98 precession cycles of 22kyr, and 107 precession cycles of 18.82kyr. In addition, this method also identified distinct sub-meter scale cycles, with cycle periods ranging from 1000 to 8000 years. Solar radiation, as the energy source for the Earth system, is the fundamental driving force for climate formation and evolution. Over long time scales of thousands of years, variations in solar radiation reaching the Earth are closely related to Earth\u0026apos;s climate (Xiao, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to previous research (Ma et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), solar radiation exhibits different periodicities, including the 11-year Schwabe cycle, the 80\u0026ndash;100-year Gleissberg cycle, the 210-year de Vries-Suess cycle, the 1000-year Eddy cycle, and the 2400-year Hallstatt cycle. Studies on these periodic variations have primarily focused on Holocene records, such as tree rings, stalagmites, polar ice sheets, lacustrine varves, and marine sediments. The 1000\u0026ndash;8000 year periodicity identified by the wavelet transform in this study is likely associated with the Eddy and Hallstatt cycles. The 11-year cycle in solar radiation corresponds to the solar sunspot cycle, which is an important indicator of solar radiation variations. Within a single sunspot cycle, the periodic changes in solar irradiance influence climate variations, which in turn affect the periodic changes in sediment deposition within sedimentary strata. This is reflected as periodicity in the rhythmic layers of lacustrine deposits. This is consistent with the later-estimated average sedimentation rate of 0.0661 m/kyr for the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e formation in well niuye 1. Under the assumption of no compaction effects, the sedimentary thickness over the 1000-8000-year period would be approximately 6.61\u0026ndash;52.88 cm, corresponding to the scale of stratigraphic layers. In the presence of an 11-year sunspot cycle, the sedimentary thickness would be about 0.727 mm, corresponding to the scale of varve-like laminae. At the same time, some researchers have also discovered a correlation between the variations in varve-like laminae and sunspot activity (Shi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhao et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The scale of sedimentary rhythms in lacustrine strata varies, ranging from fine laminae formed on seasonal or even shorter time scales, to larger-scale cycles corresponding to interannual, centennial, or even millennial orbital periods. The study of periodic cyclic records in layers or varve-like laminae is of great significance for the cyclostratigraphy of fine-grained lacustrine sediments. Therefore, subsequent chapters will further explore the periodic cyclic records of layers or varve-like laminae.\u003c/p\u003e\n \u003cp\u003e③Estimation of sedimentation rates using COCO and ECOCO\u003c/p\u003e\n \u003cp\u003eIn order to find the optimal sedimentation rate for the study interval, sedimentation rate assessments using COCO and ECOCO were conducted on the natural gamma logging depth-domain data, resulting in the optimal sedimentation rate. In this study, a 2000-run Monte Carlo simulation was used to perform COCO analysis on the natural gamma logging data series from the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well niuye 1. The tested sedimentation rate range was 1\u0026ndash;15 cm/kyr. The results show several peaks at sedimentation rates of 2.3 cm/kyr, 3.1 cm/kyr, 4 cm/kyr, 7.9 cm/kyr, 9.7 cm/kyr, and 13.4 cm/kyr (Fig. 9a to Fig. 9c). Among these peaks, the most significant cluster occurs between 3.1 cm/kyr and 7.9 cm/kyr, which may indicate that the average sedimentation rate lies between 3.1 cm/kyr and 7.9 cm/kyr. The average sedimentation rate of 3.1 cm/kyr exceeds the critical significance level. At this rate, all six astronomical orbital cycle components are included, and the null hypothesis of orbital signal is not rejected at a significance level below 0.1% (Fig. 9a to Fig. 9c). The astronomical timescale established through astronomical tuning indicates that the average sedimentation rate for this section is 6.61 cm/kyr. The corresponding correlation coefficient in the COCO analysis is greater than 0.15, suggesting that the sedimentation rate calculated using the timescale is reliable. Furthermore, the sliding correlation analysis of the correlation coefficient, H\u003csub\u003e0\u003c/sub\u003e, and orbital parameters (ECOCO) for this well ((Fig. 9d to Fig. 9f)) shows that all these sedimentation rates have an insignificant hypothesis significance level below 0.01 (Fig. 9).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.2 Cycle analysis of grayscale values\u003c/h2\u003e\n \u003cp\u003eSpectrum analysis and fast Fourier transform sliding window spectrum analysis of the core grayscale data from well Niuye 1, depth range 3396.45m to 3403.94m, were conducted using Acycle software. The analysis results show higher peak values at frequencies of 0.1405, 0.3204, 0.3417, 0.5705, 0.6105, and 0.721 (Fig. 10b to Fig. 10c). Based on the dominant frequencies, the dominant cycle thicknesses can be determined, which correspond to clear peaks in the wavelength bands of 7.119m, 3.121m, 2.881m, 1.753m, 1.638m, and 1.387m. These wavelengths exceed the 99% confidence level, indicating that these thicknesses represent the dominant cycle thicknesses in the sedimentary records of the formation. By using the cycle wavelengths, the dominant cycle thicknesses are obtained, and the ratio between the cycle thicknesses is calculated as 7.119: 3.121: 2.881: 1.753: 1.638: 1.387\u0026thinsp;=\u0026thinsp;5.1327: 2.2502: 2.0771: 1.2639: 1.181: 1. This ratio closely matches the theoretical orbital periods of 95.24kyr: 39.76kyr: 38.54kyr: 23.28kyr: 22kyr: 18.82kyr. Therefore, it can be interpreted as follows: 7.119m corresponds to the short eccentricity cycle of 95.24kyr, 3.121m and 2.881m correspond to the obliquity cycles of 39.76kyr and 38.54kyr, while 1.753m, 1.638m, and 1.387m correspond to the precession cycles of 23.28kyr, 22kyr, and 18.82kyr, respectively. The Acycle spectral analysis and sliding window spectral analysis of the core grayscale data indicate that the studied stratigraphic section is clearly controlled by eccentricity, obliquity, and precession cycles, with a distinct cyclicity observed.\u003c/p\u003e\n \u003cp\u003ePrevious studies have shown that solar activity exhibits cycles such as the Little Ice Age cycle of 1200\u0026ndash;1800 years, a long cycle of around 400 years, a long cycle of around 250 years, a solar activity double-century cycle of around 200 years, the Gleissberg cycle of 70\u0026ndash;90 years (the solar activity century cycle of 80\u0026ndash;90 years), a 60-year cycle, the 22-year Hale cycle (solar magnetic cycle), and the 11-year Schwabe sunspot cycle (sunspot cycle) (Shi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yi et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). To further investigate smaller-scale cycles, Acycle software was used to perform spectral analysis and fast Fourier transform (FFT) sliding window spectral analysis on the grayscale data, identifying annual-scale cycles.\u003c/p\u003e\n \u003cp\u003eThe analysis results (Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) show that several high peaks appear at frequencies with wavelengths above the 99% confidence level. Based on the dominant frequencies, the dominant cycle thickness can be determined, which corresponds to the dominant cycle thickness in the stratigraphic sedimentary record. Therefore, the cycle thickness corresponding to the Little Ice Age cycle of 1200\u0026ndash;1800 years in the studied section is 0.0609\u0026ndash;0.1204 m, with approximately 62.2\u0026ndash;123 cycles; the cycle thickness corresponding to the long cycle of around 400 years is 0.0226\u0026ndash;0.0376 m, with approximately 199.2-331.4 cycles; the cycle thickness corresponding to the long cycle of around 250 years is 0.0151\u0026ndash;0.0178 m, with approximately 420.8\u0026ndash;496 cycles; the cycle thickness corresponding to the solar activity double-century cycle of around 200 years is 0.0103\u0026ndash;0.0145 m, with approximately 516.6-727.2 cycles; and the cycle thickness corresponding to the 70\u0026ndash;90 year Gleissberg cycle is 0.0058\u0026ndash;0.0096 m, with approximately 780.2-1291.4 cycles. Based on subsequent research, the sedimentation rate of the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e formation in well Niuye 1 is approximately 0.069 m/kyr. Therefore, at this sedimentation rate, the sedimentary thickness for cycles of 1200\u0026ndash;1800 years, 400 years, 250 years, 200 years, and 70\u0026ndash;90 years should be 0.0828\u0026ndash;0.1242 m, 0.0276 m, 0.01725 m, 0.0138 m, and 0.00483\u0026ndash;0.00621 m, respectively. The cycle thicknesses corresponding to each period obtained from the Acycle spectral analysis and sliding window spectral analysis align with the cycle thicknesses calculated based on the sedimentation rate. Thus, it can be confirmed that the identified cycles in the studied section are accurate and reliable on an annual scale.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.2.3 Comparison of cycle classification results based on various alternative parameters and different analysis methods\u003c/h2\u003e\n \u003cp\u003eIndicators that are sensitive to climate and environmental changes are ideal data for Milankovitch cycle analysis. That is, various substitute parameters reflecting changes in sedimentary conditions, such as sedimentary stratigraphic sequences, can all be used in astronomical cycle studies. Different types of sedimentary sequence substitute parameters respond differently to astronomical orbital cycles, and astronomical timescales based on a single indicator may have uncertainties. To reduce these uncertainties, it is necessary to use a combination of various sedimentary sequence substitute parameters or apply mathematical transformations to the indicators before use. Based on this, the present study primarily involves five major types of parameters, including geophysical parameters, geochemical parameters, environmental magnetism parameters, mineral content parameters, and core grayscale parameters. The time series analysis methods mainly involve two sets of processing methods and workflows: First, Matlab data preprocessing to Past3.0 spectral analysis to Matlab one-dimensional continuous wavelet transform to power spectrum estimation to filtering analysis and extract cycle periods. This workflow is referred to as TSAM1. Second, Acycle data preprocessing to Acycle spectral analysis to FFT sliding window spectral analysis to Acycle wavelet transform to COCO and ECOCO estimation of sedimentation rates. This workflow is referred to as TSAM2. In the study, it was found that the cycle results obtained by applying different time series analysis methods to various substitute parameters differed. To ensure that the various parameters and analysis methods better serve cycle research, a comparison and optimization of the sedimentary stratigraphic sequence substitute parameters and time series analysis methods were conducted, based on the cycle analysis results (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), as shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe comparison of cycle research results (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) shows that, among geophysical parameters, the TSAM2 method yields the best results for GR, AC, RL, RN, principal component analysis of various logging series, and factor analysis of various logging series. It is able to identify orbital parameters at different scales. On the other hand, the use of single logging data with the TSAM1 method also provides relatively good cycle recognition results, although it is slightly less effective than the results obtained by the TSAM2 method. Furthermore, principal component analysis and factor analysis of various logging series data are not well-suited for cycle analysis using the TSAM1 method. For environmental magnetism parameters, the magnetic susceptibility data analyzed using the TSAM2 method yields the best results, while the cycle recognition results from the TSAM1 method are less effective than those from TSAM2. Among geochemical parameters, TSAM2 is the most effective for Fe, Mn, Ti, Ca, Al, Si, CaO/MgO, (CaO\u0026thinsp;+\u0026thinsp;K\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eO)/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, (Fe\u0026thinsp;+\u0026thinsp;Al)/(Ca\u0026thinsp;+\u0026thinsp;Mg), as well as principal component analysis and factor analysis of elemental data. On the other hand, the cycle recognition results obtained using the TSAM1 method are generally less effective, with some cases only able to identify the eccentricity cycle. In the analysis of cycle recognition using mineral content parameters, the TSAM2 method is not applicable, while the TSAM1 method can only identify the eccentricity and obliquity cycles, but fails to recognize the precession cycle. When using core grayscale data for cycle research, the TSAM2 method yields the best results and can identify solar activity cycles at a scale smaller than the precession cycle, down to the annual level. However, the TSAM1 method is not suitable for magnetic susceptibility cycle studies.\u003c/p\u003e\n \u003cp\u003eThe study shows that various substitute parameters for sedimentary stratigraphic sequences and time series analysis methods used for cycle analysis each have their own advantages and disadvantages (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Only by thoroughly understanding these strengths and weaknesses can the optimal substitute parameters and analysis methods be selected for cycle research, ensuring the reliability and accuracy of the cycle analysis results. For substitute parameters in cycle analysis, geophysical parameters are easily accessible, have good continuity, and high resolution, making them an important choice for cycle research. Environmental magnetism parameters, such as magnetic susceptibility data, are also crucial in cycle studies. Since astronomical cycle research requires high continuity of substitute parameter data and demands a large number of evenly spaced samples for testing and analysis, the testing methods need to be simple, cost-effective, and easy to interpret. Environmental magnetism measurements are convenient and low-cost, making them well-suited for astronomical stratigraphic analysis. Therefore, this parameter, like logging data, has a wide range of applications. However, a drawback of this method is that it may be subject to human interference during testing. Thus, it is important to create a good testing environment to minimize such influences. Geochemical parameters and mineral content parameters are rarely used in cycle recognition, primarily due to limitations in testing technology, cost, and sample availability. The acquisition of these two types of data is restricted, making it difficult to obtain high-resolution and continuous data series for astronomical cycle analysis. On the other hand, core grayscale data has high resolution and is a good indicator for cycle analysis. However, the process of obtaining, processing, and extracting grayscale values is complicated and challenging to operate.\u003c/p\u003e\n \u003cp\u003eFor time series analysis methods in cycle analysis, the TSAM1 time series analysis method uses spectral analysis to transform periodic signals from the time domain to the frequency domain. However, it cannot observe and analyze periodic signals in both the time-frequency domain, nor can it reflect the periodicity of depth positions. Additionally, it cannot divide the different levels of sedimentary cycle interfaces as a whole (Yan et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Wavelet analysis, as a time-frequency domain analysis method, can observe and process signals at different scales and frequencies. Specifically, wavelet transform can decompose complex signals into cyclical curves of different frequencies or periods, breaking them down into independent sedimentary cycles with distinct periods, which are displayed at different scales. This allows for the examination of the variation of local energy clusters in the wavelet time-frequency energy map and the periodic oscillation characteristics at various scaling levels (Gambacorta et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although wavelet transform has the advantage of multi-resolution capability in identifying sedimentary cycles of different levels and overcomes the limitation of spectral analysis, which cannot reflect the local features of the time and frequency domains, it can more accurately display the changes of different frequency (period) components in the time (depth) domain. This, in turn, facilitates better identification of sedimentary discontinuities and reflects changes in sedimentation rates. However, when using wavelet time-frequency energy spectra to delineate sedimentary cycle interfaces, certain errors may arise, leading to inaccurate or imprecise interface readings. Power spectral estimation, based on wavelet transform analysis, uses the Morlet wavelet basis to perform multi-scale wavelet decomposition of logging curves. This method applies a wavelet transform-based power spectral estimation to extract cycles of different levels, aiming to reflect the energy changes of stratigraphic sedimentary units from small scales to large scales (Yan et al.,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the TSAM2 time series analysis method, spectral analysis is used to assess the distribution of time series as a function of frequency. The main purpose of spectral analysis is to identify periodic or quasi-periodic components in the data sequence, ultimately producing an MTM spectral analysis plot with a noise model. The sliding window spectral analysis method provides a large number of \u0026quot;windows\u0026quot; and can accurately determine the variations of different frequencies in the depth (time) domain. This helps in identifying changes in sedimentation rates and potential sedimentary discontinuities. Additionally, this method uses the same program under the same operating system, eliminating the need to switch between different operating systems or software, making the operation convenient and straightforward.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 The cycle division results of various alternative parameters and different analytical methods of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eAlternative Parameters of Sedimentary Sequences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e处理流程\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e旋回划分结果\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e5.7个E\u003csub\u003e1\u003c/sub\u003e；24个E\u003csub\u003e2\u003c/sub\u003e；53个O\u003csub\u003e1\u003c/sub\u003e；110.5个P\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e5个E\u003csub\u003e1\u003c/sub\u003e；P2个E\u003csub\u003e2\u003c/sub\u003e；52个O\u003csub\u003e1\u003c/sub\u003e；108个P\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e5.5个E1；23个E2；52个O\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003e5个E\u003csub\u003e1\u003c/sub\u003e；25个E\u003csub\u003e2\u003c/sub\u003e；51个O\u003csub\u003e1\u003c/sub\u003e；108个P\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\" style=\"width: 272px;\"\u003e\n \u003cp\u003e5个E\u003csub\u003e1\u003c/sub\u003e；P2个E\u003csub\u003e2\u003c/sub\u003e；48个O\u003csub\u003e1\u003c/sub\u003e；53个O\u003csub\u003e2\u003c/sub\u003e；87个P\u003csub\u003e1\u003c/sub\u003e；98个P\u003csub\u003e2\u003c/sub\u003e；107个P\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eElectrical Logging Series 1 Principal Component Analysis of PC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1+TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eRadioactive Logging Series Principal Component Analysis Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1+TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eImaging Array Induction Logging Series Principal Component Analysis Data of PC2 and PC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1+TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eFe、Mn、Ti、Ca、Al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eCaO/MgO、(CaO+K\u003csub\u003e2\u003c/sub\u003eO+Na\u003csub\u003e2\u003c/sub\u003eO)/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e、(Fe+Al)/(Ca+Mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eFactor Analysis Data of Fe、Mn、Ti、Ca、Al、Si、(CaO+K\u003csub\u003e2\u003c/sub\u003eO+Na\u003csub\u003e2\u003c/sub\u003eO)/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e、CaO/MgO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eElectrical Logging Series 2 Principal Component Analysis Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1+TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003ePC1：5个E\u003csub\u003e1\u003c/sub\u003e；22个E\u003csub\u003e2\u003c/sub\u003e；48个O\u003csub\u003e1\u003c/sub\u003e；53个O\u003csub\u003e2\u003c/sub\u003e；87个P\u003csub\u003e1\u003c/sub\u003e；98个P\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003ePC2：5个E\u003csub\u003e1\u003c/sub\u003e；22个E\u003csub\u003e2\u003c/sub\u003e；48个O\u003csub\u003e1\u003c/sub\u003e；53个O\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eImaging Array Induction Logging Series Principal Component Analysis Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1+TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003ePC1：5个E\u003csub\u003e1\u003c/sub\u003e；22个E\u003csub\u003e2\u003c/sub\u003e；48个O\u003csub\u003e1\u003c/sub\u003e；53个O\u003csub\u003e2\u003c/sub\u003e；87个P\u003csub\u003e1\u003c/sub\u003e；98个P\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eFactor Analysis Data of Various Logging Series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eTSAM1+TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 272px;\"\u003e\n \u003cp\u003eF1和F4：5个E\u003csub\u003e1\u003c/sub\u003e；22个E\u003csub\u003e2\u003c/sub\u003e；48个O\u003csub\u003e1\u003c/sub\u003e；53个O\u003csub\u003e2\u003c/sub\u003e；87个P\u003csub\u003e1\u003c/sub\u003e；98个P\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eF2：5个E\u003csub\u003e1\u003c/sub\u003e；P2个E\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003eF3：5个E\u003csub\u003e1\u003c/sub\u003e；22个E\u003csub\u003e2\u003c/sub\u003e；48个O\u003csub\u003e1\u003c/sub\u003e；53个O\u003csub\u003e2\u003c/sub\u003e；87个P\u003csub\u003e1\u003c/sub\u003e；98个P\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote: E1 - 405 kyr eccentricity long cycle; E2 - 95.24 kyr eccentricity short cycle; O1 - 39.76 kyr obliquity cycle; O2 - 35.54 kyr obliquity cycle; P1 - 23.28 kyr precession cycle; P2 - 22 kyr precession cycle; P3 - 18.82 kyr precession cycle.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of cyclic analysis results between alternative parameters of sedimentary stratigraphic sequences and time series analysis methods of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSedimentary Stratigraphic Sequence Substitution Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Series Analysis Methods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCycle Analysis Results\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eGeophysical Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGR、AC、RL、RN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood performance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGR、AC、RL、RN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood performance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrincipal Component Analysis of R25, R4, RL, RN Logging Series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM1\u0026thinsp;+\u0026thinsp;TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC2 cycle analysis shows good performance, while PC1 analysis shows poor results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrincipal Component Analysis of SP, Rt, Rxo, CON Logging Series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC1 cycle analysis performs slightly better than PC2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrincipal Component Analysis of Radioactive Logging Series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM1\u0026thinsp;+\u0026thinsp;TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC1 cycle analysis is good, while PC2, PC3, and PC4 cycle analyses show poor results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrincipal Component Analysis of Imaging Array Induction Logging Series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM1\u0026thinsp;+\u0026thinsp;TSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePC2 and PC3 cycle analyses slightly outperform PC1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFactor Analysis of Various Logging Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF1 and F4 cycle analysis results show similar cyclic patterns, F3 shows a similar pattern to F1 and F4, but F2 cycle analysis results are relatively poor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnvironmental Magnetism Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnetic Susceptibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood performance in cycle analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGeochemical Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFe, Mn, Ti, Ca, Al, Si, CaO/MgO, (CaO\u0026thinsp;+\u0026thinsp;K\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eO)/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, (Fe\u0026thinsp;+\u0026thinsp;Al)/(Ca\u0026thinsp;+\u0026thinsp;Mg), Principal Component Analysis and Factor Analysis of Elemental Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage performance with no precession period identified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcellent cycle analysis performance with consistent results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMineral Content Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClay Minerals, Carbonate Minerals, Pyrite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage performance with no precession period identified, but the TSAM2 method cannot be used to detect the cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCore gray scale data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGray Scale Values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSAM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcellent cycle analysis performance, identifying smaller scale periods, but the TSAM1 method cannot be used to detect the cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Optimization of multiple alternative indicators and methods\u003c/h2\u003e\n \u003cp\u003eIn summary, by comparing the cycle research results of sedimentary stratigraphic sequence alternative parameters and time series analysis methods, as well as analyzing their advantages and disadvantages, an optimal ranking of the selection order for various indicators and analysis methods has been made. This is to ensure that the cycle research can avoid unnecessary detours while guaranteeing the selection of the most optimal alternative parameters and analysis methods to obtain the best and most accurate cycle analysis results.\u003c/p\u003e\n \u003cp\u003eFirst, the optimal alternative indicators are geophysical parameter data, especially single well log data such as GR, AC, RL, and RN, which can be used for cycle analysis with the TSAM1 method. Additionally, cycle research can be conducted through principal component analysis (PCA) and factor analysis of various element data obtained by mathematical processing methods. Secondly, the TSAM2 method can also be used for cycle analysis, although the processing steps may be slightly more complex than those of TSAM1. Second, in cases where well log data cannot be used for cycle analysis, environmental magnetism parameters, such as magnetic susceptibility data, can be selected, and the TSAM2 method can be applied for cycle analysis. Third, core grayscale data can be used to identify cycles by applying the TSAM2 time series analysis method, and high-resolution data can help identify smaller-scale cycles. Fourth, when other data cannot reliably support cycle analysis, single-element geochemical data such as Fe, Mn, Ti, Ca, Al, Si, or element combination data like CaO/MgO, (CaO\u0026thinsp;+\u0026thinsp;K\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eO)/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, and (Fe\u0026thinsp;+\u0026thinsp;Al)/(Ca\u0026thinsp;+\u0026thinsp;Mg) can serve as excellent alternative parameters for cycle analysis, using the TSAM2 time series analysis method to identify cycles. At the same time, data obtained through mathematical processing methods such as principal component analysis (PCA) and factor analysis of element data can also serve as alternative parameters for cycle analysis. These can be used to calibrate and verify the reliability of cycle analysis results based on single-element data or element combination data, making the cycle analysis results more convincing. In conclusion, the optimal alternative indicator for the sedimentary sequence selected in this study is geophysical parameter data. The optimal time series analysis method is as follows: Matlab data preprocessing to Past3.0 spectral analysis to Matlab one-dimensional continuous wavelet transform to power spectrum estimation to filtering analysis and extract cycle periods.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Establishment of the Astronomical Time Scale and Estimation of Sedimentation Rate\u003c/h2\u003e\n \u003cp\u003eA comparison and analysis of the results from cycle research using sedimentary stratigraphic sequence proxy parameters and time series analysis methods were conducted, highlighting their advantages and disadvantages. An optimal selection sequence for various indicators and analysis methods was made, choosing the most suitable proxy parameters and analytical methods to obtain the best and most accurate cyclic analysis results. This approach allows for the establishment of a chronological scale and the estimation of sedimentation rates.\u003c/p\u003e\n \u003cp\u003eIn the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e strata of well Niuye 1, approximately 6 long eccentricity cycles of 405 kyr, 24 short eccentricity cycles of 95.24 kyr, 53 obliquity cycles of 39.76 kyr, and 110 precession cycles of 18.82 kyr were identified (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Using the research results of Laskar on Earth\u0026apos;s orbital parameter solutions, theoretical curves for Earth\u0026apos;s orbital elements were generated (Laskar et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). The filtered analysis results of the main peak values were compared with the theoretical curves, revealing that the filtered short eccentricity cycles closely match the theoretical eccentricity curve in frequency, and there is a strong correspondence between the two. Therefore, in this study, a \u0026quot;floating\u0026quot; astronomical timescale for the Es4scs of the Niuye 1 well in the Dongying Depression was established based on the prominent 95.24 kyr short eccentricity cycle. This was combined with the absolute age of 42 Ma for the top of the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003es\u003c/sup\u003e, as determined by previous studies using paleomagnetic and volcanic rock isotope dating (Yao et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Jin et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Using the theoretical eccentricity cycle curve as the target curve and the short eccentricity cycle filter curve extracted from the natural gamma log curve as the tuning curve, the short eccentricity cycles between every two tuning lines were used to divide the strata into 24 cycles. Under the control of the time point of 42 Ma, starting from the age of the top of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003es\u003c/sup\u003e, the age values of each cycle interface were calculated. Based on these calculations, the sedimentation time for the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 was estimated to be approximately 2.286 Myr. Therefore, the absolute geological age at the bottom of the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e is 44.286 Ma. The results of this study show that the absolute age at the bottom of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1, estimated to be 44.286 Ma, is in relative agreement with the 3 Ma duration of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003es\u003c/sup\u003e (including both Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e and Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escx\u003c/sup\u003e) determined through paleomagnetic, volcanic rock isotope dating, and stratigraphic correlation. Specifically, the volcanic rock isotope age at the base of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003es\u003c/sup\u003e is 45 Ma. This indicates that the Milankovitch cycles identified in this study are accurate and reliable, thereby establishing a geological timescale for the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). This has significant implications for determining the geological age of the strata.\u003c/p\u003e\n \u003cp\u003eTo make the establishment of the geological timescale for Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in Niuye 1 well more reliable and convincing, this study selected five core samples near the top boundary of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e for Re-Os isotopic dating at the National Geological Laboratory. The key aspect of this research lies in the precise dating of the mudstone strata. Based on the current status of isotopic dating research, Re-Os isotopic dating is considered the most suitable method for mudstone strata due to its high precision and applicability to fine-grained sediments like mudstones. This approach strengthens the overall reliability of the geological timescale for Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e. By using precise Re-Os isotopic dating, combined with the latest Paleogene astronomical timescale, existing stratigraphic absolute age data from previous studies in the region, and the orbital cycle stratigraphy results from the project\u0026apos;s earlier work, a high-precision isotopic age stratigraphic profile for the study interval can be established. On this basis, the sedimentation rates for astronomical cycles at different scales can be further calculated, converting the astronomical orbital cycles from the depth domain to the time domain. The Re-Os isotopic dating results for this study are shown in Figure (Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e). The age value at the top boundary of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e is 45.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 Ma. Although the error margin is \u0026plusmn;\u0026thinsp;4.1 Ma, meaning the age could range from 41.8 to 50 Ma, the result still demonstrates consistency with previous paleomagnetic and volcanic rock isotope dating data, which also provided an age for the top of Es4s in the Dongying Depression. Therefore, using 42 Ma as the absolute geological age for the top of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1 is reliable.\u003c/p\u003e\n \u003cp\u003eTo study the vertical variation of sedimentation rates, the sediment thickness between adjacent peaks of the 95.24 kyr eccentricity short cycle and the sedimentation duration, based on the established timescale, were used to calculate the average sedimentation rate for Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e in well Niuye 1, which is 0.0661 m/kyr (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). The sedimentation rate ranges from 0.0486 to 0.0984 m/kyr. As shown in the figure, the rate increases in the middle to upper sections of Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e, between depths of 3337.07 to 3394.09 m, suggesting a more pronounced event-driven sedimentation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e(1) By integrating geophysical, geochemical, and environmental magnetism parameters such as magnetic susceptibility data and other sedimentary sequence proxies, the TSAM1 and TSAM2 methods are used to optimize the cyclic analysis methods and proxy indicators. Among the geophysical parameters, the TSAM2 method shows the best performance when applied to data such as GR, AC, RL, RN, principal component analysis of various logging series, and factor analysis of various logging series. The TSAM1 method also performs well for cyclic identification using single well log data, but its processing effect is slightly inferior to that of the TSAM2 method. Additionally, principal component analysis and factor analysis data of various logging series are not well-suited for cyclic analysis using the TSAM1 method. The TSAM2 method produces the best results for magnetic susceptibility data, while the cyclic identification effect of the TSAM1 method is inferior to that of the TSAM2 method. Among the geochemical parameters, the TSAM2 method produces the best results for data such as Fe, Mn, Ti, Ca, Al, Si, CaO/MgO, (CaO+K\u003csub\u003e2\u003c/sub\u003eO+Na\u003csub\u003e2\u003c/sub\u003eO)/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, (Fe+Al)/(Ca+Mg), as well as principal component analysis and factor analysis data of various elements. The cyclic identification effect using the TSAM1 method is generally less effective, and in some cases, it can only identify the eccentricity cycle. For mineral content parameters, the TSAM2 method is not applicable, while the TSAM1 method can only identify the eccentricity and obliquity cycles, and is unable to identify the precession cycle. When core grayscale data is used for cyclic research, the TSAM2 method provides the best results. It is capable of identifying solar activity cycles at a smaller scale than the precession cycle, down to an annual level. However, the TSAM1 method is not suitable for use in magnetic susceptibility cyclic studies. This study indicates that the optimal substitute index for sedimentary sequences is natural gamma logging data. The best time series analysis method is as follows: Matlab data preprocessing to Past3.0 spectral analysis to One-dimensional continuous wavelet transform of Matlab to power spectrum estimation to filtering analysis and extract cyclic periods.\u003c/p\u003e\n\u003cp\u003e(2) By combining the two sets of time series analysis processes, five long eccentricity cycles of 405 kyr, 22 short eccentricity cycles of 95.24 kyr, 48 obliquity cycles of 39.76 kyr, 53 obliquity cycles of 38.54 kyr, 87 precession cycles of 23.28 kyr, 98 precession cycles of 22 kyr, and 107 precession cycles of 18.82 kyr were identified in the Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e section of the Niuye1 well. The sedimentary time is approximately 2.038 Myr, and the estimated average sedimentation rate is 0.074 m/kyr. In addition to the aforementioned cycles, the use of magnetic susceptibility data also identified sedimentary cycles of 1200-1800 years, 400 years, 250 years, 200 years, and 70-90 years. The study found that for the same set of strata and data, different analysis methods can yield varying cycle analysis results. Furthermore, the periods of Earth\u0026apos;s orbital parameters are variable and not absolutely fixed. Therefore, sedimentary cycles identified using wavelet coefficients with fixed scales will inevitably differ from those classified using sedimentological indicators.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAcknowledge\u003c/h1\u003e\n\u003cp\u003eThis research was jointly supported by the National Natural Science Foundation of China project \u0026quot;Astronomical Stratigraphy Cycles and High-Resolution Sedimentary Cycle Well Log Study of Lacustrine Shale\u0026quot; (41872166) and the Exploration and Development Research Institute of Sinopec Shengli Oilfield.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLedan Yu wrote the main manuscript text and \"3 Data and Experimental Methods\" and \"4 Results and Discussion\". Jiao Wang wrote the \"2 Geological Settings\". Jun Peng wrote the \"1 Introduction\". 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Journal of Earth Sciences and Environment 43 (4): 710-723.\u003c/li\u003e\n\u003cli\u003eYao Y M., Xiu S C., Wei X L., Meng S H., Ye Y G., Diao S B (2002) Researches on the ESR geochronometry in Palaeogene of Dongying depression. Petroleum Geology and Recovery Efficiency 9 (02): 31-34+3.\u003c/li\u003e\n\u003cli\u003eYi H S., Shi Z Q., Yang W., Hui B (2010) Astronomical Periodicity Signals from Lamina Growth Rhythm Records of Lacustrine Stromatolites. Acta Sedimentologica Sinica 28 (03): 405-411.\u003c/li\u003e\n\u003cli\u003eYu L D., Peng J., Xu T Y., Han H D., Yang Y M (2024) Analysis of Organic Matter Enrichment and Influences in Fine-grained Sedimentary Strata in Saline Lacustrine Basins of Continental Fault Depressions: Case study of the upper sub-section of the upper 4th member of the Shahejie Formation in the Dongying Sag. 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Acta Sedimentologica Sinica 40 (03): 801-812.\u003c/li\u003e\n\u003cli\u003eZhao K (2019) Characteristics and genesis analysis of carbonate lamina in the upper fourth member and the lower third member of Shahejie Formation in Dongying Depression, Bohai Bay Basin, China University of Geosciences, Wuhan.\u003c/li\u003e\n\u003cli\u003eZhao K, Du X B, Lu Y C, Xiong S P., Wang Y (2019) Are light-dark coupled laminae in lacustrine shale seasonally controlled? A case study using astronomical tuning from 42. 2 to 45. 4 Ma in the Dongying Depression, Bohai Bay Basin, eastern China. Palaeogeography, Palaeoclimatology, Palaeoecology, 528: 35-49. https://doi.org/10.1016/j.palaeo.2019.04.034\u003c/li\u003e\n\u003cli\u003eZong Y (2018). High frequency cyclic sequence of UpperPermian coal measures in Panguan area ofWestern Guizhou Province. China University of Mining and Technology.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"geomechanics-and-geophysics-for-geo-energy-and-geo-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gggg","sideBox":"Learn more about [Geomechanics and Geophysics for Geo-Energy and Geo-Resources](http://link.springer.com/journal/40948)","snPcode":"40948","submissionUrl":"https://submission.nature.com/new-submission/40948/3","title":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"lacustrine mud shale, astronomical orbital period, substitution index of deposition sequence, time series analysis method, preferred, Dongying Sag","lastPublishedDoi":"10.21203/rs.3.rs-6651998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6651998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, great progress has been made in fine-grain sedimentary oil and gas exploration. However, the rotary division of fine grain sedimentary strata is an important foundation of fine grain deposition oil and gas exploration. With the development of research, the complexity and differences of indicators and methods in the rotary division have led to the increasing contradiction of research results. The lacustrine shale from the upper interval of the upper sub-member of the 4th member of the Paleogene Shahejie Formation（Es\u003csub\u003e4\u003c/sub\u003e\u003csup\u003escs\u003c/sup\u003e）in Well Niuye 1，Dongying Sag，Jiyang Depression，Bohai Bay Basin was used as the study object. This study was conducted by combining testing methods and techniques such as X-ray diffraction whole-rock analysis, X-ray fluorescence spectroscopy, TOC analysis, and magnetic susceptibility testing. Based on the data of geophysical, geochemical, environmental magnetic parameters magnetic susceptibility, mineral content and core grayscale, and using TSAM 1 and TSAM 2 methods to optimize the rotary analysis method and sedimentation series substitution indexes. The research results are shown as follows. First, The TSAM 2 method has the best results for GR, AC, RL, RN, log series principal component analysis and log series factor analysis. Second, The TSAM 2 method works the best for the magnetic susceptibility data. Third, TSAM 2 was best for Fe, Mn, Ti, Ca, Al, Si, CaO / MgO, (CaO + K\u003csub\u003e2\u003c/sub\u003eO + Na\u003csub\u003e2\u003c/sub\u003eO) / MgO Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, (Fe + Al) / (Ca + Mg), principal component analysis and factor analysis of each element data. Fourth, In the core grayscale data, the TSAM 2 method has the best effect. The data identifies smaller solar cycles than the transition cycle scale. Fifth, The optimal deposited sequence substitutability index is the natural gamma logging data, and the optimal time series analysis method is TSAM 1. Sixth, The study strata identified 5 long eccentricity cycle of 405 kyr , 22 short eccentricity cycle of 95.24kyr , 48 obliquity cycle of 39.76kyr, 53 obliquity cycle of 38.54kyr , 87 precession cycle of 23.28kyr, 98 precession cycle of 22kyr and 107 precession cycle of 18.82kyr. The study concluded that the deposition time was roughly 2.038Ma, and the average deposition rate is estimated to be 0.074m / kyr. This research is helpful to promote the research of scientific problems such as the establishment of rotary geological age and the determination of terrestrial rotation level. Meanwhile, it has a good application value and research prospect for the development of fine-grain sedimentary spiral stratigraphy.\u003c/p\u003e","manuscriptTitle":"Analysis Method and Indicator Optimization of Astronomical Cycles in Lacustrine Fine-Grained Sedimentary Strata- A case study of Es4scs in Well Niuye 1, Dongying Sag, Jiyang Depression, Bohai Bay Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 09:02:24","doi":"10.21203/rs.3.rs-6651998/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-16T04:26:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T01:32:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T08:57:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156727987559356993694986250407859561858","date":"2025-11-27T00:44:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88666370609152192122021935337596638442","date":"2025-11-26T08:31:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145811340851020352098100001447017441758","date":"2025-10-26T04:11:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234430814698000283978520349615405577319","date":"2025-09-16T12:00:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T14:00:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254003276912580735053373908235619841489","date":"2025-08-04T05:44:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T13:04:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T12:00:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T03:20:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources","date":"2025-05-13T06:17:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"geomechanics-and-geophysics-for-geo-energy-and-geo-resources","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gggg","sideBox":"Learn more about [Geomechanics and Geophysics for Geo-Energy and Geo-Resources](http://link.springer.com/journal/40948)","snPcode":"40948","submissionUrl":"https://submission.nature.com/new-submission/40948/3","title":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"88ee64e1-8969-4d15-a665-e2f4f0b2b903","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:05:23+00:00","versionOfRecord":{"articleIdentity":"rs-6651998","link":"https://doi.org/10.1007/s40948-026-01154-2","journal":{"identity":"geomechanics-and-geophysics-for-geo-energy-and-geo-resources","isVorOnly":false,"title":"Geomechanics and Geophysics for Geo-Energy and Geo-Resources"},"publishedOn":"2026-04-16 15:58:37","publishedOnDateReadable":"April 16th, 2026"},"versionCreatedAt":"2025-07-28 09:02:24","video":"","vorDoi":"10.1007/s40948-026-01154-2","vorDoiUrl":"https://doi.org/10.1007/s40948-026-01154-2","workflowStages":[]},"version":"v1","identity":"rs-6651998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6651998","identity":"rs-6651998","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}