Paleo-megalake termination in the Quaternary: Paleomagnetic and water-level evidence from south Bohai Sea, China

Long, Yi; Deng, Chenglong; Xu, Xingyong; Yu, Hongjun; Qiang, Xiaoke; Jiang, Xingyu; Chen, Yanping; Su, Qiao; Chen, Guangquan; Ping, Li; Ge, Junyi; Li, Yan

doi:10.1016/j.sedgeo.2015.01.005

Cited by 35 publications

(20 citation statements)

References 34 publications

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“…According to Table 3 and Figure 9, we divide the Sample network model into three clusters calculated by the Mini Batch K-means algorithm. Yi et al divided the sedimentary environment of Lz908 through a variety of indices in the representative manuscripts [12,16]. Compared with their results, the three clusters correspond three sedimentary environments: marine, fluvial, and lacustrine.…”

Section: Network Characteristic Analysis Of Clustering Results Andmentioning

confidence: 99%

“…The grain-size analysis is one of the basic tools for classifying sedimentary environments, an analysis which can provide important clues to the provenance, transport history, and depositional conditions [12]. In general, the representative statistical parameters of grain-size analysis involve median, mode, mean, separation parameter, skewness, and kurtosis [13].…”

Section: Introductionmentioning

confidence: 99%

“…In general, the representative statistical parameters of grain-size analysis involve median, mode, mean, separation parameter, skewness, and kurtosis [13]. During the last few decades, two computing methods of grain-size parameters were developed: the graphical method and the moment method [12]. Blott and Pye (2011) presented that these two analysis methods had some advantages and disadvantages in computing sediment grain-size samples with various parameters.…”

Section: Introductionmentioning

confidence: 99%

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Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm

Zhu

Jia

et al. 2018

Geofluids

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During the last several decades, researchers have made significant advances in sedimentary environment interpretation of grain-size analysis, but these improvements have often depended on the subjective experience of the researcher and were usually combined with other methods. Currently, researchers have been using a larger number of data mining and knowledge discovering methods to explore the potential relationships in sediment grain-size analysis. In this paper, we will apply bipartite graph theory to construct a Sample/Grain-Size network model and then construct a Sample network model projected from this bipartite network. Furthermore, we will use the Mini Batch K-means algorithm with the most appropriate parameters (reassignment ratio ϵ=0.025 and mini batch = 25) to cluster the sediment samples. We will use four representative evaluation indices to verify the precision of the clustering result. Simulation results demonstrate that this algorithm can divide the Sample network into three sedimentary categorical clusters: marine, fluvial, and lacustrine. According to the results of previous studies obtained from a variety of indices, the precision of experimental results about sediment grain-size category is up to 0.92254367, a fact which shows that this method of analyzing sedimentary environment by grain size is extremely effective and accurate.

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Section: Network Characteristic Analysis Of Clustering Results Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm

Zhu

Jia

et al. 2018

Geofluids

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“…It is crucial to set up a well-constrained chronology of the transgression-regression series for better geological understanding. Paleomagnetic dating results showed that three transgressive phases (T-1, T-2, and T-3 from top to bottom) occurred in the Bohai Coast (Yao et al, 2014;Yi et al, 2015;Liu et al, 2016).…”

Section: Introductionmentioning

confidence: 98%

Transgressive Events since the Late Pleistocene in the Yellow River Delta: Grain‐size Distribution and Palynological Results

Lü

Liu

Huang

et al. 2020

Acta Geologica Sinica (Eng)

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This study deals with the relationship between sea‐level changes and paleoclimatic fluctuations based on the analysis of stratigraphy, grain sizes, palynology, and radiometric dating of the Yellow River delta since the Late Pleistocene. Evidence from the sedimentary record, grain sizes, and pollen provides a paleoenvironmental history of the Late Pleistocene from the boreholes of the delta. Based on a combination of grain‐size analysis with lithological studies, marine deposit units contain the intervals of 13.85–16.9, 18.5–19.69, 27.9–34.8, 36.4–37.2, 48.4–51.6, and 54.1–55.9 m, and transitional facies units contain the intervals of 10.25–13.85, 16.9–18.5, 19.69–27.9, 34.8–36.4, 37.2–48.4, 51.6–54.1, and 55.9–60 m, compared with fluvial (terrestrial facies) deposit units (3.36–10.25 m). Based on pollen analysis and pollen assemblages, there were three warm‐wet periods from 9.1–0.16 ka BP, 16.1–60 ka BP, and 90.1–94.6 ka BP From the top to the bottom of the borehole, the paleoclimate has an evident fluctuation: warm and moist (Holocene Optimum) —cool and dry (Younger Dryas Event)—mild semi cool—cool and dry—warm and moist. There were three warm‐wet periods from 9.1–0.16 ka BP, 16.1–60 ka BP, and 90.1–94.6 ka BP, corresponding to the Holocene Optimum stage, MIS 3, and MIS 5, respectively. The warm period allowed monsoonal evergreen and broadleaved deciduous forests that corresponded to Holocene hypsithermal climatic conditions and the Late Pleistocene climatic Optimum. Three warm‐wet periods occurred in marine deposit units from 9.1–0.16 ka BP, 60.1–16.1 ka BP, and 94.6–90.1 ka BP. These periods correspond to the Cangzhou transgression, Xianxian transgression, and Huanghua transgression, respectively. From 90.1–60.1 ka BP, 17.5–9.1 ka BP, and 0. 16 ka BP–1855 AD, three dry and cold phases are recognized. The phases indicate the fluvial (flood plain) sedimentary environment, corresponding to cooler and mild dry periods based on palynological results and grain‐size distribution.

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“…Alterations of marine and terrestrial sediment strata were recognized from numerous sediment cores (e.g. Zhao et al, 1978;Yao et al, 2006;Yi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Quartz OSL and K-feldspar post-IR IRSL dating of sand accumulation in the Lower Liao Plain (Liaoning, NE China)

Tsukamoto

Frechen

2017

Geochronometria

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Abstract:The timing of the formation of extensively distributed sand dunes in the Bohai coastal area and its forcing factors are poorly understood. In this study, the chronology of a well-preserved sand dune located in Panjin Forest Park (PJ) in the Lower Liao Plain (LLP) is investigated using quartz optically stimulated luminescence (OSL) and K-feldspar postinfrared (IR) infrared stimulated luminescence (IRSL) (pIRIR) dating. For the pIRIR measurements, the combination of preheating at 180°C and pIRIR stimulation at 150°C (pIRIR 150 ) is exploited. The quartz results show that the sand dune accumulated from c. 120 a (1890 AD) to c. 70 a (1940 AD) before present, and the underlying sandy soil sediments deposited from c. 5.0 ka to c. 0.13 ka as marsh sediment after the sea level highstand since the mid-Holocene. From the evidence in historical coastline records, the PJ sand dune is an inland sand dune and not a coastal sand dune. Based on further information of climate and temperature change after the Little Ice Age (LIA) and human activity in northeastern China, we conclude that the PJ sand dune accumulation was very likely impacted by the immigrants and land reclamation at the end of Qing dynasty. The fading corrected IR 50 ages, the apparent and fading corrected pIRIR 150 ages are consistent with quartz ages for two sandy soil samples but overestimate those for six sand samples. The overestimation of the feldspar ages is derived from the residual signal which has not been bleached before burial. The offset obtained from the difference between the quartz OSL and the feldspar pIRIR 150 ages are ~20-160 a (predicted residual dose: ~0.08-0.60 Gy), whereas the measured residual dose after bleaching 4 h in a solar simulator yielded age overestimation of ~10-40 a (~0.05-0.16 Gy). The age discrepancy calculated from the predicted residual was larger than those obtained from the laboratory measured residuals. We conclude that the pIRIR 150 of aeolian sediment is applicable for samples older than ~1000 years where the effect of the residual dose become negligible.

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Paleo-megalake termination in the Quaternary: Paleomagnetic and water-level evidence from south Bohai Sea, China

Cited by 35 publications

References 34 publications

Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm

Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm

Transgressive Events since the Late Pleistocene in the Yellow River Delta: Grain‐size Distribution and Palynological Results

Quartz OSL and K-feldspar post-IR IRSL dating of sand accumulation in the Lower Liao Plain (Liaoning, NE China)

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