scholarly journals Holocene Sedimentary Environment in HP1 Core at Duong Kinh, Haiphong

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Nguyen Thi Thu Cuc ◽  
Nguyen Thuy Duong ◽  
Nguyen Thi Minh Phuong ◽  
Doan Dinh Lam ◽  
Vu Van Loi ◽  
...  
Keyword(s):  
Grain Size ◽  
Coastal Area ◽  
Sedimentary Environment ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Diatom Species ◽  
Delta Front ◽  
Environment Change ◽  
Delta Plain

Holocene environment change in Hai Phong coastal area was reconstructed based on diatom and grain-size analysis in the HP1 core at Duong Kinh, Hai Phong. 52 diatom species were identified and divided in five diatom ecozones by changing of four diatom groups including marine planktonic, brackish planktonic, brackish benthic and freshwater one. The sedimentary environment at the Hai Phong coastal area was estuary- bay condition in the Flandrian trangression (Z1, Z2 and Z3 Unit). Deltaic environment changed from prodelta (Z4), delta front (Z5) to delta plain (Z6 and Z7) corresponding to the Flandrian regression.

Sedimentary environment prediction of grain-size data based on machine learning approach

2020 ◽  
Vol 8 (3) ◽  
pp. SL71-SL78
Author(s):  
Qiao Su ◽  
Yanhui Zhu ◽  
Fang Hu ◽  
Xingyong Xu
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Prediction Model ◽  
Sedimentary Environment ◽  
Machine Learning Algorithms ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Data Set ◽  
Sedimentary Environments ◽  
Size Data

Grain size is one of the most important records for sedimentary environment, and researchers have made remarkable progress in the interpretation of sedimentary environments by grain size analysis in the past few decades. However, these advances often depend on the personal experience of the scholars and combination with other methods used together. Here, we constructed a prediction model using the K-nearest neighbors algorithm, one of the machine learning methods, which can predict the sedimentary environments of one core through a known core. Compared to the results of other studies based on the comprehensive data set of grain size and four other indicators, this model achieved a high precision value only using the grain size data. We have also compared our prediction model with other mainstream machine learning algorithms, and the experimental results of six evaluation metrics shed light on that this prediction model can achieve the higher precision. The main errors of the model reflect the length of the conversation area of sedimentary environment, which is controlled by the sedimentary dynamics. This model can provide a quick comparison method of the cores in a similar environment; thus, it may point out the preliminary guidance for further study.

Analysis on Grain-Size Characteristics and Sedimentary Environment of Conglomerate Reservoirs in Ying-4 Section of Xingcheng Gas Field of Songliao Basin

2013 ◽  
Vol 734-737 ◽  
pp. 286-289
Author(s):  
Yu Sun ◽  
Xiu Li Zhang ◽  
Jin Yan Zhang ◽  
Chen Chen
Keyword(s):  
Grain Size ◽  
Sedimentary Environment ◽  
Songliao Basin ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Gas Field ◽  
Environment Analysis ◽  
Two Dimension ◽  
Sedimentary Characteristics ◽  
Fan Deltas

Aimed at difficulty of conglomerate in Ying-4 Section of Xingcheng Gas Field, Songliao Basin, which it is impossible to be carried out, using routine granularity analysis methods, and the iconic grain-size analysis is carried out. According to iconic two-dimension grain-size analysis, and contacting to fundamental sedimentary characteristics of conglomerate, sedimentary environment analysis is carried out. It is thought that this area is Shallow gravel-bed braided fluvial fan deltas, to braided fluvial delta transition, which supported an important geological foundation for the next disposition of exploration in Xingcheng Gas Field.

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

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qiao Su ◽  
Yanhui Zhu ◽  
Yalin Jia ◽  
Ping Li ◽  
Fang Hu ◽  
...  
Keyword(s):  
Grain Size ◽  
Network Model ◽  
Subjective Experience ◽  
Sedimentary Environment ◽  
Grain Size Analysis ◽  
Size Analysis ◽  
Bipartite Network ◽  
Size Category ◽  
Sediment Grain Size ◽  
Environment Analysis

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.

Grain size analysis and characterization of sedimentary environment along Thiruchendur coast, Tamilnadu, India

2012 ◽  
Vol 6 (12) ◽  
pp. 4717-4728 ◽  
Author(s):  
V. C. Rajganapathi ◽  
N. Jitheshkumar ◽  
M. Sundararajan ◽  
K. H. Bhat ◽  
S. Velusamy
Keyword(s):  
Grain Size ◽  
Sedimentary Environment ◽  
Grain Size Analysis ◽  
Size Analysis

Grain size analysis and characterization of sedimentary environment along the Atlantic Coast, Kenitra (Morocco)

2020 ◽  
pp. 1-8
Author(s):  
Hind Azidane ◽  
Boko Michel ◽  
Mohamed El Bouhaddioui ◽  
Soufiane Haddout ◽  
Bouchaib Magrane ◽  
...  
Keyword(s):  
Grain Size ◽  
Atlantic Coast ◽  
Sedimentary Environment ◽  
Grain Size Analysis ◽  
Size Analysis
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