Some methods for statistical analysis of multimodal distributions and their application to grain-size data

1976 ◽  
Vol 8 (3) ◽  
pp. 267-282 ◽  
Author(s):  
Malcolm W. Clark
Keyword(s):  
Grain Size ◽  
Statistical Analysis ◽  
Multimodal Distributions ◽  
Size Data

The Description and Classification of Grain Size Data from Ancient and Modern Shoreline Sands at Lepcis Magna using Log Skew Laplace Distributions

1990 ◽  
Vol 21 ◽  
pp. 49-59 ◽  
Author(s):  
N. R. J. Fieller ◽  
E. C. Flenley ◽  
D. D. Gilbertson ◽  
C. O. Hunt
Keyword(s):  
Particle Size ◽  
Grain Size ◽  
Depositional Environments ◽  
Field Observations ◽  
Size Distributions ◽  
Particle Size Distributions ◽  
Multimodal Distributions ◽  
A New Technique ◽  
Size Data

AbstractThe description and analysis of particle size distributions using log skew Laplace distributions is a new technique designed to overcome various mathematical and computational problems associated with other approaches. This paper presents an application of the method. In particular, it describes fitting log skew Laplace distributions to modern and ancient shoreline sands from Lepcis Magna (near Horns), Tripolitania, with the purpose of discriminating between modern environments and so classifying the ancient samples. Satisfactory discrimination was not always achieved between some of the modern ‘calibration’ shoreline sand samples of known provenance — possibly as a result of the presence of multimodal distributions. One layer in the harbour-infill sequence, previously of unknown provenance, was shown to have collected at, or close to, an ancient shoreline which developed within the ancient harbour. The majority of the ‘ancient’ samples of unknown depositional environments which were excavated from exposures in the Romano-Libyan harbour-infill sands, were shown by this analysis to be of neither beach nor aeolian origin. This conclusion supports field observations which suggested that the greater part of the harbour-infill sequence represented reworked dune palaeosols, developing dune soils and fluvial and lagoonal facies.

scholarly journals Paleoenvironments from robust loess stratigraphy using high-resolution color and grain-size data of the last glacial Krems-Wachtberg record (NE Austria)

2020 ◽  
Vol 248 ◽  
pp. 106602
Author(s):  
Tobias Sprafke ◽  
Philipp Schulte ◽  
Simon Meyer-Heintze ◽  
Marc Händel ◽  
Thomas Einwögerer ◽  
...  
Keyword(s):  
Grain Size ◽  
High Resolution ◽  
Last Glacial ◽  
Size Data ◽  
The Last Glacial

As-Cast Grain Size of Aluminum Alloys Refined by Al-Ti-B Master Alloys

2019 ◽  
Author(s):  
Mark Easton ◽  
David St John ◽  
Prasad Arvind
Keyword(s):  
Grain Size ◽  
Al Alloys ◽  
Good Ability ◽  
Refined Microstructure ◽  
Critical Technology ◽  
Master Alloys ◽  
Near Net Shape ◽  
Empirical Approaches ◽  
Cast Parts ◽  
Size Data

Grain refinement is a critical technology for the successful production of cast parts; whether that be preforms such as extrusion billet or rolling slab, or near net-shape castings. A refined microstructure has many advantages with reduced defects and improved mechanical properties. This article describes the approaches to the prediction of grain size in Al-alloys refined by Al-Ti-B master alloys. Included are empirical approaches based on the generation and analysis of grain size data, the development of analytical equations, and the use of finite element approaches to the prediction of grain sizes. It is clear that researchers have a good ability to predict grain size of Al-alloys grain refined by Al-Ti-B master alloys, although there are still some outstanding challenges, particularly in considering more extreme solidification conditions and poisoning of the master alloys.

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.

scholarly journals Statistical Analysis of Grain-Scale Effects of Twinning Deformation for Magnesium Alloys under Cyclic Strain Loading

Materials ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2454
Author(s):  
Damin Lu ◽  
Shuai Wang ◽  
Yongting Lan ◽  
Keshi Zhang ◽  
Wujun Li ◽  
...  
Keyword(s):  
Grain Size ◽  
Statistical Analysis ◽  
Magnesium Alloys ◽  
Probability Function ◽  
Volume Fraction ◽  
Scale Effects ◽  
Low Cycle Fatigue ◽  
Cyclic Strain ◽  
Statistical Distribution ◽  
Twin Thickness

To reveal the relationship between grain size and twinning deformation of magnesium alloys under cyclic strain, this study carried out a group of strain-controlled low-cycle fatigue experiments and statistical analysis of microstructures. Experimental results show that the shape of the hysteresis loop exhibits significant asymmetry at different strain amplitudes, and the accumulation of residual twins plays an important role in subsequent cyclic deformation. For the different strain amplitudes, the statistical distribution of the grain size of magnesium alloy approximately follows the Weibull probability function distribution, while the statistical distribution of twin thickness is closer to that of Gaussian probability function. The twin nucleation number (TNN) increases with the increase of grain size, but there is no obvious function relationship between twin thickness and grain size. Twin volume fraction (TVF) increases with the increase of grain size, which is mainly due to the increase of TNN. This work can provide experimental evidence for a more accurate description of the twinning deformation mechanism.

A basic end-member model algorithm for grain-size data of marine sediments

2020 ◽  
Vol 236 ◽  
pp. 106656 ◽  
Author(s):  
Xiaodong Zhang ◽  
Hongmin Wang ◽  
Shumei Xu ◽  
Zuosheng Yang
Keyword(s):  
Grain Size ◽  
Marine Sediments ◽  
Member Model ◽  
Model Algorithm ◽  
Size Data ◽  
End Member Model
Sign in / Sign up

Export Citation Format

Share Document