A quantitative analysis on the sources of dune sand in the Hulun Buir Sandy Land:application of stepwise discriminant analysis (SDA) to the granulometric data

Quantitatively determining the sources of dune sand is one of the problems necessarily and urgently to be solved in aeolian landforms and desertification research. Based on the granulometric data of sand materials from the Hulun Buir Sandy Land, the paper employs the stepwise discriminant analysis technique (SDA) for two groups to select the principal factors determining the differences between surface loose sediments. The extent of similarity between two statistical populations can be described quantitatively by three factors such as the number of principal variables, Mahalanobis distance D 2 and confidence level 琢for F-test. Results reveal that: 1) Aeolian dune sand in the region mainly derives from Hailar Formation (Q 3 ), while fluvial sand and palaeosol also supply partially source sand for dunes; and 2) in the vicinity of Cuogang Town and west of the broad valley of the lower reaches of Hailar River, fluvial sand can naturally become principal supplier for dune sand.

Identification of Geographical Origins for Blackcurrant Cultivars Based on Amino Acid Compositions

In this study,a total of 36 blackcurrant(Ribes nigrum L.)cultivars grown in the Northeast of China were selected,including 12 cultivars introduced from Russia,10 from Poland and the rest from local areas.The physicochemical properties and amino acid compositions of these varieties were studied,and the geographical origins of blackcurrants were tracked by multivariate statistical analysis.A total of 23 amino acids were detected in all cultivars,which were rich in glutamine,glutamate,aspartate,asparagine,α-alanine,γ-aminobutyric acid,valine and serine.The content of the total amino acids in these cultivars was from 31.21 mg•100 g-1 to 319.40 mg•100 g-1.Stepwise linear discriminant analysis(SLDA)was introduced to perform satisfactory categorization for blackcurrant cultivars,which achieved a success rate of 88.9%for the identification of geographical origins.These results suggested that the compositions of amino acids in blackcurrants could effectively predict geographical origins.

Using endocarp-remains of seeds of wild apricot Prunus armeniaca to identify rodent seed predators

Some rodent-dispersed seeds have a hard seed-coat(e.g.woody endocarp).Specific scrapes or dental marks on the hard seed-coat left by rodents when they eat these seeds can be used to identify seed predators.In this study we measured the morphological traits of endocarp-remains of seeds of wild apricot Prunus armeniaca used by Chinese white-bellied rats Niviventor confucianus and Korean field mice Apodemus peninsulae.We established their Fisher's linear discriminant functions to separate endocarp-remains between the two predators.A total of 90.0% of the endocarp-remains left by Korean field mice and 88.0% of those left by Chinese white-bellied rats were correctly classified.The overall percentage of correct classification was 89.0%.One hundred and sixty endocarp-remains of unknown what species predated them were classified using the functions.The method may allow more reliable quantitative studies of the effects of Chinese white-bellied rats and Korean field mice on seed consumption and dispersal of wild apricot and this study might be used for reference in other studies of seed predators identification on hard seeds.

Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.