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.

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.

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.

Identification of Three Common Loliginidae Squid Species in the South China Sea by Analyzing Hard Tissues with Geometric Outline Method

The hard tissues of squid can provide important information for species identification. In this study, we used statolith and beak to identify three squid species including Uroteuthis duvaucelii, Loliolus beka, and U. edulis in the South China Sea. Because of the highly overlapping habitat and similar body morphology of the three squid species, we explored four different ways to identify them, by using statolith, upper beak, lower beak and a combination of statolith and beak. An outline geometric morphometric method and stepwise discriminant analysis were used to evaluate the most suitable method for the identification. We found that the combination of statolith and beak had the highest cross validation rate that was 75.0%, 87.5% and 88.7% for U. duvaucelii, L. beka and U. edulis, respectively. Using two beaks had similar results and the lowest cross validation rate was 60.0%, 50.0%, and 73.7% for the upper beak, 46.9%, 58.5% and 75.3% for the lower beak of U. duvaucelii, L. beka and U. edulis, respectively. Analyzing with the statolith had moderate cross validation which was 72.2%, 80.0%, and 87.7% for U. duvaucelii, L. beka and U. edulis, respectively. From the results it is suggested when the entire body of a squid is available, a combination of statolith and beak should be used for the identification. When only one hard tissue is available, species identification can be subjected to large errors.

Auto recognition of carbonate microfacies based on an improved back propagation neural network

Though traditional methods could recognize some facies, e.g. lagoon facies, backshoal facies and foreshoal facies, they couldn't recognize reef facies and shoal facies well. To solve this problem, back propagation neural network(BP-ANN) and an improved BP-ANN with better stability and suitability, optimized by a particle swarm optimizer(PSO) algorithm(PSO-BP-ANN) were proposed to solve the microfacies' auto discrimination of M formation from the R oil field in Iraq. Fourteen wells with complete core, borehole and log data were chosen as the standard wells and 120 microfacies samples were inferred from these 14 wells. Besides, the average value of gamma, neutron and density logs as well as the sum of squares of deviations of gamma were extracted as key parameters to build log facies(facies from log measurements)-microfacies transforming model. The total 120 log facies samples were divided into 12 kinds of log facies and 6 kinds of microfacies, e.g. lagoon bioclasts micrite limestone microfacies, shoal bioclasts grainstone microfacies, backshoal bioclasts packstone microfacies, foreshoal bioclasts micrite limestone microfacies, shallow continental micrite limestone microfacies and reef limestone microfacies. Furthermore, 68 samples of these 120 log facies samples were chosen as training samples and another 52 samples were gotten as testing samples to test the predicting ability of the discrimination template. Compared with conventional methods, like Bayes stepwise discrimination, both the BP-ANN and PSO-BP-ANN can integrate more log details with a correct rate higher than 85%. Furthermore, PSO-BP-ANN has more simple structure, smaller amount of weight and threshold and less iteration time.