Many multivariate statistical techniques have the ability to handle large data sets or a great number of parameters. Therefore, these multivariate statistical approaches are widely used in clastic ...sedimentology for facies analysis. Furthermore, most of the techniques which try to separate more or less homogeneous subsets can be subjective. This subjectivity raises several questions about the significance and confidence of clustering. The goal of this study is to optimize clustering and to evaluate the proper number of clusters needed in order to describe sedimentary and lithological facies through common characteristics. Also, with the interpretation of the clusters, the parametrized geometry adds further but quasi-subjective information to a 3D geological model. Two assumptions must be met: (1) well-definable geometries must correspond to the architectural elements (2) it is assumed that exactly one sedimentary or lithological facies belongs to each structural element and the flow properties are determined by these structural elements. This approach was applied to the clastic depositional data from a Miocene hydrocarbon reservoir (Algyő field, Hungary) to demonstrate the fidelity of the clustering method yielding an optimum of five cluster facies. The revealed clusters represent lithological characteristics within a (delta fed) submarine fan system. The paper deals with two stressed clusters in particular, showing sinusoid channels which were recognizable and measureable using parametrisation.
Traditional techniques of identification of a depositional body from core data are costly and sometimes difficult to extrapolate to uncored wells. However, application of Kohonen’s Self Organized Map ...(SOM) approach may be regarded to be a potential method for pattern recognition problems. A combination of Indicator Kriging and SOM for log-porosity and sand content data coming from quantitative well-log interpretations is used for identifying the spatial pattern of some delta-plain sub-environments. The basic high-dimensional property fields are defined by 3D shapes of well known depositional facieses. Many parameters as log-porosity and sand content data can be used to determine geo-property as a lithological pattern using SOM. This step of method can discover spatial patterns as clusters in unstructured data set because SOM is based on clustering algorithm. However, this approach not necessarily makes sure, that the resulting disjunctive clusters can show any meaningful depositional geometry. So at last the final geometry is given using Indicator Kriging method, which uses threshold values derived from property values of clusters. <-->Traditional techniques to identify a depositional body from core data are costly and sometimes difficult to extrapolate to uncored wells. The application of Kohonen’s Self Organized Map (SOM) approach may be useful for the interpretation of a depositional rock body through well-log data. SOM is based on a clustering algorithm and this method can be used to discover spatial patterns occurring as clusters in unstructured data sets. An example of the application of SOM is presented whereby clusters through SOM can indicate the contours of well-known depositional patterns such as sub-environments.
The present paper aims to introduce the current problems of geomathematics along with giving on overview on the papers published in the special issue covering the Croatian-Hungarian Geomathematical ...Congress of 2015 in Hungary.
The present paper aims to introduce the current problems of geomathematics along with giving on overview on the papers published in the special issue covering the Croatian-Hungarian Geomathematical ...Congress of 2015 in Hungary.
During the past few decades a huge number of papers have introduced different multivariate statistical methods and workflows to identify subsurface facies analysis. Most of them have relied on ...clustering the objects in the sample, but few (if any) have tried to use these classifying methods under the surface combined with lateral extension of cluster members. In fact, this approach can be expected to have significant uncertainty because of the scattered lateral distribution of sample points (wells). This dissertation aimed to contribute this issue by addressing several main points: (1) cluster separation using neural network technique; (2) the lateral estimation of point-like qualitative information of cluster members using indicator kriging (IK); (3) the interpretation of the geometry presented by IK; (4) a comparison of the efficiency of UNN and K-means clustering on the basis of results provided by the previous three analyses.There are many, widely used clustering techniques but in this dissertation the separation of subsets, based on a neural network approach, the so-called Kohonen network was demonstrated. This method was applied because neural network cluster separation requires associative ability, learning ability and non-linear separation techniques. Often, a database cannot be divided in a linear way. This may be the reason that in some cases the separation procedures misclassify at relatively large rates. Using a suitable non-linear transformation these linked clusters can be separated.The Kohonen neural network is a non-linear separation technique. The K-means approach is regarded the most similar to the Kohonen clustering. But the K-means is one of from classical clustering techniques. Some papers have dealt with their comparison using statistical tests and found that K-means sometimes failed to find any reasonable clusters. In this study a comparison of these two methods relies on the results of variance analysis. Clustering seeks to minimise within-group variance (WGV) and maximise between-group variance (BGV) and it can rarely reach a substantial difference between them. The difference between WGV and BGV can demonstrate the suitability of cluster results. The relatively low WGV and larger BGV mean that cluster analysis has a number of heterogeneous groups with homogeneous contents.A comparison of data separation by Kohonen neural network and K-means algorithm pointed out that: (1) UNN is able to recognise clusters as facies even in such a situation where K-means clustering techniques fail to find any reasonable depositional units; (2) one of the advantages of using UNN in facies analysis is that its cluster-forming ‘capacity’ is selfregulated, which is why it is more efficient than ‘classic’ clustering.The applied Kohonen neural network is an unsupervised neural network. This is also an analogy of the manner by which the human brain can logically arrange data, and new information. This is a kind of associative memory, which supports the systematic organisation process without any external help. To this end it was used as a clustering process to separate the cluster facies in the data space.The results of clustering mean only categorical information in well points.
Metoda združivanja (engl. clustering) i indikatorski kriging standardni su istraživački alati u rješavanju geoloških problema, a u ovom je radu prikazan primjer identifikacije taložnih okoliša ...koristeći dva postupka združivanja i indikatorski kriging. Združivanje se temelji na principu Kohonenove neuronske mreže (engl. Kohonen's neural network) i hierarhijskom aglomerativnom združivanju. Rezultatom se može vizualizirati geometrija taložnih podokoliša te interpretirati pomoću vjerojatnosti dobivene za svaki klaster indikatorskim krigingom. U tom se slučaju združivanje može definirati kao metoda za prostorno prepoznavanje uzorka. Istraživano područje prikazano u radu nalazi se u Savskoj depresiji. Sedimenti u polju neogenske su i kvartarne starosti, a analizirane stijene pripadaju gornjem panonu, kada je na sedimentaciju utjecao paleoreljef pred-neogenske podine te mehanizmi turbiditnih sustava.
A limited number of studies have focused on the mutational landscape of breast cancer in different ethnic populations within Europe and compared the data with other ethnic groups and databases. We ...performed whole-genome sequencing of 63 samples from 29 Hungarian breast cancer patients. We validated a subset of the identified variants at the DNA level using the Illumina TruSight Oncology (TSO) 500 assay. Canonical breast-cancer-associated genes with pathogenic germline mutations were
and
. Nearly all the observed germline mutations were as frequent in the Hungarian breast cancer cohort as in independent European populations. The majority of the detected somatic short variants were single-nucleotide polymorphisms (SNPs), and only 8% and 6% of them were deletions or insertions, respectively. The genes most frequently affected by somatic mutations were
(31%),
(34%),
(18%), and
(34%). Copy number alterations were most common in the
,
,
, and
genes. For many samples, the somatic mutational landscape was dominated by mutational processes associated with homologous recombination deficiency (HRD). Our study, as the first breast tumor/normal sequencing study in Hungary, revealed several aspects of the significantly mutated genes and mutational signatures, and some of the copy number variations and somatic fusion events. Multiple signs of HRD were detected, highlighting the value of the comprehensive genomic characterization of breast cancer patient populations.
PDF
