scholarly journals ELECTROFACIES CLASSIFICATION OF PONTA GROSSA FORMATION BY MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) AND SELF-ORGANIZING MAPS (SOM) METHODS

2020 ◽  
Vol 38 (1) ◽  
pp. 52
Author(s):  
Felipe Vasconcelos dos Passos ◽  
Marco Antonio Braga ◽  
Thiago Gonçalves Carelli ◽  
Josiane Branco Plantz
Keyword(s):  
Gamma Ray ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Resolution Graph ◽  
Lithology Prediction ◽  
Paraná Basin ◽  
Parana Basin ◽  
Graph Based Clustering ◽  
Self Organizing

ABSTRACT. In Ponta Grossa Formation, devonian interval of Paraná Basin, Brazil, sampling restrictions are frequent, and lithological interpretations from gamma ray logs are common. However, no single log can discriminate lithology unambiguously. An alternative to reduce the uncertainty of these assessments is to perform multivariate analysis of well logs using data clustering methods. In this sense, this study aims to apply two different clustering algorithms, trained with gamma ray, sonic and resistivity logs. Five electrofacies were differentiated and validated by core data. It was found that one of the electrofacies identified by the model was not distinguished by macroscopic descriptions. However, the model developed is sufficiently accurate for lithological predictions.Keywords: geophysical well logging, lithology prediction, Paraná Basin. CLASSIFICAÇÃO DE ELETROFÁCIES DA FORMAÇÃO PONTA GROSSA UTILIZANDO OS MÉTODOS MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) E SELF-ORGANIZING MAPS (SOM)RESUMO. Na Formação Ponta Grossa, intervalo devoniano da Bacia do Paraná, Brasil, restrições de amostragem são frequentes e interpretações litológicas dos registros de raios gama são comuns. No entanto, nenhum perfil geofísico único pode discriminar litologias sem ambiguidade. Uma alternativa para reduzir a incerteza dessas avaliações é executar uma análise multivariada combinando vários perfis geofísicos de poços por meio de métodos de agrupamento de dados. Nesse sentido, este estudo tem como objetivo aplicar dois algoritmos de agrupamento aos registros de raios gama, sônico e resistividade para fins de predição litológica. Cinco eletrofácies foram diferenciadas e validadas por dados de testemunhos. Verificou-se que uma classe identificada pelo modelo não foi identificada por descrições macroscópicas. Porém, o modelo é suficientemente preciso para predições litológicas.Palavras-chave: geofísica de poços, predição litológica, correlação rocha-perfil, Bacia do Paraná.

Classifying Gamma‐Ray Bursts using Self‐organizing Maps

2002 ◽  
Vol 566 (1) ◽  
pp. 202-209 ◽  
Author(s):  
H. J. Rajaniemi ◽  
P. Mahonen
Keyword(s):  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Machine Learning-Based Probabilistic Lithofacies Prediction from Conventional Well Logs: A Case from the Umiat Oil Field of Alaska

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4862
Author(s):  
Nilesh Dixit ◽  
Paul McColgan ◽  
Kimberly Kusler
Keyword(s):  
Machine Learning ◽  
Oil Field ◽  
Machine Learning Algorithms ◽  
Self Organizing Maps ◽  
Log Data ◽  
Core Data ◽  
Core Samples ◽  
Resolution Graph ◽  
Reservoir Facies ◽  
Self Organizing

A good understanding of different rock types and their distribution is critical to locate oil and gas accumulations in the subsurface. Traditionally, rock core samples are used to directly determine the exact rock facies and what geological environments might be present. Core samples are often expensive to recover and, therefore, not always available for each well. Wireline logs provide a cheaper alternative to core samples, but they do not distinguish between various rock facies alone. This problem can be overcome by integrating limited core data with largely available wireline log data with machine learning. Here, we presented an application of machine learning in rock facies predictions based on limited core data from the Umiat Oil Field of Alaska. First, we identified five sandstone reservoir facies within the Lower Grandstand Member using core samples and mineralogical data available for the Umiat 18 well. Next, we applied machine learning algorithms (ascendant hierarchical clustering, self-organizing maps, artificial neural network, and multi-resolution graph-based clustering) to available wireline log data to build our models trained with core-driven information. We found that self-organizing maps provided the best result among other techniques for facies predictions. We used the best self-organizing maps scheme for predicting similar reservoir facies in nearby uncored wells—Umiat 23H and SeaBee-1. We validated our facies prediction results for these wells with observed seismic data.

Semiautomated geologic mapping using self-organizing maps and airborne geophysics in the Brazilian Amazon

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. K17-K24 ◽  
Author(s):  
Cleyton de Carvalho Carneiro ◽  
Stephen James Fraser ◽  
Alvaro Penteado Crósta ◽  
Adalene Moreira Silva ◽  
Carlos Eduardo de Mesquita Barros
Keyword(s):  
Gamma Ray ◽  
Gold Mineralization ◽  
Brazilian Amazon ◽  
Geophysical Data ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Spectrometric Data ◽  
Rock Types ◽  
Geologic Map ◽  
Self Organizing

A self-organizing map (SOM) approach has been used to provide an integrated spatial analysis and classification of airborne geophysical data collected over the Brazilian Amazon. Magnetic and gamma ray spectrometric data were used to extract geophysical signatures related to the spatial distribution of rock types and to produce a geologic map over the prospective Anapu-Tuerê region. Particular emphasis was given to discriminating and identifying rock types, and the processes related to gold mineralization, which are known to occur in the Anapu-Tuerê region. SOM was able to identify and map distinctive geophysical signatures related to the various geologic units identified on the published geologic map. Furthermore, SOM was able to identify and enhance very subtle signatures derived jointly from the magnetic and gamma ray spectrometric data that could be related to geologic processes present in the area. These results demonstrate the effectiveness of using SOM as a tool for geophysical data analysis and for semiautomated mapping in regions such as the Amazon.

scholarly journals SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Microarray Images

2018 ◽  
Vol 7 (1) ◽  
pp. 78
Author(s):  
Durga Prasad Kondisetty ◽  
Mohammed Ali Hussain
Keyword(s):  
Computational Cost ◽  
Microarray Technology ◽  
Qualitative And Quantitative Analysis ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Microarray Image ◽  
Segmentation Quality ◽  
Som Algorithm ◽  
Simple Linear Iterative Clustering ◽  
Self Organizing

We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy c-means and self organizing maps clustering methods.

scholarly journals Mobile Anomaly Detection Based on Improved Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Chunyong Yin ◽  
Sun Zhang ◽  
Kwang-jun Kim
Keyword(s):  
Anomaly Detection ◽  
Mobile Devices ◽  
Clustering Algorithms ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Optimal Method ◽  
Self Organizing Maps ◽  
Different Characteristics ◽  
Precision Rate ◽  
Self Organizing

Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

scholarly journals Business Client Segmentation in Banking Using Self-Organizing Maps

2014 ◽  
Vol 8 (2) ◽  
pp. 32-41 ◽  
Author(s):  
Mirjana Pejić Bach ◽  
Sandro Juković ◽  
Ksenija Dumičić ◽  
Nataša Šarlija
Keyword(s):  
Foreign Trade ◽  
Selection Criteria ◽  
Clustering Algorithm ◽  
Annual Income ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Marketing Activities ◽  
Ward Clustering ◽  
Statistical Clustering ◽  
Self Organizing

Abstract Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means). The goal of the paper is to demonstrate that self-organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

Self-organizing maps and clustering methods for matrix data

2004 ◽  
Vol 17 (8-9) ◽  
pp. 1211-1229 ◽  
Author(s):  
Sambu Seo ◽  
Klaus Obermayer
Keyword(s):  
Clustering Methods ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals Using Self-Organizing Maps for Rural Territorial Typology

2011 ◽  
pp. 107-126 ◽  
Author(s):  
Marcos Santos da Silva ◽  
Edmar Ramos de Siqueira ◽  
Olívio Teixeira ◽  
Maria Manos ◽  
Antônio Monteiro
Keyword(s):  
Neural Network ◽  
Temporal Analysis ◽  
The Self ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Policy Makers ◽  
Spatial Temporal Analysis ◽  
Self Organizing

This work assessed the capacity of the self-organizing map, an unsupervised artificial neural network, to aid the process of territorial design through visualization and clustering methods applied to a multivariate geospatial temporal dataset. The method was applied in the case study of Sergipe‘s institutional regional partition (Territories of Identity). Results have shown that the proposed method can improve the exploratory spatial-temporal analysis capacity of policy makers that are interested in territorial typology. A new partition for rural planning was elaborated and confirmed the coherence of the Territories of Identity.

Assessment of Clusteranalysis and Self-Organizing Maps

1998 ◽  
Vol 06 (02) ◽  
pp. 139-149 ◽  
Author(s):  
Helge Petersohn
Keyword(s):  
Neural Networks ◽  
Market Segmentation ◽  
Clustering Methods ◽  
Clustering Method ◽  
Self Organizing Maps ◽  
Methodical Approach ◽  
Starting Conditions ◽  
Marketing Activities ◽  
Special Approach ◽  
Self Organizing

Market segmentation represents a central problem of preparing marketing activities. The methodical approach of this problem is supported by clustering methods. Available data are used to detect common grounds regarding their quality structures. Therefore statistics provides various methods for cluster analysis. Self-organizing maps are another possibility to form classes. They are a special approach of the artificial neural networks. The statistical methods and these methods, which are based on organic processes of our brain, offer different solutions although the starting conditions are the same. Often decisions about investigations are based on such solutions. Therefore the results of clustering are very important to reveal systematic information about the size of classes and their structure. Methodical notes are needed for the use of any clustering method. This paper offers a simplified way to select the best result for clustering.

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