... The paper introduces a novel algorithmic approach for interpreting seismic data, aiming to automate spectral decomposition interpretation and cluster distinct 3D seismic facies. Utilizing Self-Organizing Maps (SOM) and Kmeans, the proposed algorithm effectively identifies and clusters geological...

... complex reservoir machine learning upstream oil & gas reservoir characterization som facies neuron facies classification american association roden interpretation international applied geoscience energy 10 artificial intelligence classification exploration geophysicist...

... Self-organizing mapping (SOM) is one of the most famous classification method in seismic facies analysis. Traditional self-organizing map network and its variation methods usually use the Euclidean distance to measure the similarity between input data and weights of neuron node. A Euclidean...

... fluvial system rgb color blending self-organizing map som malay basin envelope seg international exposition fluvial depositional element Multiattribute analysis of a Pleistocene fluvial system using RGB color blending and self- organizing maps Ismailalwali Babikir*, Ahmed Salim, Maman Hermana...

...Application of SOMs and K-Means clustering to geophysical mapping Lessons Learned Angela Carter-McAuslan* and Colin Farquharson, Memorial University of Newfoundland Summary Machine learning techniques are of growing interest to the geosciences. We discuss the use of self-organizing maps and k...

... on attributes generated on angle stacks to enhance facies identification in seismic data. Internal heterogeneities are identified within the reef core reservoir facies on the near angle self-organizing map not seen in a SOM calculated on the PSTM volume. Underlying low porosity bioherm facies are accurately...

... ABSTRACT Self organizing maps (SOM) provide an intuitive and effective way of classifying seismic data using various pattern recognition techniques. They provide better supervised or unsupervised strategies or both simultaneously for seismic clustering. Conventional approaches involve...

... Summary The self-organizing map (SOM) is one of the most effective pattern recognition techniques, and is commonly used tool for non-supervised seismic facies analysis. Early SOM implementations required estimating the number of clusters. Current implementations avoid this choice by over...

..., and bumpy. When a texture is rough to the touch, the surface exhibits sharp differences in elevation within the space of your fingertip. seg houston 2009 reservoir geometry geology reservoir characterization interpretation texture analysis international exposition som artificial intelligence...

... ABSTRACT A new alternative to detect time-lapse seismic effects is presented herein. We propose to use clustering of self organizing maps (SOM) associated with the wavelet transform to detect time-lapse changes. The wavelet transform is used to detect seismic traces singularities of each time...

... of the human hearing system, it is possible to develop a procedure in which instantaneous amplitude spectral information obtained from seismic sub-band analyses are used as input to Kohonen Self Organizing Map (SOM) artificial neural networks. One can thereby produce both uncalibrated as well as calibrated...

... ABSTRACT Artificial neural networks are highly simplified computer models that can simulate the human brain’s functionality to solve problems. A self-organizing map (SOM) is a type of neural network that uses a two-dimensional feature map to find significant patterns. In this paper, an SOM...