Modelling discontinuous well log signal to identify lithological boundaries via wavelet analysis: An example from KTB borehole data

Singh, Amrita; Maiti, Saumen; Tiwari, R. K.

doi:10.1007/s12040-016-0701-2

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…There are many decomposition methods for wavelet decomposition, as long as the direct sum of the set of subspaces can cover the V space without overlapping each other. Wavelet decomposition is actually just a special case of wavelet packet decomposition [22]. The diverse and flexible decomposition characteristics of wavelet provide the possibility to select the optimal decomposition according to different purposes [23,24].…”

Section: Wavelet Analysismentioning

confidence: 99%

Financial Time Series Image Algorithm Based on Wavelet Analysis and Data Fusion

Liu

Yan

2021

Journal of Sensors

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In recent years, people are more and more interested in time series modeling and its application in prediction. This paper mainly discusses a financial time series image algorithm based on wavelet analysis and data fusion. In this research, we conducted an in-depth study on the scale decomposition sequence and wavelet transform sequence in different scale domains of wavelet transform according to the scale change rule based on wavelet transform. We use wavelet neural network with different input neurons and hidden neurons to predict, respectively. Finally, the prediction results are integrated into the final prediction results based on the original time series by using wavelet reconstruction technology. Using RBF algorithm in neural network and SPSS Clementine, the wavelet transform sequences on five scales are modeled. Each network model has three layers: one input layer, one hidden layer, and one output layer, and each output layer has only one output element. In order to compare the prediction effect of the model proposed in this study, the ordinary RBF network is used to model and predict the log yield itself. When the input sample is 5, the minimum mean square error is obtained when the hidden layer is 6, and the mean square error is 1.6349. The mean square error of the training phase is 0.0209, and the validation error is 1.6141. The results show that the prediction results of the wavelet prediction method combined with the RBF network prediction method are better than those of wavelet prediction or RBF network prediction.

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Section: Wavelet Analysismentioning

confidence: 99%

Financial Time Series Image Algorithm Based on Wavelet Analysis and Data Fusion

Liu

Yan

2021

Journal of Sensors

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“…All images were obtained by optical microscope. The Kocurek Carbonates Dataset, which represents the different thin section of plugs of the BBS, DPL, EYC, GBS, IBS, IL and SD carbonate rock classes, produced in CENPES 5 Laboratory by the CENPES Tomography group led by R. Surmas; Granite sample images from the GeoSecSlides group 6 ; and a group of Olivinite sample images from the NCPTT 7 of the National Park Service public images 8 .…”

Section: About the Kth-tips Datasetmentioning

confidence: 99%

“…This is the reason why oil companies use different probes to gather information about the oil field in order to estimate the probability, presence and quantity of oil or gas on that certain field. The analysis of well logs has been relied over the years as a very powerful tool to aid analysts on deciding whether a field is suitable for exploration or not (see [6]).…”

mentioning

confidence: 99%

On a method for Rock Classification using Textural Features and Genetic Optimization

Valentin¹,

Albuquerque²,

Albuquerque³

et al. 2017

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In this work we present a method to classify a set of rock textures based on a Spectral Analysis and the extraction of the texture Features of the resulted images. Up to 520 features were tested using 4 different filters and all 31 different combinations were verified. The classification process relies on a Naïve Bayes classifier. We performed two kinds of optimizations: statistical optimization with covariance-based Principal Component Analysis (PCA) and a genetic optimization, for 10,000 randomly defined samples, achieving a final maximum classification success of 91% against the original ∼ 70% success ratio (without any optimization nor filters used). After the optimization 9 types of features emerged as most relevant.Keywords: Haralick Features, Genetic Algorithm, Texture Classification, Naïve Bayes, Image Processing, Principal Component Analysis.Resumo: Neste trabalho apresentamos um método para classificar um conjunto de texturas de rocha baseado na Análise espectral e na extração de features texturais das imagens resultantes. Um conjunto de 520 features foi testado usando 4 filtros diferentes e todas as 31 combinações dos mesmos foram verificadas. O processo de classificação propostoé baseado em um classificador Naïve Bayes. Foram realizados dois tipos de otimização nos parâmetros extraídos: uma otimização estatística usando uma Análise de Componente Principal por covariância (PCA) e uma otimização genética, para todas as 10.000 permutações aleatórias das imagens, obtendo um sucesso máximo final de classificação de 91%, sendo o sucesso inicial, sem nenhum tipo de otimização do 70%. Depois da aplicação do método aqui descrito 9 tipos diferentes de features emergeram como as mais relevantes para o problema de classificação de texturas de rochas.

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