Novi algoritam za kompresiju seizmičkih podataka velike amplitudske rezolucije

Renewable sources cannot meet energy demand of a growing global market. Therefore, it is expected that oil & gas will remain a substantial sources of energy in a coming years. To find a new oil & gas deposits that would satisfy growing global energy demands, significant efforts are constantly involved in finding ways to increase efficiency of a seismic surveys. It is commonly considered that, in an initial phase of exploration and production of a new fields, high-resolution and high-quality images of the subsurface are of the great importance. As one part in the seismic data processing chain, efficient managing and delivering of a large data sets, that are vastly produced by the industry during seismic surveys, becomes extremely important in order to facilitate further seismic data processing and interpretation. In this respect, efficiency to a large extent relies on the efficiency of the compression scheme, which is often required to enable faster transfer and access to data, as well as efficient data storage. Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, this work explores a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. It is proposed to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, this work modifies almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. In addition, optimized block selection, reduced intra prediction modes, and flexible motion estimation are tested to adapt to the structure of seismic data. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. There is no similar work in the field of the seismic data compression that uses the HEVC as a base codec setting. Thus, a specific codec design has been tailored which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-tonoise- ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a proposed configurations, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, an optimized encoder is proposed in this work. It reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses. As a side contribution of this work, HEVC is analyzed within all of its functional units, so that the presented work itself can serve as a specific overview of methods incorporated into the standard

Novi algoritam za kompresiju seizmičkih podataka velike amplitudske rezolucije

Renewable sources cannot meet energy demand of a growing global market. Therefore, it is expected that oil & gas will remain a substantial sources of energy in a coming years. To find a new oil & gas deposits that would satisfy growing global energy demands, significant efforts are constantly involved in finding ways to increase efficiency of a seismic surveys. It is commonly considered that, in an initial phase of exploration and production of a new fields, high-resolution and high-quality images of the subsurface are of the great importance. As one part in the seismic data processing chain, efficient managing and delivering of a large data sets, that are vastly produced by the industry during seismic surveys, becomes extremely important in order to facilitate further seismic data processing and interpretation. In this respect, efficiency to a large extent relies on the efficiency of the compression scheme, which is often required to enable faster transfer and access to data, as well as efficient data storage. Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, this work explores a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. It is proposed to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, this work modifies almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. In addition, optimized block selection, reduced intra prediction modes, and flexible motion estimation are tested to adapt to the structure of seismic data. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. There is no similar work in the field of the seismic data compression that uses the HEVC as a base codec setting. Thus, a specific codec design has been tailored which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-tonoise- ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a proposed configurations, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, an optimized encoder is proposed in this work. It reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses. As a side contribution of this work, HEVC is analyzed within all of its functional units, so that the presented work itself can serve as a specific overview of methods incorporated into the standard

Post-stack seismic data compression with multidimensional deep autoencoders

Seismic data are surveys from the Earth's subsurface with the goal of representing the geophysical characteristics from the region where they were obtained in order to be interpreted. These data can occupy hundreds of Gigabytes of storage, motivating their compression. In this work, we approach the problem of three-dimensional post-stack seismic data using models based on deep autoencoders. The deep autoencoder is a neural network that allows representing most of the information of a seismic data with a lower cost in comparison to its original representation. To the best of our knowledge, this is the rst work to deal with seismic compression using deep learning. Four compression methods for post-stack data are proposed: two based on a bi-dimensional compression, named 2D-based Seismic Data Compression(2DSC) and 2D-based Seismic Data Compression using Multi-resolution (2DSC-MR), and two based on three-dimensional compression, named 3D-based Seismic Data Compression (3DSC) and 3D-based Seismic Data Compression using Vector Quantization (3DSC-VQ). The 2DSC is our simplest method for seismic compression, in which the volume is compressed through its bi-dimensional sections. The 2DSC-MR extends the previous method by introducing the data compression in multiple resolutions. The 3DSC extends the 2DSC method by allowing the seismic data compression by using the three-dimensional volume instead of 2D slices. This method considers the similarity between sections to compress a whole volume with the cost of a single section. The 3DSC-VQ uses vector quantization aiming to extract more information from the seismic volumes in the encoding part. Our main goal is to compress the seismic data at low bit rates, attaining a high quality reconstruction. Experiments show that our methods can compress seismic data yielding PSNR values over 40 dB and bit rates below 1.0 bpv.Dados sísmicos s~ao mapeamentos da subsuperfície terrestre que têm como objetivo representar as características geofísicas da região onde eles foram obtidos de forma que possam ser interpretados. Esses dados podem ocupar centenas de Gigabytes de armazenamento, motivando sua compressão. Neste trabalho o problema de compressão de dados sísmicos tridimensionais pós-pilha é abordado usando modelos baseados em autocodificadores profundos. O autocodificador profundo é uma rede neural que permite representar a maior parte da informação contida em um dado sísmico com um custo menor que sua representação original. De acordo com nosso conhecimento, este é o primeiro trabalho a lidar com compressão de dados sísmicos utilizando aprendizado profundo. Dessa forma, através de aproximações sucessivas, são propostos quatro métodos de compressão de dados tridimensionais pós-pilha: dois baseados em compressão bidimensional, chamados Método de Compressão 2D de Dado Sísmico (2DSC) e Método de Compressão 2D de Dado Sísmico usando Multi-resolução (2DSC-MR), e dois baseados em compressão tridimensional, chamados Método de Compressão 3D de Dado Sísmico (3DSC) e Método de Compressão 3D de Dado Sísmico usando Quantização Vetorial (3DSC-VQ). O método 2DSC é o nosso método de compressão do dado sísmico mais simples, onde o volume é comprimido a partir de suas seções bidimensionais. O método 2DSC-MR estende o método anterior introduzindo a compressão do dado em múltiplas resoluções. O método 3DSC estende o método 2DSC permitindo a compressão do dado sísmico em sua forma volumétrica, considerando a similaridade entre seções para representar um volume inteiro com o custo de apenas uma seção. O método 3DSC-VQ utiliza quantização vetorial para relaxar a etapa de codificação do método anterior, dando maior liberdade à rede para extrair informação dos volumes sísmicos. O objetivo deste trabalho é comprimir o dado sísmico a baixas taxas de bits e com alta qualidade de reconstrução em termos de PSNR e bits-por-voxel (bpv). Experimentos mostram que os quatro métodos podem comprimir o dado sísmico fornecendo valores de PSNR acima de 40 dB a taxas de bits abaixo de 1.0 bpv.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Compressão de dados sísmicos pós-pilha usando uma rede adversária generativa com função de perda composta

Seismic data provide structural and static information of the region where they were acquired, used to determine regions to explore oil and gas. The improvements in the acquisition methods, especially in the high quality of the sensors, have also increased the size of the seismic data. The motivation for compressing them comes from the demand for hundreds of terabytes to transmit and store seismic data. This work presents a method for three-dimensional (3D) poststack seismic data compression integrating a 3D convolutionbased autoencoder to a generative adversarial network (GAN). The main challenge of the 3D convolutional autoencoder is how to exploit volumetric redundancy, keeping the latent representation dimensions. The proposed method is based on a convolutional neural network for seismic data compression called 3DSC. The main hypothesis is that 3DSC architecture can be improved by adversarial training. Thus, a new 3D-based seismic data compression method (3DSC-GAN) is proposed by coupling the 3DSC network to a GAN. The decoder module is used as a generator of poststack seismic data integrated with a discriminator module to better exploit the volumetric redundancy of the 3D data. Also, a new fashion to calculate the distortion for multidimensional data is proposed, such as seismic data. Since the generic functions ignore the 3D data structure and consider it as a 1D vector, the idea is to apply a different loss function for each axis, for a reduction in dimensionality that better captures the error according to its magnitude. For this purpose, an extensive study is performed to analyze the possible combinations of functions for the 3D poststack seismic data compression problem. Results indicate that the 3DSC-GAN method outperforms previous ones for very low target bit rates, increasing the peak signal-to-noise ratio (PSNR) with high visual reconstruction quality. In addition, the experiments using the new distortion function show that it benefits the network learning process, generating a superior reconstruction compared to methods that use PSNR as a distortion function, in quantitative and qualitative terms.Dados sísmicos fornecem informações estruturais e estáticas da região de onde foram coletados, usadas para determinar regiões para explorar petróleo e gás. As melhorias nos métodos de aquisição, especialmente na alta qualidade dos sensores, também aumentaram o tamanho dos dados sísmicos. A motivação para comprimi-los vem da necessidade de centenas de terabytes para transmitir e armazenar dados sísmicos. Este trabalho apresenta um método para compressão de dados sísmicos pós-pilha tridimensional (3D) integrando um autocodificador baseado em convoluções 3D e uma rede adversária generativa (GAN). O principal desafio dos autocodificadores é como explorar a redundância volumétrica, mantendo as dimensões da representação latente. O método proposto é baseado em uma rede neural convolucional para compressão de dados sísmicos chamada 3DSC. A principal hipótese é que a arquitetura da 3DSC pode ser melhorada pelo treinamento adversário. Assim, é proposto um novo método de compressão de dados sísmicos baseado em 3D (3DSC-GAN) ao acoplar a rede 3DSC a uma GAN. O módulo decodificador é utilizado como um gerador de dados sísmicos pós-pilha integrado a um módulo discriminador para melhor explorar a redundância volumétrica presente no dado 3D. Também é proposta uma nova forma para calcular a distorção em dados multidimensionais, como dados sísmicos. Visto que funções genéricas ignoram a estrutura do dado 3D e o consideram como um vetor 1D, a ideia consiste em aplicar uma função de perda diferente para cada eixo, para uma redução de dimensionalidade que melhor capture o erro de acordo com sua grandeza. Para isso, é feito um estudo extensivo para analisar as possíveis combinações de funções para o problema de compressão de dados sísmicos pós-pilha 3D. Os resultados indicam que o método 3DSC-GAN supera os métodos anteriores para taxas de bits alvo muito baixas, aumentando a relação sinal-ruído de pico (PSNR) com alta qualidade visual de reconstrução. Além disso, os experimentos realizados aplicando a nova função de distorção mostram que ela auxilia no processo de aprendizado da rede, gerando uma reconstrução superior comparado com métodos que utilizam PSNR como função de distorção, em termos quantitativos e qualitativos.FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerai

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

The doctoral research abstracts. Vol:10 2016 / Institute of Graduate Studies, UiTM

Foreword: Congratulations to Institute of Graduate Studies on the continuous efforts to publish the 10th issue of the Doctoral Research Abstracts which showcases the research carried out in the various disciplines range from science and technology, business and administration to social science and humanities. This issue captures the novelty of research contributed by seventy (70) PhD graduands receiving their scrolls in the UiTM’s 85th Convocation. As of October 2016, this year UiTM has produced 138 PhD graduates soaring from125 in the previous year (2015). It shows that UiTM is in the positive direction to achive the total of 1200 PhD graduates in 2020. To the 70 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholarly world with its endless challenges and obstacles, and by persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into more innovative research from knowledge gained during this academic journey, for the community and country. This year marks UiTM’s 60th Anniversary and we have been producing many good quality graduates that have a major impact on the socio-economic development of the country and the bumiputeras. As alumni of UiTM, we hold you dear to our hearts. We sincerely wish you all the best and may the Almighty guide you to a path of excellence and success. As you leave the university as alumni we hope a new relationship will be fostered between you and the faculty in soaring UiTM to greater heights. “UiTM Sentiasa di Hati Ku” / Prof Emeritus Dato’ Dr Hassan Said Vice Chancellor Universiti Teknologi MAR