A Quantitative Study of Scaling Properties of Fracture Networks

Fracture networks and their scaling properties are important from both an academic and practical perspective since they play a significant role in many areas ranging from crustal fluid flow to studies of earthquakes. Over the years, researchers have employed a wide variety of techniques to quantify the complexities of fractured media. These range from deterministic, process-based approaches employing the laws of physics, to ones involving the applications of geostatistics and more recently, fractal geometry. Fractals are irregular entities that show self-similarity over a wide range of scales and can be quantified by the fractal dimension, D. It is important that the D-values of such networks are properly evaluated. The box-counting algorithm is a widely used technique for characterizing fracture networks as fractals and estimating their D-values. If this analysis yields a power law distribution given by N ∝ r−D , where N is the number of boxes containing one or more fractures and r is the box size, the network is considered to be fractal. However, researchers are divided in their opinion about issues such as the best box-counting algorithm for estimating the ‘correct’ D-value or whether a fracture network is indeed fractal. For instance, a closer look at the N vs. r plots for a set of previously published fracture trace maps shows that such distributions do not follow power law scaling. As part of the present work, a synthetic fractal-fracture network with a known theoretical fractal dimension, D, was used to develop an improved algorithm for the box-counting method that returns “unbiased” D-values. A suite of 17 fracture trace maps that had previously been evaluated for their fractal nature was reanalyzed using the improved technique. “Unbiased” estimates of D for these maps ranged from 1.56±0.02 to 1.79±0.02, and were much higher than the original estimates. The fractal dimension of a pattern however, does not capture all of the heterogeneity present. For instance, two patterns that have the same fractal dimension may have very different appearances. We investigated the applicability of a new parameter, namely lacunarity, L, for distinguishing between different fracture networks having the same fractal dimension. The lacunarity is the degree of clustering in a pattern and is a geostatistical parameter that can be used to study patterns that are both fractals non-fractal. The gliding-box algorithm is a popular technique for computing lacunarities as a function of the box-size, r. In the present work it has been successfully used for the first time to analyze fracture networks. Apart from computing lacunarity curves for a set of synthetic patterns generated in MATLAB, we also analyzed a set of 7 nested natural fracture maps with similar D values ranging from 1.80±0.05 to 1.84±0.04. Our results show that differences between maps are most pronounced when L values are determined using intermediate box sizes. Estimates of L based on such box sizes indicate that fractures are more clustered at smaller scales. Future work in this area should explore the use of the gliding box algorithm to see whether fracture networks are self-similar over a given range of scales and if lacunarity analysis alone can furnish information on the “unbiased” fractal dimensions of such networks

Scale-dependent heterogeneity in fracture data sets and grayscale images

Lacunarity is a technique developed for multiscale analysis of spatial data and can quantify scale-dependent heterogeneity in a dataset. The present research is based on characterizing fracture data of various types by invoking lacunarity as a concept that can not only be applied to both fractal and non-fractal binary data but can also be extended to analyzing non-binary data sets comprising a spectrum of values between 0 and 1. Lacunarity has been variously modified in characterizing fracture data from maps and scanlines in tackling five different problems. In Chapter 2, it is shown that normalized lacunarity curves can differentiate between maps (2-dimensional binary data) belonging to the same fractal-fracture system and that clustering increases with decreasing spatial scale. Chapter 4 analyzes spacing data from scanlines (1-dimensional binary data) and employs log-transformed lacunarity curves along with their 1st derivatives in identifying the presence of fracture clusters and their spatial organization. This technique is extended to 1-dimensional non-binary data in chapter 5 where spacing is integrated with aperture values and a lacunarity ratio is invoked in addressing the question of whether large fractures occur within clusters. Finally, it is investigated in chapter 6 if lacunarity can find differences in clustering along various directions of a fracture netowork thus identifying differentially-clustered fracture sets. In addition to fracture data, chapter 3 employs lacunarity in identifying clustering and multifractal behavior in synthetic and natural 2-dimensional non-binary patterns in the form of soil thin sections. Future avenues for research include estimation of 2-dimensional clustering from 1-dimensional samples (e.g., scanlines and well-data), forward modeling of fracture networks using lacunarity, and the possible application of lacunarity in delineating shapes of other geologic patterns such as channel beds

Oil transportation in the global landscape : the Murmansk Oil Terminal and Pipeline proposal evaluated

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2003.Includes bibliographical references (leaves 60-62).Oil and transportation have been commingled since the first oil reserves were discovered. The importance of energy, namely oil, and the transportation of that energy from the producers to the consumers is persistently monitored and evaluated. Oil producers often seek novel transportation channels to increase oil production, thereby increasing revenues. Oil consumers seek unique transportation nodes to reduce their reliance on a single set of producers while potentially reducing prices. An example of the transportation interplay between global producers and consumers is highlighted by the Murmansk Oil Terminal and Pipeline proposal that seeks to provide Russian oil to the United States in a safe, efficient, and economic manner. The framework and corresponding feasibility analysis highlight the importance of oil transportation in a global landscape and peruse the macro and micro variables that intertwine and impact that landscape. A thorough evaluation of both Russian and US oil reliance must be understood, while extrapolating the influence of ancillary players such as OPEC, West Siberian Oil Reserves, the Murmansk locality, and the marine transportation industry. This thesis seeks to provide a overview of the oil industry generally, while specifically focusing on marine oil transportation. The thesis does so with a case evaluation of the Murmansk Oil Terminal and Pipeline project.by Ankur Roy.M.Eng.in Logistic

AstroSat observation of rapid Type-I thermonuclear burst from the low mass X-ray binary GX 3+1

We report the results of an observation of low mass X-ray binary GX 3+1 with {\it AstroSat}'s Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) instruments on-board for the first time. We have detected one Type-1 thermonuclear burst ($\sim$ 15 s) present in the LAXPC 20 light curve, with a double peak feature at higher energies and our study of the hardness-intensity diagram reveals that the source was in a soft banana state. The pre-burst emission could be described well by a thermally Comptonised model component. The burst spectra is modelled adopting a time-resolved spectroscopic method using a single color blackbody model added to the pre-burst model, to monitor the parametric changes as the burst decays. Based on our time-resolved spectroscopy, we claim that the detected burst is a photospheric radius expansion (PRE) burst. During the PRE phase, the blackbody flux is found to be approximately constant at an averaged value $\sim$ 2.56 in $10^{-8}$ ergs s$^{-1}$ cm$^{-2}$ units. On the basis of literature survey, we infer that \textit{AstroSat}/LAXPC 20 has detected a burst from GX 3+1 after more than a decade which is also a PRE one. Utilising the burst parameters obtained, we provide a new estimation to the source distance, which is $\sim$ 9.3 $\pm$ 0.4 kpc, calculated for an isotropic burst emission. Finally, we discuss and compare our findings with the published literature reports.Comment: 14 pages, 10 figures, accepted for publication in The Journal of Astrophysics and Astronom

Fractal Characterization of Fracture Networks: An Improved Box-counting Technique

Box counting is widely used for characterizing fracture networks as fractals and estimating their fractal dimensions (D). If this analysis yields a power law distribution given by N $\propto$ r−D, where N is the number of boxes containing one or more fractures and r is the box size, then the network is considered to be fractal. However, researchers are divided in their opinion about which is the best box‐counting algorithm to use, or whether fracture networks are indeed fractals. A synthetic fractal fracture network with a known D value was used to develop a new algorithm for the box‐counting method that returns improved estimates of D. The method is based on identifying the lower limit of fractal behavior (rcutoff) using the condition ds/dr → 0, where s is the standard deviation from a linear regression equation fitted to log(N) versus log(r) with data for r \u3c rcutoff sequentially excluded. A set of 7 nested fracture maps from the Hornelen Basin, Norway was used to test the improved method and demonstrate its accuracy for natural patterns. We also reanalyzed a suite of 17 fracture trace maps that had previously been evaluated for their fractal nature. The improved estimates of D for these maps ranged from 1.56 ± 0.02 to 1.79 ± 0.02, and were much greater than the original estimates. These higher D values imply a greater degree of fracture connectivity and thus increased propensity for fracture flow and the transport of miscible or immiscible chemicals

Analysis and design optimization of enhanced swing CMOS LC oscillators based on a phasor based approach

Analysis and design optimization of enhanced swing, low power CMOS LC oscillators is presented. A phasor analysis based approach for determining the amplitude and phase noise of these oscillators is used. MOSFET operation in cut-off, linear and saturation regions is included. The calculated steady state output amplitude and phase noise from this analysis are in good agreement with Cadence Spectre simulations for different bias conditions. Application of this analysis to the design optimization of LC oscillators is demonstrated.Keywords: ISF, Phasor model, PPV, Spectrum conversion, Design oriented analysis, Voltage biased, Enhanced swing oscillator, Oscillator design, Amplitude analysis, X-coupled, Phase noise, Colpitts oscillator, LC oscillator, Design optimizatio

Multimodal Fusion Transformer for Remote Sensing Image Classification

Vision transformer (ViT) has been trending in image classification tasks due to its promising performance when compared to convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViT models in hyperspectral image (HSI) classification tasks, but without achieving satisfactory performance. To this paper, we introduce a new multimodal fusion transformer (MFT) network for HSI land-cover classification, which utilizes other sources of multimodal data in addition to HSI. Instead of using conventional feature fusion techniques, other multimodal data are used as an external classification (CLS) token in the transformer encoder, which helps achieving better generalization. ViT and other similar transformer models use a randomly initialized external classification token {and fail to generalize well}. However, the use of a feature embedding derived from other sources of multimodal data, such as light detection and ranging (LiDAR), offers the potential to improve those models by means of a CLS. The concept of tokenization is used in our work to generate CLS and HSI patch tokens, helping to learn key features in a reduced feature space. We also introduce a new attention mechanism for improving the exchange of information between HSI tokens and the CLS (e.g., LiDAR) token. Extensive experiments are carried out on widely used and benchmark datasets i.e., the University of Houston, Trento, University of Southern Mississippi Gulfpark (MUUFL), and Augsburg. In the results section, we compare the proposed MFT model with other state-of-the-art transformer models, classical CNN models, as well as conventional classifiers. The superior performance achieved by the proposed model is due to the use of multimodal information as external classification tokens

350 mV, 5 GHz Class-D Enhanced Swing Differential and Quadrature VCOs in 65 nm CMOS

A new enhanced swing class-D VCO which operates from a supply voltage as low as 300 mV is presented. The architectural advantages are described along with an analysis for the oscillation frequency. Prototype differential and quadrature variants of the proposed VCO have been implemented in a 65 nm RF CMOS process with a 5 GHz VCO oscillation frequency. At a 350 mV supply, the measured phase noise performance for the quadrature VCO with a 5% tuning range is -137.1 dBc/Hz at 3 MHz offset with a power dissipation of 2.1 mW from a 0.35 V supply. The highest resulting figure-of-merit (FoM) is 198.3 dBc/Hz.©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works