scholarly journals Methodology for dense spatial sampling of multicomponent recording of converted waves in shallow marine environments

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. WB29-WB37 ◽  
Author(s):  
Nihed Allouche ◽  
Guy G. Drijkoningen ◽  
Joost van der Neut
Keyword(s):  
Spatial Sampling ◽  
Danube River ◽  
Converted Wave ◽  
Data Set ◽  
Shallow Marine ◽  
Spatial Aliasing ◽  
Receiver Array ◽  
Effective Manner ◽  
Processing Step ◽  
Converted Waves

A widespread use of converted waves for shallow marine applications is hampered by spatial aliasing and field efficiency. Their short wavelengths require dense spatial sampling which often needs to be achieved by receivers deployed on the seabed. We adopted a new methodology where the dense spatial sampling is achieved in the common-receiver domain by reducing the shot spacing. This is done by shooting one track multiple times and merging the shot lines in an effective manner in a separate processing step. This processing step is essential because positioning errors introduced during the field measurement can become significant in the combined line, particularly when they exceed the distance between two adjacent shot positions. For this processing step, a particular shot line is used as a reference line and relative variations in source and receiver positions in the other shot lines are corrected for using crosscorrelation. The combined shot line can subsequently be regularized for further processing. The methodology is adopted in a field experiment conducted in the Danube River in Hungary. The aim of the seismic experiment was to acquire properly sampled converted-wave data using a multicomponent receiver array. The dense spatial sampling was achieved by sailing one track 14 times. After correcting for the underwater receiver positions using the direct arrival, the crosscorrelation step was applied to merge the different shot lines. The successfully combined result is regularized into a densely sampled data set free of visible spatial aliasing and suitable for converted-wave processing.

Joint inversion of P- and PS-waves in orthorhombic media: Theory and a physical modeling study

Geophysics ◽  
1999 ◽  
Vol 64 (1) ◽  
pp. 146-161 ◽  
Author(s):  
Vladimir Grechka ◽  
Stephen Theophanis ◽  
Ilya Tsvankin
Keyword(s):  
Shear Wave ◽  
Physical Modeling ◽  
Horizontal Plane ◽  
Joint Inversion ◽  
Symmetry Plane ◽  
P Wave ◽  
Simple Relationship ◽  
Converted Wave ◽  
Data Set ◽  
Converted Waves

Reflection traveltimes recorded over azimuthally anisotropic fractured media can provide valuable information for reservoir characterization. As recently shown by Grechka and Tsvankin, normal moveout (NMO) velocity of any pure (unconverted) mode depends on only three medium parameters and usually has an elliptical shape in the horizontal plane. Because of the limited information contained in the NMO ellipse of P-waves, it is advantageous to use moveout velocities of shear or converted modes in attempts to resolve the coefficients of realistic orthorhombic or lower‐symmetry fractured models. Joint inversion of P and PS traveltimes is especially attractive because it does not require shear‐wave excitation. Here, we show that for models composed of horizontal layers with a horizontal symmetry plane, the traveltime of converted waves is reciprocal with respect to the source and receiver positions (i.e., it remains the same if we interchange the source and receiver) and can be adequately described by NMO velocity on conventional‐length spreads. The azimuthal dependence of converted‐wave NMO velocity has the same form as for pure modes but requires the spatial derivatives of two-way traveltime for its determination. Using the generalized Dix equation of Grechka, Tsvankin, and Cohen, we derive a simple relationship between the NMO ellipses of pure and converted waves that provides a basis for obtaining shear‐wave information from P and PS data. For orthorhombic models, the combination of the reflection traveltimes of the P-wave and two split PS-waves makes it possible to reconstruct the azimuthally dependent NMO velocities of the pure shear modes and to find the anisotropic parameters that cannot be determined from P-wave data alone. The method is applied to a physical modeling data set acquired over a block of orthorhombic material—Phenolite XX-324. The inversion of conventional‐spread P and PS moveout data allowed us to obtain the orientation of the vertical symmetry planes and eight (out of nine) elastic parameters of the medium (the reflector depth was known). The remaining coefficient (c12 or δ(3) in Tsvankin’s notation) is found from the direct P-wave arrival in the horizontal plane. The inversion results accurately predict moveout curves of the pure S-waves and are in excellent agreement with direct measurements of the horizontal velocities.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

scholarly journals Optimization through Bayesian Classification on the k-Anonymized Data

2012 ◽  
pp. 245-249
Author(s):  
L Mohana Tirumala ◽  
S. Srinivasa Rao
Keyword(s):  
Cost Effective ◽  
Privacy Preserving ◽  
Bayesian Classification ◽  
Data Sets ◽  
Data Set ◽  
Equal Importance ◽  
Cost Effective Approach ◽  
Effective Manner ◽  
Anonymized Data ◽  
Refinement Algorithm

Privacy preserving in Data mining & publishing, plays a major role in today networked world. It is important to preserve the privacy of the vital information corresponding to a data set. This process can be achieved by k-anonymization solution for classification. Along with the privacy preserving using anonymization, yielding the optimized data sets is also of equal importance with a cost effective approach. In this paper Top-Down Refinement algorithm has been proposed which yields optimum results in a cost effective manner. Bayesian Classification has been proposed in this paper to predict class membership probabilities for a data tuple for which the associated class label is unknown.

scholarly journals AUTOMATIC BLOCKED ROADS ASSESSMENT AFTER EARTHQUAKE USING HIGH RESOLUTION SATELLITE IMAGERY

2015 ◽  
Vol XL-1-W5 ◽  
pp. 601-605 ◽  
Author(s):  
H. Rastiveis ◽  
E. Hosseini-Zirdoo ◽  
F. Eslamizade
Keyword(s):  
High Resolution ◽  
World View ◽  
Satellite Image ◽  
Data Set ◽  
The Road ◽  
Vector Map ◽  
Processing Step ◽  
Image Quality Improvement ◽  
High Resolution Satellite Imagery ◽  
The City

In 2010, an earthquake in the city of Port-au-Prince, Haiti, happened quite by chance an accident and killed over 300000 people. According to historical data such an earthquake has not occurred in the area. Unpredictability of earthquakes has necessitated the need for comprehensive mitigation efforts to minimize deaths and injuries. Blocked roads, caused by debris of destroyed buildings, may increase the difficulty of rescue activities. In this case, a damage map, which specifies blocked and unblocked roads, can be definitely helpful for a rescue team. <br><br> In this paper, a novel method for providing destruction map based on pre-event vector map and high resolution world view II satellite images after earthquake, is presented. For this purpose, firstly in pre-processing step, image quality improvement and co-coordination of image and map are performed. Then, after extraction of texture descriptor from the image after quake and SVM classification, different terrains are detected in the image. Finally, considering the classification results, specifically objects belong to “debris” class, damage analysis are performed to estimate the damage percentage. In this case, in addition to the area objects in the “debris” class their shape should also be counted. The aforementioned process are performed on all the roads in the road layer.In this research, pre-event digital vector map and post-event high resolution satellite image, acquired by Worldview-2, of the city of Port-au-Prince, Haiti's capital, were used to evaluate the proposed method. The algorithm was executed on 1200×800 m2 of the data set, including 60 roads, and all the roads were labelled correctly. The visual examination have authenticated the abilities of this method for damage assessment of urban roads network after an earthquake.

Survey design for coal-scale 3D-PS seismic reflection

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. P57-P70 ◽  
Author(s):  
Shaun Strong ◽  
Steve Hearn
Keyword(s):  
Survey Design ◽  
High Amplitude ◽  
Azimuthal Anisotropy ◽  
Mine Planning ◽  
P Wave ◽  
Depth Range ◽  
Low Velocity ◽  
Converted Wave ◽  
Data Set ◽  
Economic Considerations

Survey design for converted-wave (PS) reflection is more complicated than for standard P-wave surveys, due to raypath asymmetry and increased possibility of phase distortion. Coal-scale PS surveys (depth [Formula: see text]) require particular consideration, partly due to the particular physical properties of the target (low density and low velocity). Finite-difference modeling provides a pragmatic evaluation of the likely distortion due to inclusion of postcritical reflections. If the offset range is carefully chosen, then it may be possible to incorporate high-amplitude postcritical reflections without seriously degrading the resolution in the stack. Offsets of up to three times target depth may in some cases be usable, with appropriate quality control at the data-processing stage. This means that the PS survey design may need to handle raypaths that are highly asymmetrical and that are very sensitive to assumed velocities. A 3D-PS design was used for a particular coal survey with the target in the depth range of 85–140 m. The objectives were acceptable fold balance between bins and relatively smooth distribution of offset and azimuth within bins. These parameters are relatively robust for the P-wave design, but much more sensitive for the case of PS. Reduction of the source density is more acceptable than reduction of the receiver density, particularly in terms of the offset-azimuth distribution. This is a fortuitous observation in that it improves the economics of a dynamite source, which is desirable for high-resolution coal-mine planning. The final-survey design necessarily allows for logistical and economic considerations, which implies some technical compromise. However, good fold, offset, and azimuth distributions are achieved across the survey area, yielding a data set suitable for meaningful analysis of P and S azimuthal anisotropy.

scholarly journals A fixed functional set genetic algorithm (FFSGA) approach for function approximation

2006 ◽  
Vol 8 (3) ◽  
pp. 193-206 ◽  
Author(s):  
Mohammad Tufail ◽  
Lindell E. Ormsbee
Keyword(s):  
Genetic Algorithm ◽  
Friction Factor ◽  
Linear Regression Analysis ◽  
Turbulent Pipe Flow ◽  
Decision Variable ◽  
Data Set ◽  
Functional Formulation ◽  
Effective Manner ◽  
Least Squares Optimization ◽  
Moody Diagram

This paper describes a simple mathematical technique that uses a genetic algorithm and least squares optimization to obtain a functional approximation (or computer program) for a given data set. Such an optimal functional form is derived from a pre-defined general functional formulation by selecting optimal coefficients, decision variable functions, and mathematical operators. In the past, functional approximations have routinely been obtained through the use of linear and non-linear regression analysis. More recent methods include the use of genetic algorithms and genetic programming. An example application based on a data set extracted from the commonly used Moody diagram has been used to demonstrate the utility of the proposed method. The purpose of the application was to determine an explicit expression for friction factor and to compare its performance to other available techniques. The example application results in the development of closed form expressions that can be used for evaluating the friction factor for turbulent pipe flow. These expressions compete well in accuracy with other known methods, validating the promise of the proposed method in identifying useful functions for physical processes in a very effective manner. The proposed method is simple to implement and has the ability to generate simple and compact explicit expressions for a given response function.

scholarly journals Application of multivariate statistical techniques in the water quality assessment of Danube river, Serbia

2015 ◽  
Vol 41 (4) ◽  
pp. 96-103 ◽  
Author(s):  
Danijela Voza ◽  
Milovan Vukovic ◽  
Ljiljana Takic ◽  
Djordje Nikolic ◽  
Ivana Mladenovic-Ranisavljevic
Keyword(s):  
Water Quality ◽  
Water Quality Management ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Water Quality Monitoring ◽  
Danube River ◽  
Multivariate Techniques ◽  
Data Set ◽  
Political Issue

AbstractThe aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.

Performance Analysis of Machine Learning Based Classifiers for the Diagnosis of Melanoma Cancer and Comparison

2018 ◽  
Vol 15 (2) ◽  
pp. 558-575
Author(s):  
A. Anto Spiritus Kingsly ◽  
B. Sankaragomathi
Keyword(s):  
Detection System ◽  
Noise Removal ◽  
Morphological Processing ◽  
Svm Classifier ◽  
Bag Of Visual Words ◽  
Data Set ◽  
Visual Words ◽  
Melanoma Cancer ◽  
Processing Step ◽  
Ann Classifier

Melanoma cancer is the most injurious form of cancer which affects the human. Skin cancer has quickly increased in western part of the country among the world. In this paper, diagnosing melanoma in premature stages a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy image of skin is taken, and it is subjected to the pre-processing step for noise removal and post-processing step for image enhancement. Then the processed image undergoes image segmentation using Otsu method and Morphological processing. Second, features are extracted using feature extraction technique-ABCD parameter, GLCM, and FOS. Various feature combinations are given as the input to the KNN, SVM, ANN and Bag of Visual Words classifiers. KNN classifier is used to classify the data set into two classes, SVM classifier is used to classify the data set into three classes, ANN classifier is used to classify the data set based on the number of layers and Bag of Visual Words are used to classify the data set into two classes. Performance is analyzed based on the accuracy of the learning classifier output.

Organizational dynamics: exploring the factors affecting knowledge sharing behavior

Kybernetes ◽  
2019 ◽  
Vol 49 (1) ◽  
pp. 165-181 ◽  
Author(s):  
Nóra Obermayer ◽  
Viktoria Erika Toth
Keyword(s):  
Knowledge Sharing ◽  
Organizational Factors ◽  
Organizational Characteristics ◽  
Organizational Dynamics ◽  
Quantitative Methodology ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting ◽  
Effective Manner ◽  
The Individual

Purpose The purpose of this study is to identify the individual and organizational factors that influence knowledge sharing (KS) behavior within Hungarian organizations. Design/methodology/approach The data were obtained from 238 completed questionnaires collected via the LimeSurvey system. The analysis is based on applied quantitative methodology, both descriptive and inferential statistics were used. The research investigated the relationships between individual and organizational characteristics and the KS behavior at individual and global levels. Findings Among individual factors, significant relationships have been identified regarding the generation and position of individuals, and KS behavior, while gender and education do not seem to play a significant role. With respect to organizational factors, the size of the organization and the tenure of individuals are found to be significant. Research limitations/implications The results of the analysis are limited because the data set was not large enough to investigate inter- and intra-industry variability. Practical implications The outcome of this research can support the design of managerial and organizational processes and incentives that will potentially facilitate KS in a more efficient and effective manner. Such improved KS is likely to improve the overall performance of knowledge-intensive organizations. Originality/value The original value of this research is that individual and organizational characteristics have been identified that influence KS behavior. The study focuses on a single country, Hungary, and provides relevant insight into the organizational dynamics of a specific national context.

High resolution linkage of channel-coastal plain and shallow marine facies belts, Desert Member to Lower Castlegate Sandstone stratigraphic interval, Book Cliffs, Utah-Colorado, USA

2019 ◽  
Vol 131 (9-10) ◽  
pp. 1643-1672 ◽  
Author(s):  
Simon A.J. Pattison
Keyword(s):  
Coastal Plain ◽  
Sediment Supply ◽  
Data Set ◽  
Shallow Marine ◽  
Book Cliffs ◽  
Marine Facies ◽  
Sequence Boundaries ◽  
Allogenic Processes ◽  
Stacking Patterns ◽  
Order Sequence

AbstractThe Campanian Desert Member and Lower Castlegate Sandstone in the Book Cliffs of east-central Utah to western Colorado, USA, has served as a foundational data set in the development of sequence stratigraphy. Contrary to previous work, no third-order sequence boundaries are recognized. These were originally thought to partition the neighboring coastal plain and shallow marine facies belts into separate systems tracts, unlinked in time or space. In contrast, adjoining channel-coastal plain and shallow marine facies belts are genetically-, temporally-, and spatially-related. Evidence includes the (i) synchronous, strongly progradational stacking patterns within each facies belt, (ii) gradational and conformable transitions between adjoining facies belts, accentuated by the ubiquity of flat-topped, rooted foreshore sandstones passing upwards into carbonaceous-rich-mudstone-dominated coastal plain, (iii) parasequence-scale interfingering of coastal plain-channel and foreshore-shoreface deposits, with channels, white caps and coals embedded within stacked shoreface parasequences, (iv) regional correlation of coals and flooding surfaces, and (v) near orthogonal paleocurrent relationship between channels and shorelines. Terminal channels incise into proximal foreshore-shoreface sandstones in most Desert-Castlegate parasequences. Incisions are generally confined to the parasequence in which the channels are nested, rarely cutting deeper. These shoreface-incised channels are cut and filled at a parasequence-scale, and are bounded above by the same flooding surface that caps each foreshore-shoreface package. The ubiquity of ascending regressive shoreface trajectories and near absence of descending regressive trajectories that intersect depositional slope argues against any significant sea level fall. Increased rates of sediment supply, driven by autogenic and/or allogenic processes, likely generated the strongly progradational Desert-Castlegate great tongue of sandstone.

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