A skeptic's view of VVAz and AVAz

We have applied a modern amplitude- and azimuth-preserving seismic data processing workflow to the SEG Advanced Modeling Program (SEAM) Phase II Barrett classic data set — an orthorhombic synthetic seismic model that has extremely dense sampling of all azimuths and offsets. We analyze the resulting prestack depth-migrated offset vector tiles with a variety of methods and software. Note that we only analyze the P-P wave mode, which is the focus of our study. We demonstrate that observed azimuthal changes cannot be correlated with the model's reservoir properties. We have made the migrated data available through SEAM. Compared to modeled data, real onshore seismic data have significantly lower amplitude fidelity, higher noise levels, and more uncertainty in the migration velocity field used for processing. Since we are unable to relate the anisotropy measured from the fully sampled clean SEAM Phase II Barrett synthetic seismic data to the model's known anisotropy, we conclude that it is highly unlikely that azimuthal variations observed on real onshore seismic data will be predictive of reservoir fracture properties.

Obtaining Fixed Effects for Between-Within Designs in Explanatory Longitudinal Item Response Models Using Mplus

Between-within designs that include a person group (i.e., a between-subjects factor) and repeated measures of binary responses over time (i.e., a within-subjects factor) are common in educational and psychological research. This software note describes how explanatory item response models can be specified to analyze longitudinal item-level data to detect fixed effects in Mplus for between-within designs. In particular, a necessary parameter transformation is illustrated in detail to obtain the fixed effects in Mplus.

Providing a Combination Classification (Honeybee Clooney and Decision Tree) Based on Developmental Learning

<p class="zhengwen">The aim of this study is to provide a combination classification based on developmental learning in the proposed method using algorithms inspired by nature (honeybee Clooney) and decision tree, by using algorithm classifier consensus is proposed that this method, at first classifier once implemented and based on the detection rate of input data agreement in the final consensus which is an innovation in this research. To implement the proposed algorithms used MATLAB software. Note that, this is an increase compared to the classifiers ensemble, it have accuracy and fix. This shows that this method of making the ensemble by helping bee Clooney algorithm, when appropriate and effective which the number of data collection records is high or the number of study characteristics is high. In this study, we proposed algorithm on 8 samples tested. However, training time of this method compared with simple ensemble is a slower process but this method compared with simple ensemble method has higher accuracy, this shows, if we want a higher accuracy, we should be spent more time.</p>In general, if the accuracy of the process have a large importance for us, this method can be a good option to get the results that almost optimal.

Afar Print

The growth and development of reader appliances and to get better improvement of screen expertise have made reading on screens less burdensome. The acts of reading are not univocal, as read in many diverse ways with many different target in mind. Reader software can afford different levels of steering support for the exploitation of digital text, presenting potentialities for systematic reading not available in the print-on-paper reading experience and compensating for lack of compass reading and feeling of invincible dominance of text. The parameters of e-text reading and the issues of access remain central to readers and researchers, whether the electronic text is devised and packaged as an “e-book” for portable reading devices, or be located in a server for distribution to library terminals to be downloaded to desktop PCs, laptops or tablet PCs. The power and functionality of reading software – note-taking, highlighting and indexing capabilities, robust open searching across databases – are ultimately linked to open access issues: interoperability, text standards, and digital rights management. These remain key questions for libraries, publishers and researchers.