Direct elastic properties to resistivity transforms

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. IM109-IM118
Author(s):  
Dimitrios Economou ◽  
Behzad Alaei
Keyword(s):  
Water Saturation ◽  
Data Sets ◽  
Seismic Velocities ◽  
Even Start ◽  
Novel Approach ◽  
Multivariate Nonlinear Regression ◽  
Subsurface Resistivity ◽  
Relationship Of ◽  
Norwegian Continental Shelf ◽  
Good Agreement

Numerous publications have dealt with estimations of resistivity from elastic parameters and vice versa. Attempts have been made in the cross-property relationship of elastic and electric properties, in particular, velocity to resistivity using different parameters, such as porosity and water saturation. These types of transforms are currently used to predict background seismic velocities and resistivities, or even start models for seismic or controlled source electromagnetic (CSEM) inversions. However, they are not reliable predictors because they depict the regional elastic or electric variations with limited accuracy. We present a novel approach for the development of models capable of estimating the regional subsurface resistivity based on information from regional wells and seismic inversions. We apply multivariate nonlinear regression on data derived from regional wells and seismic inversions and subsequently produced an estimation of subsurface horizontal resistivity that could be either used as a direct hydrocarbon indicator or provide a constraint on the horizontal resistivity in anisotropic CSEM inversions. We have verified the validity of the approach using two data sets from the Norwegian continental shelf. We found very good agreement between the borehole-measured and predicted resistivity.

scholarly journals Integrated magnetotelluric and seismic investigation of Cenozoic graben structure near Obrzycko, Poland

2021 ◽  
Author(s):  
Adam Cygal ◽  
Michał Stefaniuk ◽  
Anna Kret
Keyword(s):  
Seismic Data ◽  
Geophysical Methods ◽  
Geological Structure ◽  
Data Sets ◽  
Low Velocity ◽  
Shallow Subsurface ◽  
Geophysical Model ◽  
Novel Approach ◽  
Loose Deposits ◽  
Static Corrections

AbstractThis article presents the results of an integrated interpretation of measurements made using Audio-Magnetotellurics and Seismic Reflection geophysical methods. The obtained results were used to build an integrated geophysical model of shallow subsurface cover consisting of Cenozoic deposits, which then formed the basis for a detailed lithological and tectonic interpretation of deeper Mesozoic sediments. Such shallow covers, consisting mainly of glacial Pleistocene deposits, are typical for central and northern Poland. This investigation concentrated on delineating the accurate geometry of Obrzycko Cenozoic graben structure filled with loose deposits, as it was of great importance to the acquisition, processing and interpretation of seismic data that was to reveal the tectonic structure of the Cretaceous and Jurassic sediments which underly the study area. Previously, some problems with estimation of seismic static corrections over similar grabens filled with more recent, low-velocity deposits were encountered. Therefore, a novel approach to estimating the exact thickness of such shallow cover consisting of low-velocity deposits was applied in the presented investigation. The study shows that some alternative geophysical data sets (such as magnetotellurics) can be used to significantly improve the imaging of geological structure in areas where seismic data are very distorted or too noisy to be used alone

scholarly journals Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonas Albers ◽  
Angelika Svetlove ◽  
Justus Alves ◽  
Alexander Kraupner ◽  
Francesca di Lillo ◽  
...  
Keyword(s):  
Three Dimensional ◽  
High Specificity ◽  
Histopathological Analysis ◽  
Specific Information ◽  
Data Sets ◽  
Virtual Histology ◽  
Novel Approach ◽  
Cell Tissue ◽  
Immuno Histochemistry ◽  
3D Information

AbstractAlthough X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.

Systematic calculations of energy levels and transitions rates in Mo XXVIII

2020 ◽  
Vol 75 (8) ◽  
pp. 739-747
Author(s):  
Feng Hu ◽  
Yan Sun ◽  
Maofei Mei
Keyword(s):  
Energy Levels ◽  
Oscillator Strengths ◽  
Atomic Data ◽  
Data Sets ◽  
Electron Systems ◽  
Excitation Energies ◽  
Experimental Values ◽  
Qed Effects ◽  
Hyperfine Structures ◽  
Good Agreement

AbstractComplete and consistent atomic data, including excitation energies, lifetimes, wavelengths, hyperfine structures, Landé gJ-factors and E1, E2, M1, and M2 line strengths, oscillator strengths, transitions rates are reported for the low-lying 41 levels of Mo XXVIII, belonging to the n = 3 states (1s22s22p6)3s23p3, 3s3p4, and 3s23p23d. High-accuracy calculations have been performed as benchmarks in the request for accurate treatments of relativity, electron correlation, and quantum electrodynamic (QED) effects in multi-valence-electron systems. Comparisons are made between the present two data sets, as well as with the experimental results and the experimentally compiled energy values of the National Institute for Standards and Technology wherever available. The calculated values including core-valence correction are found to be in a good agreement with other theoretical and experimental values. The present results are accurate enough for identification and deblending of emission lines involving the n = 3 levels, and are also useful for modeling and diagnosing plasmas.

PWA OF πN SCATTERING WITH FIXED-t DISPERSION RELATIONS

2005 ◽  
Vol 20 (08n09) ◽  
pp. 1810-1813
Author(s):  
PEKKO PIIROLA ◽  
M. E. SAINIO
Keyword(s):  
Data Base ◽  
Forward Direction ◽  
Dispersion Relations ◽  
Data Sets ◽  
Coupling Constant ◽  
Partial Waves ◽  
Scattering Measurements ◽  
Pion Nucleon ◽  
Good Agreement

The πN scattering measurements from last couple of decades are not in very good agreement with each other. In fact, using the different data sets one finds different values for the pion-nucleon coupling constant. An analysis with theoretical constraints is the only way to produce accurate partial waves. In this analysis, the fixed-t dispersion relations are used to ensure analyticity in the invariant amplitudes and to decrease the effects of inaccuracies in the data base. Pietarinen's expansion is the method used to enforce the dispersion constraints. The strength of the analyticity constraints is illustrated with C± amplitudes in the forward direction.

Comparison of Small-Angle Neutron Scattering of δ' Precipitation in an Al–Li Alloy at a High-Flux Reactor and at a Pulsed-Neutron Source

1997 ◽  
Vol 30 (5) ◽  
pp. 602-606 ◽  
Author(s):  
G. Albertini ◽  
F. Carsughi ◽  
R. Coppola ◽  
R. K. Heenan ◽  
M. Stefanon
Keyword(s):  
Neutron Scattering ◽  
Small Angle ◽  
Small Angle Neutron Scattering ◽  
Size Distribution Function ◽  
Data Sets ◽  
Flux Reactor ◽  
Average Size ◽  
Pulsed Neutron ◽  
Pulsed Neutron Source ◽  
Good Agreement

Two different small-angle neutron scattering (SANS) facilities, the D11 camera at the Institut Laue–Langevin (ILL, Grenoble, France) and the LOQ time-of-flight diffractometer at the Rutherford Appleton Laboratory (RAL, Didcot, Oxon, England), were used in the investigations of δ′-Al3Li precipitation at 463 K in Al–Li 3% alloy. The results obtained from the steady-state reactor and from the pulsed source by using two different data-acquisition techniques and two different procedures for data analysis are compared. The SANS curves for the same set of samples investigated using the two different instruments are in good agreement within the experimental uncertainties. A check was also made on the metallurgically relevant quantities, namely the average size and the size-distribution function of the δ′ precipitates at the various stages of the ageing process, obtained from the two sets of SANS curves by applying the same numerical method. Good agreement was found between the results from the two data sets.

A Novel Approach for Crawling the Opinions from World Wide Web

2016 ◽  
Vol 6 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Surbhi Bhatia ◽  
Manisha Sharma ◽  
Komal Kumar Bhatia
Keyword(s):  
World Wide ◽  
Opinion Mining ◽  
Real Data ◽  
User Generated Content ◽  
Decision Making Process ◽  
Web Pages ◽  
Data Sets ◽  
Web Technologies ◽  
Design And Implementation ◽  
Novel Approach

Due to the sudden and explosive increase in web technologies, huge quantity of user generated content is available online. The experiences of people and their opinions play an important role in the decision making process. Although facts provide the ease of searching information on a topic but retrieving opinions is still a crucial task. Many studies on opinion mining have to be undertaken efficiently in order to extract constructive opinionated information from these reviews. The present work focuses on the design and implementation of an Opinion Crawler which downloads the opinions from various sites thereby, ignoring rest of the web. Besides, it also detects web pages which frequently undergo updation by calculating the timestamp for its revisit in order to extract relevant opinions. The performance of the Opinion Crawler is justified by taking real data sets that prove to be much more accurate in terms of precision and recall quality attributes.

An evaluation of fire-plume properties simulated with the Fire Dynamics Simulator (FDS) and the Clark coupled wildfire model

2006 ◽  
Vol 36 (11) ◽  
pp. 2894-2908 ◽  
Author(s):  
Ruiyu Sun ◽  
Mary Ann Jenkins ◽  
Steven K Krueger ◽  
William Mell ◽  
Joseph J Charney
Keyword(s):  
Fluid Dynamics ◽  
Data Sets ◽  
Fire Plume ◽  
Fire Dynamics ◽  
Fire Model ◽  
Fire Dynamics Simulator ◽  
Physically Based ◽  
Vertical Grid ◽  
Simplifying Assumptions ◽  
Good Agreement

Before using a fluid dynamics physically based wildfire model to study wildfire, validation is necessary and model results need to be systematically and objectively analyzed and compared to real fires, which requires suitable data sets. Observational data from the Meteotron experiment are used to evaluate the fire-plume properties simulated by two fluid dynamics numerical wildfire models, the Fire Dynamics Simulator (FDS) and the Clark coupled atmosphere–fire model. Comparisons based on classical plume theory between numerical model and experimental Meteotron results show that plume theory, because of its simplifying assumptions, is a fair but restricted rendition of important plume-averaged properties. The study indicates that the FDS, an explicit and computationally demanding model, produces good agreement with the Meteotron results even at a relatively coarse horizontal grid size of 4 m for the FDS, while the coupled atmosphere–fire model, a less explicit and less computationally demanding model, can produce good agreement, but that the agreement is sensitive to surface vertical-grid sizes and the method by which the energy released from the fire is put into the atmosphere.

scholarly journals A Novel Approach to Measure the Hydraulic Capacitance of a Microfluidic Membrane Pump

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chun-Hui Wu ◽  
Chia-Wei Chen ◽  
Long-Sheng Kuo ◽  
Ping-Hei Chen
Keyword(s):  
Linear Correlation ◽  
Experimental Results ◽  
Membrane Thickness ◽  
Membrane Pump ◽  
Novel Approach ◽  
Hydraulic Capacitance ◽  
Linear And Nonlinear ◽  
Center Deflection ◽  
Membrane Deflection ◽  
Good Agreement

A novel approach was proposed to measure the hydraulic capacitance of a microfluidic membrane pump. Membrane deflection equations were modified from various studies to propose six theoretical equations to estimate the hydraulic capacitance of a microfluidic membrane pump. Thus, measuring the center deflection of the membrane allows the corresponding pressure and hydraulic capacitance of the pump to be determined. This study also investigated how membrane thickness affected the Young’s modulus of a polydimethylsiloxane (PDMS) membrane. Based on the experimental results, a linear correlation was proposed to estimate the hydraulic capacitance. The measured hydraulic capacitance data and the proposed equations in the linear and nonlinear regions qualitatively exhibited good agreement.

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

scholarly journals Clustering and enhanced classification using a hybrid quantum autoencoder

2021 ◽  
Author(s):  
Maiyuren Srikumar ◽  
Charles Daniel Hill ◽  
Lloyd Hollenberg
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Quantum Information Theory ◽  
Quantum States ◽  
Data Sets ◽  
New Paradigm ◽  
Novel Approach ◽  
Representational Space ◽  
Quantum Machine Learning ◽  
Pure States

Abstract Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify – and classically represent – their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states – which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

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