1,720,979 research outputs found
Equivariant imaging for self-supervised regularly undersampled seismic data interpolation
No full textBecause of the restriction of complex field conditions and economic circumstance, seismic data is usually undersampled in the spatial domain, which needs to be interpolated to meet the requirements of following seismic data processing such as seismic imaging. In this abstract, we present a seismic data interpolation method via an end-to-end self-supervised deep learning framework. Specifically, a CNN is trained only using the observed undersampled seismic data itself. Furthermore, based on the equivariance of seismic data with respect to shift and undersampling, a training strategy that enforces both the measurement consistency and the equivalence is utilized. Experiments on regularly undersampled synthetic and field data interpolation show the effectiveness of our presented method in comparison with deep image prior (DIP) based interpolation method
Deep prior based seismic data interpolation via multi-res U-net
No full textInterpolation of seismic data is an important pre-processing step in most seismic processing workflows. Through the deep image prior paradigm, it is possible to use Convolutional Neural Networks for seismic data interpolation without the costly and prone-to-overfitting training stage. The proposed method makes use of the multi-res U-net architecture as a deep prior to perform interpolation of time slices in order to reconstruct 3D shot gathers. Numerical examples on different corrupted synthetic datasets demonstrate the validity and effectiveness of the proposed approach
INTERPOLATION OF MISSING SHOTS VIA PLUG AND PLAY METHOD WITH CSGS TRAINED DEEP DENOISER
No full textDue to the restriction of complex field conditions, the trace interval in common receiver gathers (CRGs) is often larger than that in common shot gathers (CSGs). This impacts on the stability and precision of the following seismic data processing steps. To solve this issue, we present a Plug and Play method CSGs-trained deep denoiser for the interpolation of missing shots. Specifically, based on the spatial reciprocity theorem, instead of collecting or constructing training datasets, CSGs are used as the training dataset to train a deep convolutional neural network (CNN) Gaussian denoiser. This trained denoiser is then plugged into the alternating direction method of multiplier (ADMM) framework to solve the interpolation inverse problem. A numerical example on field data shows the effectiveness of the presented method in comparison to CNN-POCS method
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Segni sui libri e non solo: il “Fondo Bonarelli-Modena” della Biblioteca Comunale Benincasa di Ancona
No full textPost-Stack Inversion with Uncertainty Estimation through Bayesian Deep Image Prior
No full textWe propose a Bayesian framework for post-stack inversion and uncertainty estimation based on deep priors. A Convolutional Neural Network acts like a nonlinear preconditioner to the inversion problem, capturing the priors from the data in its inner layers. At the same time, it also provides an estimate of the aleatoric uncertainty; this is achieved by minimizing a joint objective function in the CNN parameters space. Then, in a Bayesian framework, Montecarlo dropout is leveraged in order to sample the posterior and characterize the inherent uncertainty. Through synthetic examples we prove our methodology to be effective
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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