1,720,965 research outputs found
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
Identification and recognition of landmine internal structure scattering contribution from GPR data
No full textThe aim of the study was to quantify the potential increase in the information level produced by an increase in the data dimensionality, i.e. from analysing a 1D signature to the investigation of a 3D GPR volume. The experimental campaign was carried out employing two different neutralised landmines, characterised by a different internal structure and buried in controlled conditions. Obviously, the acquisition of a single monodimensional signature of the target has the advantage of being almost effortless, but shows significant limitations in achieving adequate performance, in particular for landmines showing an irregular internal structure. This is a consequence of the impossibility of effectively separating the different scattering contribution. As well, despite producing a clearer and more intuitive image of the target, a single 2D profile is not able to provide reliable performance, hence there is little benefit in acquiring a 2D profile as it still suffers from not producing unambiguous results. The analysis of a 3D volume, instead, allows for an accurate delineation of the internal structure of the target, providing a reliable solution to the complex target design critical issue
Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric Data
Full text linkBuried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of casualties has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance. Nonetheless, being able to detect and localize buried landmines with high precision in an automatic fashion is still considered a challenging task due to the many different boundary conditions that characterize this problem (e.g., several kinds of objects to detect, different soils and meteorological conditions, etc.). In this article, we propose a novel technique for buried object detection tailored to unexploded landmine discovery. The proposed solution exploits a specific kind of convolutional neural network (CNN) known as autoencoder to analyze volumetric data acquired with ground penetrating radar (GPR) using different polarizations. This method works in an anomaly detection framework, indeed we only train the autoencoder on GPR data acquired on landmine-free areas. The system then recognizes landmines as objects that are dissimilar to the soil used during the training step. Experiments conducted on real data show that the proposed technique requires little training and no ad hoc data preprocessing to achieve accuracy higher than 93% on challenging data sets
Post-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
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
A Deep Prior Convolutional Autoencoder for Seismic Data Interpolation
No full textA properly designed skip-connection convolutional autoencoder deep generator is able to capture the inner structure of shot gathers from subsampled seismic data without any pre-training procedure. The complete interpolated data can be reconstructed by feeding the autoencoder with multidimensional random noise and minimizing the mean squared error between generated and measured data. The performances achieved on synthetic and field data show the effectiveness of the proposed method
A tool for processing seismic images: Study on CycleGAN
No full textThe advent of deep learning techniques had a huge impact in the geophysical community. Convolutional Neural Networks have been investigated for interpretation tasks and, lately, for seismic imaging problems traditionally approached with analytical methods. In this manuscript we employ a state-of-the-art CycleGAN for processing migrated images. We show an application scenario in which this post-processing machine transforms migrated images into corresponding reflectivity model of the subsurface. The proposed methodology achieves promising results in both synthetic and field data. The reconstructed reflectivity model exhibits a meaningful topology. However, the results suggest that computer-vision techniques need to be adapted to tackle the complexity of seismic imaging problems. For this purpose, we believe that geophysical knowledge can be embedded into more accurate network design
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