1,720,976 research outputs found
Potential fields data modeling: new frontiers in forward and inverse problems
No full textSince the '50, potential fields data modeling has played an important role in analyzing the density and magnetization distribution in Earth's subsurface for a wide variety of applications. Examples are the characterization of ore deposits and the assessment of geothermal and petroleum potential, which turned out to be key contributors for the economic and industrial development after World War II.
Current modeling methods mainly rely on two popular parameterization approaches, either involving a discretization of target geological bodies by means of 2D to 2.75D horizontal prisms with polygonal vertical cross-section (polygon-based approach) or prismatic cells (prism-based approach). Despite the great endeavour made by scientists in recent decades, inversion methods based on these parameterization approaches still suffers from a limited ability to (i) realistically characterize the variability of density and magnetization expected in a study area and (ii) take into account the strong non-uniqueness affecting potential fields theory. The prism-based approach is used in linear deterministic inverse methods, which provide just one single solution, preventing uncertainty estimation and statistical analysis on the parameters we would like to characterize (i.e, density or magnetization). On the contrary, the polygon-based approach is almost exclusively exploited in a trial-and-error modeling strategy, leaving the potential to develop innovative inverse methods untapped. The reason is two-fold, namely (i) its strongly non-linear forward problem requires an efficient probabilistic inverse modeling methodology to solve the related inverse problem, and (ii) unpredictable cross-intersections between polygonal bodies during inversion represent a challenging task to be tackled in order to achieve geologically plausible model solutions.
The goal of this thesis is then to contribute to solving the critical issues outlined above, developing probabilistic inversion methodologies based on the polygon- and prism-based parameterization approaches aiming to help improving our capability to unravel the structure of the subsurface.
Regarding the polygon-based parameterization strategy, at first a deep review of its mathematical framework has been performed, allowing us (i) to restore the validity of a recently criticized mathematical formulation for the 2D magnetic case, and (ii) to find an error sign in the derivation for the 2.75D magnetic case causing potentially wrong numerical results. Such preliminary phase allowed us to develop a methodology to independently or jointly invert gravity and magnetic data exploiting the Hamilton Monte Carlo approach, thanks to which collection of models allow researchers to appraise different geological scenarios and fully characterize uncertainties on the model parameters. Geological plausibility of results is ensured by automatic checks on the geometries of modelled bodies, which avoid unrealistic cross-intersections among them.
Regarding the prism-based parameterization approach, the linear inversion method based on the probabilistic approach considers a discretization of target geological scenarios by prismatic bodies, arranged horizontally to cover it and finitely extended in the vertical direction, particularly suitable to model density and magnetization variability inside strata. Its strengths have been proven, for the magnetic case, in the characterization of the magnetization variability expected for the shallower volcanic unit of the Mt. Melbourne Volcanic Field (Northern Victoria Land, Antarctica), helping significantly us to unravel its poorly known inner geophysical architecture
Magnetic Anomalies Caused by 2D Polygonal Structures With Uniform Arbitrary Polarization: New Insights From Analytical/Numerical Comparison Among Available Algorithm Formulations
No full textSince the '60s of the last century, the calculation of the magnetic anomalies caused by 2D uniformly polarized bodies with polygonal cross‐section has been mainly performed using the popular algorithm of Talwani and Heirtzler (1962, 1964). Recently, Kravchinsky et al. (2019, https://doi.org/10.1029/2019GL082767) claimed errors in the above algorithm formulation, proposing new corrective formulas and questioning the effectiveness of almost 60 years of magnetic calculations. Here we show that the two approaches are equivalent and Kravchinsky et al.'s formulas simply represent an algebraic variant of those of Talwani and Heirtzler. Moreover, we analyze a large amount of random magnetic scenarios, involving both changing‐shape polygons and a realistic geological model, showing a complete agreement among the magnetic responses of the two discussed algorithms and the one proposed by Won and Bevis (1987, https://doi.org/10.1190/1.1442298). We release the source code of the algorithms in Julia and Python languages
Imaging the Mt. Melbourne Volcanic Field (Northern Victoria Land, Antarctica): a Hamiltonian Monte Carlo approach applied to high-resolution aeromagnetic data
No full textThe Mt. Melbourne Volcanic Complex (MMVC) is located in Northern Victoria Land (Antarctica) along the western flank of the West Antarctic Rift System, at the boundary with the Transantarctic
Mountains. It is constituted by two main volcanic areas, i.e. the Mt. Melbourne Edifice (MME) and the Cape Washington Shield (CWS), and some other minor centres.
To date, the inner structure of this volcanic complex is still poorly known, being the direct geological information on site confined to either glacial erratics or a few rock outcrops not hidden
by the ice sheet. Consequently, even the temporal building up and evolution of the MMVC as well as its primary magmatic source are still under investigation (debated).
Recently, we attempted to define the geological structure of the MMVC by means of digital enhancement and forward modeling performed on a high-resolution aeromagnetic dataset
(Ghirotto et al. 2020, EGU). Coupling both information derived from past geological/geophysical studies and unpublished magnetic susceptibility measurements from rock samples collected in
the field, we proposed two models to explain the chronological evolution of the MME and CWS. These models involve either i) major magmatic events occurred in periods of both normal and
reverse magnetic polarity or ii) only magmatic flows with normal polarity.
To gain further insights into the geological structure and the geodynamic evolution of the MMVC in relation to the two proposed models, we develop here a Hamiltonian Monte Carlo (HMC)
algorithm (Fichtner et al. 2018) based on the probabilistic approach to inverse problems. To date, this methodology has never been applied to aeromagnetic data for geological studies. In detail,
the above proposed models provide some soft a priori information from which to start exploring potential solutions. The parameterization of the volcanic area is defined in terms of 2-D polygonal
bodies, representing e.g. magmatic lava flows, where the unknown parameters are represented by both the position of the vertices and/or the magnetization (induced and/or remnant), resulting in a
non-linear forward model. The HMC algorithm requires the computation of gradients of the posterior probability density (PPD), i.e., derivatives of the objective functional with respect to the
position of vertices of the bodies and magnetization, in order to better move the inversion process toward high-probability areas in the model space manifold. We implement such calculations using automatic differentiation, a tool which is very accurate and fast compared to other approaches such as finite difference. The result of the inversion is then a collection of models representing the
PPD, from which statistical analysis can provide measures of uncertainty and plausible geological scenarios.
In this study we present some preliminary results of applying the above-mentioned methodology, which finally could help unravel the framework of the MMVC
HMCLab: a framework for solving diverse geophysical inverse problems using the Hamiltonian Monte Carlo method
Full text linkThe use of the probabilistic approach to solve inverse problems is becoming
more popular in the geophysical community, thanks to its ability to address
nonlinear forward problems and to provide uncertainty quantification. However,
such strategy is often tailored to specific applications and therefore there is
a lack of a common platform for solving a range of different geophysical
inverse problems and showing potential and pitfalls. We demonstrate a common
framework to solve such inverse problems ranging from, e.g, earthquake source
location to potential field data inversion and seismic tomography. Within this
approach, we can provide probabilities related to certain properties or
structures of the subsurface. Thanks to its ability to address high-dimensional
problems, the Hamiltonian Monte Carlo (HMC) algorithm has emerged as the
state-of-the-art tool for solving geophysical inverse problems within the
probabilistic framework. HMC requires the computation of gradients, which can
be obtained by adjoint methods, making the solution of tomographic problems
ultimately feasible. These results can be obtained with "HMCLab", a tool for
solving a range of different geophysical inverse problems using sampling
methods, focusing in particular on the HMC algorithm. HMCLab consists of a set
of samplers and a set of geophysical forward problems. For each problem its
misfit function and gradient computation are provided and, in addition, a set
of prior models can be combined to inject additional information into the
inverse problem. This allows users to experiment with probabilistic inverse
problems and also address real-world studies. We show how to solve a selected
set of problems within this framework using variants of the HMC algorithm and
analyze the results. HMCLab is provided as an open source package written both
in Python and Julia, welcoming contributions from the community.Comment: 21 pages, 4 figure
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
Focused Inversion of Gravimetric and Magnetotelluric Data for Geothermal Investigations
No full textFocused inversion techniques may be applied to geophysical data inversion in order to image complex structures in the subsoil. These algorithms may image complex “blocky” structures giving useful information in geothermal exploration that may be smoothed out by standard inversion algorithms that use stabilizer that penalize sharp transitions. We have tested the modified total variation and the maximum gradient support stabilizers in the inversion of synthetic and field magnetotelluric and gravimetric data. The gravimetric data from the Luhoi geothermal prospect have been used to map the sharp density transition between the sandstone and the overlying claystone layers. The resulting horst structure imaged in 2D and 3D models by the maximum gradient support stabilizer solution allow to trace the main fault system that drives the up-flow of hydrothermal waters. The 1D magnetotelluric “blocky” models with lateral constrain (pseudo-3D) image the lithological contact between the claystone and sandstone far from the horst area and reveal resistivity variations in the claystone layer associated with sand lenses. In the horst area, resistivity models image hydrothermal alteration affecting the sandstone layer
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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