Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Sérgio Luiz E F da Silva"'
Autor:
Gustavo Z Dos Santos Lima, João V T de Lima, João M de Araújo, Gilberto Corso, Sérgio Luiz E F da Silva
Publikováno v:
PLoS ONE, Vol 18, Iss 3, p e0282578 (2023)
The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associ
Externí odkaz:
https://doaj.org/article/f7c410d8aae34d588a70831286d171e0
Publikováno v:
PLoS ONE, Vol 17, Iss 11, p e0275416 (2022)
The estimation of physical parameters from data analyses is a crucial process for the description and modeling of many complex systems. Based on Rényi α-Gaussian distribution and patched Green's function (PGF) techniques, we propose a robust framew
Externí odkaz:
https://doaj.org/article/26d2567dfb6e4ff89a4457ef9d3bcf81
Autor:
Sérgio Luiz E F da Silva, Pedro T C Carvalho, Carlos A N da Costa, João M de Araújo, Gilberto Corso
Publikováno v:
PLoS ONE, Vol 15, Iss 10, p e0240999 (2020)
Full-waveform inversion (FWI) is a powerful technique to obtain high-resolution subsurface models, from seismic data. However, FWI is an ill-posed problem, which means that the solution is not unique, and therefore the expert use of the information i
Externí odkaz:
https://doaj.org/article/46e1efaf05824b27bdad5507070825a4
Autor:
Suzane A. Silva, Sérgio Luiz E. F. da Silva, Renato F. de Souza, Andre A. Marinho, João M. de Araújo, Claudionor G. Bezerra
Publikováno v:
Entropy, Vol 23, Iss 8, p 1081 (2021)
The seismic data inversion from observations contaminated by spurious measures (outliers) remains a significant challenge for the industrial and scientific communities. This difficulty is due to slow processing work to mitigate the influence of the o
Externí odkaz:
https://doaj.org/article/255f691e825a42279e1fc0ec9a6dbc7e
Autor:
Adson Alexandre Quirino da Silveira, Renato Ferreira de Souza, Jonathas da Silva Maciel, Jessica Lia Santos da Costa, Daniel Teixeira dos Santos, João Medeiros de Araujo, Sérgio Luiz E. F. da Silva, Gilberto Corso
Publikováno v:
The European Physical Journal B. 96
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::529924b2f3b855358982f14f47de8cdb
https://doi.org/10.1140/epjb/s10051-023-00496-0
https://doi.org/10.1140/epjb/s10051-023-00496-0
Publikováno v:
Entropy, Vol 22, Iss 4, p 464 (2020)
The nonextensive statistical mechanics proposed by Tsallis have been successfully used to model and analyze many complex phenomena. Here, we study the role of the generalized Tsallis statistics on the inverse problem theory. Most inverse problems are
Externí odkaz:
https://doaj.org/article/7e128ddd65a3425caf70339c091712f2
Publikováno v:
Pure and Applied Geophysics. 178:3415-3426
Full-waveform inversion (FWI) is a powerful methodology employed in estimating subsurface physical parameters. FWI is classically formulated as a data-fitting problem based on minimizing the squared $$l_2$$ -norm of the difference between the observe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a6c8267b39fed9c020ad31ff8970f7c
https://doi.org/10.1007/s00024-021-02839-3
https://doi.org/10.1007/s00024-021-02839-3
Publikováno v:
Physical Review E. 106
Extracting physical parameters that cannot be directly measured from an observed data set remains a great challenge in several fields of science and physics. In many of these problems, the construction of a physical model from waveforms is hampered b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::590ebd6f812feefd9e2b1f7a39940175
https://doi.org/10.1103/physreve.106.034113
https://doi.org/10.1103/physreve.106.034113
Publikováno v:
The European Physical Journal B. 95
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba7256fdf4483438a838c3e941ccea41
https://doi.org/10.1140/epjb/s10051-022-00361-6
https://doi.org/10.1140/epjb/s10051-022-00361-6
Autor:
Gustavo Z. dos Santos Lima, João V. T. de Lima, João M. de Araújo, Gilberto Corso, Sérgio Luiz E. F. da Silva
The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92e2f80df01112089a377be36b213665
