Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Daniela Szwarcman"'
Autor:
Daniela Szwarcman, Jorge Guevara, Maysa M. G. Macedo, Bianca Zadrozny, Campbell Watson, Laura Rosa, Dario A. B. Oliveira
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract The stochastic synthesis of extreme, rare climate scenarios is vital for risk and resilience models aware of climate change, directly impacting society in different sectors. However, creating high-quality variations of under-represented samp
Externí odkaz:
https://doaj.org/article/ecf02ba2308946bba52f3089348e791a
Autor:
Jorge Guevara, Maria Garcia, Priscilla Avegliano, Debora Lima, Dilermando Queiroz, Maysa Macedo, Leonardo Tizzei, Daniela Szwarcman, Bianca Zadrozny, Campbell Watson, Anne Jones
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 11, Pp n/a-n/a (2023)
Abstract Resampling‐based weather generators simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are consistent with the observed ones. These generators are fully data‐dri
Externí odkaz:
https://doaj.org/article/afcc9709b07f469c94951c5df36a0b8b
Autor:
Dario Augusto Borges Oliveira, Daniela Szwarcman, Rodrigo da Silva Ferreira, Semen Zaytsev, Daniil Semin
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Abstract Current seismic processing workflows in the oil and gas industry involve several interactions between different experts to optimize the overall data quality in various tasks, such as noise attenuation, velocity analysis and horizon picking.
Externí odkaz:
https://doaj.org/article/3058d7ac66554cf2a714ebf9122e8ffb
Autor:
Paula Harder, Venkatesh Ramesh, Alex Hernandez-Garcia, Qidong Yang, Prasanna Sattigeri, Daniela Szwarcman, Campbell Watson, David Rolnick
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational cos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0ae96d87ebd55554092e19965e0e358
https://doi.org/10.5194/egusphere-egu23-4350
https://doi.org/10.5194/egusphere-egu23-4350
Autor:
Jorge Luis Guevara Diaz, Bianca Zadrozny, Campbell Watson, Daniela Szwarcman, Debora Lima, DIlermando Queiroz, Leonardo Tizzei, Maria Garcia, Maysa Macedo, Priscilla Avegliano, Anne Jones
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2178081b336a3c6515cbdc8606542795
https://doi.org/10.1002/essoar.10512970.1
https://doi.org/10.1002/essoar.10512970.1
Autor:
Dario Augusto Borges Oliveira, Andrea Britto Mattos, Semen Zaytsev, Daniil Semin, Daniel Civitarese, Daniela Szwarcman, Matheus Oliveira
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 59:5317-5324
Seismic interpretation is a complex procedure that depends on many and interdependent data analyses. One of the essential steps in this process is picking horizons in seismic images, which is time-consuming and prone to errors when performed manually
Autor:
Daniela Szwarcman, Daniil Semin, Dario Augusto Borges Oliveira, Semen Zaytsev, Rodrigo S. Ferreira
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Scientific Reports
Scientific Reports
Current seismic processing workflows in the oil and gas industry involve several interactions between different experts to optimize the overall data quality in various tasks, such as noise attenuation, velocity analysis and horizon picking. While man
Autor:
Campbell Watson, Jorge Guevara, Daniela Szwarcman, Dario Oliveira, Leonardo Tizzei, Maria Garcia, Priscilla Avegliano, Bianca Zadrozny
Climate change is making extreme weather more extreme. Given the inherent uncertainty of long-term climate projections, there is growing need for rapid, plausible “what-if” climate scenarios to help users understand climate exposure and examine r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7890c8d3fa981435c96e72825b9b81d4
https://doi.org/10.5194/egusphere-egu22-10773
https://doi.org/10.5194/egusphere-egu22-10773
Publikováno v:
Women in Computational Intelligence ISBN: 9783030790912
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2817f782df5423300d60ef98f30c0858
https://doi.org/10.1007/978-3-030-79092-9_14
https://doi.org/10.1007/978-3-030-79092-9_14
Publikováno v:
Applied Soft Computing. 120:108674