Date: 15th October, 09:00 - 17:00
Target Audience: From introductory to expert knowledge in seismic processing.
Max participants:
Requeriments: No
Carlos Calderón-Macías joined TGS in 2022 as is currently Senior Manager in R&D. Carlos graduated with a PhD in geophysics from the University of Texas at Austin in 1997 and earned a bachelor’s degree in geophysical engineering from the National Autonomous University of Mexico in 1992. Upon graduating from UT Austin, Carlos joined Mobil Technology as a visiting scientist conducting research in multiple elimination and imaging technology. He joined the Mexican Institute of Petroleum (2000) conducting research in multicomponent seismic for hydrocarbon exploration and joined ION Geophysical in 2006. Within ION Geophysical, Carlos advised imaging/model building projects in the GOM and in offshore Brazil. Carlos is currently an associate editor for Geophysical Prospecting.
A major underlying assumption of migration is that the input data are adequately sampled in terms of surface coverage, that the subsurface is adequately illuminated, and that the migration algorithm itself is based on an acceptable numerical approximation of the wave equation. However, in general these assumptions are never fully met, leading to amplitude imbalance and blurring of the output image. To some extent, this blurring and amplitude imbalance can be compensated for via application of some form of localized deconvolution, generally referred to as least-squares migration. This image modification can be performed in either the data or the image domains and can be achieved via an iterative or a single pass process, under the assumption that the velocity model is acceptable. The course reviews concepts in imaging towards improving the conventional migration image and provides an analysis of its application, including possible pitfalls. The main focus of the workshop is to gain a clear understanding on these concepts with open participation and discussions on data problems at all times. A special area of interest are resolution enhancement and amplitude improvements on migrated angle gathers. With this understanding, the course focuses then on what are the main benefits to consider performing imaging with a FWI workflow.
Date: 15th October, 09:00 - 17:00
Target Audience: Students should have familiarity with the Python Programming Language, an
understanding of partial differential equations and their use in physics, and familiarity with
machine learning concepts like training and inference.
Max participants:
Requeriments: No
Pedro Mário Cruz e Silva as Bachelor's degree in Mathematics (1995) and Master's degree in Applied Mathematics and Optimization (1998) from UFPE. Ph.D. in Computer Graphics from PUC-Rio (2004). Worked for 15 years at Tecgraf/PUC-Rio Institute where he created the Computational Geophysics Group. During this period, he led several software development projects as well as research projects in the field of Geophysics. Recently completed an MBA in Business Management at FGV-Rio. Currently, he is the Solutions Architecture Manager for NVIDIA in Latin America.
Caio Araujo da Silva Leao as Geophysicist at Senai Cimatec. Works with informed physics networks in search of optimized solutions in seismic methods.Degree in Geophysics from the Federal University of Bahia. Master in Geophysics in modeling from the Federal University of Bahia in probabilistic modeling of porous media.
High-fidelity simulations in science and engineering are computationally expensive and time-prohibitive for quick iterative use cases, from design analysis to optimization. NVIDIA Modulus, the physics machine learning platform, turbocharges such use cases by building physics-based deep learning models that are 100,000x faster than traditional methods and offer high-fidelity simulation results. In this course, you'll learn how to:
Date: 15th October, 09:00 - 17:00
Target Audience: Geophysicist, Senior undergraduates and Graduate students.
Max participants: 25
Requeriments: No
Associate Professor at the Instituto de Geociencias of Universidade de Brasilia. Graduated in Geology at UFRRJ, specialized in Marine Geology and Geophysics at UFF. M.Sc. in Geophysics from Observatorio Nacional and Ph.D. in Geophysics at the University of Leeds. Has experience in Applied Geophysics, with emphasis on gravity, magnetic and gamma-ray spectrometry methods applied to regional geology, geotectonic, sedimentary basins and mineral prospecting. SBGf President, from September 2021 to October 2023. Has published 29 papers on peer reviewed journals.
Gravimetry was the first geophysical method to aid in the discovery of an oil field in the 20s of the last century. Potential methods (gravity and magnetic) survived the 1980s when there was great progress in other oil exploration techniques and still provide important information about the tectonic and structural configuration of sedimentary basins. There is a large amount of public data available on these methods and knowing how to use them is a fundamental knowledge for anyone who intends to study sedimentary basins. Thus, this course aims to present how potential methods (gravity and magnetic) can help in exploration and in the study and tectonic of sedimentary basins.
Date: 16th October, 08:30 - 16:30
Target Audience: The course is designed to equip geologists, geoscientists, and professionals in related fields.
Max participants: 30
Requeriments: Laptops to run the software, if possible with an installation of python such as Anaconda.
Geophysicist of the Geological Survey of Brazil, with expertise in computational methods, applying in multiple areas such as seismology, airborne geophysics, and artificial intelligence.
The course aims to provide a hands-on approach to generating predictive geological mapping using machine learning and teaches students how to assess the results. Participants will work with real-world cases to address problems and difficulties specific to their own scenarios. There are no prerequisites for taking this course. The course covers an introduction to machine learning, preparing a dataset for geological predictive mapping, and finally, creating their own predictive maps.
Date: 16th October, 08:30 - 16:30
Target Audience: Geophysicist, Senior undergraduates and Graduate students.
Max participants:
Requeriments: Notebooks for running Julia examples
Mauricio D. Sacchi obtained a Diploma in Geophysics from The National University of La Plata, Argentina, in 1988 and a Ph.D., also in Geophysics, from UBC, Canada, in 1996. He joined the Department of Physics at the University of Alberta in 1997. His research interests include seismology, geophysical signal analysis, inverse problems, and seismic imaging methods. M D Sacchi directs the Signal Analysis and Imaging Group, an initiative for advanced geophysical signal processing and imaging research. M D Sacchi is the recipient of the 2012 Medal of the Canadian Society of Exploration Geophysicists (CSEG), was 2014 Central and South America Honorary Lecturer for the Society of Exploration Geophysicists, and the 2016 CSEG Distinguished lecturer. Also, recipient of 2019 Virgil Kauffman Gold Medal. He was the Editor-in-chief of the journal Geophysics from 2016-2018. M D Sacchi was Chair of the Department of Physics at the University of Alberta for two terms (2010-2015 and 2016-2021).
Signal processing methods for seismic record enhancement and reconstruction have been a central part of efforts to improve the quality of seismic datasets and indispensable components of data preconditioning strategies before reliable imaging and inversion. In these lectures, attendees will explore classical methods for seismic signal representation based on transform-based techniques and predictability of signals and then dive into modern aspects of signal processing using assumptions of sparsity and reduced-rank filtering. Attendees will be able to acquire sufficient knowledge to develop algorithms and understand technologies offered by seismic data contractors. 1. The signal representation problem: Expansion of signal in known bases, Fourier, localized transforms, frames, and classical algorithms for retrieval of coefficients that model signals. 2. Predictability of seismic signals in the FX domain: Application of prediction filters to SNR enhancement and data interpolation. Classical Spitz’s interpolation, FK Gülünay’s interpolation. 3. Fourier reconstruction methods for multidimensional seismic signals: Spatial sampling and Fourier Domain. Application of Matching Pursuit, ALFT, Minimum Weighted Norm interpolation, POCS, and other sparsity-promoting algorithms for reconstructing ND seismic volumes. Recovering of regular acquisition from irregular 5D dataset. Examples showing the impact of 5D as a preconditioning tool for SNR enhancement and fold homogenization. 4. Compressed Sensing Methods: What is CS and its connection to classical 5D interpolation. Value added by CS to optimal acquisition design. Reconstruction and deblending methods inspired in CS strategies. Classical solvers for the CS problem. 5. Reduced-rank signal enhancement and reconstruction: Dimensionality reduction as a tool to enhance seismic records in FXY domain. Hankel-based (Cadzow/MSSA) SNR enhancement. Strategies for fast implementation of Hankel-based multidimensional denoising methods. Imputation algorithms for reduced-rank signal reconstruction. 6. Tensor-based data processing: Application of multi-linear algebra methods to SNR enhancement. High-order SVD, Parallel Matrix Factorization and other tensor factorization algorithms. Application to 5D reconstruction and fold homogenization. 7. Emerging methods for SNR enhancement and reconstruction: Dictionary learning, convolutional dictionary learning and Machine Learning methods for SNR enhancement and interpolation. I will provide Julia language templates to reproduce examples.
Date: 16th October, 08:30 - 16:30
Target Audience: Geophysicists and Geologists not specialized in the seismic reservoir characterization. Or seismic reservoir characterization professionals at the beginning of career.
Max participants:
Requeriments: No
Ekaterina Kneller received a B.Sc. (2003), M.Sc. (2004) in Geophysics from the Moscow State University in Russia. Since then, she has been working with seismic reservoir characterization and integrated projects. Her portfolio includes 20+ years in seismic reservoir characterization services both on-site and off-site for oil and gas companies globally, with a focus on seismic inversions, quantitative data analysis, geostatistics. Ekaterina is part of CGG GeoConsulting team in Brazil since 2011.
Ulisses Correia, with a geology background (BSc 2012 Univ. of Lisbon, MSc 2015 and PhD 2019 Univ. of Campinas) is building his career as a geophysicist expert in advanced reservoir characterization. Currently based in Rio de Janeiro, Brazil, for the past 4+ years his focus has been in developing projects with major international and national energy companies ranging from 4D and 3D deterministic and geostatistical inversion. Ulisses is part of CGG GeoConsulting team in Brazil since 2019.
The objective of this course is to acquire theoretical and practical knowledge of the key concepts and methods used in seismic reservoir characterization. The course covers both deterministic and stochastic approaches to characterize the reservoir, focusing on the uncertainty and ways to deal with it. Particular attention is drawn to presenting reservoir characterization as a data integration process. The main concepts discussed in the course are illustrated through several case studies including Brazilian data. Course outline:
Date: 16th October, 08:30 - 16:30
Target Audience: Students, researchers and professionals in Earth Sciences and related fields.
Max participants:
Requeriments: No
Andréa Teixeira Ustra, Professor at the Department of Geophysics of the Institute of Astronomy, Geophysics and Atmospheric Sciences of the University of São Paulo (IAG/USP) and currently coordinator of the USPMag Multiuser Center. Secretary of Academic Relations of the Brazilian Society of Geophysics (SBGf) since September 2021 for the biennium 2021-2023. She holds a bachelor’s degree in physics from the State University of Campinas (2004), a specialization in Environmental Engineering from the State University of Campinas (2005) and a masters and PhD in Geophysics from the University of São Paulo (2008 and 2013). Her main lines of research are Biogeophysics and Environmental Magnetism. Andrea is a level 2 CNPq research productivity grantee.
Rosely Aparecida Liguori Imbernon, degree in Chemical Engineering from the University of Mogi das Cruzes (1986), Master's and Doctorate in Geosciences (Geochemistry and Geotectonics-1993 and 1998) from the University of São Paulo (1998), and Associate Professor at the University of São Paulo (2012). Associate Professor MS-5.3 at the School of Arts, Sciences and Humanities - EACH at the University of São Paulo. Research Areas in Geosciences, with emphasis on Geochemistry (environmental geochemistry) and Environmental Education (geoethics, scientific education and teaching of geosciences).
This course aims to present to students the principles of the critical zone science and the USP Leste Critical Zone Observatory – CZO seed site, at the School of Arts Sciences and Humanities - EACH / USP, and to conduct didactic activities with the data obtained in this CZO. The course will approach Geoethics concepts and the use and occupation of the geosphere; 2030 Agenda – the sustainable development goals (SDGs) achieved from the CZ Science; The Critical Zone (CZ); CZ Science Methods; Architecture and evolution of the CZ; Water transfer through the CZ; Geochemistry and biogeochemistry; Humanity and the CZ.
Date: 16th October, 08:30 - 16:30
Target Audience: Undergraduate students, geophysics professionals and people interested in
the topic.
Max participants: 20
Requeriments: Notebook with the latest version of octave installed to run the examples.
Professor at the University of Brasilia – UnB. Head of the Seismological Observatory at UnB. Doctor in Geosciences (Applied Geophysics) from UnB. Master’s degree in civil engineering (Spatial Information) and graduated in Surveying Engineering from Federal University of Viçosa - UFV. Specialist in Project Management from Getúlio Vargas Foundation - FGV. Has experience in Geodesy (physical and spatial) and Geophysics (potential methods and seismology).
Concepts of direct and inverse problems in geophysics; basic definitions; nomenclatures; basic linear algebra operations; direct problem; overdetermined and underdetermined inverse problems; linear and non-linear problems; weighting, regularization; constraint; and basic applications involving potential methods and seismic methods. The entire course will be taught in Portuguese.
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