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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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ISBN 10: 1119654467 ISBN 13: 9781119654469
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Añadir al carritoPaperback. Condición: New. A textbook applying fundamental seismology theories to the latest computational tools The goal of computational seismology is to digitally simulate seismic waves, create subsurface models, and match these models with observations to identify subsurface rock properties. With recent advances in computing technology, including machine learning, it is now possible to automate matching procedures and use waveform inversion or optimization to create large-scale models. Computation, Optimization, and Machine Learning in Seismology provides students with a detailed understanding of seismic wave theory, optimization theory, and how to use machine learning to interpret seismic data. Volume highlights include: Mathematical foundations and key equations for computational seismologyEssential theories, including wave propagation and elastic wave theoryProcessing, mapping, and interpretation of prestack dataModel-based optimization and artificial intelligence methodsApplications for earthquakes, exploration seismology, depth imaging, and multi-objective geophysics problemsExercises applying the main concepts of each chapter.
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Añadir al carritoPaperback. Condición: new. Paperback. A textbook applying fundamental seismology theories to the latest computational tools The goal of computational seismology is to digitally simulate seismic waves, create subsurface models, and match these models with observations to identify subsurface rock properties. With recent advances in computing technology, including machine learning, it is now possible to automate matching procedures and use waveform inversion or optimization to create large-scale models. Computation, Optimization, and Machine Learning in Seismology provides students with a detailed understanding of seismic wave theory, optimization theory, and how to use machine learning to interpret seismic data. Volume highlights include: Mathematical foundations and key equations for computational seismologyEssential theories, including wave propagation and elastic wave theoryProcessing, mapping, and interpretation of prestack dataModel-based optimization and artificial intelligence methodsApplications for earthquakes, exploration seismology, depth imaging, and multi-objective geophysics problemsExercises applying the main concepts of each chapter Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por Amer Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Añadir al carritoPaperback. Condición: Brand New. 400 pages. 6.90x0.80x9.80 inches. In Stock.
Idioma: Inglés
Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Añadir al carritoPaperback. Condición: new. Paperback. A textbook applying fundamental seismology theories to the latest computational tools The goal of computational seismology is to digitally simulate seismic waves, create subsurface models, and match these models with observations to identify subsurface rock properties. With recent advances in computing technology, including machine learning, it is now possible to automate matching procedures and use waveform inversion or optimization to create large-scale models. Computation, Optimization, and Machine Learning in Seismology provides students with a detailed understanding of seismic wave theory, optimization theory, and how to use machine learning to interpret seismic data. Volume highlights include: Mathematical foundations and key equations for computational seismologyEssential theories, including wave propagation and elastic wave theoryProcessing, mapping, and interpretation of prestack dataModel-based optimization and artificial intelligence methodsApplications for earthquakes, exploration seismology, depth imaging, and multi-objective geophysics problemsExercises applying the main concepts of each chapter Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Idioma: Inglés
Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Publicado por John Wiley & Sons Inc Okt 2025, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - A textbook applying fundamental seismology theories to the latest computational tools.
Idioma: Inglés
Publicado por American Geophysical Union, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
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Idioma: Inglés
Publicado por John Wiley and Sons Inc, US, 2025
ISBN 10: 1119654467 ISBN 13: 9781119654469
Librería: Rarewaves.com UK, London, Reino Unido
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Añadir al carritoPaperback. Condición: New. A textbook applying fundamental seismology theories to the latest computational tools The goal of computational seismology is to digitally simulate seismic waves, create subsurface models, and match these models with observations to identify subsurface rock properties. With recent advances in computing technology, including machine learning, it is now possible to automate matching procedures and use waveform inversion or optimization to create large-scale models. Computation, Optimization, and Machine Learning in Seismology provides students with a detailed understanding of seismic wave theory, optimization theory, and how to use machine learning to interpret seismic data. Volume highlights include: Mathematical foundations and key equations for computational seismologyEssential theories, including wave propagation and elastic wave theoryProcessing, mapping, and interpretation of prestack dataModel-based optimization and artificial intelligence methodsApplications for earthquakes, exploration seismology, depth imaging, and multi-objective geophysics problemsExercises applying the main concepts of each chapter.
Librería: preigu, Osnabrück, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Computation, Optimization, and Machine Learning in Seismology | Subhashis Mallick | Taschenbuch | AGU Advanced Textbooks | Einband - flex.(Paperback) | Englisch | 2025 | John Wiley & Sons Inc | EAN 9781119654469 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Añadir al carritoPaperback. Condición: new. Paperback. A textbook applying fundamental seismology theories to the latest computational tools The goal of computational seismology is to digitally simulate seismic waves, create subsurface models, and match these models with observations to identify subsurface rock properties. With recent advances in computing technology, including machine learning, it is now possible to automate matching procedures and use waveform inversion or optimization to create large-scale models. Computation, Optimization, and Machine Learning in Seismology provides students with a detailed understanding of seismic wave theory, optimization theory, and how to use machine learning to interpret seismic data. Volume highlights include: Mathematical foundations and key equations for computational seismologyEssential theories, including wave propagation and elastic wave theoryProcessing, mapping, and interpretation of prestack dataModel-based optimization and artificial intelligence methodsApplications for earthquakes, exploration seismology, depth imaging, and multi-objective geophysics problemsExercises applying the main concepts of each chapter Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.