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:
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Subhashis Mallick, University of Wyoming, USA
Computation, Optimization, and Machine Learning in Seismology
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:
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers 9781119654469
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Paperback. Zustand: 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. Bestandsnummer des Verkäufers LU-9781119654469
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