Deep Learning For Seismic Data Enhancement And Representation

Deep Learning for Seismic Data Enhancement and Representation

Seismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns various factors such as acquisition design, environmental constraints, sampling resolution, and noises. The focus of this book is to investigate efficient seismic data representation and signal enhancement solutions by leveraging the powerful feature engineering capability of deep learning. The book delves into seismic data representation and enhancement issues, ranging from seismic acquisition design to subsequent quality improvement and compression technologies. Given the challenges of obtaining suitable labeled training datasets for seismic data processing problems, we concentrate on exploring deep learning approaches that eliminate the need for labels. We combined novel deep learning techniques with conventional seismic data processing methods, and construct networks and frameworks tailored for seismic data processing. The editors and authors of this book come from both academia and industry with hands-on experiences in seismic data processing and imaging.

Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.

Deconvolution of Seismic Data