Deep Learning Empowers The Next Generation Of Seismic Interpretation
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Deep Learning Empowers the Next Generation of Seismic Interpretation
With the ever developing data acquisition techniques, seismic processing deals with massive amount of high quality 3-D data with greater pressure to interpret the data more efficiently. Currently, seismic interpretation such as fault analysis and salt detection is a tedious, manual, and time-consuming process. Modern interpretive tools still rely on interpreter while only utilizing the data qualitatively as a backdrop or indirect guide. Therefore, the seismic analysis iterations could take multiple months with human expertise. The advancements in computer technology creates opportunities to develop automated tools for seismic interpretation that only a few years ago would have been prohibitively expensive. In this dissertation, I address the problem by investigating efficient seismic interpretation tools, designing related algorithms, and show the feasibility and effectiveness of applying them to various demanding interpretation problems on 2D/3D datasets. The tools are based on deep neural networks and employ convolutional layers to achieve artificial visual understanding of the datasets. First, I formulate salt detection as an image segmentation problem and develop a CNN to solve this problem with high efficiency and accuracy. CNNs with encoder-decoder architecture and skip-connections allows for extracting essential information from training data, thus results in high accuracy and great generalization across different type of datasets. Further extending from the segmentation end-to-end network framework, I introduce a recurrent style network for tracking irregular geobodies. The improvement is two-fold: the tracking algorithm allows for instance separation during segmentation, and the atomic design allows for more interaction on the user side to control the model application on various datasets. Apart from these supervised learning frameworks, I found that unsupervised learning provides even more powerful tools in other interpretation tasks. In the following chapter, I investigate the possibility to exploit the deep CNN architecture itself as a model parameterization method and perform image enhancing tasks. The deep network is optimized iteratively and can constrain the space of solutions to admissible models. Inspired by automatic recommendation system, in the next chapter, I propose a network that transforms seismic waveforms into a latent space in which they are aligned by similarities. Waveforms that belong to the same horizon, which are more similar to each other, can be extracted from the latent space more easily. In the last chapter, I propose a network architecture, plane-wave neural networks (PWNN), combining plane-wave destruction (PWD) filters and CNN into a single architecture. CNN can extract nonlinear features from spatial information, however, lacks the ability to understand spectral information. On the other hand, PWD filter, a local plane-wave model tailored specifically for representing seismic data, is effective to extract signals aligned along dominant seismic events. Finally, I discuss known limitations and suggest possible future research topics
CubeSat Science
Discover the limitless possibilities of space exploration with "CubeSat Science," an insightful eBook that opens the door to the world of miniature satellites revolutionizing scientific research. From monitoring Earth's climate to unlocking the mysteries of distant exoplanets, CubeSats have become the small but mighty pioneers of space science, showing that bigger isn't always better. Begin your journey with an introduction to the fascinating history and potential of CubeSats, comparing these compact marvels to their traditional satellite counterparts. Uncover why these small satellites are making big waves in the scientific community and how they are democratizing access to space. Delve into the mechanics of CubeSats, exploring their innovative design, propulsion, and communication systems that allow them to operate efficiently in the vastness of space. Learn how these technologies enable groundbreaking research that was once unthinkable. Venture across diverse scientific domains where CubeSats are leading the charge. Track climate change from above, study space weather phenomena, and delve into the search for life beyond Earth. From geoscience applications like earthquake monitoring to cutting-edge space physics and astronomy, CubeSats are at the forefront of discovery. Explore the exciting potential of these satellites in educational settings, where they inspire and engage the next generation of scientists. Practical chapters provide guidance on developing a CubeSat mission, from planning and funding to launch and operations, paving the way for hands-on learning and innovation. "CubSat Science" also addresses the challenges these tiny titans face, including technical constraints and regulatory hurdles, ensuring a comprehensive understanding of both their strengths and limitations. As the future of CubeSat technology continues to evolve, this eBook serves as a beacon, highlighting emerging trends and collaborative missions shaping tomorrow's cosmic adventures. Join the CubeSat revolution today and be a part of the endless frontier of exploration.
Marketing Intelligence for the New Normal: Next Generation Solutions to Drive Business Success
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-03-07
In today's rapidly evolving digital landscape, businesses face unprecedented challenges and opportunities in understanding and engaging with their customers. Marketing Intelligence for the New Normal: Next Generation Solutions to Drive Business Success provides a comprehensive roadmap for navigating this complex terrain, empowering businesses to harness the power of data and technology to drive growth and innovation. This book is a must-read for marketing professionals, business leaders, and anyone seeking to stay ahead of the curve in the digital age. With its in-depth analysis of emerging trends, practical case studies, and actionable insights, Marketing Intelligence for the New Normal offers a wealth of knowledge and strategies for thriving in the new normal. Key Features: * Comprehensive Coverage: Explores the fundamental principles of marketing intelligence, the latest market research techniques, and the ethical and legal considerations associated with data-driven decision-making. * Real-World Examples: Provides real-world examples and case studies that illustrate how businesses across various industries have successfully leveraged marketing intelligence to drive growth and innovation. * Practical Insights: Offers actionable insights and strategies for harnessing the power of big data, artificial intelligence, and social media to gain a deeper understanding of customers and optimize marketing strategies. * Future-Focused Perspective: Examines emerging trends and technologies that are shaping the future of marketing intelligence, helping businesses stay ahead of the curve and anticipate future challenges and opportunities. With its comprehensive coverage, practical guidance, and forward-looking perspective, Marketing Intelligence for the New Normal is an indispensable resource for anyone seeking to unlock the full potential of data-driven marketing and achieve measurable business success. About the Author: Pasquale De Marco is a renowned expert in the field of marketing intelligence, with over two decades of experience in helping businesses leverage data and technology to drive growth. A sought-after speaker and consultant, Pasquale De Marco has worked with leading organizations across various industries, providing strategic guidance and innovative solutions to complex marketing challenges. Pasquale De Marco is passionate about empowering businesses to harness the power of data and technology to achieve measurable success. With Marketing Intelligence for the New Normal, Pasquale De Marco provides a comprehensive guide for navigating the rapidly evolving digital landscape and unlocking the full potential of data-driven marketing. If you like this book, write a review!