This book provides a practical guide to applying soft-computing methods to interpret geophysical data. It discusses the design of neural networks with Matlab for geophysical data, as well as fuzzy logic and neuro-fuzzy concepts and their applications. In addition, it describes genetic algorithms for the automatic and/or intelligent processing and interpretation of geophysical data.

This book presents intelligent methods like neural, neuro-fuzzy, machine learning, deep learning and metaheuristic methods and their applications in both volcanology and seismology. The complex system of volcanoes and also earthquakes is a big challenge to identify their behavior using available models, which motivates scientists to apply non-model based methods. As there are lots of seismology and volcanology data sets, i.e., the local and global networks, one solution is using intelligent methods in which data-based algorithms are used.

This volume presents peer reviewed and selected papers of the International Youth Conference on Electronics, Telecommunications and Information Technologies (YETI-2020), held in Peter the Great St. Petersburg Polytechnic University, St. Petersburg on July 10–11, 2020. It discusses current trends and major advances in electronics, telecommunications, optical and information technologies, focusing, in particular, on theoretical and practical aspects of developing novel devices and materials, improving data processing methods and technologies. The conference brings together young researchers and early-career scientists participating in a series of lectures and presentations, establishing contacts with potential partners, sharing new project ideas and starting new collaborations.

The design, construction, and upkeep of infrastructure is comprised of a multitude of dimensions spanning a highly complex paradigm of interconnected opportunities and challenges. While traditional methods fall short of adequately accounting for such complexity, artificial intelligence (AI) presents novel and out-of-the-box solutions that effectively tackle the growing demands of our infrastructure. The convergence between AI and civil engineering is an emerging frontier with tremendous potential. The book is likely to provide a boost to the state of infrastructure engineering by fostering a new look at civil engineering that capitalizes on AI as its main driver. It highlights the ongoing push to adopt and leverage AI to realize contemporary, intelligent, safe, and resilient infrastructure. The book comprises interdisciplinary and novel works from across the globe. It presents findings from innovative efforts supplemented with physical tests, numerical simulations, and case studies – all of which can be used as benchmarks to carry out future experiments and/or facilitate the development of future AI models in structural engineering, traffic engineering, construction engineering, and construction materials. The book will serve as a guide for a wide range of audiences, including senior undergraduate and graduate students, professionals, and government officials of civil, traffic, and computer engineering backgrounds, as well as for those engaged in urban planning and human sciences.

This comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects.It spans a wide spectrum of applications in the oil industry, crossing many discipline boundaries such as geophysics, geology, petrophysics and reservoir engineering. It is complemented by several tutorial chapters on fuzzy logic, neural networks and genetic algorithms and geostatistics to introduce these concepts to the uninitiated. The application areas include prediction of reservoir properties (porosity, sand thickness, lithology, fluid), seismic processing, seismic and bio stratigraphy, time lapse seismic and core analysis.There is a good balance between introducing soft computing and geostatistics methodologies that are not routinely used in the petroleum industry and various applications areas. The book can be used by many practitioners such as processing geophysicists, seismic interpreters, geologists, reservoir engineers, petrophysicist, geostatistians, asset mangers and technology application professionals. It will also be of interest to academics to assess the importance of, and contribute to, R&D efforts in relevant areas.

In most oil exploration and production problems, we deal with limited and incomplete data. We are constantly trying to extrapolate information from sparse measurements, for example, sparse well data and limited core measurements on the one hand and large volumes of seismic data with limited spatial resolution on the other hand. We resort to statistical methods to accomplish the data extrapolation and the integration of diverse data sets in constructing a coherent and meaningful model of the subsurface. Traditional statistical methods both for spatial and temporal extrapolation have been used in E&P for several decades. One of the main uses of statistics has been for reservoir characterization through integrating information and data from various sources with varying degrees of uncertainty such as log, well tests, and seismic data. Other applications include establishing relationships between measurements and reservoir properties, and reserve estimation and oil field economics along with the associated risk factors.