This book provides a comprehensive introduction to techniques for quantitative subsidence analysis and visualization with example applications. Subsidence analysis is an essential step to understand basin evolution through geologic time and space in the study of sediments and sedimentary basins. Quantifying techniques have been developed and applied in many basin research projects to evaluate total, tectonic and thermal subsidence. They are also a pre-requisite for basin evolution modelling. Recent studies have applied visualization techniques to understand regional subsidence contexts and trends, which confirmed that three-dimensional visualization of the basin subsidence is highly helpful to gain insight into basin evolution. In this book, we show how geoscience and computer science can be effectively combined in advanced basin analysis, especially in terms of basin subsidence. Each type of subsidence analysis is introduced with example applications. In particular we present a study of the Vienna basin using BasinVis, a MATLAB-based program for analyzing and visualizing basin subsidence. Given its breadth of coverage, this book will benefit students in undergraduate and postgraduate courses and provide helpful information for research projects and industry applications.

Sedimentology is a core discipline of earth and environmental sciences. It enquires the origins, transport and deposition of mineral sediment on the Earth's surface. The subject is a link between positive effects arising from the building of relief by tectonics and the negative action of denudation in drainage catchments and tectonic subsidence in sedimentary basins. The author addresses the principles of the subject, emphasising the advantages of a general science approach and the importance of understanding modern processes. Sedimentology and Sedimentary Basins is not an encyclopaedia, but attempts to stimulate interdisciplinary thought across the whole subject area and related disciplines. The book has been designed to meet the needs of earth and environmental science undergraduates.

The outer parts of collision mountain belts are commonly represented by fold and thrust belts. Major advances in understanding these tectonic settings have arisen from regional studies that integrate diverse geological information in quests to find and produce hydrocarbons. Drilling has provided tests of subsurface forecasts, challenging interpretation strategies and structural models. This volume contains 19 papers that illustrate a diversity of methods and approaches together with case studies from Europe, the Middle East and the Asia-Pacific region. Collectively they show that appreciating diversity is key for developing better interpretations of complex geological structures in the subsurface – endeavours that span applications beyond the development of hydrocarbons.

This Research Topic is Volume III of a series. The previous volume can be found here: Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods - Volume II and Spatial Modelling and Failure Analysis of Natural and Engineering Disasters through Data-based Methods Natural and engineering disasters, which include landslides, rock fall, rainstorm, dam failure, floods, earthquakes, road and building disasters and wildfires, appear as results of the progressive or extreme evolution of climatic, tectonic and geomorphological processes and human engineering activities. It is significant to explore the failure mechanism and carry out spatial modeling of these engineering and natural disasters due to their serious harm to the safety of people's lives and property. The data-based methods, including advanced and successful remote sensing, geographic information systems, machine learning and numerical simulation techniques methods, are promising tools to analyze these complex disasters. Machine Learning models such as neurofuzzy logic, decision tree, artificial neural network, deep learning and evolutionary algorithms are characterized by their abilities to produce knowledge and discover hidden and unknown patterns and trends from large databases, whereas remote sensing and Geographic Information Systems appear as significant technology equipped with tools for data manipulation and advanced mathematical modeling. What is more, the numerical simulation can also be acknowledged as advanced technologies for discovering hidden failure mechanism of disasters. The main objective of this Research Topic is to provide a scientific forum for advancing the successful implementation of Machine Learning (ML) and numerical simulation techniques in operation rules, failure mechanism, spatial and time series prediction, susceptibility mapping, hazard assessment, vulnerability modeling, risk assessment and early warning of complex natural and engineering disasters.