Computational Neural Networks For Geophysical Data Processing

Computational Neural Networks for Geophysical Data Processing

This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances in network design, data set design, testing strategy, and error analysis.Computational, rather than artificial, modifiers are used for neural networks in this book to make a distinction between networks that are implemented in hardware and those that are implemented in software. The term artificial neural network covers any implementation that is inorganic and is the most general term. Computational neural networks are only implemented in software but represent the vast majority of applications.While this book cannot provide a blue print for every conceivable geophysics application, it does outline a basic approach that has been used successfully.

Computational neural networks for geophysical data processing

Monitoring Continuous Phenomena

Monitoring continuous phenomena by stationary and mobile sensors has become a common due to the improvement in hardware and communication infrastructure and decrease in it’s cost. Sensor data is now available in near real time via web interfaces and in machine-readable form, facilitated by paradigms like the Internet of Things (IoT). There are still some obstacles in the usability of the data since the positions (in space and time) of observation and the positions of interest usually do not coincide. Interpolation is the technique to fill such gaps and there are manifold methods to perform it. To actually operate a monitoring system, there are problems like unambiguous identification of interpolation method and associated parameters, appropriate interface to store observations and retrieve interpolated data, continuous update of the interpolation model for real time monitoring, compression and progressive retrieval of observational data and critical states definition and notification by using aggregation of values. This book proposes a general system architecture that addresses these problems. It is not confined to details about particular interpolation methods but rather takes a holistic view on the problem of monitoring. State-of-the-art technologies like geostatistics, sensor web enablement and field data types are introduced and applied in order to provide a viable toolset for the problem domain. The focus is on the overall organization of the monitoring and the architectural design of the software system and the associated simulation framework that is used to systematically evaluate different monitoring approaches. The whole cycle of a monitoring entailing observation, interpolation, discretization, storage, retrieval and notification is covered. Concrete solutions for several common problems in this context are provided.