Metal adsorption and desorption are crucial processes in the remediation of contaminated surficial geomedia (e.g., soils) and aquifers. Contaminant transport modeling, which necessarily includes some adsorption parameter, has important uses in anticipating the direction, speed, and extent of contaminant migration in ground water. Adsorption processes are of vital importance in many industrial processes in the beneficiation of various ores (some of which were formed largely as the result of adsorption processes).

Seismic data must be interpreted using digital signal processing techniques in order to create accurate representations of petroleum reservoirs and the interior structure of the Earth. This book provides an advanced overview of digital signal processing (DSP) and its applications to exploration seismology using real world examples. The book begins by introducing seismic theory, describing how to identify seismic events in terms of signals and noise, and how to convert seismic data into the language of DSP. Deterministic DSP is then covered, together with non-conventional sampling techniques. The final part covers statistical seismic signal processing via Wiener optimum filtering, deconvolution, linear-prediction filtering, and seismic wavelet processing. With over 60 end-of-chapter exercises, seismic data sets, and data processing MATLAB codes included, this is an ideal resource for electrical engineering students unfamiliar with seismic data, and for earth scientists and petroleum professionals interested in DSP techniques.

Soil–structure interaction is a topic of significant importance in the solution of problems in geotechnical engineering. Conventional and ad hoc techniques are usually not sufficient to understand the mechanism and model the challenging behavior at the interfaces and joints prevalent in most structural and foundation systems. In addition to mechanical loading, the behavior of structures containing interfaces can be affected by environmental factors such as fluids, temperature, and chemicals.

In this introductory chapter we describe general problems of spatial environmental data analysis, modeling, validation and visualization. Many of these problems are considered in detail in the following chapters using geostatistical models, machine learning algorithms (MLA) of neural networks and Support Vector Machines, and the Bayesian Maximum Entropy (BME) approach. The term “mapping” in the book is considered not only as an interpolation in two- or threedimensional geographical space, but in a more general sense of estimating the desired dependencies from empirical data.