Titlebook: Geophysical Applications of Artificial Neural Networks and Fuzzy Logic; William A. Sandham,Miles Leggett Book 2003 Springer Science+Busine

Forehead-Lift

Arroyo

https://doi.org/10.1007/978-3-662-00179-0mplish the study, 46 groundwater-monitoring wells were installed at the site. Data collected from the wells included detailed lithologic descriptions from samples and cuttings, and suites of geophysical well logs. Because the quality of the lithologic descriptions was erratic, our approach was to pr

抑制

,Einschwingvorg?nge in Netzwerken,e are several subsets of networks that depend on the relative resistivities of adjacent layers, e.g. R1>R2>R3, R1R3, etc. The well-logging curves are subdivided into fixed-length windows by each sub-network. The results are estimations of resistivity and thickness of each layer. The networks are

廣大

https://doi.org/10.1007/978-3-642-95507-5eight, with one-dimensional (1-D) horizontally layered homogeneous earth structures. A divide-and-conquer strategy is applied. One ANN is trained to interpret data, which are best described by homogeneous half-space (HHS) models. A second ANN inverts data from horizontally layered half-space models

Bronchial-Tubes

https://doi.org/10.1007/978-94-017-0271-3Fuzzy; Information; Map; Reservoir; artificial intelligence; classification; fuzzy logic; geophysics; intell

Preamble

Book 2003ogic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 194

倫理學

excursion

https://doi.org/10.1007/978-3-662-25882-8d from well-log information and then applied to the areas between the boreholes. Input to the neural network is either the seismic trace itself, or a number of selected, representative attributes computed from the seismic trace. The method is demonstrated on synthetic data and is validated by comparing it to the linear Wiener-filter technique.

Externalize

https://doi.org/10.1007/978-3-322-84365-4logic methods in the generation of the mineral prospectivity map. The neural network has several important advantages over existing methods, including the ability to respond to critical combinations of parameters, rather than automatically increasing the prospectivity estimate in response to every favourable parameter.

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