Journal: Mathematical Geosciences

Abbreviation

Math Geosci

Publisher

Springer

Journal Volumes

ISSN

1874-8961
1874-8953

Description

Filters

2010 - 201962020 - 20241
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Publications 1 - 7 of 7
  • Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
    Item type: Journal Article
    Micheletti, Natan; Foresti, Loris; Robert, Sylvain; et al. (2014)
    Mathematical Geosciences
  • The Use of Geographically Weighted Regression for Spatial Prediction
    Item type: Journal Article
    Harris, P.; Fotheringham, A.S.; Crespo, R.; et al. (2010)
    Mathematical Geosciences
  • Erratum to: The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets (vol 42, pg 657, 2010)
    Item type: Other Journal Item
    Harris, P.; Fotheringham, A.S.; Crespo, R.; et al. (2011)
    Mathematical Geosciences
  • High-Performance Parallel Solver for Integral Equations of Electromagnetics Based on Galerkin Method
    Item type: Journal Article
    Kruglyakov, Mikhail; Bloshanskaya, Lidia (2017)
    Mathematical Geosciences
    A new parallel solver for the volumetric integral equations (IE) of electrodynamics is presented. The solver is based on the Galerkin method, which ensures convergent numerical solution. The main features include: (i) memory usage eight times lower compared with analogous IE-based algorithms, without additional restrictions on the background media; (ii) accurate and stable method to compute matrix coefficients corresponding to the IE; and (iii) high degree of parallelism. The solver’s computational efficiency is demonstrated on a problem of magnetotelluric sounding of media with large conductivity contrast, revealing good agreement with results obtained using the second-order finite-element method. Due to the effective approach to parallelization and distributed data storage, the program exhibits perfect scalability on different hardware platforms.
  • Predicting Threshold Exceedance by Local Block Means in Soil Pollution Surveys
    Item type: Journal Article
    Hofer, Christoph; Papritz, Andreas Jürg (2010)
    Mathematical Geosciences
  • History Matching Through a Smooth Formulation of Multiple-Point Statistics
    Item type: Journal Article
    Melnikova, Yulia; Zunino, Andrea; Lange, Katrine; et al. (2015)
    Mathematical Geosciences
    We propose a smooth formulation of multiple-point statistics that enables us to solve inverse problems using gradient-based optimization techniques. We introduce a differentiable function that quantifies the mismatch between multiple-point statistics of a training image and of a given model. We show that, by minimizing this function, any continuous image can be gradually transformed into an image that honors the multiple-point statistics of the discrete training image. The solution to an inverse problem is then found by minimizing the sum of two mismatches: the mismatch with data and the mismatch with multiple-point statistics. As a result, in the framework of the Bayesian approach, such a solution belongs to a high posterior region. The methodology, while applicable to any inverse problem with a training-image-based prior, is especially beneficial for problems which require expensive forward simulations, as, for instance, history matching. We demonstrate the applicability of the method on a two-dimensional history matching problem. Starting from different initial models we obtain an ensemble of solutions fitting the data and prior information defined by the training image. At the end we propose a closed form expression for calculating the prior probabilities using the theory of multinomial distributions, that allows us to rank the history-matched models in accordance with their relative posterior probabilities.
  • Towards a Model-Based Interpretation of Measurements of Mineralogical and Chemical Compositions
    Item type: Journal Article
    Hauser, Juerg; Miron, George D.; Kyas, Svetlana; et al. (2024)
    Mathematical Geosciences
    We introduce a new methodology for inference of fluid composition from measurements of mineralogical or chemical compositions, expanding upon the use of reactive transport models to understand hydrothermal alteration processes. The reactive transport models are used to impute a latent variable explanatory mechanism in the formation of hydrothermal alteration zones and mineral deposits. An expectation maximisation algorithm is then employed to solve the joint problem of identifying alteration zones in the measured data and estimating the fluid composition, based on the fit between the mineral abundances in the measured and predicted alteration zones. Using the hydrothermal alteration of granite as a test case (greisenisation), a range of synthetic tests is presented to illustrate how the methodology enables objective inference of the mineralising fluid. For field data from the East Kemptville tin deposit in Nova Scotia, the technique generates inferences for the fluid composition which compare favourably with previous independent estimates, demonstrating the feasibility of the proposed calibration methodology.
Publications 1 - 7 of 7