Multi Population Genetic Algorithm to estimate snow properties from GPR data

Abstract Multi-population genetic algorithms (DGA or MGA) are based on the partition of the population into several semi-isolated subpopulations (demes). Each sub-population is associated to an independent GA and explores different promising regions of the search space. We evaluate the sensitivity of some parameters to solve a non-linear problem in georadar data analysis. Particularly, we adapt the DGAs to optimize the model parameters of a data set of variable-offset data, collected in variable offset modality with Ground Penetrating Radar, to estimate porosity, saturation and density of snowpack in a glacial environment. The data set comes from investigation on glaciers to estimate the thickness and density of the seasonal snow. The main strategies to select the best parameters of the optimization process are outlined. We analyze the sensitivity on the solution of the optimization problems of some parameters of DGA; we deal with the effects of population and sub-population, and mutation properties. We consider the reflection traveltimes in a layered medium including a relationship between the traveltimes, porosity and saturation of the snow. We solve the problem for the layer thickness and the porosity, saturation and structural exponent of the snow. Reliable results are obtained in the snow density estimating, while the evaluation of free water content into the snow still remains challenging.

[1]  Mrinal K. Sen,et al.  2-D MIGRATION VELOCITY ESTIMATION USING A GENETIC ALGORITHM , 1993 .

[2]  Mrinal K. Sen,et al.  Bayesian inference, Gibbs' sampler and uncertainty estimation in geophysical inversion , 1996 .

[3]  Alberto Bellin,et al.  Decay of a long-term monitored glacier: The Careser glacier (Ortles-Cevedale, European Alps) , 2013 .

[4]  R. B. Rege,et al.  Multimodal inversion of guided waves in georadar data , 2012 .

[5]  D. Gong,et al.  Multi-population Genetic Algorithms with Space Partition for Multi-objective Optimization Problems , 2006 .

[6]  Mrinal K. Sen,et al.  Non‐linear inversion of resistivity profiling data for some regular geometrical bodies , 1995 .

[7]  Mike Dentith,et al.  Inversion of potential field data by genetic algorithms , 1997 .

[8]  F. Boschetti,et al.  Inversion of seismic refraction data using genetic algorithms , 1996 .

[9]  S. Ferraris,et al.  Validation of spatial variability of snowpack thickness and density obtained with GPR and TDR methods , 2011 .

[10]  Michele Pipan,et al.  Velocity analysis from common offset GPR data inversion: theory and application to synthetic and real data , 2014 .

[11]  R. B. Rege,et al.  The mechanical properties of snow and ice of an alpine glacier inferred by integrating seismic and GPR methods , 2015 .

[12]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Michele Pipan,et al.  A new fast methodology to estimate the density of frozen materials by means of common offset GPR data , 2013 .

[16]  L. Sambuelli,et al.  Staggered grid inversion of cross hole 2-D resistivity tomography , 2014 .

[17]  H. Looyenga.  Dielectric constants of heterogeneous mixtures , 1965 .

[18]  C. G. Gardner,et al.  High dielectric constant microwave probes for sensing soil moisture , 1974 .

[19]  Alberto Godio,et al.  Open ended-coaxial Cable Measurements of Saturated Sandy Soils , 2007 .

[20]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[21]  C. H. Dix.  SEISMIC VELOCITIES FROM SURFACE MEASUREMENTS , 1955 .

[22]  Mrinal K. Sen,et al.  Rapid sampling of model space using genetic algorithms: examples from seismic waveform inversion , 1992 .

[23]  Michele Pipan,et al.  Rayleigh wave dispersion curve inversion via genetic algorithms and Marginal Posterior Probability Density estimation , 2007 .

[24]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[25]  Alberto Godio,et al.  Georadar measurements for snow cover density , 2009 .

[26]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.

[27]  Michael Jervis,et al.  Prestack migration velocity estimation using nonlinear methods , 1996 .

[28]  A. Godio.  Performance and experimental evidence of GPR in density estimates of snowpack , 2008 .

[29]  M. Jha,et al.  Vertical electrical sounding survey and resistivity inversion using genetic algorithm optimization technique , 2008 .

[30]  F. Boschetti,et al.  A staged genetic algorithm for tomographic inversion of seismic refraction data , 1995 .

[31]  Mrinal K. Sen,et al.  Genetic inversion of AVO , 1992 .