01905nab a2200241 4500 20260520003100.0 260224s2012 xxu ing Inglés Application of Hierarchical Matrices to Linear Inverse Problems in Geostatistics sept./oct. 2012 13/05/2013 ; 13/05/2013 18 p. ; 857-875 Traducción del resúmen del autor: Characterizing the uncertainty in the subsurface is an important step for exploration and extraction of natural resources, the storage of nuclear material and gasses such as natural gas or CO2. Imaging the subsurface can be posed as an inverse problem and can be solved using the geostatistical approach [Kitanidis P.K. (2007) Geophys. Monogr. Ser. 171, 19-30, doi:10.1029/171GM04; Kitanidis (2011) doi: 10.1002/9780470685853. ch4, pp. 71-85] which is one of the many prevalent approaches. We briefly describe the geostatistical approach in the context of linear inverse problems and discuss some of the challenges in the large-scale implementation of this approach. Using the hierarchical matrix approach, we show how to reduce matrix vector products involving the dense covariance matrix from (m2) to (m log m), where m is the number of unknowns. Combined with a matrix-free Krylov subspace solver, this results in a much faster algorithm for solving the system of equations that arise from the geostatistical approach. We illustrate the performance of our algorithm on an application, for monitoring CO2 concentrations using crosswell seismic tomography. 5 Oil and Gas Science and Technology 67 ARTICULO Saibaba, A.K. 53141 Ambikasaran, S. 53142 Yue Li, J. 53143 Kitanidis, P.K. 53144 Darve, E.F. 53145 187677 187677 0 0 0 0 255496 BAAB BAAB 2026-03-06 200059819 0 200059819 2026-03-06 00:17:39 2026-03-06 ARTICULO