000			01905nab a2200241 4500
005			20260520003100.0
008			260224s2012 xxu ing
041			_aInglés
245	0	0	_aApplication of Hierarchical Matrices to Linear Inverse Problems in Geostatistics
260			_a _b _csept./oct. 2012
270			_a13/05/2013 ; 13/05/2013
300			_a18 p. ; 857-875
520			_aTraducción 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.
581			_a5
773	0		_tOil and Gas Science and Technology _g67
942			_cARTICULO
100	1		_aSaibaba, A.K. _953141
100	1		_aAmbikasaran, S. _953142
100	1		_aYue Li, J. _953143
100	1		_aKitanidis, P.K. _953144
100	1		_aDarve, E.F. _953145
999			_c187677 _d187677