Full-waveform inversion (FWI) tries to estimate velocity models of the subsurface with improved accuracy and resolution compared to conventional methods. To be successful, it needs input data that is rich in low frequencies and possibly characterized by long source-to-receiver offsets. The correct solution of the inverse problem by means of local methods is facilitated if the starting model lies in the “valley” of the cost-function global minimum. We explore the possibility of relaxing this requirement by using genetic algorithms, a stochastic optimization method, as the driver of the FWI (GA FWI). However, stochastic methods are affected by the “curse of dimensionality,” meaning that they require huge and sometimes even unaffordable computer resources for inverse problems with many unknowns and costly forward modeling. Therefore, we need to adopt proper stratagems in the inversion and limit our goal to the estimation of a velocity macromodel that is of a model with only the long-wavelength velocity structures, which could eventually act as the starting model for a local, higher-resolution gradient-based inversion. To this end, in the GA FWI we parametrize the subsurface with two grids: (1) a coarse grid with widely spaced nodes, that is unknowns, for the inversion, and (2) a fine grid with shorter spacing for the modeling. As a side result, we can also have an estimate of the uncertainty at the solution nodes of the grid. The approach we discuss is 2D acoustic in the time domain, with finite difference forward modeling. The examples we show refer to the Marmousi model and to a marine field data set.

Two-grid genetic algorithm full-waveform inversion

MAZZOTTI, ALFREDO;STUCCHI, EUSEBIO MARIA;TOGNARELLI, ANDREA;ALEARDI, MATTIA;SAJEVA, ANGELO
2016-01-01

Abstract

Full-waveform inversion (FWI) tries to estimate velocity models of the subsurface with improved accuracy and resolution compared to conventional methods. To be successful, it needs input data that is rich in low frequencies and possibly characterized by long source-to-receiver offsets. The correct solution of the inverse problem by means of local methods is facilitated if the starting model lies in the “valley” of the cost-function global minimum. We explore the possibility of relaxing this requirement by using genetic algorithms, a stochastic optimization method, as the driver of the FWI (GA FWI). However, stochastic methods are affected by the “curse of dimensionality,” meaning that they require huge and sometimes even unaffordable computer resources for inverse problems with many unknowns and costly forward modeling. Therefore, we need to adopt proper stratagems in the inversion and limit our goal to the estimation of a velocity macromodel that is of a model with only the long-wavelength velocity structures, which could eventually act as the starting model for a local, higher-resolution gradient-based inversion. To this end, in the GA FWI we parametrize the subsurface with two grids: (1) a coarse grid with widely spaced nodes, that is unknowns, for the inversion, and (2) a fine grid with shorter spacing for the modeling. As a side result, we can also have an estimate of the uncertainty at the solution nodes of the grid. The approach we discuss is 2D acoustic in the time domain, with finite difference forward modeling. The examples we show refer to the Marmousi model and to a marine field data set.
2016
Mazzotti, Alfredo; Bienati, Nicola; Stucchi, EUSEBIO MARIA; Tognarelli, Andrea; Aleardi, Mattia; Sajeva, Angelo
File in questo prodotto:
File Dimensione Formato  
TLE_two-gridGA-FWI.pdf

solo utenti autorizzati

Tipologia: Versione finale editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 421.75 kB
Formato Adobe PDF
421.75 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
TLE FWI FIRST_LOOK_PDF0001.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.62 MB
Formato Adobe PDF
2.62 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/854245
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 32
  • ???jsp.display-item.citation.isi??? ND
social impact