Banca de DEFESA: SUZANE ADRIELLY DA SILVA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : SUZANE ADRIELLY DA SILVA
DATE: 19/02/2020
TIME: 14:00
LOCAL: Auditório do Departamento de Física Teórica e Experimental
TITLE:

Seismic Waveforms Inversion Based on Hybrid Optimization

KEY WORDS:

Seismic Waveform Inversion, Progressive Matching, Hybrid Optimization, Adjoint Method

PAGES: 43
BIG AREA: Ciências Exatas e da Terra
AREA: Física
SUMMARY:

The Oil today is a vital resource for society. Besides being a great player in the energy sector, it is also a raw material for many products that are essential in our daily lives. However, the increase of its production is a consequence of the technological advance we have had over the last decades. This advance in data storage and processing has greatly favored an important step in reservoir characterization: subsurface imaging. The purpose of this work is to use Full Waveform Inversion with a hybrid inversion methodology that extracts advantages from two optimization classes, Derivative Free Optimization (DFO) and Gradient Based Optimization (traditional FWI), to obtain an estimate of the model. In practice we use an Adaptation of Particle Swarm Optimization (APSO) where we add two new terms, one of them being a gradient that serves as a guide and a regularization in particle dynamics. The gradient leads us to a derivative-based inversion, while the Particle Swarm Optimization leads us to a naturalistic approach, so we have a hybrid strategy. In the modeling step we use an acoustic approach doing a fourth order finite difference discretization in space and second in time, the gradient term was computed with the adjoint method to approximate the objective function gradient using the image condition and the adjoint field. Another feature of the method proposed in this work is that we use a Progressive Matching inversion strategy in order to reduce the processing and storage cost, so it is necessary to evaluate only the inversion spatial window parameters at each step only, instead of involving all parameters of the model. To evaluate the accuracy of the method we compared our hybrid inversion with a derivative based inversion, using the LBFGS-B. The results shows that for the model used in this work the PSO-based method provided a better estimate of the model, and there is a gain in processing time compared to the traditional FWI which was much more expensive. In all tests we used a resampled cutout of the Marmousi model.

BANKING MEMBERS:
Presidente - 2492756 - JOAO MEDEIROS DE ARAUJO
Interno - 1379465 - GILBERTO CORSO
Externo à Instituição - MARCOS VINICIUS CANDIDO HENRIQUES - UFERSA