Elastic Imaging using Multiprocessor Computing Systems

5 Jul 2016, 14:00
15m
LIT Conference Hall

LIT Conference Hall

Sectional reports 8. High performance computing, CPU architectures, GPU, FPGA 8. High performance computing, CPU architectures, GPU, FPGA

Speaker

Dr Vasily Golubev (Moscow Institute of Physics and Technology)

Description

One of the main method for search and exploration of oil and gas deposits and understanding the structure of the Earth’s crust is the seismic survey. The migration process allows us to estimate the position of geological boundaries under the day surface. A lot of different migration techniques were developed using the acoustic approximation. One of the obvious improvements is the transfer to the elastic medium model that successfully describes P-waves and S-waves. Consequences of it – the grows of numerical complexity of the mathematical problem and the increase of computer resource requirements. Real field data contain the information about kilometers of area and for the reasonable imaging the step of computational mesh must be from one to ten meters. Thus, for the realization of elastic migration procedures it is necessary to use modern HPC systems. The goal of this work was the development and investigation of the elastic imaging method using Born approximation for quasi-homogeneous elastic media. The research software in Mathematica system was developed, and a set of calculations for simple geological models were carried out using 12-cores shared memory system. The assessment of the scalability shows approximately 90 % of effectiveness. The study was funded by Ministry of Education and Science of the Russian Federation under grant agreement No. 14.575.21.0084 on October 20, 2014 (the unique identifier PNI: RFMEFI57514X0084) in the Moscow Institute of Physics and Technology (State University).

Summary

The research software based on the new elastic imaging method using Born approximation for quasi-homogeneous media was developed. The assessment of the scalability on 12-cores shared memory system shows approximately 90 % of effectiveness.

Primary author

Dr Vasily Golubev (Moscow Institute of Physics and Technology)

Co-authors

Prof. Igor Petrov (Moscow Institue of Physics and Technology) Mr Oleg Voinov (Moscow Institute of Physics and Technology)

Presentation materials