Accelerating geostatistical seismic inversion using TensorFlow: A heterogeneous distributed deep learning framework

doi:10.1016/j.cageo.2018.12.007

Return

Accelerating geostatistical seismic inversion using TensorFlow: A heterogeneous distributed deep learning framework

2019-03-01

PRE

Mingliang Liu *

Grana, Dario

DOI：10.1016/j.cageo.2018.12.007

Original

Original request for help

Save

Abstract

En 中文

Geostatistical seismic inversion is one of emerging technologies in reservoir characterization and reservoir uncertainty quantification. However, the challenge of intensive computation often restricts its application in practical studies. To circumvent the computational limitation, in this work, we present a distributed parallel approach using TensorFlow to accelerate the geostatistical seismic inversion. The approach provides a general parallel scheme to efficiently take advantage of all the available computing resources, i.e. CPUs and GPUs: the computational workflow is expressed and organized as a Data Flow Graph, and the graph can be divided into several sub-graphs which are then mapped to multiple computing devices to concurrently evaluate the operations in them. The high-level interface and the feature of automatic differentiation provided by TensorFlow also makes it much easy for users to implement their algorithms in an efficient parallel manner, and allows employing programs on any computing platform almost without alteration. The proposed method was validated on a 3D seismic dataset consisting of 600 x 600 x 200 grid nodes. The results indicate that it is feasible to the practical application and the computational time can be largely reduced by using multiple GPUs.

Keywords:

Seismic inversion

Geostatistics

TensorFlow

GPU

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.