Communication-efficient decentralised algorithms for seismic tomography with sensor networks. Issue 5 (2nd September 2020)

• Record Type: Journal Article
• Authors: Zhao, Liang
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 35:Issue 5(2020)
• Page Start: 550
• Page End: 570
• Publication Date: 2020-09-02
• Subjects: Big data -- decentralised computing -- in-network processing -- seismic tomography -- sensor network; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2019.1577416
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract : We consider the decentralised consensus optimisation problem arising from in-situ seismic tomography in large-scale sensor networks. Unlike traditional seismic imaging performed in a centralised location, each node in this setting privately holds an objective function and partial data. The goal of each node is to obtain the optimal solution of the whole seismic image, while communicating only with its immediate neighbours. We present a fast decentralised gradient descent method and prove that this novel method can reach optimal convergence rate of O ( 1 / k 2 ) where k is the number of communication/iteration rounds. A gossip-based asynchronous version is also proposed which is preferable when there is a divergence on the processing speed of the nodes. Extensive numerical experiments on synthetic and real-world sensor network seismic data demonstrate that the proposed algorithms significantly outperform existing methods. GRAPHICAL ABSTRACT: