English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Modern seismic sensors are capable of recording high precision vibration data continuously for several months. Seismic raw data consists of information regarding earthquake's origin time, location, wave velocity, etc. Currently, these high volume data are gathered manually from each station for analysis. This process restricts us from obtaining high-resolution images in real-time. A new in-network distributed method is required that can obtain a high-resolution seismic tomography in real time. In this paper, we present a distributed multigrid solution to reconstruct seismic image over large dense networks. The algorithm performs in-network computation on large seismic samples and avoids expensive data collection and centralized computation. Our evaluation using synthetic data shows that the proposed method accelerates the convergence and reduces the number of messages exchanged. The distributed scheme balances the computation load and is also tolerant to severe packet loss.

Decentralized multigrid for in-situ big data computing