Stephen Monna and Giuditta Marinaro, INGV, Italy

Stephen and Giuditta's team at INGV, the Italian National Institute of Geophysics and Volcanology (, study the marine environment focussing on natural hazards, analyzing multiparametric (geophysical, geochemical and oceanographic) observations. Data are collected by EMSO seafloor and water column observatories in different nodes throughout the European seas. Using the INDIGO platform the team will be able to examine and analyze multiparametric time series and perform a continuous quality control and assessment of EMSO time series  more easily and effectively.





Stephen Monna, INGV, Italy

Champion’s team

The INGV team includes geophysicists, physicists, engineers and technicians (electronics and computer science). Everyone is engaged in operating and running experiments at the Western Ionian node of the European Research Infrastructure EMSO – the European Multidisciplinary Seafloor and water-column Observatory. The team is responsible for the enhancement, long-term operation, and maintenance of geophysical and oceanographic equipment of the NEMO-SN1 observatory. NEMO-SN1 is located in the central Mediterranean at 2000m water depth. The research activity includes seismology, volcanology, tectonics, physical oceanography, and geochemistry studies of natural phenomena and their mutual interactions.

The case study: A cloud data analysis for the European Multidisciplinary Seafloor and water-column Observatory

The team studies the marine environment focussing on natural hazards (transient and long-term signals) analyzing multiparametric (geophysical, geochemical and oceanographic) observations. Data are collected by EMSO seafloor and water column observatories in different nodes throughout the European seas.

Thanks to INDIGO solutions they will be able to:

  • Perform a continuous quality control and assessment of EMSO time series. Similar to other research infrastructures, EMSO is committed in providing scientists with high quality data, this service will include the application of Quality Contro/Quality Assessment procedures. This task has to be performed as a routine. Since the data production amounts in hundreds of TB/year, thanks to INDIGO powerful computational environment this task can be more easily accomplished.
  • Apply multidisciplinary research. To understand and model complex natural processes we need to analyze simultaneously data series from many sensors. Using the INDIGO platform we will able to examine and analyze multiparametric time series.


Case Study & User story

Our case study is based on MOIST (Multidisciplinary Oceanic Information SysTem) a data portal-management system presently in use within EMSO for some multi-parametric observatories focused on standards, open accessibility and web services. This web interface allows discovering, visualizing and downloading the metadata and data related to seafloor campaigns with GEOSTAR-type observatories from 1998 to present. The datasets discovery can be done through the navigation menu (in particular by sites, projects, instruments). A representative large user story (or perhaps epic) for MOIST is about the study of some of the geohazards in the Mount Etna area (the largest sub-areal volcano in Europe) based on NEMO-SN1 observatory data. The analysed data comes from a broadband seafloor seismometer, an absolute pressure gauge and a hydrophone. The focus is on these three main non-earthquake signals:

  • Seismic signals associated with submarine landslides caused by slope instabilities of the volcano’s submerged flanks, moreover tectonic shifts can lead to submarine landslides.
  • Volcanic tremor signals, it is the result of sustained pressure fluctuations, probably related to stress variations induced by magma movement
  • Short duration ground-motion events (SDEs).

At the Western Ionian EMSO node recorded volcanic tremor signal is directly connected to Mt. Etna's activity, while landslide and other transient seismic signals might be connected to this volcanic activity. On the other hand, some of these signals can have a non-volcanic origin, for example they could be driven also by the water column pressure. A multiparametric analysis can help us to identify the causes behind these signals, their connection and their possible association with Mt. Etna's activity.

Status today (January 2016)

Data management has recently became a big challenge in terms of storage capacity and data access. MOIST is a relational database hosting data and metadata and it is organised in two functional blocks. The first, the core part, is the harvesting engine that indexes data and keeps track of the data source. The second one, the e-infrastructure, determines the overall MOIST configuration and takes care of all the data flow from acquisition to dissemination. 

To optimize the working environment special attention is devoted to all standardisation aspects in terms of file formats, metadata, interoperability, transport protocols and shared vocabulary for keywords and parameters. MOIST is developed to adopt the most common standards (e.g., OGC, NASA, INSPIRE).

The EMSO community

EMSO ( is a large-scale European Research Infrastructure in the field of marine environmental sciences supported by the EC. It is a geographically distributed infrastructure composed of several deep-sea monitoring systems deployed on specific sites in the European seas, from the Arctic to the Black Sea through the Atlantic Ocean and the Mediterranean, thus forming a widely distributed pan-European infrastructure.

EMSO scientific community focusses on a wide spectrum of natural processes: climate change, marine ecosystems evolution and marine geo-hazards. The community includes geophysicists for the study of the energy and mass exchange at the Benthic Boundary Layer (BBL) with an multi- and inter- disciplinary approach, biologists and biogeochemists for studying the influence of the local marine environmental changes on the ecosystems, physical-oceanographers for climate changes and regional and local dynamics.

The final user perspective 

A final user will interact with the MOIST system in the following ways:

  • Working on a remote platform: The user ideally can access a web shell interface, such as a Linux X-windows environment. In this way the user has the same “experience” as if she/he was working on a local machine. This machine complies with the requirements described in this paragraph as part of the INDIGO solutions.
  • Software on a remote platform: The availability of software that is updated in a stable version by the INDIGO infrastructure. The possibility of using up-to-date and well documented software in the future. For example there are different tools for global optimization that are readily implementable.
  • Metadata-documentation available: Since relevant information included in the metadata-documentation is readily available on the platform the user will be aware of all the relevant technical information.

INDIGO innovation

We would like to shift, thanks to INDIGO solutions, from a local data analysis to a cloud data analysis. It will be much easier to share software and scientific results, and to share on-going research among the community.

INDIGO solutions can help EMSO researchers:

  • In storing and handling large quantities of data.
  • In running and maintaining software, both commercial and in-house, for multiparametric and multi-node data series.
  • In running computational intensive calculations such as global optimization procedures

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