EPOS-GNSS

Aline Déprez, Anne Socquet, Nathalie Cotte, Andrea Walpersdorf, M.G. Bato

► To cite this version:

Aline Déprez, Anne Socquet, Nathalie Cotte, Andrea Walpersdorf, M.G. Bato. EPOS-GNSS: Proto-
type solution in double difference from CNRS-UGA analysis center. 18th General Assembly of WE-
GENER: Understanding Earth deformation at plate boundaries, Sep 2016, Ponta Delgada, Azores,
Portugal., 2016. hal-01958414

HAL Id: hal-01958414

https://hal.science/hal-01958414

Submitted on 18 Dec 2018

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# EPOS-GNSS: prototype solution in double difference from CNRS-UGA analysis center

A. Déprez(1) (a.deprez@univ-grenoble-alpes.fr), A. Socquet(1), N. Cotte(1), A. Walpersdorf(1) and M.G. Bato(2)

(1) ISTerre, CNRS-Université Grenoble Alpes, FRANCE

(2) ISTerre, CNRS-Université Savoie Mont-Blanc, FRANCE

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## 1. Data bases

The prototype EPOS solution spans the **2005-2015 period** and includes about **600 European permanent stations**.

<figure><img src="image_5.png" /><figcaption>Network of the European permanent stations used for the prototype EPOS solution</figcaption></figure>

The RInEx data and metadata were downloaded from archive centres (GSAC nodes) maintained in :

- **France** (CNRS-OCA): RENAG cGPS network

- **Greece**: NOA cGPS network

- **Italy** (INGV): RING cGPS network

Data from the European Permanent Network (EPN), downloaded from the EPN server, were also included.

Our data set is stored on a distributed data-management system called **IRODS**, maintained in the University Grenoble Alpes (UGA), France. It consists in about **1 850 000 rinex files** of about **3kb**, being an amount of data of about **750G**.

Data were processed in double difference with **GAMIT/GLOBK** software.

## 2. Processing chart

<figure><img src="image_6.png" /></figure>

## 3. Daily processing

### a. Division in N sub-networks

For each day independantly, the network is first split into **sub-networks** (between 8 and 14 subnetworks) using **NETSEL** tool included in the GAMIT/GLOBK package. The sub-networks consist in about **40 stations**, with **2 overlapping** stations.

<figure><img src="image_7.png" /><figcaption>Example of division in 8 sub-networks for the day 100 in 2006. The tie sub-nework, in black, consists in a set of station couples belonging to the 7 others sub-networks</figcaption></figure>

It allowed us to define parameters of the jobs (GAMIT processing) to launch on a distributed system.

### b. All sub-network processing

For each day and for each sub-network the GAMIT processing is **conducted independently** on the **high performance computing platform** CIMENT hosted at UGA. Theoretically, for the EPOS prototype solution, 56 400 jobs have to be launched. These jobs, launched in **best-effort** on 3 different clusters, present an identical format with a walltime of 2h. The mean duration of these jobs is **25 min**. At the end of this step, a **GAMIT h-file** is available for each couple day/sub-network.

### c. Daily combination

An other set of about 4000 (in theory) very short jobs has to be launch to **combine the h-files** of the different sub-networks and obtain a daily solution for the whole network. These jobs last a **few minutes**.

The **daily solution** is obtained at that time in the form of a **SINEX file**. The whole SINEX file set consists in an amount of about **400G**.

## 4. Quality check

Along our processing chain, we included **two quality checks**. After the GAMIT processing of the sub-networks, we checked the **RMS value**. The unexpected high RMS values were due to no accurate enough **a priori coordinates** for some sites. We re-estimated them and re-launched the processing of the sub-network. After the daily combination a few SINEX files were associated with high **CHI**\*\*\*2 value. A **sub-networks reorganisation** allowed us to optimize the consistency within the daily sub-network set.

## 5. Products

### a. Time series combination

<figure><img src="image_8.png" /><figcaption>Time series after jump estimation on March 27, 2007 due to antenna and receiver change</figcaption></figure>

The **PYACS** software was used to **compute the time series**. PYACS consists in a set of tools, written in python language and dedicated to the analysis of geodetic data. It was developed in Observatoire de la Côte d'Azur in Nice (France) by J.M. Nocquet. This package allowed us to **visualize the position time series** for each station, to **detrend** it, to **remove potential outliers** and to **estimate** and **correct** for the **jumps** associated with **material changes** (antenna or receiver).

### b. Velocity field computation

<figure><img src="image_9.png" /><figcaption>Histogram showing the repartition of the velocity difference between the 2 processings with GLOBK and MIDAS</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>noisy time series or with seasonal signal</figcaption></figure>

Explanations for the velocity differences.

- Time series to short, no result with MIDAS (eg DIPP)

- Noisy time series (eg PESA, difference of 7.8 mm/yr for the horizontal component and 2.1 mm/yr for the vertical one)

- Time series offset by geophysical signal (eg AQUI, station near to the Mw6.3 l'Aquila earthquake that occurred on 6/04/2009.

Difference of 4.7 mm/yr for the horizontal component and 8.7 for the vertical one)

### c. Strain rate computation

<figure><img src="image_11.png" /><figcaption>On the left: 2005-2015 velocity field computed with GLOBK software</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>On the right: strain rate map deduced from the velocity field on the left. The pink star ★ shows the Mw6.2 Amatrice earthquake that occurred on 25/08/2016</figcaption></figure>

After a selection of reliable sites in Italy and surrounding areas, we made some tests for the **strain rate computation**, using a set of tools developed by Y. Ziegler in Ecole et Observatoire des Sciences de la Terre, Strasbourg (France).

**References:**

Blewitt, G., C. Kreemer, W.C. Hammond, J. Gazeaux, 2016, MIDAS robust trend estimator for accurate GPS station velocities without step detection, accepted for publication in the Journal of Geophysical Research, doi: 10.1002/2015JB012552

Masson, F., Lehujeur, M., Ziegler, Y., and Doubre, C. (2014). Strain rate tensor in Iran from a new GPS velocity field. Geophys. J. Int., 197(1):10-21.