# Toward the generation of EPOS-GNSS products

Aline Déprez, Anne Socquet, Nathalie Cotte, Andrea Walpersdorf

► To cite this version:

Aline Déprez, Anne Socquet, Nathalie Cotte, Andrea Walpersdorf. Toward the generation of EPOS-GNSS products: Massive GNSS processing and evaluation of a statistical approach for the velocity field computation. 19th General Assembly of WEGENER: on Earth deformation & the study of earthquakes using geodesy and geodynamics, Sep 2018, Grenoble, France., 2018. hal-01958416

HAL Id: hal-01958416

https://hal.science/hal-01958416

Submitted on 9 Jan 2019

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Distributed under a Creative Commons Attribution 4.0 International License
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## 1. Massive data set & processing method

The GNSS solution presented here is the result of a **massive GNSS processing** based on a double difference method (using GAMIT / GLOBK software, Herring et al., 2010).

Our data set is composed of rinex files from more than **1500 permanent stations** over **18 years** (2000-2017 included).

<figure><img src="image_7.png" /><figcaption>Network of the European permanent stations used in our solution</figcaption></figure>

**References:**

**RENAG:** doi:10.15778/RESIF.RG

**RGP:** http://rgp.ign.fr

**ORPHEON:** http://reseau-orpheon.fr

**RING (EPOS-glass node):**

http://ring.gm.ingv.it
**NOA (EPOS-glass node):**

http://194.177.194.238:8080/noanetgsac/gsacapi/site/search)
**SMARTNET:** personal communication
Athanassios G.

This represents more than **4 millions of rinex files** being an amount of data of more than **2000G**.

Such a processing justifies the use of a high performance computing platform (CIMENT hosted at

<figure><img src="image_8.png" /><figcaption>Processing chart from the data download to the position time series generation</figcaption></figure>

## 2. Approaches tested to evaluate the velocity field

The massive processing implementation requires innovative strategies in order satisfy the **efficiency, robustness & automation** criteria for the computation of GNSS velocity field in a **"big data"** context.

**MIDAS software** appears to be a good, **fast** and the most appropriate method (Blewitt et al., 2016).

<figure><img src="image_9.png" /><figcaption>Vertical velocity field computed with MIDAS</figcaption></figure>

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We obtained a "MIDAS velocity" for 1362 stations. In order to validate this "unusual" approach on our data set, we compared these velocities with the ones obtained using a more "traditional" approach (using PYACS, Nocquet et al. in prep.) i.e. : estimation of a **linear trend** on position time series after an automatic or semi-automatic **cleanage**.

<figure><img src="image_11.png" /><figcaption>**PYACS manual**</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**MIDAS with pre-processing TS**</figcaption></figure>

## 3. Comparison of the results in terms of velocity

We presented here the statistics associated with the comparison between the MIDAS evaluation and the linear regression of the time series after spending a lot of time in order to carefully visualize the time series data set and to correct them using human eyes and knowledge.

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The standard deviations of the differences were 0.182, 0.240 and 0.509 mm/yr respectively for the east, north and vertical component.

<figure><img src="image_15.png" /><figcaption>Differences in terms of horizontal velocities between the statistical approach (MIDAS) and the linear regression (PYACS), with respect to Eurasia fixe.</figcaption></figure>

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<figure><img src="image_17.png" /><figcaption>Differences in terms of vertical velocities between the statistical approach (MIDAS) and the linear regression (PYACS).</figcaption></figure>

There were still 74 remaining stations with a difference > 0.5 mm/yr for the horizontal velocity and 21 stations with a difference > 1.5 mm/yr for the vertical velocity. However they are mainly located in active zones and are associated with area for which a constant velocity is meaningless, regardless the chosen method. Some differences in the vertical component were also associated with a high level of seasonal noise due to snow.

**References:**

Herring, T. A., King, R. W. & McClusky, S. C. Reference Manual (GPS Analysis at MIT) Gamit-Globk Blog http://www-gpsg.mit.edu/~simon/gtgk/GAMIT_Ref.pdf (2010)

Blewitt, G., Kreemer, C., Hammond, W. C. & Gazeaux J., MIDAS robust trend estimator for accurate GPS station velocities without step detection, J. Geophys. Res. Solid Earth, 121, 2054-2068 (2016), doi:10.1002/2015JB012552

Nocquet et al., in preparation PYACS, a framework (i.e. a set of tools and a way to think) to analyze and model geodetic data