EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

New insight into the December 2018 Etna eruption through the joint inversion of ground deformation and gravity data

Mahak Singh Chauhan, Flavio Cannavò, Daniele Carbone, and Filippo Greco
Mahak Singh Chauhan et al.
  • Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo, Italy (

We focus on the eruption of Mt. Etna which took place on 24 December, 2018. The eruption occurred after a month of unrest and was accompanied by a seismic swarm that culminated in the M4.9 earthquake on the 26th, with epicentre on the eastern flank of the volcano. We jointly analyse ground deformation and gravity data to estimate the geometrical and kinematic parameters of the source structure, together with the density of the intruding material. The data used in this study were recorded by stations in the INGV-OE monitoring network (21 GPS stations and 2 gravity stations equipped with superconducting gravimeters), during the interval of 23 to 28 December (pre to post eruption). We assume a dike-type source for the forward calculation in the defined objective function. A pattern search algorithm (PSA) is used for the iterative minimization of the misfit error. In order to estimate the posterior probability density function (PDFs) of the model parameters, we also use a Markov Chain Monte Carlo (MCMC) approach. Indeed, the calculated PDFs provide more information about the uncertainties of the model parameters, which helps to understand overall tendencies of the solutions. We first test the constrained inversion of the gravity data, to calculate the density of eruptive magmatic body, by fixing the geometrical parameters of the dike, previously retrieved through inversion of the deformation data only. Using this approach, it is possible to suitable explain the deformation data and the gravity change observed at the station in the near field (MNT), while the gravity change at the other station (SLN) remain unexplained. We then invert jointly both deformation and gravity datasets, in order to adequately fit all the observations. The final model gives a density value of ~1.8-2.0 g/cm3. This value is significantly lower than the density of bubble-free magma and indicates either the involvement of gas in the intrusive process, or the formation of dry fissures during the emplacement of the dyke.

How to cite: Chauhan, M. S., Cannavò, F., Carbone, D., and Greco, F.: New insight into the December 2018 Etna eruption through the joint inversion of ground deformation and gravity data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-564,, 2019

Comments on the presentation

AC: Author Comment | CC: Community Comment | Report abuse

Presentation version 2 – uploaded on 07 May 2020
Only added the Licence icon
  • CC1: Comment on EGU2020-564, Peter Lelièvre, 07 May 2020

    Hello. I'm interested in your work and keen to discuss further. I have several questions for you:

    How did you spatially represent the dike? That is, what were the geometrical parameters for the dike?

    Did you let all of those parameters change freely or did you prescribe any constraints on them?

    Why did you use a pattern search algorithm (PSA) in combination with MCMC? Did you use the PSA to obtain a starting model for the MCMC sampling?
    Which pattern search algorithm (PSA) did you use? Have you considered using any metaheuristic global optimization algorithms instead of that PSA?

    - Peter Lelievre, Memorial University of Newfoundland,

    • AC1: Reply to CC1, Mahak Singh Chauhan, 08 May 2020

      We thank you for showing the interest in our work.

      1. The geometrical parameters of the dike are: Location (x, y), depth, Azimuth, Dip, Length, Width and Tensile.
      2. The only constrain we used is the bounds for each parameter and same for the MCMC, we used the bounded uniform prior to sample from.
      3. Actually both are used independently, so no starting model is used in MCMC from PSA. Since PSA gives only the best fit model, so we used MCMC for estimating the model uncertainty from the posteriors density functions (PDFs).
      4. We tried genetic algorithms, but obtained the best results using pattern search with an adaptive mesh and genetic algorithm to search for a solution at each iteration prior to the polling.
  • CC2: Questions and answers from the live chat during EGU2020, Michael Heap, 11 May 2020

    Q: Have you tried other source geometries? Is decision to go with planar dislocation mainly based on seismicity pattern, or other?

    A: We are working now to incorporate two source, Sphere (Mogi source) and Dike (Okada) together, mainly to explain deflation near the flank.

    Q: Only one dike? at which depth?

    A: In this study ony one dike, depth is very shallow, almost near to the assumed elastic half space.

    Q: So no evidence of deeper intrusion?

    A: There is possibality of deeper intrusion, and modeling that may explain the gravity change at SLN station.

    Q: And any idea why one gravity stations is poorly fitted? In relation to Valerio's question?

    A: In order to fit the deformation field, estimated source is very shallow which can't explain the gravity at one station. There could be possibality of another source at deeper depth of the feeding system of volcano, that may explain the gravity. Another possibality of the void creation near the gravity station.

    Q: Do you have any control of the groundwater level to calibrate your gravity measurements?

    A: In this study not. the peirod is four days, pre and post eruption and data is from continous gravity time series.

    Q: Nice study! would it be possible/useful to add additional data in the inversion, like seismicity?

    A: Yes it is possible, We can use any data and incromaorate the information in the joint objective function to minimize by the optimization algorithm.

Presentation version 1 – uploaded on 07 May 2020 , no comments