Earth crustal modelling

Satellite gravity models can be used to infer the Earth structure, particularly the crust structure, by solving a so-called inverse gravimetric problem. Gravity observations are generally weaker than seismic observations, since gravity is the effect of all Earth masses and it is not easy to disentagle the different contributions coming from different mass anomalies. On the other hand, satellite gravity missions, like GOCE, provide information also in areas where seismic data are very poor or not available at all. Moreover gravity observations can be used to check the truthfulness of the estimated crustal model by performing a signal forward operation.

The activities performed by GEOlab as a continuation of an ESA-STSE project called GEMMA consist in estimating and refining a global crustal model derived from GOCE data, and in particular from grids of second radial derivatives of the gravitational potential derived by the space-wise approach. The main characteristic of this model is that the Earth crust is subdivided into geological provinces, each of them with its own mass density characterization, and the inverse gravimetric problem is consistently performed. Note that the main information derived from this model is the Moho, namely the discontinuity surface between the Earth crust and mantle.

The GEMMA model has been computed when the GOCE mission was still on orbit. Now that the mission is concluded the future activity is to recompute this model ingesting all the data provided by GOCE and processed by the space-wise approach. Next to this global model, regional solutions are also possible.

Reguzzoni M, Sampietro D, Sansò F (2013) Global Moho from the combination of the CRUST2. 0 model and GOCE data. Geophysical Journal International, ggt247.

Reguzzoni M, Sampietro D (2015) GEMMA: An Earth crustal model based on GOCE satellite data. International Journal of Applied Earth Observation and Geoinformation, 35, 31-43.

Sampietro D (2016) Crustal Modelling and Moho Estimation with GOCE Gravity Data. In Remote Sensing Advances for Earth System Science (pp. 127-144). Springer International Publishing.

Geomatics and Earth Observation laboratory