Investigation of symmetry attribute analysis on the phase measurements of marine controlled-sourceelectromagnetic surveys

In this paper we study the potential of symmetry attribute analysis applied to the phase component of electric field observations of marine controlled-source electromagnetic data. The effectiveness of symmetry attribute analysis on the phase component of marine controlled-source electromagnetic data in detecting the boundaries of resistive layer(s), such as hydrocarbon accumulation, is investigated. A comparison between symmetry attribute analysis on the phase and magnitude component of 2.5D synthetic data and a real data set is also discussed. The results presented a clear response indicative of the locations of the subsurface resistors. The phase symmetry attribute analysis proved to be effective for qualitative detection of the lateral extent of embedded resistors

Controlled laboratory test for the investigation of LNAPL contamination using a 2.0 GHz ground penetrating radar

Groundwater is an important source of fresh water and, consequently, its quality should be properly monitored. Different contaminants can be identified with different types of equipment and/or measurement procedures. Fuel oil contamination forms a "floating" layer over the water table, which has different electrical properties, therefore electromagnetic techniques can be used to image such contaminants. This paper presents a scale-laboratory test where a 2.0 GHz ground penetrating radar (GPR) is used to assess a controlled-fuel oil injection in a shallow sand tank setup. The test examined several scenarios involving different levels of water saturation and fuel oil contamination. The increase of water content produces a reduction of EM wave propagation velocity, moving some fixed/reference targets to higher reflection times. We use simplified relations to obtain approximated dielectric permittivity values, where the inverted results are consistent with those available in the literature for similar scenarios. Rather than suggesting a true quantitative procedure, these observations could be exploited in a qualitative long-term monitoring strategy in common field situations where a contaminant enters a soil matrix and moves through its pore spaces. Finally, the integration of GPR measurements with other monitoring techniques could increase the reliability of the interpretation and the sensitivity to the contaminant concentration

Space-Wise approach for airborne gravity data modelling

Regional gravity field modelling by means of remove-compute-restore procedure is nowadays widely applied in different contexts: it is the most used technique for regional gravimetric geoid determination, and it is also used in exploration geophysics to predict grids of gravity anomalies (Bouguer, free-air, isostatic, etc.), which are useful to understand and map geological structures in a specific region. Considering this last application, due to the required accuracy and resolution, airborne gravity observations are usually adopted. However, due to the relatively high acquisition velocity, presence of atmospheric turbulence, aircraft vibration, instrumental drift, etc., airborne data are usually contaminated by a very high observation error. For this reason, a proper procedure to filter the raw observations in both the low and high frequencies should be applied to recover valuable information. In this work, a software to filter and grid raw airborne observations is presented: the proposed solution consists in a combination of an along-track Wiener filter and a classical Least Squares Collocation technique. Basically, the proposed procedure is an adaptation to airborne gravimetry of the Space-Wise approach, developed by Politecnico di Milano to process data coming from the ESA satellite mission GOCE. Among the main differences with respect to the satellite application of this approach, there is the fact that, while in processing GOCE data the stochastic characteristics of the observation error can be considered a-priori well known, in airborne gravimetry, due to the complex environment in which the observations are acquired, these characteristics are unknown and should be retrieved from the dataset itself. The presented solution is suited for airborne data analysis in order to be able to quickly filter and grid gravity observations in an easy way. Some innovative theoretical aspects focusing in particular on the theoretical covariance modelling are presented too. In the end, the goodness of the procedure is evaluated by means of a test on real data retrieving the gravitational signal with a predicted accuracy of about 0.4 mGal