GNGTS 2014 - Atti del 33° Convegno Nazionale

potential electrodes) permits the error assessment: in fact, each ERT sequence is made up of 4885 measurements, comprising both direct and reciprocal values. The latter, DTS (Distributed Temperature Sensing), has been employed in hydrogeophysics only for the last dozen of years. This methodology is based on the Raman effect, one of the scattering phenomena arising from the interaction between light photons and the noncrystalline structure of fiber-optic: Such an interaction generates a backscattered signal, whose intensity is temperature dependent (Selker et al. , 2006). Therefore, with an appropriate data processing, it is possible to compute how temperature varies along the whole fiber-optic cable. The usage of this methodology enables then to exploit heat as a natural tracer, showing its temporal and spatial changes in the domain of interest. Given our aim of characterizing the hyporheic zone of the Vermigliana creek, also for this technique we opted for an innovative deployment, which consists of a 200 m long fiber-optic located in a horizontal borehole a few meters downstream from the ERT perforation and parallel to it: Thus, also the fiber-optic is placed inside the HZ (Fig. 1). For a better comprehension of the site geometry, it is necessary to underline that the DTS perforation has a 100 m linear length. Therefore, the fiber-optic is folded, creating the “double-ended” configuration required (i.e., both ends are connected to the DTS instrument). For every DTS survey we used the AP Sensing N4386A Distributed Sensing system with a double-ended fiber-optic configuration, a sampling interval equal to 1 m and a spatial resolution equal to 1 m. In each survey, we acquired three single traces with update time and measurement time both equal to 30 s (i.e., every 30 s a new trace acquisition begins and lasts 30 s) and then averaged the three temperature values thus obtained for every sampling point: The result consists of a single profile with 200 temperature values, spaced 1 m one from the other. The ERT time-lapse monitoring started in July 2013 and still is being carried out approximately once or twice a month, mostly according to the weather conditions. On the other hand, we have been performing the DTS time-lapse monitoring since June 2014 approximately once a month. Because of the typical continental climate characterizing the Upper Val di Sole, the time- lapse monitoring (both ERT and DTS) can be performed only from spring to autumn, given the absence of snow and of seasonally frozen ground that would lead to high resistivity values and noisy data. Data processing, results and discussions. The ERT data at our disposal call for two different types of inversion. First of all, from each dataset (one from every survey) the correspondent absolute resistivity (ρ) cross-section is obtained with an appropriate inverse modelling, in order to represent the state of the hyporheic zone at the measurement time. After the error calculation based on the combination of each direct measurement with the correspondent reciprocal one, every dataset is refined applying an error threshold equal to 10%, which reduces each sequence to 1400 measurements on average. Despite 10%may seem a quite high error limit, it is perfectly compatible with the heterogeneous material whereof the investigated domain is composed [i.e., clay, tonalite boulders, gravelly-sand and silty deposits, as described by Dal Piaz et al. (2007)]. Then, we performed the ERT data inversion thanks to the R2 code (Lancaster University, UK), fixing an error equal to 10% and using a triangular mesh with 5039 nodes and 9729 triangular elements. An example of the result of such a processing is the resistivity cross-section depicted in Fig.2, representing the acquisition carried out on August 30, 2013. The resistivity distribution here illustrated is largely comparable to those of the other ERT surveys. A very low resistivity domain characterizes the area beneath the Vermigliana creek and extends till a depth of 4 m below ground level, with an average resistivity value of 50 Ωm. At first glance, the presence of such a domain may be justified by the seepage process, which allows the –total or partial– sub-riverbed saturation with the consequent overall resistivity modification (as expeted, according to Archie’s law). If we focus on resistivity values instead, a discrepancy emerges: An average resistivity of 50 Ωm is incompatible with the values characterizing both GNGTS 2014 S essione 3.2 131