Roberto Bruno

A software tool for geostatistical analysis of thermal response test data: GA-TRT

In this paper we present a new method (DCE - Drift and Conditional Estimation), coupling Infinite Line Source (ILS) theory with geostatistics, to interpret thermal response test (TRT) data and the relative implementing user-friendly software (GA-TRT). Many methods (analytical and numerical) currently exist to analyze TRT data. The innovation derives from the fact that we use a probabilistic approach, able to overcome, without excessively complicated calculations, many interpretation problems (choice of the guess value of ground volumetric heat capacity, identification of the fluctuations of recorded data, inability to provide a measure of the precision of the estimates obtained) that cannot be solved otherwise. The new procedure is based on a geostatistical drift analysis of temperature records which leads to a precise equivalent ground thermal conductivity (@lg) estimation, confirmed by the calculation of its estimation variance. Afterwards, based on @lg, a monovariate regression on the original data allows for the identification of the theoretical relationship between ground volumetric heat capacity (cg) and borehole thermal resistance (Rb). By assuming the monovariate Probability Distribution Function (PDF) for each variable, the joint conditional PDF to the cg-Rb relationship is found; finally, the conditional expectation allows for the identification of the correct and optimal couple of the cg-Rb estimated values.

Thermal response test for shallow geothermal applications: a probabilistic analysis approach

BackgroundThermal Response Test (TRT) is an onsite test used to characterize the thermal properties of shallow underground, when used as heat storage volume for shallow geothermal application. It is applied by injecting/extracting heat into geothermal closed-loop circuits inserted into the ground. The most common types of closed loop are the borehole heat exchangers (BHE), horizontal ground collectors (HGC), and energy piles (EP). The interpretation method of TRT data is generally based on a regression technique and on the calculation of thermal properties through different models, specific for each closed loop and test conditions.MethodsA typical TRT record is a graph joining a series of experimental temperatures of the thermal carrier fluid. The proposed geostatistical approach considers the temperature as a random function non-stationary in time, with a given trend, therefore the record is considered as a ‘realization’, one of the possible results; the random nature of the test results is transferred to the fluctuations and a variogram modeling can be applied, which may give many information on the TRT behavior.ResultsIn this paper, a nested probabilistic approach for TRT output interpretation is proposed, which can be applied for interpreting TRT data, independently of the different methodologies and technologies adopted. In the paper, for the sake of simplicity, the probabilistic approach is applied to the ‘infinite line source’ (ILS) methodology, which is the most commonly used for BHE.ConclusionsThe probabilistic approach, based on variogram modeling of temperature residuals, is useful for identifying with robust accuracy the time boundaries (initial time t0 and the final time tf) inside which makes temperature regression analysis possible. Moreover, variograms are used into the analysis itself to increase estimation precision of thermal parameter calculation (ground conductivity λg, ground capacity cg, borehole resistance Rb). Finally, the probabilistic approach helps keep under control the effect of any cause of result variability. Typical behaviors of power, flows, and temperatures and of their interaction with the specific closed-loop circuit and geo-hydrological system are deepened by variogram analysis of fluctuations.

Quantitative identification of marbles aesthetical features

The use of image analysis for the aesthetical characterisation of stone slab surfaces has been studied during last ten years and has proved efficiency for an industrial and commercial application. This work aims to identify operational parameters specifically conceived for the classification of marble tiles. In this specific case the meaningful aesthetical properties are mainly linked to the anisotropy of the RGB intensities and, specifically, to the “veins”. Starting from the classical geostatistical and morphological modelling (variograms, granulometries, etc.), specific operational parameters have been obtained for a quantitative measurement of veins density, colour, and geometrical features (width, shape, continuity, etc.). The actual methodology defines commercial categories on the base of a self-appraisal process, which identifies intervals of several parameters. The current procedure is too rigid and doesnt allow choosing in an intuitive way the discriminating properties. The proposed approach identifies understandable characteristics (vein features), and proposes quantitative indexes which actually satisfy the commercial classification of marbles.

Statistical and Geostatistical Analysis of Drilling Parameters in the Brenner Base Tunnel

Currently, a number of very long and very deep tunnels are being excavated under the Alps to create safer and faster connections between several European countries. In these conditions, geological and geotechnical knowledge before construction is relatively limited and, for this reason, many projects contemplate the preliminary excavation of an explorative tunnel to provide the necessary information for a better design of the main tunnels. In this context, a number of investigations have been developed in order to continuously explore the ground conditions ahead the tunnel face (i.e. Exadaktylos et al. 2008), including the down-the-hole percussive hammer drilling considered in this study. In particular, the automatic measurement of different drilling parameters recorded during hammer drilling tests, called in this paper DAC tests (from the Italian Diagrafia Automatica Continua), has recently raised significant interest, due to its relatively low cost, fast implementation and long extension (e.g. Fortunati and Manassero 1999; Fortunati and Pellegrino 1998; Hameli et al. 1983; Colosimo 1998; Deveaux et al. 1983; Kahraman et al. 2003; Rostami et al. 2015) . The aim of this paper, focused on the segment of the Brenner Base tunnel excavated in the Val di Mules fault, is twofold:

How different data supports affect geostatistical modelling: the new aggregation method and comparison with the classical regularisation and the theoretical punctual model

ABSTRACT In geostatistical analysis, often the data have different support (volume). Data with different supports can be treated separately or together but in a consistent way. In many applications, data are considered as point variable averaged over the sample volume. Regularisation of data has a significant impact on variograms and geostatistical estimations. Two methods of managing data with different supports (“integrating”) are compared: regularisation and aggregation. This paper examines the consequences of the regularisation on sample variograms and proposes another procedure to integrate samples called “aggregation”. The variogram models of integrated data are compared with the theoretical punctual model. The point-support variogram presents some advantages, such as the possibility of utilizing samples without compositing their values. But, this implies to modify the classical kriging system. The ways of managing data with different supports are applied to a complex dataset of an iron ore body. The spatial variable analysis from the composited borehole samples demonstrates the impact of the integrating methods through cross validation. Results show the effect of the variogram model in the kriging system and the accuracy of the evaluation. Also evidenced are the sensitivity to the integration method, the selected parameters and the benefits of the theoretical punctual variogram.

Estimating Thermal Response Test Coefficients: Choosing Coordinate Space of The Random Function

In shallow geothermal systems, the main equivalent underground thermal properties are commonly calculated with a thermal response test (TRT). This is a borehole heat exchanger production test where the temperature of a heat transfer fluid is recorded over time at constant power heat injection/extraction. The equivalent thermal parameters (thermal conductivity, heat capacity) are simply deduced from temperature data regression analysis that theoretically is a logarithmic function in the time domain, or else a linear function in the log-time domain. By interpreting the recorded temperatures as a regionalized variable whose drift is the regression function, in both cases the formal problem is a linear estimation of the mean. If the autocorrelation function (variogram, covariance) of residuals is known, coefficient variance can be directly deduced. Coefficient estimates are independent of the drift form adopted, and the residuals are the same in the same points. The random function is different in the time domain, however, and in the log-time domain. In fact, residual variograms are different due to the transformation of the coordinate space. This paper uses a TRT case study to examine the consequences of coordinate space transformation for a random function, namely its variogram. The specific question addressed is the choice of coordinate space and variogram.

Application of low frequency georadar antenna to fractures detection and 3D visualization in a new quarry bench

Recently, laser scanning systems (airborne and terrestrial mobile mapping systems) have been established as a leading technology for collecting high density 3D information from an objects surface. The availability of generated surface models is very important for various industrial, military, environmental, and public applications. The accuracy of the derived point cloud coordinates from a LiDAR system is affected by inherent systematic and random errors. The impact of random errors depends on the precision of the system’s measurements, which comprise position and orientation information from the GPS/INS unit, mirror angles, and ranges. On the other hand, systematic errors are mainly caused by biases in the mounting parameters (i.e., lever arm offset and boresight angles) relating the system components as well as biases in the system measurements (e.g., ranges and mirror angles). In order to ensure the geometric quality of the collected point cloud, the LiDAR systems should undergo a rigorous calibration procedure to estimate the system parameters that minimize the discrepancies between conjugate surface elements in overlapping LiDAR strips. The main objective of this paper is to look into an existing LiDAR system calibration technique, which is based on manual selection of overlapping regions between LiDAR strips and how to increase the efficiency of this technique by automatic selection of appropriate overlapping strip pairs, which should achieve the minimum optimal flight configuration that maximizes the impact of the discrepancies among conjugate surface elements in overlapping strips as well as automatic selection of regions within the appropriate overlapping strip pairs. The methodology of the proposed technique can be summarized as follows: first, the LiDAR strip pairs are grouped based on the flight configuration; second, appropriate overlapping strip pairs from each group is automatically selected; third, regions within the appropriate overlapping strip pairs are automatically selected based on their angles (slopes and aspects) and distribution; finally, the calibration procedure is applied. The experimental results have shown that the quality of the estimated parameters using the automatic selection is quite comparable to the estimated parameters using the manual selection while the proposed method is fully automated, and much faster.

Formazione avanzata nel settore delle rocce ornamentali e delle geoelaborazioni

Questa e la prima Edizione che raccoglie i principali lavori scientifici dei partecipanti al programma di scambio FARO - Formacao Avancada no Setor das Rochas Ornamentais e do Geoprocessamento (2005-2009), una rete centrata sulle rocce ornamentali o, per dirla in maniera corrente, su “marmi e graniti”.Un “filo”comune sottintende tutti i lavori, ed e la questione della “caratterizzazione”. La caratterizzazione e l’azione che produce l’informazione necessaria a prendere le decisioni e ciascuna decisione ha bisogno dei propri dati caratterizzanti. Le scuole aderenti alla rete FARO affrontano diverse questioni, svolgono delle analisi specifiche e tentano di offrire delle soluzioni mediante delle caratterizzazioni le piu avanzate possibile. nI contributi sono sati raggruppati in due Sezioni: I) La tecnologia della caratterizzazione; II) La caratterizzazione dei materiali. Nella prima sezione sono stati raccolti i lavori in cui e prevalente l’attenzione alle nuove tecnologie o alle nuove applicazioni delle tecnologie. La seconda sezione raccoglie contributi maggiormente centrati sui materiali nuovi o poco noti.

The expansion of environmental geostatistics

Environment, and more precisely pollution, appeared very early among the initial applications of geostatistics [15, 19, 33], but is seldom mentioned in Matheron’s writing. Nevertheless, it is indeed in reference to the pollution context that he examines, at the end of his essay on the practice of probabilities, “Estimating and choosing”, the operational character of non-linear estimators [25, 31].