High-resolution coherency functionals for improving the velocity analysis of ground-penetrating radar data

We aim at verifying whether the use of high-resolution coherency functionals could improve the signal-to-noise ratio (S/N) of Ground-Penetrating Radar data by introducing a variable and precisely picked velocity field in the migration process. After carrying out tests on synthetic data to schematically simulate the problem, assessing the types of functionals most suitable for GPR data analysis, we estimated a varying velocity field relative to a real dataset. This dataset was acquired in an archaeological area where an excavation after a GPR survey made it possible to define the position, type, and composition of the detected targets. Two functionals, the Complex Matched Coherency Measure and the Complex Matched Analysis, turned out to be effective in computing coherency maps characterized by high-resolution and strong noise rejection, where velocity picking can be done with high precision. By using the 2D velocity field thus obtained, migration algorithms performed better than in the case of constant or 1D velocity field, with satisfactory collapsing of the diffracted events and moving of the reflected energy in the correct position. The varying velocity field was estimated on different lines and used to migrate all the GPR profiles composing the survey covering the entire archaeological area. The time slices built with the migrated profiles resulted in a higher S/N than those obtained from non-migrated or migrated at constant velocity GPR profiles. The improvements are inherent to the resolution, continuity, and energy content of linear reflective areas. On the basis of our experience, we can state that the use of high-resolution coherency functionals leads to migrated GPR profiles with a high-grade of hyperbolas focusing. These profiles favor better imaging of the targets of interest, thereby allowing for a more reliable interpretation

SH-wave seismic reflection at a landslide (Patigno, NW Italy) integrated with P-wave

The aim of this paper is to present the acquisition and processing up to the depth migrated section of an SH-wave reflection seismic profile. This experience is conducted on a deep-seated gravitational slope deformation located in the Northern Apennines in Italy. The SH-wave depth-migrated image in the investigated area provides a detailed description of the small reactivation slip surfaces delineating minor landslides at shallow depths, which are responsible for the major damages observed. These results are integrated with a recently acquired P-wave seismic reflection profile investigating the same slope and delineating the highly deformed layer at depth, liable for the deep-seated gravitational slope deformation. The combined use of P-waves and SH-waves allows to gain a deeper knowledge of the landslide internal setting that is necessary to mitigate the risk associated with the mass movement

Machine learning-accelerated gradient-based Markov Chain Monte Carlo inversion applied to electrical resistivity tomography

Expensive forward model evaluations and the curse of dimensionality usually hinder applications of Markov chain Monte Carlo algorithms to geophysical inverse problems. Another challenge of these methods is related to the definition of an appropriate proposal distribution that simultaneously should be inexpensive to manipulate and a good approximation of the posterior density. Here we present a gradient-based Markov chain Monte Carlo inversion algorithm that is applied to cast the electrical resistivity tomography into a probabilistic framework. The sampling is accelerated by exploiting the Hessian and gradient information of the negative log-posterior to define a proposal that is a local, Gaussian approximation of the target posterior probability. On the one hand, the computing time to run the many forward evaluations needed for both the data likelihood evaluation and the Hessian and gradient computation is decreased by training a residual neural network to predict the forward mapping between the resistivity model and the apparent resistivity value. On the other hand, the curse of dimensionality issue and the computational effort related to the Hessian and gradient manipulation are decreased by compressing data and model spaces through a discrete cosine transform. A non-parametric distribution is assumed as the prior probability density function. The method is first demonstrated on synthetic data and then applied to field measurements. The outcomes provided by the presented approach are also benchmarked against those obtained when a computationally expensive finite-element code is employed for forward modelling, with the results of a gradient-free Markov chain Monte Carlo inversion, and also compared with the predictions of a deterministic inversion. The implemented approach not only guarantees uncertainty assessments and model predictions comparable with those achieved by more standard inversion strategies, but also drastically decreases the computational cost of the probabilistic inversion, making it similar to that of a deterministic inversion

Probabilistic inversions of electrical resistivity tomography data with a machine learning-based forward operator

Casting a geophysical inverse problem into a Bayesian setting is often discouraged by the computational workload needed to run many forward modeling evaluations. Here we present probabilistic inversions of electrical resistivity tomography data in which the forward operator is replaced by a trained residual neural network that learns the non-linear mapping between the resistivity model and the apparent resistivity values. The use of this specific architecture can provide some advantages over standard convolutional networks as it mitigates the vanishing gradient problem that might affect deep networks. The modeling error introduced by the network approximation is properly taken into account and propagated onto the estimated model uncertainties. One crucial aspect of any machine learning application is the definition of an appropriate training set. We draw the models forming the training and validation sets from previously defined prior distributions, while a finite element code provides the associated datasets. We apply the approach to two probabilistic inversion frameworks: a Markov Chain Monte Carlo algorithm is applied to synthetic data, while an ensemble-based algorithm is employed for the field measurements. For both the synthetic and field tests, the outcomes of the proposed method are benchmarked against the predictions obtained when the finite element code constitutes the forward operator. Our experiments illustrate that the network can effectively approximate the forward mapping even when a relatively small training set is created. The proposed strategy provides a forward operator three that is orders of magnitude faster than the accurate but computationally expensive finite element code. Our approach also yields most likely solutions and uncertainty quantifications comparable to those estimated when the finite element modeling is employed. The presented method allows solving the Bayesian electrical resistivity tomography with a reasonable computational cost and limited hardware resources

A Career in Catalysis: Laura Prati

This account celebrates the long and successful scientific career of Laura Prati, recalling her most important scientific achievements since the beginning of her work as a researcher in inorganic chemistry. Laura went through many aspects of liquid-phase heterogeneous catalysis, taking her first steps in the field of catalysts synthesis, where she pursued the development of innovative strategies for preparing catalysts until laying the foundations of the colloidal synthesis of metal nanoparticles, with particular interest in gold. Her investigations in colloids for catalysis had a natural outcome on catalysts synthesis and optimization. In her career, she dealt with liquidphase oxidation reactions, with particular attention to biomass valorization processes. According to this, she could not help to deal also with hydrogenation and hydrogenolysis reactions, to which she dedicated herself, especially in the more recent years. Her discoveries have influenced many researchers in the area of heterogeneous catalysis and design of materials

SH-wave reflection seismic survey at the Patigno landslide: integration with a previously acquired P-wave seismic profile

Seismic investigation on landslide is hampered by several factors that could prevent the use of the reflection seismic method to characterize the subsurface architecture (Jongmans and Garambois, 2007). Moreover, acquisition and processing of reflection seismic data are more time consuming compared with other geophysical techniques such as refraction seismic and electrical resistivity tomography (ERT), leading inevitably to higher costs. Notwithstanding these difficulties, recently some attempts to delineate the deep slip surface of large landslides have been carried out using P-wave reflection seismic surveys (Apuani et al., 2012; Stucchi and Mazzotti, 2009; Stucchi et al., 2014;). P-wave reflection seismic method is effective in imaging the slip surface at a depth sufficiently greater than the seismic wavelength, whereas, for very shallow horizons, it suffers from the limited resolution that can be obtained by the use of compressional waves. In this regards, SH-waves can be used to overcome this limitation (Deidda and Balia, 2001; Guy, 2006; Pugin et al., 2006,), but they require a specifically-designed energy source for waves generation, geophones measuring horizontal components of particles motion and an accurate choice of acquisition parameters. On the contrary, due to attenuation, the depth of investigation for SHwaves can be lower than for P-waves (Pugin et al., 2006). Therefore the geological understanding of a mass movement can take advantage of a combined use of both these geophysical methodologies. This is the case of the Patigno landslide, a great landslide located in the upper basin of Magra River, in the Northern Appennines, Italy (Fig.1), where a P-wave study carried out in the last years (Stucchi et al., 2014) was able to image the deepest discontinuity of the landslide body at around 40-50 m depth, but no description of the shallower layers can be inferred. Because these surface layers are the slip surfaces of quick reactivation movements of the landslide, an SH high-resolution reflection seismic survey was planned along the previous P-wave profile (Fig.1). This new survey associated to the P-wave investigation allows a more robust description of the landslide body, from the deepest discontinuity up to the very shallow portions of the landslide. This work describes the planning, acquisition and processing of the SH reflection seismic survey, and also gives a possible combined interpretation of both P and SH seismic images

Seismic hazard assessment in terms of macroseismic intensity for the Italian area

A seismic hazard map, in terms of macro seismic intensity with 10% probability of exceedance in 50 years, is proposed for the Italian territory. The input elements used to evaluate the seismic hazard are: the seismogenic zoning ZS9 (Meletti et al., 2007), the earthquake catalogue CPTI04 (Gruppo di lavoro CPTI04, 2004) and intensity attenuation relationships. The first two elements and the historical and statistical completeness of the catalogue are those used in the national seismic hazard map for Italy MPS04 (Gruppo di Lavoro MPS, 2004). Two intensity attenuation models are used: 1) one national relationship obtained with a new approach by Pasolini et al. (2006) and a relationship for the Etna volcanic zone proposed by Azzaro et al. (2006) 2) a set of regional relationships derived from a previous cubic model (Berardi et al., 1993) which is recalibrated in the present study using the macro seismic intensity database DBMI04 (Stucchi et al., 2007), which was used for compiling CPTI04. The computer code adopted to evaluate the seismic hazard, with the elements cited above, is SeisRisk III (Bender and Perkins, 1987), which has been modified within this study to incorporate the aleatory variability of the ground motion (macroseismic intensity). A logic-tree framework allowed to explore some possible alternatives of epistemic character. The seismic hazard map obtained in terms of intensity was subsequently transformed into PGA by means of a linear relation between intensity and PGA, in order to compare it with the recently national seismic hazard map MPS04

Ruling factors in cinnamaldehyde hydrogenation: Activity and selectivity of pt-mo catalysts

To obtain selective hydrogenation catalysts with low noble metal content, two carbon-supported Mo-Pt bimetallic catalysts have been synthesized from two different molybdenum precursors, i.e., Na2MoO4 and (NH4)6Mo7O24. The results obtained by X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) combined with the presence and strength of acid sites clarified the different catalytic behavior toward cinnamaldehyde hydrogenation. After impregnating the carbon support with Mo precursors, each sample was used either as is or treated at 400 °C in N2 flow, as support for Pt nanoparticles (NPs). The heating treatment before Pt deposition had a positive effect on the catalytic performance. Indeed, TEM analyses showed very homogeneously dispersed Pt NPs only when they were deposited on the heat-treated Mo/C supports, and XPS analyses revealed an increase in both the exposure and reduction of Pt, which was probably tuned by different MoO3/MoO2 ratios. Moreover, the different acid properties of the catalysts resulted in different selectivity

Dabigatran overload in acute kidney injury: haemodialysis or idarucizumab? A case report and proposal for a decisional algorithm

Dabigatran overload has been reported in acute kidney injury (AKI), leading to occasional major bleeding. Haemodialysis (HD) was the method used for reversing dabigatran anticoagulant effects before the approval of idarucizumab, which is now indicated for dabigatran reversal in major bleeding or surgical emergencies. There have been reports of rebound of dabigatran levels following idarucizumab administration in AKI, requiring HD to achieve effective dabigatran clearance. However, a decisional algorithm to individualize treatments for dabigatran overload seems lacking. We present a case of dabigatran accumulation in obstructive AKI with minor bleeding that was successfully treated with HD and tranexamic acid without using idarucizumab, and propose a decision-making algorithm including different pathways in the management of suspected dabigatran overload in AKI