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Mattia Aleardi 《Geophysical Prospecting》2020,68(9):2738-2761
Markov chain Monte Carlo algorithms are commonly employed for accurate uncertainty appraisals in non-linear inverse problems. The downside of these algorithms is the considerable number of samples needed to achieve reliable posterior estimations, especially in high-dimensional model spaces. To overcome this issue, the Hamiltonian Monte Carlo algorithm has recently been introduced to solve geophysical inversions. Different from classical Markov chain Monte Carlo algorithms, this approach exploits the derivative information of the target posterior probability density to guide the sampling of the model space. However, its main downside is the computational cost for the derivative computation (i.e. the computation of the Jacobian matrix around each sampled model). Possible strategies to mitigate this issue are the reduction of the dimensionality of the model space and/or the use of efficient methods to compute the gradient of the target density. Here we focus the attention to the estimation of elastic properties (P-, S-wave velocities and density) from pre-stack data through a non-linear amplitude versus angle inversion in which the Hamiltonian Monte Carlo algorithm is used to sample the posterior probability. To decrease the computational cost of the inversion procedure, we employ the discrete cosine transform to reparametrize the model space, and we train a convolutional neural network to predict the Jacobian matrix around each sampled model. The training data set for the network is also parametrized in the discrete cosine transform space, thus allowing for a reduction of the number of parameters to be optimized during the learning phase. Once trained the network can be used to compute the Jacobian matrix associated with each sampled model in real time. The outcomes of the proposed approach are compared and validated with the predictions of Hamiltonian Monte Carlo inversions in which a quite computationally expensive, but accurate finite-difference scheme is used to compute the Jacobian matrix and with those obtained by replacing the Jacobian with a matrix operator derived from a linear approximation of the Zoeppritz equations. Synthetic and field inversion experiments demonstrate that the proposed approach dramatically reduces the cost of the Hamiltonian Monte Carlo inversion while preserving an accurate and efficient sampling of the posterior probability. 相似文献
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Jacques Mourey Ludovic Ravanel Christophe Lambiel Joël Strecker Mattia Piccardi 《Norsk geografisk tidsskrift. Norwegian journal of geography》2019,73(4):215-228
ABSTRACTIn the European Alps, high mountain environments are subject to major impacts resulting from climate change, which strongly affect human activities such as mountaineering. The purpose of the study was to examine changes in access routes to 30 high mountain huts in the Western Alps since the 1990s. Data were derived from the use of two different methods, geo-historical studies and a questionnaire, and were used to identify both the climate-related processes affecting the climbing routes and the strategies implemented by public entities, Alpine clubs, guide companies, and hut keepers to maintain acceptable safety and technical conditions. The case studies revealed issues affecting three access routes and the results from the questionnaire showed that the main processes affecting access routes were loss of ice thickness and retreat from the front of the glaciated areas. Commonly, in situ equipment was installed to facilitate access for mountaineers and/or a part of a route was relocated to a safer area. The authors conclude that in most cases, the measures were effective but they were limited by financial, ethical and legal issues, especially in protected or classified areas that could jeopardise their durability and effectiveness. 相似文献
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Climate sciences foresee a future where extreme weather events could happen with increased frequency and strength, which would in turn increase risks of floods (i.e. the main source of losses in the world). The Mediterranean basin is considered a hot spot in terms of climate vulnerability and risk. The expected impacts of those events are exacerbated by land-use change and, in particular, by urban growth which increases soil sealing and, hence, water runoff. The ultimate consequence would be an increase of fatalities and injuries, but also of economic losses in urban areas, commercial and productive sites, infrastructures and agriculture. Flood damages have different magnitudes depending on the economic value of the exposed assets and on level of physical contact with the hazard. This work aims at proposing a methodology, easily customizable by experts’ elicitation, able to quantify and map the social component of vulnerability through the integration of earth observation (EO) and census data with the aim of allowing for a multi-temporal spatial assessment. Firstly, data on employment, properties and education are used for assessing the adaptive capacity of the society to increase resilience to adverse events, whereas, secondly, coping capacity, i.e. the capacities to deal with events during their manifestation, is mapped by aggregating demographic and socio-economic data, urban growth analysis and memory on past events. Thirdly, the physical dimension of exposed assets (susceptibility) is assessed by combining building properties acquired by census data and land-surface characteristics derived from EO data. Finally, the three components (i.e. adaptive and coping capacity and susceptibility) are aggregated for calculating the dynamic flood vulnerability index (FVI). The approach has been applied to Northeast Italy, a region frequently hit by floods, which has experienced a significant urban and economic development in the past decades, thus making the dynamic study of FVI particularly relevant. The analysis has been carried out from 1991 to 2016 at a 5-year steps, showing how the integration of different data sources allows to produce a dynamic assessment of vulnerability, which can be very relevant for planning in support of climate change adaptation and disaster risk reduction.
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Carlos M. Alonso-Hernández Fabio Conte Cristina Misic Mattia Barsanti Miguel Gómez-Batista Misael Díaz-Asencio Anabella Covazzi-Harriague Federica G. Pannacciulli 《Continental Shelf Research》2011,31(7-8):749-757
The main environmental features of the Gulf of Batabanó, Cuba, Caribbean, were investigated through the analyses of surface sediments collected at 23 sites. In order to highlight the potential threats affecting the sedimentary compartment of this area, samples were analysed for: granulometry, mineralogy, heavy metals concentration (As, Cd, Cu, Mn, Ni, Pb, Zn), organic carbon, total nitrogen and radionuclides. Findings were compared with published data and “grey” literature.Results showed: granulometric homogeneity and a widespread carbonatic condition all over the gulf, probably due to stable bathymetry and lack of terrigenous input (except for the La Coloma basin); a rather pristine environment for what concerns heavy metals pollution, except for La Coloma where a large arsenic input was recorded; very low levels of natural and artificial radioactivity; a relevant quantity of sedimentary organic matter, providing biota with useful substrate for feeding and enhancing the food-web development while indirectly supplying lobster fisheries. Combined data highlighted the impact of the Dique Sur in reducing terrigenous input in the coastal area.Future studies should focus on dating of sediment cores for identifying and quantifying the changes acting in the gulf and on investigating the origins of the large arsenic input to La Coloma. 相似文献
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1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach 
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下载免费PDF全文 Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step. 相似文献
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Colucci?SimoneEmail authorView author&#;s OrcID profile de’?Michieli?Vitturi?Mattia Landi?Patrizia 《Contributions to Mineralogy and Petrology》2017,172(11-12):100
Nucleation and growth of crystals, and the resulting crystal size distribution, play a fundamental role in controlling the physical properties of magmas and consequently the dynamics of the eruptions. In the past decades, laboratory experiments demonstrated that size and shape of crystals strongly control the physical properties of magma and lava. Additionally, natural and experimental samples are usually characterized in terms of their crystal size distribution to link it with physical processes that are not directly observable, such as cooling or decompression mechanisms. In this paper, we present CrystalMoM, a new predictive model, based on the quadrature-based method of moments, developed for studying the kinetic of crystallization in volcanic systems. The quadrature-based method of moments, well established in the field of chemical engineering, represents a mesoscale modelling approach that rigorously simulates the space–time evolution of a distribution of particles, by considering its moments. The method is applied here, for the first time, for studying the equilibrium/disequilibrium crystallization in magma, modelling the temporal evolution of the moments of a crystal size distribution. The model, verified against numerical and experimental data, represents a valuable tool to infer the cooling and decompression rates from the crystal size distribution observed in natural samples. 相似文献
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Basement lithostratigraphy of the Adula nappe: implications for Palaeozoic evolution and Alpine kinematics 总被引：1，自引：0，他引：1
Mattia Cavargna-Sani Jean-Luc Epard François Bussy Alex Ulianov 《International Journal of Earth Sciences》2014,103(1):61-82
The Adula nappe belongs to the Lower Penninic domain of the Central Swiss Alps. It consists mostly of pre-Triassic basement lithologies occurring as strongly folded and sheared gneisses of various types with mafic boudins. We propose a new lithostratigraphy for the northern Adula nappe basement that is supported by detailed field investigations, U–Pb zircon geochronology, and whole-rock geochemistry. The following units have been identified: Cambrian clastic metasediments with abundant carbonate lenses and minor bimodal magmatism (Salahorn Formation); Ordovician metapelites associated with amphibolite boudins with abundant eclogite relicts representing oceanic metabasalts (Trescolmen Formation); Ordovician peraluminous metagranites of calc-alkaline affinity ascribed to subduction-related magmatism (Garenstock Augengneiss); Ordovician metamorphic volcanic–sedimentary deposits (Heinisch Stafel Formation); Early Permian post-collisional granites recording only Alpine orogenic events (Zervreila orthogneiss). All basement lithologies except the Permian granites record a Variscan + Alpine polyorogenic metamorphic history. They document a complex Paleozoic geotectonic evolution consistent with the broader picture given by the pre-Mesozoic basement framework in the Alps. The internal consistency of the Adula basement lithologies and the stratigraphic coherence of the overlying Triassic sediments suggest that most tectonic contacts within the Adula nappe are pre-Alpine in age. Consequently, mélange models for the Tertiary emplacement of the Adula nappe are not consistent and must be rejected. The present-day structural complexity of the Adula nappe is the result of the intense Alpine ductile deformation of a pre-structured entity. 相似文献
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