(Table 1) Gravity and pressure within the Sleipner area relative to ones at the reference station in 2002 and 2005

At Sleipner, CO2 is being separated from natural gas and injected into an underground saline aquifer for environmental purposes. Uncertainty in the aquifer temperature leads to uncertainty in the in situ density of CO2. In this study, gravity measurements were made over the injection site in 2002 and 2005 on top of 30 concrete benchmarks on the seafloor in order to constrain the in situ CO2 density. The gravity measurements have a repeatability of 4.3 µGal for 2003 and 3.5 µGal for 2005. The resulting time-lapse uncertainty is 5.3 µGal. Unexpected benchmark motions due to local sediment scouring contribute to the uncertainty. Forward gravity models are calculated based on both 3D seismic data and reservoir simulation models. The time-lapse gravity observations best fit a high temperature forward model based on the time-lapse 3D seismics, suggesting that the average in situ CO2 density is about to 530kg/m**3. Uncertainty in determining the average density is estimated to be ±65 kg/m**3 (95% confidence), however, this does not include uncertainties in the modeling. Additional seismic surveys and future gravity measurements will put better constraints on the CO2 density and continue to map out the CO2 flow.

Indagine modellistica sulla tenuta idraulica delle difese arginali deputate alla protezione idraulica del territorio

La prima parte di questo lavoro di tesi tratta dell’interazione tra un bacino di laminazione e il sottostante acquifero: è in fase di progetto, infatti, la costruzione di una cassa di espansione sul torrente Baganza, a monte della città di Parma. L’obiettivo di tale intervento è di ridurre il rischio di esondazione immagazzinando temporaneamente, in un serbatoio artificiale, la parte più pericolosa del volume di piena che verrebbe rilasciata successivamente con portate che possono essere agevolmente contenute nel tratto cittadino del torrente. L’acquifero è stato preliminarmente indagato e monitorato permettendone la caratterizzazione litostratigrafica. La stratigrafia si può riassumere in una sequenza di strati ghiaioso-sabbiosi con successione di lenti d’argilla più o meno spesse e continue, distinguendo due acquiferi differenti (uno freatico ed uno confinato). Nel presente studio si fa riferimento al solo acquifero superficiale che è stato modellato numericamente, alle differenze finite, per mezzo del software MODFLOW_2005. L'obiettivo del presente lavoro è di rappresentare il sistema acquifero nelle condizioni attuali (in assenza di alcuna opera) e di progetto. La calibrazione è stata condotta in condizioni stazionarie utilizzando i livelli piezometrici raccolti nei punti d’osservazione durante la primavera del 2013. I valori di conducibilità idraulica sono stati stimati per mezzo di un approccio geostatistico Bayesiano. Il codice utilizzato per la stima è il bgaPEST, un software gratuito per la soluzione di problemi inversi fortemente parametrizzati, sviluppato sulla base dei protocolli del software PEST. La metodologia inversa stima il campo di conducibilità idraulica combinando osservazioni sullo stato del sistema (livelli piezometrici nel caso in esame) e informazioni a-priori sulla struttura dei parametri incogniti. La procedura inversa richiede il calcolo della sensitività di ciascuna osservazione a ciascuno dei parametri stimati; questa è stata valutata in maniera efficiente facendo ricorso ad una formulazione agli stati aggiunti del codice in avanti MODFLOW_2005_Adjoint. I risultati della metodologia sono coerenti con la natura alluvionale dell'acquifero indagato e con le informazioni raccolte nei punti di osservazione. Il modello calibrato può quindi essere utilizzato come supporto alla progettazione e gestione dell’opera di laminazione. La seconda parte di questa tesi tratta l'analisi delle sollecitazioni indotte dai percorsi di flusso preferenziali causati da fenomeni di piping all’interno dei rilevati arginali. Tali percorsi preferenziali possono essere dovuti alla presenza di gallerie scavate da animali selvatici. Questo studio è stato ispirato dal crollo del rilevato arginale del Fiume Secchia (Modena), che si è verificato in gennaio 2014 a seguito di un evento alluvionale, durante il quale il livello dell'acqua non ha mai raggiunto la sommità arginale. La commissione scientifica, la cui relazione finale fornisce i dati utilizzati per questo studio, ha attribuito, con molta probabilità, il crollo del rilevato alla presenza di tane di animali. Con lo scopo di analizzare il comportamento del rilevato in condizioni integre e in condizioni modificate dall'esistenza di un tunnel che attraversa il manufatto arginale, è stato realizzato un modello numerico 3D dell’argine mediante i noti software Femwater e Feflow. I modelli descrivono le infiltrazioni all'interno del rilevato considerando il terreno in entrambe le porzioni sature ed insature, adottando la tecnica agli elementi finiti. La tana è stata rappresentata da elementi con elevata permeabilità e porosità, i cui valori sono stati modificati al fine di valutare le diverse influenze sui flussi e sui contenuti idrici. Per valutare se le situazioni analizzate presentino o meno il verificarsi del fenomeno di erosione, sono stati calcolati i valori del fattore di sicurezza. Questo è stato valutato in differenti modi, tra cui quello recentemente proposto da Richards e Reddy (2014), che si riferisce al criterio di energia cinetica critica. In ultima analisi è stato utilizzato il modello di Bonelli (2007) per calcolare il tempo di erosione ed il tempo rimanente al collasso del rilevato.

Bayesian retrieval of scatterometer wind fields

The retrieval of wind fields from scatterometer observations has traditionally been separated into two phases; local wind vector retrieval and ambiguity removal. Operationally, a forward model relating wind vector to backscatter is inverted, typically using look up tables, to retrieve up to four local wind vector solutions. A heuristic procedure, using numerical weather prediction forecast wind vectors and, often, some neighbourhood comparison is then used to select the correct solution. In this paper we develop a Bayesian method for wind field retrieval, and show how a direct local inverse model, relating backscatter to wind vector, improves the wind vector retrieval accuracy. We compare these results with the operational U.K. Meteorological Office retrievals, our own CMOD4 retrievals and a neural network based local forward model retrieval. We suggest that the neural network based inverse model, which is extremely fast to use, improves upon current forward models when used in a variational data assimilation scheme.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

A pragmatic Bayesian approach to wind field retrieval

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about 800 km, carrying a C-band scatterometer. A scatterometer measures the amount of backscatter microwave radiation reflected by small ripples on the ocean surface induced by sea-surface winds, and so provides instantaneous snap-shots of wind flow over large areas of the ocean surface, known as wind fields. Inherent in the physics of the observation process is an ambiguity in wind direction; the scatterometer cannot distinguish if the wind is blowing toward or away from the sensor device. This ambiguity implies that there is a one-to-many mapping between scatterometer data and wind direction. Current operational methods for wind field retrieval are based on the retrieval of wind vectors from satellite scatterometer data, followed by a disambiguation and filtering process that is reliant on numerical weather prediction models. The wind vectors are retrieved by the local inversion of a forward model, mapping scatterometer observations to wind vectors, and minimising a cost function in scatterometer measurement space. This thesis applies a pragmatic Bayesian solution to the problem. The likelihood is a combination of conditional probability distributions for the local wind vectors given the scatterometer data. The prior distribution is a vector Gaussian process that provides the geophysical consistency for the wind field. The wind vectors are retrieved directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density network, that incorporates the knowledge that the conditional probability distribution of the observation process is predominantly bi-modal, is developed. The optimal model, which generalises across a swathe of scatterometer readings, is better on key performance measures than the current operational model. Wind field retrieval is approached from three perspectives. The first is a non-autonomous method that confirms the validity of the model by retrieving the correct wind field 99% of the time from a test set of 575 wind fields. The second technique takes the maximum a posteriori probability wind field retrieved from the posterior distribution as the prediction. For the third technique, Markov Chain Monte Carlo (MCMC) techniques were employed to estimate the mass associated with significant modes of the posterior distribution, and make predictions based on the mode with the greatest mass associated with it. General methods for sampling from multi-modal distributions were benchmarked against a specific MCMC transition kernel designed for this problem. It was shown that the general methods were unsuitable for this application due to computational expense. On a test set of 100 wind fields the MAP estimate correctly retrieved 72 wind fields, whilst the sampling method correctly retrieved 73 wind fields.

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

Stochastic control strategies and adaptive critic methods

Adaptive critic methods have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, nonlinear and nonstationary environments. In this study, a novel probabilistic dual heuristic programming (DHP) based adaptive critic controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) adaptive critic method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterized by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the critic network is then calculated and shown to be equal to the analytically derived correct value.

Distribution modeling of nonlinear inverse controllers under a Bayesian framework

The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems and is demonstrated on nonlinear single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) examples. © 2006 IEEE.

A Bayesian approach to modeling the conditional density of the inverse controller

The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems. For illustration purposes, the proposed methodology is applied to linear Gaussian systems. © 2004 IEEE.

Origin of the Grande Ronde Formation flows, Columbia River flood basalt group

Lavas belonging to the Grande Ronde Formation (GRB) constitute about 63% of the Columbia River Basalt Group (CRBG), a flood basalt province in the NW United States. A puzzling feature is the lack of phenocrysts (< 5%) in these chemically evolved lavas. Based mainly on this observation it has been hypothesized that GRB lavas were nearly primary melts generated by large-scale melting of eclogite. Another recent hypothesis holds that GRB magmas were extremely hydrous and rose rapidly from the mantle such that the dissolved water kept the magmas close to their liquidi. I present new textural and chemical evidence to show that GRB lavas were neither primary nor hydrous melts but were derived from other melts via efficient fractional crystallization and mixing in shallow intrusive systems. Texture and chemical features further suggest that the melt mixing process may have been exothermic, which forced variable melting of some of the existing phenocrysts. ^ Finally, reported here are the results of efforts to simulate the higher pressure histories of GRB using COMAGMAT and MELTS softwares. The intent was to evaluate (1) whether such melts could be derived from primary melts formed by partial melting of a peridotite source as an alternative to the eclogite model, or if bulk melting of eclogite is required; and (2) at what pressure such primary melts could have been in equilibrium with the mantle. I carried out both forward and inverse modeling. The best fit forward model indicates that most primitive parent melts related to GRB could have been multiply saturated at ∼1.5--2.0 GPa. I interpret this result to indicate that the parental melts last equilibrated with a peridotitic mantle at 1.5--2.0 GPa and such partial melts rose to ∼0.2 GPa where they underwent efficient mixing and fractionation before erupting. These models suggest that the source rock was not eclogitic but a fertile spinel lherzolite, and that the melts had ∼0.5% water. ^

The influence of actions on auditory perception: cognitive and neural mechanisms

It is well known that self-generated stimuli are processed differently from externally generated stimuli. For example, many people have noticed since childhood that it is very difficult to make a self-tickling. In the auditory domain, self-generated sounds elicit smaller brain responses as compared to externally generated sounds, known as the sensory attenuation (SA) effect. SA is manifested in reduced amplitudes of evoked responses as measured through MEEG, decreased firing rates of neurons and a lower level of perceived loudness for self-generated sounds. The predominant explanation for SA is based on the idea that self-generated stimuli are predicted (e.g., the forward model account). It is the nature of their predictability that is crucial for SA. On the contrary, the sensory gating account emphasizes a general suppressive effect of actions on sensory processing, regardless of the predictability of the stimuli. Both accounts have received empirical support, which suggests that both mechanisms may exist. In chapter 2, three behavioural studies concerning the influence of motor activation on auditory perception were presented. Study 1 compared the effect of SA and attention in an auditory detection task and showed that SA was present even when substantial attention was paid to unpredictable stimuli. Study 2 compared the loudness perception of tones generated by others between Chinese and British participants. Compared to externally generated tones, a decrease in perceived loudness for others generated tones was found among Chinese but not among the British. In study 3, partial evidence was found that even when reading words that are related to action, auditory detection performance was impaired. In chapter 3, the classic SA effect of M100 suppression was replicated with MEG in study 4. With time-frequency analysis, a potential neural information processing sequence was found in auditory cortex. Prior to the onset of self-generated tones, there was an increase of oscillatory power in the alpha band. After the stimulus onset, reduced gamma power and alpha/beta phase locking were found. The three temporally segregated oscillatory events correlated with each other and with SA effect, which may be the underlying neural implementation of SA. In chapter 4, a TMS-MEG study was presented investigating the role of the cerebellum in adapting to delayed presentation of self-generated tones (study 5). It demonstrated that in sham stimulation condition, the brain can adapt to the delay (about 100 ms) within 300 trials of learning by showing a significant increase of SA effect in the suppression of M100, but not M200 component. Whereas after stimulating the cerebellum with a suppressive TMS protocol, the adaptation in M100 suppression disappeared and the pattern of M200 suppression reversed to M200 enhancement. These data support the idea that the suppressive effect of actions on auditory processing is a consequence of both motor driven sensory predictions and general sensory gating. The results also demonstrate the importance of neural oscillations in implementing SA effect and the critical role of the cerebellum in learning sensory predictions under sensory perturbation.

An analysis of US greenhouse gas cap-and-trade proposals using a forward-looking economic model

We develop a forward-looking version of the recursive dynamic MIT Emissions Prediction and Policy Analysis (EPPA) model, and apply it to examine the economic implications of proposals in the US Congress to limit greenhouse gas (GHG) emissions. We find that shocks in the consumption path are smoothed out in the forward-looking model and that the lifetime welfare cost of GHG policy is lower than in the recursive model, since the forward-looking model can fully optimize over time. The forward-looking model allows us to explore issues for which it is uniquely well suited, including revenue-recycling and early action crediting. We find capital tax recycling to be more welfare-cost reducing than labor tax recycling because of its long-term effect on economic growth. Also, there are substantial incentives for early action credits; however, when spread over the full horizon of the policy they do not have a substantial effect on lifetime welfare costs.

Development of a subject-specific musculoskeletal shoulder model : inverse and forward dynamics applications

A prevalência de pessoas que referem dor no complexo articular do ombro, com concomitante limitação na capacidade para realizar atividades da vida diária, é elevada. Estes níveis de prevalência sobrecarregam quer os utentes, como a própria sociedade. A evidência científica atual indicia a existência de uma relação entre as alterações da articulação escápulo-torácica e as patologias associadas à articulação gleno-umeral. A capacidade de quantificar, cinemática e cineticamente, as disfunções ao nível das articulações escápulo-torácica e gleno-umeral, é algo de enorme importância, quer para a comunidade biomecânica, como para a clínica. No decorrer dos trabalhos desta tese foi desenvolvido, através do software OpenSim, um modelo tridimensional músculo-esquelético do complexo articular do ombro que inclui a representação do tórax/coluna, clavícula, omoplata, úmero, rádio, cúbito e articulações que permitem os movimentos relativos desses segmentos, assim como, 16 músculos e 4 ligamentos. Com um total de 11 graus de liberdade, incluindo um novo modelo articular escápulo-torácico, os resultados demonstram que este é capaz de reconstruir de forma precisa e rápida os movimentos escápulo-torácicos e glenoumerais, recorrendo para tal, à cinemática inversa, e à dinâmica inversa e direta. Conta ainda com um método de transformação inovador para determinar, com base nas especificidades dos sujeitos, os locais de inserção muscular. As principais motivações subjacentes ao desenvolvimento desta tese foram contribuir para o aprofundar do atual conhecimento sobre as disfunções do complexo articular do ombro e, simultaneamente, proporcionar à comunidade clínica uma ferramenta biomecânica de livre acesso com o intuito de melhor suportar as decisões clínicas e dessa forma concorrer para uma prática mais efetiva.

A feed-forward spiking model of shape-coding by IT cells

The ability to recognize a shape is linked to figure-ground (FG) organization. Cell preferences appear to be correlated across contrast-polarity reversals and mirror reversals of polygon displays, but not so much across FG reversals. Here we present a network structure which explains both shape-coding by simulated IT cells and suppression of responses to FG reversed stimuli. In our model FG segregation is achieved before shape discrimination, which is itself evidenced by the difference in spiking onsets of a pair of output cells. The studied example also includes feature extraction and illustrates a classification of binary images depending on the dominance of vertical or horizontal borders.