Proposed Data Assimilation Framework Research Articles

Contaminant source and aquifer characterization: An application of ES-MDA demonstrating the assimilation of geophysical data

Contaminant source and aquifer characterization (CSAC) is critical in groundwater pollution evaluation and remediation. The ensemble smoother with multiple data assimilation (ES-MDA) is utilized to jointly identify contaminant source information and hydraulic conductivities by assimilating time-lapse electrical resistivity tomography (ERT) data. In a synthetic profile with a time-varying release history in a heterogeneous aquifer, we verify the performance of the proposed data assimilation framework. The results show that the CSAC problem could be handled by the proposed approach. The time-varying release history and the high permeability area can be identified with adequate time-lapse ERT measurements. Further reproduction of the evolution of the plume after CSAC also shows consistency with the reference plume. The poor conditioning inversion caused by the filter inbreeding is analyzed by comparing four scenarios with different apparent resistivity measurements. Furthermore, we also evaluate the impact of uncertainties in the petrophysical properties and geophysical observations on our data assimilation framework. The results show that the proposed ES-MDA data assimilation framework could provide a convincing inversion of the time-varying release history and hydraulic conductivities.

Regulating the time of the crop model clock: A data assimilation framework for regions with high phenological heterogeneity

in crop growth data assimilation systems, the mismatch between simulated and observed phenology significantly deteriorates the performance of crop growth modeling. This situation may be more severe for smallholder farmers-managed fields, where the phenological heterogeneity was high even when climate condition was relatively uniform. Previous studies investigated the non-sequential methods to retrospectively assimilate historical phenology observations. However, approaches to dynamically assimilating phenological measurements through sequential data assimilation methods remain unexplored one of the most intractable challenges of dynamic phenology assimilation is that a considerable proportion of model parameters and variables are entangled with phenology, therefore simply assimilating phenological measurements could disturb the model clock. This study aims to establish a robust crop data assimilation framework capable of assimilating phenological measurements in real time without disturbing the model clock the framework used an open-source version of the AquaCrop model to simulate crop growth and used the ensemble Kalman filter (EnKF) to assimilate observations sequentially. A parameter refresh method was proposed to restore the phenological consistency of model parameters after updating the phenology state. Assimilation strategies with different observation types and compositions of state vectors were designed after a global sensitivity analysis of model parameters. These strategies were evaluated through the Observing System Simulation Experiments (OSSE), and the selected strategies were tested in a real-world case. the results of the OSS Experiments show that the phenological mismatch problem greatly affects crop growth simulation, and this mismatch could not be narrowed effectively by assimilating non-phenological observations. Assimilating phenological measurements with the proposed parameters refresh method and assimilation strategies closed this mismatch and produced better performance compared to the Restart-EnKF method. In the real-world paddy rice case, assimilating phenology with the proposed strategies significantly improved yield estimation in low-yield plots (less than 4 ton/ha) compared to assimilating canopy cover (CC) alone, with an R2 increase from 0.07 to 0.48. Assimilating CC, biomass and phenology simultaneously produced the best yield estimation for all plots, with R2 = 0.57 and RMSE = 1.00 ton/ha. assimilating phenology under a consistent model clock significantly improved yield estimation when the phenological heterogeneity of plots was high. the results highlight the effectiveness and robustness of the established data assimilation framework for dynamic crop growth simulation, indicating the potentials of the proposed data assimilation framework for regional in-season crop modeling and yield forecasting.

Mean wind flow reconstruction of a high-rise building based on variational data assimilation using sparse pressure measurements

The paper investigates the applicability of the variational data assimilation approach to reconstruct three-dimensional wind flows around a high-rise building model, and provides guidelines towards an efficient reconstruction. The objective is to determine the best distributed control parameters based on the Reynolds-averaged-Navier–Stokes (RANS) model coupled with pressure data. The strategy considers a distributed additive forcing control parameter, acting on the momentum equation and/or the transport equation of turbulent dissipations, where the closure is performed. To avoid unphysical solutions and accelerate the optimization convergence a Sobolev gradient descent regularization is proposed and compared with penalty techniques. The proposed data assimilation framework is applied to a realistic wind engineering estimation problem combining sparse wall pressure measurements with a routinely used industrial three-dimensional RANS numerical code. Results demonstrate that wall-pressure data is a meaningful enough piece of information to recover accurately the wake flow extension. A hybrid control parameter on the transport of the dissipation and the momentum equations leads to very precise results, whereas the Sobolev gradient descent direction gives efficient regularization and a fast convergence. These findings can be useful for future application of such a data assimilation technique to wind engineering problems.

Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information

To minimize systematic differences between soil moisture (SM) time series derived from remote sensing (RS) and land surface model (LSM), RS-based SM climatology information is typically discarded during land data assimilation (DA). However, recent studies have demonstrated that SM climatology estimates provided by L-band microwave RS retrievals can significantly outperform comparable estimates derived from LSMs. Consequently, neglecting a RS-based SM climatology may lead to degraded SM spatial patterns generated by land DA. Here, we propose a climatology-optimized SM DA framework, which first calibrates LSM parameters to leverage the RS SM climatology information. Next, multi-sensor RS SM retrievals are assimilated into the calibrated LSM using Ensemble Kalman Filter (EnKF). This framework is demonstrated using the Variable Infiltration Capacity (VIC) model and RS SM retrievals derived from the L-band Soil Moisture Active Passive (SMAP) and C-band Advanced SCATterometer (ASCAT) sensors. Here, both SMAP and ASCAT SM retrievals are assimilated into the VIC model after calibrating VIC to match spatial variations captured in the SMAP SM climatology. The DA SM results are validated using in-situ SM observations derived from 820 stations. Results show that SMAP-based SM climatology calibration directly improves the quality of SM spatial patterns estimated by VIC. In addition, the SMAP-based calibration also benefits our model-error representation and thereby yields better Kalman gains that improve temporal SM DA accuracy. Relative to typical DA approaches, this newly proposed DA framework improves both spatial (0.26 versus 0.51 (−)) and temporal correlations (0.48 versus 0.52 (−)) versus in-situ SM observations. In addition, SM improvements are effectively propagated into improved streamflow estimates – leading to an average increase of Nash and Sutcliffe coefficient from 0.74 to 0.76 (−). Overall, we demonstrate that RS SM climatology information is valuable for land DA and our climatology-optimized framework successfully retains such information to the benefit of land DA performance.

A newly proposed data assimilation framework to enhance predictions for reflood tests

Data assimilation is the process that can enhance the predictions by adjusting the input parameters inside their uncertainties range. In this study, the predicted results of the reflood tests, which were simulated by the SPACE code, were examined using the data assimilation technique. Crucially, a newly proposed data assimilation framework, STARU (Sampling meThod for highly non-lineAR system Uncertainty analysis), was developed. To propose the new data assimilation framework, we combined a new accuracy evaluation method with the Monte Carlo sampling algorithm to enhance the predictions for such a complex system as reflooding phenomena (many input parameters and non-linear responses). Subsequently, the outcomes of STARU were compared with the performances of data assimilation in PAPIRUS, a toolkit with many functions and modules for uncertainty analysis, which was developed by Heo and Kim (2015). Conversely, STARU only focused on solving problems with many parameters and highly non-linear systems in which implementation of the Monte Carlo sampling method is required because of the complexity of the systems. The main objectives of developing STARU are to reduce the computation time and increase the acceptance rate of the data assimilation process for complex systems. Consequently, we found that STARU effectively enhanced the data assimilation results for the reflood tests with higher acceptance rates and faster convergence. Furthermore, better improvements and more stable convergence of the system states were also observed, which effectively facilitated the search process for the most sensitive physical models in the predictions. In the future, STARU may be suitable for implementation in the other complex problems to calibrate the models, examine the uncertainty propagation, and study the sensitivity analysis.

Dynamic demand estimation on large scale networks using Principal Component Analysis: The case of non-existent or irrelevant historical estimates

Calibrating DTA models is complex due to the involved indeterminacy, non-linearity, and dimensionality, restricting the application of conventional calibration approaches, especially for larger networks. For this, Principal Component Analysis (PCA) is slowly establishing itself as the new state of the art because it can greatly tackle two well known challenges—i.e. problem dimensionality and non-linearity. PCA application limits the optimization search space in a lower dimension space, defined by orthogonal Principal Components, evaluated upon a set of historical estimates. In this paper, we solve practical implementation problems for PCA-based calibration techniques. Specifically, we formulate a data-assimilation framework to propose multiple OD historical data-set generation methods which allows the use of PC-based algorithms in case the historical data is irrelevant or unavailable, often the case for large-scale DTA models. Furthermore, we propose a simplified problem formulation that leverages properties of the novel data-set generation framework and helps for faster and more efficient calibration. The methodology is implemented using the PC-SPSA algorithm, which combines PCA with the popular Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, commonly used to calibrate smaller networks. The approach is tested on a large-scale case study of the Munich metropolitan urban network, with encouraging calibration results. The proposed data-assimilation framework can account for spatial, temporal, and day-to-day variations in the demand. Different methods and combinations are tested and compared. The results suggest that all these correlations should be used in order to avoid over-fitting issues. Furthermore, the implementation properties of PCA and PC-SPSA are also explored using different sensitivity analyses to assess the toll and benefits of using PCA i.e., ease in SPSA hyper-parameter, role of historical data-set generation parameters and the algorithm’s performance against different target demand fluctuations. The analysis shows encouraging results for PC-SPSA robustness and helps establishing simplified guidelines for implementing such PCA-methods practically on large-scale DTA models.

Integrating deep learning-based data assimilation and hydrogeophysical data for improved monitoring of DNAPL source zones during remediation

Data assimilation techniques allow the integration of multi-source data with physical modeling of dense nonaqueous phase liquid (DNAPL) source zones to monitor their evolution in time and thereby improve remediation efforts. The DNAPL source zone is governed by multiphase physics and its architecture is usually highly irregular and nonstationary. Traditional data assimilation methods use stationary models to describe these nonstationary DNAPL saturation fields, leading to significant errors in the estimation of unknown source zone architectures (SZAs). In this study, we propose a geostatistical data assimilation method to improve performance monitoring of DNAPL remediation. We estimate the DNAPL SZA after parameterizing the DNAPL saturation field using a deep learning method instead of a stationary statistical model. First, we train a convolutional variational autoencoder (CVAE) network to capture the physics of DNAPL infiltration and depletion. Then, we incorporate the CVAE network within the Ensemble Kalman filter (EnKF) to resolve the evolving DNAPL SZA by assimilating time-lapse hydrogeophysical measurements (i.e., oscillatory hydraulic tomography, downgradient dissolved DNAPL concentration, and electrical resistivity tomography). To assess the proposed CVAE-EnKF framework's performance, we conducted numerical experiments with evolving DNAPL SZAs in a 2D heterogeneous aquifer. Results show that the proposed framework significantly improved DNAPL saturation estimates throughout the source zone over time, compared to the standard EnKF method. CVAE-EnKF reduced the estimation error of DNAPL mass remediated by 51% from EnKF, along with better predictions of source zone longevity. It also better captured the morphology and center/spread of mass of the evolving remediated portions of the source zone and provided better estimates of pooled-DNAPL mass. Furthermore, the CVAE-EnKF method is more stable and converges faster than EnKF alone. Overall, the proposed data assimilation framework can improve real-time monitoring of DNAPL remediation and risk analysis.

A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation

Snow varies widely in space and time over high mountain regions. Accurately representing the spatial distribution of snow water equivalent (SWE) is critically important for improving our understanding of snow accumulation and melt processes. Despite its importance, in situ observations are lacking in poorly gauged regions such as the headwater region of the Yangtze River (HRYR). Traditional remotely sensed retrievals are highly uncertain due to the effect of cloudiness (e.g., optical remote sensing-derived snow cover area) and the coarse spatial resolution (e.g., passive microwave remote sensing-derived snow depth). Hydrological modeling is a powerful way to understand the snow processes, but uncertainty still exists due to model deficiency and errors of forcing data. Assimilating high-spatial-resolution remotely sensed snow data into a snowmelt model may be potentially valuable for more accurate snow predictions. In this study, a high-spatial-resolution remotely sensed snow depth data set (500 m, derived by integrating snow cover area, land surface temperature, and passive microwave brightness temperature products) was assimilated into a snowmelt model within a particle filter (PF) assimilation framework, which updates both model state variables and parameters simultaneously. After assimilation, the time series of basin-averaged SWE estimation showed an appreciable improvement with respect to the simulation with the traditional calibration (TC) method, in terms of the Nash-Sutcliffe efficiency (NSE) coefficient increased by ~ 10–20% and root‐mean‐square error (RMSE) decreased by ~ 7–18%. The PF approach also greatly improved the spatial distribution of SWE estimation with RMSE decreased by ~ 15–30%. The SWE estimation from the PF was also comparable or even better than that from another assimilation scheme, namely, the direct insertion. Comparison with in situ snowfall data indicated that the simulated snowfall from the PF outperformed the TC, with RMSE decreased by ~ 15–32% and correlation coefficient increased by ~ 58–83%. Furthermore, the evolution of parameters suggested the applicability of the PF method with spatially variable parameters. With spatiotemporally variable parameters in the PF, the snow model could perfectly simulate the actual snow distribution particularly over high elevation regions where the average temperature was lower than 0 °C, while fixed parameters in the TC cannot simulate the variable snow distribution. The proposed data assimilation framework has large potential of improving the accuracy of snow prediction across poorly gauged high mountain areas.

A data assimilation framework for data-driven flow models enabled by motion tomography

Autonomous underwater vehicles (AUVs) have become central to data collection for scientific and monitoring missions in the coastal and global oceans. To provide immediate navigational support for AUVs, computational data-driven flow models described as generic environmental models (GEMs) construct a map of the environment around AUVs. This paper proposes a data assimilation framework for the GEM to update the map using data collected by the AUVs. Unlike Eulerian data, Lagrangian data along the AUV trajectory carry time-integrated flow information. To facilitate assimilation of Lagrangian data into the GEM, the motion tomography method is employed to convert Lagrangian data of AUVs into an Eulerian spatial map of a flow field. This process allows assimilation of both Eulerian and Lagrangian data into the GEM to be incorporated in a unified framework, which introduces a nonlinear filtering problem. Considering potential complementarity of Eulerian and Lagrangian data in estimating spatial and temporal characteristics of flow, we develop a filtering method for estimation of the spatial and temporal parameters in the GEM. The observability is analyzed to verify the convergence of our filtering method. The proposed data assimilation framework for the GEM is demonstrated through simulations using two flow fields with different characteristics: (i) a double-gyre flow field and (ii) a flow field constructed by using real ocean surface flow observations from high-frequency radar.

A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters

Traffic conditions can be more accurately estimated using data assimilation techniques since these methods incorporate an imperfect traffic simulation model with the (partial) noisy measurement data. In this paper, we propose a data assimilation framework for vehicle density estimation on urban traffic networks. To compromise between computational efficiency and estimation accuracy, a mesoscopic traffic simulation model (we choose the platoon based model) is employed in this framework. Vehicle passages from loop detectors are considered as the measurement data which contain errors, such as missed and false detections. Due to the nonlinear and non-Gaussian nature of the problem, particle filters are adopted to carry out the state estimation, since this method does not have any restrictions on the model dynamics and error assumptions. Simulation experiments are carried out to test the proposed data assimilation framework, and the results show that the proposed framework can provide good vehicle density estimation on relatively large urban traffic networks under moderate sensor quality. The sensitivity analysis proves that the proposed framework is robust to errors both in the model and in the measurements.

Towards operational 3D-Var assimilation of chlorophyll Biogeochemical-Argo float data into a biogeochemical model of the Mediterranean Sea

The emerging availability of Biogeochemical-Argo (BGC-Argo) float data creates new opportunities to combine models and observations in investigations of the interior structures and dynamics of marine ecosystems. An existing variational data assimilation scheme (3DVarBio) has been upgraded and coupled with the Copernicus Marine Environment Monitoring Service biogeochemical model of the Mediterranean Sea to assimilate BGC-Argo chlorophyll profile observations. Our results show that the assimilation of BGC-Argo float data is feasible. Moreover, the proposed data assimilation framework provides significant corrections to the chlorophyll concentrations and is able to consistently re-adjust the shapes of chlorophyll profiles during surface blooms occurring in winter vertically mixed conditions, and in the case of the summer deep chlorophyll maxima.Sensitivity analysis and diagnostic metrics have been applied to evaluate the impact of assimilation and the relevance of different factors of the 3DVarBio method. A key factor is the observation error that has been tuned on a monthly basis to incorporate the representation error. Different frequencies of the assimilation cycle have been tested: daily or 3-day assimilation fosters the highest skill performances despite the reduced impacts and the increase of computational burden. Considering the present size of the BGC-Argo Mediterranean network (about 15 floats) and the estimated non-homogeneous correlation radius length scale (15.2 km on average), the chlorophyll assimilation can constrain the phytoplankton dynamics along the whole water column over an area up to 10% of the Mediterranean Sea.

A particle filter-based data assimilation framework for discrete event simulations

With the advent of new sensor technologies and communication solutions, the availability of data for discrete event systems has greatly increased. This motivates research on data assimilation for discrete event simulations that has not yet fully matured. This paper presents a particle filter-based data assimilation framework for discrete event simulations. The framework is formally defined based on the Discrete Event System Specification formalism. To effectively apply particle filtering in discrete event simulations, we introduce an interpolation operation that considers the elapsed time (i.e., the time elapsed since the last state transition) when retrieving the model state (which was ignored in related work) in order to obtain updated state values. The data assimilation problem finally boils down to estimating the posterior distribution of a state trajectory with variable dimension. This seems to be problematic; however, it is proven that in practice we can safely apply the sequential importance sampling algorithm to update the random measure (i.e., a set of particles and their importance weights) that approximates this posterior distribution of the state trajectory with variable dimension. To illustrate the working of the proposed data assimilation framework, a case is studied in a gold mine system to estimate truck arrival times at the bottom of the vertical shaft. The results show that the framework is able to provide accurate estimation results in discrete event simulations; it is also shown that the framework is robust to errors both in the simulation model and in the data.

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.

TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model

Abstract. Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce certain parameterisation errors of single compartment models, model predictions are still prone to uncertainties regarding model input variables. The resulting uncertainties of model predictions can be effectively tackled by data assimilation techniques, which allow one to correct model predictions with observations taking into account both the model and measurement uncertainties. The steadily increasing availability of computational resources makes it now increasingly possible to perform data assimilation also for computationally highly demanding integrated terrestrial system models. However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface–subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). TerrSysMP is connected via a memory-based coupling approach with the pre-existing parallel data assimilation library PDAF (Parallel Data Assimilation Framework). This framework provides a fully parallel modular environment for performing data assimilation for the land surface and the subsurface compartment. A simple synthetic case study for a land surface–subsurface system (0.8 million unknowns) is used to demonstrate the effects of data assimilation in the integrated model TerrSysMP and to assess the scaling behaviour of the data assimilation system. Results show that data assimilation effectively corrects model states and parameters of the integrated model towards the reference values. Scaling tests provide evidence that the data assimilation system for TerrSysMP can make efficient use of parallel computational resources for > 30 k processors. Simulations with a large problem size (20 million unknowns) for the forward model were also efficiently handled by the data assimilation system. The proposed data assimilation framework is useful in simulating and estimating uncertainties in predicted states and fluxes of the terrestrial system over large spatial scales at high resolution utilising integrated models.

Feasibility of improving a priori regional climate model estimates of Greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures

Abstract. The Greenland ice sheet (GrIS) has been the focus of climate studies due to its considerable impact on sea level rise. Accurate estimates of surface mass fluxes would contribute to understanding the cause of its recent changes and would help to better estimate the past, current and future contribution of the GrIS to sea level rise. Though the estimates of the GrIS surface mass fluxes have improved significantly over the last decade, there is still considerable disparity between the results from different methodologies (e.g., Rae et al., 2012; Vernon et al., 2013). The data assimilation approach can merge information from different methodologies in a consistent way to improve the GrIS surface mass fluxes. In this study, an ensemble batch smoother data assimilation approach was developed to assess the feasibility of generating a reanalysis estimate of the GrIS surface mass fluxes via integrating remotely sensed ice surface temperature measurements with a regional climate model (a priori) estimate. The performance of the proposed methodology for generating an improved posterior estimate was investigated within an observing system simulation experiment (OSSE) framework using synthetically generated ice surface temperature measurements. The results showed that assimilation of ice surface temperature time series were able to overcome uncertainties in near-surface meteorological forcing variables that drive the GrIS surface processes. Our findings show that the proposed methodology is able to generate posterior reanalysis estimates of the surface mass fluxes that are in good agreement with the synthetic true estimates. The results also showed that the proposed data assimilation framework improves the root-mean-square error of the posterior estimates of runoff, sublimation/evaporation, surface condensation, and surface mass loss fluxes by 61, 64, 76, and 62 %, respectively, over the nominal a priori climate model estimates.

Data Assimilation Using a Mesoscopic Lighthill–Whitham–Richards Model and Loop Detector Data: Methodology and Large-Scale Network Application

Traffic managers and operators need decision support systems able to provide online traffic flow monitoring and short-term traffic predictions on large-scale networks. Data assimilation (DA) techniques are used to combine observed data and a traffic model. This paper proposes a comprehensive data assimilation framework, based on a mesoscopic Lighthill–Whitham–Richards model, that has short computational times, is well suited for network discontinuities, provides individual vehicle tracking, and can easily be coupled with any dynamic traffic assignment model. The framework also relies on state variables that require adjustments of the DA framework. The requirements proposed by the paper are concerned with ( a) the model numerical scheme, ( b) the traffic state transformation operators, and ( c) updating of the model. The proposed DA framework is first applied to a simplified network. It validates the ability of the proposed framework to update and propagate traffic states accordingly. The proposed framework is then applied to a real large-scale network. The results demonstrate its ability to monitor and forecast traffic conditions with online capabilities.

Feasibility of estimating regional mechanical properties of cerebral aneurysmsin vivo

In this article, the authors studied the feasibility of estimating regional mechanical properties in cerebral aneurysms, integrating information extracted from imaging and physiological data with generic computational models of the arterial wall behavior. A data assimilation framework was developed to incorporate patient-specific geometries into a given biomechanical model, whereas wall motion estimates were obtained from applying registration techniques to a pair of simulated MR images and guided the mechanical parameter estimation. A simple incompressible linear and isotropic Hookean model coupled with computational fluid-dynamics was employed as a first approximation for computational purposes. Additionally, an automatic clustering technique was developed to reduce the number of parameters to assimilate at the optimization stage and it considerably accelerated the convergence of the simulations. Several in silico experiments were designed to assess the influence of aneurysm geometrical characteristics and the accuracy of wall motion estimates on the mechanical property estimates. Hence, the proposed methodology was applied to six real cerebral aneurysms and tested against a varying number of regions with different elasticity, different mesh discretization, imaging resolution, and registration configurations. Several in silico experiments were conducted to investigate the feasibility of the proposed workflow, results found suggesting that the estimation of the mechanical properties was mainly influenced by the image spatial resolution and the chosen registration configuration. According to the in silico experiments, the minimal spatial resolution needed to extract wall pulsation measurements with enough accuracy to guide the proposed data assimilation framework was of 0.1 mm. Current routine imaging modalities do not have such a high spatial resolution and therefore the proposed data assimilation framework cannot currently be used on in vivo data to reliably estimate regional properties in cerebral aneurysms. Besides, it was observed that the incorporation of fluid-structure interaction in a biomechanical model with linear and isotropic material properties did not have a substantial influence in the final results.