scholarly journals A novel methodological approach for land subsidence prediction through data assimilation techniques

2021 ◽  
Author(s):  
Laura Gazzola ◽  
Gazzola Ferronato ◽  
Matteo Frigo ◽  
Carlo Janna ◽  
Pietro Teatini ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Subsidence ◽  
Numerical Models ◽  
Methodological Approach ◽  
Constitutive Behavior ◽  
Test Case ◽  
Combining Data ◽  
Physically Based ◽  
Synthetic Test ◽  
Adriatic Basin

AbstractAnthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and approximations are present, as in any other modeling activity of real-world phenomena. This study aims at combining data assimilation techniques with a physically-based numerical model of anthropogenic land subsidence in a novel and comprehensive workflow, to overcome the main limitations concerning the way traditional deterministic analyses use the available measurements. The proposed methodology allows to reduce uncertainties affecting the model, identify the most appropriate rock constitutive behavior and characterize the most significant governing geomechanical parameters. The proposed methodological approach has been applied in a synthetic test case representative of the Upper Adriatic basin, Italy. The integration of data assimilation techniques into geomechanical modeling appears to be a useful and effective tool for a more reliable study of anthropogenic land subsidence.

scholarly journals Correction to: A novel methodological approach for land subsidence prediction through data assimilation techniques

2021 ◽  
Author(s):  
Laura Gazzola ◽  
Massimiliano Ferronato ◽  
Matteo Frigo ◽  
Carlo Janna ◽  
Pietro Teatini ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Subsidence ◽  
Methodological Approach ◽  
Subsidence Prediction

scholarly journals Blending measurements and numerical models: a novel methodological approach for land subsidence prediction with uncertainty quantification

2020 ◽  
Vol 382 ◽  
pp. 457-462
Author(s):  
Laura Gazzola ◽  
Massimiliano Ferronato ◽  
Matteo Frigo ◽  
Pietro Teatini ◽  
Claudia Zoccarato ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Land Subsidence ◽  
Ad Hoc ◽  
Numerical Models ◽  
Methodological Approach ◽  
Hydrocarbon Reservoirs ◽  
Subsidence Prediction ◽  
New Information ◽  
Deep Rock ◽  
Synthetic Models

Abstract. The use of numerical models for land subsidence prediction above producing hydrocarbon reservoirs has become a common and well-established practice since the early '90s. Usually, uncertainties in the deep rock behavior, which can affect the forecast capability of the models, have been taken into account by running multiple simulations with different constitutive laws and mechanical properties. Then, the most uncertain parameters were calibrated to reproduce available subsidence measurements. The objective of this work is to propose a novel methodological approach for land subsidence prediction and uncertainty quantification by integrating the available monitoring information in numerical models using ad hoc Data Assimilation techniques. The proposed approach allows to: (i) train the model with the available data and improve its accuracy as new information comes in, (ii) quantify the prediction uncertainty by providing confidence intervals and probability measures instead of deterministic outcomes, and (iii) identify the most appropriate rock constitutive model and geomechanical parameters. The methodology is tested in synthetic models of production from hydrocarbon reservoirs. The numerical experiments show that the proposed approach is a promising way to improve the effectiveness and reliability of land subsidence models.

scholarly journals Combining data assimilation and machine learning to infer unresolved scale parametrization

2021 ◽  
Vol 379 (2194) ◽  
pp. 20200086
Author(s):  
Julien Brajard ◽  
Alberto Carrassi ◽  
Marc Bocquet ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Data Assimilation ◽  
Hybrid Model ◽  
Numerical Models ◽  
Model Error ◽  
Climate Modelling ◽  
Combining Data ◽  
Full State ◽  
Direct Data

In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages high-resolution simulations to provide a dense, noiseless target state. Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrization using direct data, in the realistic scenario of noisy and sparse observations. The algorithm proposed in this work is a two-step process. First, data assimilation (DA) techniques are applied to estimate the full state of the system from a truncated model. The unresolved part of the truncated model is viewed as a model error in the DA system. In a second step, ML is used to emulate the unresolved part, a predictor of model error given the state of the system. Finally, the ML-based parametrization model is added to the physical core truncated model to produce a hybrid model. The DA component of the proposed method relies on an ensemble Kalman filter while the ML parametrization is represented by a neural network. The approach is applied to the two-scale Lorenz model and to MAOOAM, a reduced-order coupled ocean-atmosphere model. We show that in both cases, the hybrid model yields forecasts with better skill than the truncated model. Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

scholarly journals Main results of the first Data Assimilation Challenge

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Andrea Sciacchitano ◽  
Benjamin Leclaire ◽  
Andreas Schroeder
Keyword(s):  
Data Assimilation ◽  
Velocity Gradient ◽  
Linear Interpolation ◽  
Time Instant ◽  
Test Case ◽  
Lagrangian Particle ◽  
Horizon 2020 ◽  
Synthetic Test ◽  
Standard Linear ◽  
Research Grant

This work presents the main results of the first Data Assimilation (DA) challenge, conducted within the framework of the European Union’s Horizon 2020 project HOMER (Holistic Optical Metrology for Aero-Elastic Research), grant agreement number 769237. The challenge was jointly organised by the research groups of DLR, ONERA and TU Delft. The same synthetic test case as in the Lagrangian Particle Tracking (LPT) challenge (also presented in this symposium) was considered, reproducing the flow in the wake of a cylinder in proximity of a flat wall. The participants were provided with three datasets containing the measured particles locations and their trajectories identification numbers, at increasing tracers concentrations from 0.04 to 1.4 particles/mm3 . The requested outputs were the three components of the velocity, the nine components of the velocity gradient and the static pressure, defined on a Cartesian grid at one specific time instant. The results were analysed in terms of errors of the output quantities and their distributions. Additionally, the performances of the different DA algorithms were compared with that of a standard linear interpolation approach. Although the velocity errors were found to be in the same range as those of the linear interpolation algorithm, typically between 3% and 12% of the bulk velocity, the use of the DA algorithms enabled an increase of the measurement spatial resolution by a factor between 3 and 4. The errors of the velocity gradient were of the order of 10-15% of the peak vorticity magnitude. Accurate pressure reconstruction was achieved in the flow field, whereas the evaluation of the surface pressure revealed more challenging.

Ensemble-based data assimilation of volcanic aerosols using FALL3D+PDAF

2021 ◽  
Author(s):  
Leonardo Mingari ◽  
Andrew Prata ◽  
Federica Pardini
Keyword(s):  
Data Assimilation ◽  
Information Transfer ◽  
High Performance ◽  
Volcanic Ash ◽  
Numerical Models ◽  
Volcanic Eruptions ◽  
Column Height ◽  
Eruption Column ◽  
Operational Environment ◽  
Assimilation System

<p>Modelling atmospheric dispersion and deposition of volcanic ash is becoming increasingly valuable for understanding the potential impacts of explosive volcanic eruptions on infrastructures, air quality and aviation. The generation of high-resolution forecasts depends on the accuracy and reliability of the input data for models. Uncertainties in key parameters such as eruption column height injection, physical properties of particles or meteorological fields, represent a major source of error in forecasting airborne volcanic ash. The availability of nearly real time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context. Data assimilation (DA) is one of the most effective ways to reduce the error associated with the forecasts through the incorporation of available observations into numerical models. Here we present a new implementation of an ensemble-based data assimilation system based on the coupling between the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The implementation is based on the last version release of FALL3D (versions 8.x) tailored to the extreme-scale computing requirements, which has been redesigned and rewritten from scratch in the framework of the EU Center of Excellence for Exascale in Solid Earth (ChEESE). The proposed methodology can be efficiently implemented in an operational environment by exploiting high-performance computing (HPC) resources. The FALL3D+PDAF system can be run in parallel and supports online-coupled DA, which allows an efficient information transfer through parallel communication. Satellite-retrieved data from recent volcanic eruptions were considered as input observations for the assimilation system.</p>

A physically-based soil surface model and its combination with numerical models for predicting bare-soil evaporation rates

2021 ◽  
Author(s):  
Xiaocheng Liu ◽  
Chenming Zhang ◽  
Yue Liu ◽  
David Lockington ◽  
Ling Li
Keyword(s):  
Numerical Model ◽  
Surface Resistance ◽  
Numerical Models ◽  
Soil Column ◽  
Soil Surface ◽  
Bare Soil ◽  
Water Vapor Transport ◽  
Surface Model ◽  
Vapor Flow ◽  
Physically Based

<p>Estimation of evaporation rates from soils is significant for environmental, hydrological, and agricultural purposes. Modeling of the soil surface resistance is essential to estimate the evaporation rates from bare soil. Empirical surface resistance models may cause large deviations when applied to different soils. A physically-based soil surface model is developed to calculate the surface resistance, which can consider evaporation on the soil surface when soil is fully saturated and the vapor flow below the soil surface after dry layer forming on the top. Furthermore, this physically-based expression of the surface resistance is added into a numerical model that considers the liquid water transport, water vapor transport, and heat transport during evaporation. The simulation results are in good agreement with the results from six soil column drying experiments.  This numerical model can be applied to predict or estimate the evaporation rate of different soil and saturation at different depths during evaporation.</p>

Regional Bicycle Network Evaluation and Strategic Planning: A Quantitative Methodological Approach Despite Limited Data Sources for Cycling

2021 ◽  
pp. 036119812110288
Author(s):  
Alex van Dulmen ◽  
Martin Fellendorf
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Methodological Approach ◽  
Data Availability ◽  
Data Sources ◽  
Test Case ◽  
Demand Model ◽  
Limited Data ◽  
Demand Models ◽  
Bicycle Infrastructure

In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.

scholarly journals Evaluating performances of simplified physically based models for landslide susceptibility

2015 ◽  
Vol 12 (12) ◽  
pp. 13217-13256 ◽  
Author(s):  
G. Formetta ◽  
G. Capparelli ◽  
P. Versace
Keyword(s):  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Shallow Landslides ◽  
Model Parameters ◽  
Test Case ◽  
Susceptibility Analysis ◽  
Physically Based ◽  
Private And Public ◽  
Physically Based Models

Abstract. Rainfall induced shallow landslides cause loss of life and significant damages involving private and public properties, transportation system, etc. Prediction of shallow landslides susceptible locations is a complex task that involves many disciplines: hydrology, geotechnical science, geomorphology, and statistics. Usually to accomplish this task two main approaches are used: statistical or physically based model. Reliable models' applications involve: automatic parameters calibration, objective quantification of the quality of susceptibility maps, model sensitivity analysis. This paper presents a methodology to systemically and objectively calibrate, verify and compare different models and different models performances indicators in order to individuate and eventually select the models whose behaviors are more reliable for a certain case study. The procedure was implemented in package of models for landslide susceptibility analysis and integrated in the NewAge-JGrass hydrological model. The package includes three simplified physically based models for landslides susceptibility analysis (M1, M2, and M3) and a component for models verifications. It computes eight goodness of fit indices by comparing pixel-by-pixel model results and measurements data. Moreover, the package integration in NewAge-JGrass allows the use of other components such as geographic information system tools to manage inputs-output processes, and automatic calibration algorithms to estimate model parameters. The system was applied for a case study in Calabria (Italy) along the Salerno-Reggio Calabria highway, between Cosenza and Altilia municipality. The analysis provided that among all the optimized indices and all the three models, the optimization of the index distance to perfect classification in the receiver operating characteristic plane (D2PC) coupled with model M3 is the best modeling solution for our test case.

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