Computational Geosciences最新文献

{"title":"Hybrid Neural Network - Variational Data Assimilation algorithm to infer river discharges from SWOT-like data","authors":"Kevin LARNIER, Jérôme MONNIER","doi":"10.1007/s10596-023-10225-2","DOIUrl":"https://doi.org/10.1007/s10596-023-10225-2","url":null,"abstract":"Estimating discharges Q(x, t) from altimetric measurements only, for ungauged rivers (in particular, those with unknown bathymetry b(x)), is an ill-posed inverse problem. We develop here an algorithm to estimate Q(x, t) without prior flow information other than global open datasets. Additionally, the ill-posedness feature of this inverse problem is re-investigated. Inversions based on a Variational Data Assimilation (VDA) approach enable accurate estimation of spatio-temporal variations of the discharge, but with a bias scaling the overall estimate. This key issue, which was already highlighted in our previous studies, is partly solved by considering additional hydrological information (the drainage area, $$A (km^2)$$ ) combined with a Machine Learning (ML) technique. Purely data-driven estimations obtained from an Artificial Neural Network (ANN) provide a reasonably good estimation at a large scale ( $$approx 10^3$$ m). This first estimation is then employed to define the first guess of an iterative VDA algorithm. The latter relies on the Saint-Venant flow model and aims to compute the complete unknowns (discharge Q(x, t), bathymetry b(x), friction coefficient K(x, t)) at a fine scale (approximately $$10^2$$ m). The resulting complete inversion algorithm is called the H2iVDI algorithm for \"Hybrid Hierarchical Variational Discharge Inference\". Numerical experiments have been analyzed for 29 heterogeneous worldwide river portions. The obtained estimations present an overall bias (less than 30% for rivers with similar characteristics than those used for calibration) smaller than previous results, with accurate spatio-temporal variations of the flow. After a learning period of the observed rivers (e.g. one year), the algorithm provides two complementary estimators: a dynamic flow model enabling estimations at a fine scale and spatio-temporal extrapolations, and a low complexity estimator (based on a dedicated algebraic low Froude flow model). This last estimator provides reasonably accurate estimations (less than 30% for considered rivers) at a large scale from newly acquired WS measurements in real-time, therefore making it a potentially operational algorithm.","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"631 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135753937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlinear domain-decomposition preconditioning for robust and efficient field-scale simulation of subsurface flow","authors":"Olav Møyner, Atgeirr F. Rasmussen, Øystein Klemetsdal, Halvor M. Nilsen, Arthur Moncorgé, Knut-Andreas Lie","doi":"10.1007/s10596-023-10215-4","DOIUrl":"https://doi.org/10.1007/s10596-023-10215-4","url":null,"abstract":"Abstract We discuss a nonlinear domain-decomposition preconditioning method for fully implicit simulations of multicomponent porous media flow based on the additive Schwarz preconditioned exact Newton method (ASPEN). The method efficiently accelerates nonlinear convergence by resolving unbalanced nonlinearities in a local stage and long-range interactions in a global stage. ASPEN can improve robustness and significantly reduce the number of global iterations compared with standard Newton, but extra work introduced in the local steps makes each global iteration more expensive. We discuss implementation aspects for the local and global stages. We show how the global-stage Jacobian can be transformed to the same form as the fully implicit system, so that one can use standard linear preconditioners and solvers. We compare the computational performance of ASPEN to standard Newton on a series of test cases, ranging from conceptual cases with simplified geometry or flow physics to cases representative of real assets. Our overall conclusion is that ASPEN is outperformed by Newton when this method works well and converges in a few iterations. On the other hand, ASPEN avoids time-step cuts and has significantly lower runtimes in time steps where Newton struggles. A good approach to computational speedup is therefore to adaptively switch between Newton and ASPEN throughout a simulation. A few examples of switching strategies are outlined.","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135702361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An extended peridynamic bond-based constitutive model for simulation of crack propagation in rock-like materials","authors":"Gan Sun, Junxiang Wang, Haiyue Yu, Lian-quan Guo","doi":"10.1007/s10596-023-10234-1","DOIUrl":"https://doi.org/10.1007/s10596-023-10234-1","url":null,"abstract":"","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42061145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The non-monotonicity of growth rate of viscous fingers in heterogeneous porous media","authors":"I. A. Starkov, D. A. Pavlov, S. B. Tikhomirov, F. L. Bakharev","doi":"10.1007/s10596-023-10240-3","DOIUrl":"https://doi.org/10.1007/s10596-023-10240-3","url":null,"abstract":"<p>The paper presents a stochastic analysis of the growth rate of viscous fingers in miscible displacement in a heterogeneous porous medium. The statistical parameters characterizing the permeability distribution of a reservoir vary over a wide range. The formation of fingers is provided by the mixing of different-viscosity fluids — water and polymer solution. The distribution functions of the growth rate of viscous fingers are numerically determined and visualized. Careful data processing reveals the non-monotonic nature of the dependence of the front end of the mixing zone on the correlation length of the permeability of the reservoir formation. It is demonstrated that an increase in correlation length up to a certain value causes an expansion of the distribution shape and a shift of the distribution maximum to the region of higher velocities. In addition, an increase in the standard deviation of permeability leads to a slight change in the shape and characteristics of the density distribution of the growth rates of viscous fingers. The theoretical predictions within the framework of the transverse flow equilibrium approximation and the Koval model are contrasted with the numerically computed velocity distributions.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"122 14","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling 3-D anisotropic elastodynamics using mimetic finite differences and fully staggered grids","authors":"Harpreet Sethi, F. Hoxha, J. Shragge, I. Tsvankin","doi":"10.1007/s10596-023-10222-5","DOIUrl":"https://doi.org/10.1007/s10596-023-10222-5","url":null,"abstract":"","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46451071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes–Darcy flow problems","authors":"I. Kröker, S. Oladyshkin, I. Rybak","doi":"10.1007/s10596-023-10236-z","DOIUrl":"https://doi.org/10.1007/s10596-023-10236-z","url":null,"abstract":"","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45299427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Binary well placement optimization using a decomposition-based multi-objective evolutionary algorithm with diversity preservation","authors":"Matheus Bernardelli de Moraes, G. P. Coelho, A. A. S. Santos, D. Schiozer","doi":"10.1007/s10596-023-10235-0","DOIUrl":"https://doi.org/10.1007/s10596-023-10235-0","url":null,"abstract":"","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46970073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustness and efficiency of iteration schemes for variably saturated flow across the range of soils, initial and boundary conditions found in practice","authors":"Denis Maier, H. Montenegro, B. Odenwald","doi":"10.1007/s10596-023-10230-5","DOIUrl":"https://doi.org/10.1007/s10596-023-10230-5","url":null,"abstract":"","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43460130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hard enforcement of physics-informed neural network solutions of acoustic wave propagation","authors":"Harpreet Sethi, Doris Pan, Pavel Dimitrov, J. Shragge, Gunter Roth, K. Hester","doi":"10.1007/s10596-023-10232-3","DOIUrl":"https://doi.org/10.1007/s10596-023-10232-3","url":null,"abstract":"","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"1 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42020479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated framework for optimal monitoring and history matching in CO $$_{2}$$ storage projects","authors":"Dylan M. Crain, Sally M. Benson, Sarah D. Saltzer, Louis J. Durlofsky","doi":"10.1007/s10596-023-10216-3","DOIUrl":"https://doi.org/10.1007/s10596-023-10216-3","url":null,"abstract":"<p>Monitoring is an important component of geological carbon storage operations because it provides data that can be used to estimate key quantities such as CO<span>(_{2})</span> plume location. The design of the monitoring strategy is complicated, however, because the monitoring plan must be established prior to the availability of extensive flow data. In this work, we present and apply a framework that integrates monitoring well optimization and (subsequent) history matching. The monitoring well optimization entails finding the locations of monitoring wells such that, with the data acquired at those locations, the expected uncertainty reduction in a particular flow quantity is maximized. This optimization requires the simulation of a large set of prior models, though these simulations need only be performed once for a given injection scenario. Once the monitoring wells are in place and CO<span>(_{2})</span> injection begins, history matching is performed using the monitoring data. This is accomplished here using an ensemble smoother with multiple data assimilation. The overall framework is applied to variogram-based geomodels that are representative of an actual storage project under development in the USA. Two injection scenarios are considered with two different (synthetic) ‘true’ models, which provide the observed data. History matched models are constructed using data from both optimally located and heuristically placed monitoring wells. Posterior uncertainty, evaluated in terms of the cumulative distribution function for a metric related to plume extent over the ensemble of history matched models, is shown to be minimized through use of optimized monitoring wells. These results demonstrate the importance of optimizing the monitoring plan, and the degree of uncertainty reduction that can be realistically achieved.</p>","PeriodicalId":10662,"journal":{"name":"Computational Geosciences","volume":"23 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138514558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}