Multiple Realizations Research Articles

A multi-scale blocking moving window algorithm for geostatistical seismic inversion

Markov chain Monte Carlo (McMC) methods are suitable for solving the high-dimensional geostatistical seismic inverse problem. However, traditional McMC is impractical to generate desired lithofacies distribution since the repeated geostatistics and seismic forward simulators are very time-consuming. Recently, various strategies (e.g., the sequential geostatistical resampling (SGR) idea, multi-scale strategy, and annealing process) have been individually designed to alleviate the computational burden. However, the coordination between multiple corresponding key parameters (e.g., blocking window sizes, grid level, and temperatures) adds difficulties to combining multiple strategies with McMC. In this context, we propose a Multi-scale Blocking Moving Window algorithm (MsBMW) to effectively combines blocking McMC updating, a multi-scale strategy, and a new simulated annealing (SA) algorithm. In the iterative process, we monitor the mean acceptance rate (AR) to update window sizes, grid level, and temperatures, keeping the AR within the desired range (e.g., between 25% and 50%) for more efficient sampling. To assess the performance of the MsBMW, we tested it on the Standford VI synthetic reservoir. The standard Metropolis-Hastings (MH) sampling is performed to present the posterior probability density function (pdf) as the reference in this study. Compared with the Blocking Moving Window algorithm (BMW) proposed by Alcolea and Renard (2010), the MsBMW allows better quality results in less time. The results show that the multi-scale approach has the effect of accelerating the convergence of inversion. The adaptive cooling schedule circumvents the problem that the cooling rate is difficult to control in SA. Finally, we applied the proposed methodology to two-dimensional (2-D) and three-dimensional (3-D) real study areas and quickly generated multiple realizations that fit geological patterns, well data, and seismic data for uncertainty evaluation.

Flow Control Valve Valuation and Value of Information under Uncertainty

Summary An analysis is presented that quantifies the value-adding brought to a well by installing downhole flow control valves (FCVs) in a reservoir with uncertainties represented by multiple equiprobable realizations, two aquifer strengths, and three oil/water contacts (OWCs). The complexity of the model makes nonintuitive its response to FCV adjustment. From an openhole (OH) baseline net present value (NPV), it was possible to extract specific value-adding for the following, nonadjustable valves, the ability to adjust the valve, and how future information can add further value. The issue of measurement bin size is discussed, and the implication of making them too large, or too small, is described. Finally value erosion due to valve failure is also addressed. The solution presented used dynamic programming (DP), which was validated against a full enumeration of the problem using reservoir simulation. Approaches to proxies to traverse the (potentially large) decision space are also discussed.

Convolutional – recurrent neural network proxy for robust optimization and closed-loop reservoir management

Production optimization under geological uncertainty is computationally expensive, as a large number of well control schedules must be evaluated over multiple geological realizations. In this work, a convolutional-recurrent neural network (CNN-RNN) proxy model is developed to predict well-by-well oil and water rates, for given time-varying well bottom-hole pressure (BHP) schedules, for each realization in an ensemble. This capability enables the estimation of the objective function and nonlinear constraint values required for robust optimization. The proxy model represents an extension of a recently developed long short-term memory (LSTM) RNN proxy designed to predict well rates for a single geomodel. A CNN is introduced here to processes permeability realizations, and this provides the initial states for the RNN. The CNN-RNN proxy is trained using simulation results for 300 different sets of BHP schedules and permeability realizations. We demonstrate proxy accuracy for oil-water flow through multiple realizations of 3D multi-Gaussian permeability models. The proxy is then incorporated into a closed-loop reservoir management (CLRM) workflow, where it is used with particle swarm optimization and a filter-based method for nonlinear constraint satisfaction. History matching is achieved using an adjoint-gradient-based procedure. The proxy model is shown to perform well in this setting for five different (synthetic) ‘true’ models. Improved net present value along with constraint satisfaction and uncertainty reduction are observed with CLRM. For the robust production optimization steps, the proxy provides O(100) runtime speedup over simulation-based optimization.

Parametrization of Eddy Mass Transport in the Arctic Seas Based on the Sensitivity Analysis of Large-Scale Flows

The characteristics of eddy mass transport are estimated depending on the values of the parameters of a large-scale flow that forms under the conditions of the shelf seas in the Arctic. For this, the results of numerical simulation of the Kara Sea with a horizontal resolution permitting the development of mesoscale eddies are used. The multiple realizations of eddy mass flux resulting from a numerical experiment are considered as a statistical sample and are analyzed using methods of sensitivity study and clustering of sample elements. Functional dependencies are obtained that are closest to the simulated distributions of quantities. These expressions make it possible, within the framework of large-scale models, to evaluate the characteristics of the cross-isobathic eddy mass transport in the diffusion approximation with a counter-gradient flux. Numerical experiments using the SibCIOM model showed that areas along the Fram branch of the Atlantic waters trajectory in the Arctic as well as the shelf of the East Siberian and Laptev seas with adjacent deep water areas are most sensitive to the proposed parametrization of eddy exchanges. Accounting for counter-gradient eddy fluxes turned out to be less important.

Multidimensional Risk-Based Real Options Valuation for Low-Carbon Cogeneration Pathways

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility.

Unbinned likelihood analysis for X-ray polarization

ABSTRACT We present a systematic study of the unbinned, photon-by-photon likelihood technique which can be used as an alternative method to analyse phase-dependent, X-ray spectro-polarimetric observations obtained with IXPE and other photoelectric polarimeters. We apply the unbinned technique to models of the luminous X-ray pulsar Hercules X-1, for which we produce simulated observations using the ixpeobssim package. We consider minimal knowledge about the actual physical process responsible for the polarized emission from the accreting pulsar and assume that the observed phase-dependent polarization angle can be described by the rotating vector model. Using the unbinned technique, the detector’s modulation factor, and the polarization information alone, we found that both the rotating vector model and the underlying spectro-polarimetry model can reconstruct equally well the geometric configuration angles of the accreting pulsar. However, the measured polarization fraction becomes biased with respect to the underlying model unless the energy dispersion and effective area of the detector are also taken into account. To this end, we present an energy-dispersed likelihood estimator that is proved to be unbiased. For different analyses, we obtain posterior distributions from multiple ixpeobssim realizations and show that the unbinned technique yields $\sim 10{{\ \rm per\ cent}}$ smaller error bars than the binned technique. We also discuss alternative sources, such as magnetars, in which the unbinned technique and the rotating vector model might be applied.

Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data

Offshore wind is expected to play a key role in future energy systems. Wind energy resource studies often call for long-term and spatially consistent datasets to assess the wind potential. Despite the vast amount of available data sources, no current means can provide relevant sub-daily information at a fine spatial scale (~1 km). Synthetic aperture radar (SAR) delivers wind field estimates over the ocean at fine spatial resolution but suffers from partial coverage and irregular revisit times. Physical model outputs, which are the basis of reanalysis products, can be queried at any time step but lack fine-scale spatial variability. To combine the advantages of both, we use the framework of multiple-point geostatistics to realistically reconstruct wind speed patterns at time instances for which satellite information is absent. Synthetic fine-resolution wind speed images are generated conditioned to coregistered regional reanalysis information at a coarser scale. Available simultaneous data sources are used as training data to generate the synthetic image time series. The latter are then evaluated via cross validation and statistical comparison against reference satellite data. Multiple realizations are also generated to assess the uncertainty associated with the simulation outputs. Results show that the proposed methodology can realistically reproduce fine-scale spatiotemporal variability while honoring the wind speed patterns at the coarse scale and thus filling the satellite information gaps in space and time.

Proposed hybrid approach for three-dimensional subsurface simulation to improve boundary determination and design of optimum site investigation plan for pile foundations

Geological uncertainty refers to the changeability of a geomaterial category embedded in another. It arises from predicting a geomaterial category at unobserved locations using categorical data from a site investigation (SI). In the design of bridge foundations, geological uncertainty is often not considered because of the difficulties of assessing it using sparse borehole data, validating the quality of predictions, and incorporating such uncertainties into pile foundation design. To overcome these problems, this study utilizes sparse borehole data and proposes a hybrid approach of various spatial Markov Chain (spMC) models and Monte Carlo simulation to predict three-dimensional (3D) geomaterial categories and assess geological uncertainties. The 3D analysis gives realistic and comprehensive information about the site. Characteristics of the proposed hybrid approach include the estimation of transition rates, prediction of 3D geomaterial categories, and simulation of multiple realizations to propagate the uncertainties quantified by information entropy. This proposed hybrid approach leads to specific novelties that include the development of optimal SI plans to reduce geological uncertainty and the determination of geomaterial layer boundaries according to the quantified geological uncertainty. Reducing the geological uncertainties and accurately determining spatial geomaterial boundaries will improve the design reliability and safety of bridge foundations. The hybrid approach is applied to the Lodgepole Creek Bridge project site in Wyoming to demonstrate the application of the hybrid approach and the associated novelties. Outcomes are cross-validated to evaluate the geomaterial prediction accuracy of the hybrid approach.

Risk-Aware Stochastic Energy Management of Microgrid with Battery Storage and Renewables

This paper deals with optimization-based control of real microgrids with uncertain forecasts of renewable energy production and local consumption. To achieve maximum economic benefits, these uncertainties need to be accounted for in a systematic fashion. Conventionally, this task is approached by employing stochastic model predictive control. While doing so allows to account for uncertainties in the forecasts, the downside is high computational complexity that hinders implementation in real time. In this paper we therefore propose an alternative method that decreases the computational burden by an order of magnitude without inducing significant suboptimality. The approach is based on splitting the stochastic model predictive control problem into two stages, one that employs multiple realizations of the uncertainties combines with a low-fidelity prediction model, and one that uses only the risk-aware realization, combined with a high-fidelity model. The theoretical development is then showcased on a real microgrid to confirm viability of our approach.

ΣΤΕΦΑΝΙΑ ΚΟΦΙΝΑ, Ἡ μεγάλη βδομάδα τοῦ Πρεζάκη: Από το διήγημα στην κομικ-ή μετάπλαση

The Big Week of Prezakis: From the short story to the comic transformation The object of the present study is the transposition of the short story The Big Week of Prezakis by M. Karagatsis into a comic and specifically into a graphic novel, in order to investigate and analyse all the remarkable medial transformations that result from this inter-artistic transposition. The study starts from the structure in each of the two works under consideration, that is the way in which the narrative material is organized to serve the inherent imperatives of the genre in question. The transcription of narrative voice and focus in mediated adaptation is also a research desideratum as well as the multiple artistic realizations of the narrator in the comic idiom. Narrative temporality, as a predominantly graphically represented axis in the world of the comic book, as well as the imaginary soundtrack of the works, with the remarkable effect it is found to exert as a verbal statement in both cases, has also been of interest to the study. Finally, both selected characters in the works and the spaces in which they move were put under the microscope. It should be made clear, however, that this consideration is aware of the heterogeneity of the two media and the different semiotic code that governs each of them. Consequently, it is released from any attempt to identify points of convergence or divergence between the two works if these do not take the study towards interesting interpretative results about the adaptation and the transformations it brings about. After all, every adaptation constitutes a creative "departure" from the source work, and it is through this prism that this paper views the whole inter-artistic project.

Interprecedent Semiotic Space, or When the Cannons are Heard, the Muses aren’t Silent

The paper focuses on the description of chrematonyms as precedent names of Russian arms domain, developing a special semiotic space in the digital media news discourse and social networks represented in various code formats, readers and participants of which share the same axiological values as the authors of messages. The study aims to analyze the new precedent concepts, which form the new cells in the semantic network and the directions of linguistic cognitive channels generation on the Internet platform for communication. On the basis of the illustrative examples, verbalizing the Russian arms names, that is “Geran” (“Geranium”), “Bal” (“Ball”), “Topol” (“Сottonwood”), “Tishina” (“Silence”), “Burya” (“Tempest”), “Buratino” and “Cheburashka”, the issues of interprecedent links generation and rooting in the intertextual aspect are covered for the first time The description of the specific cases of the new precedent names usage is based on the complex application of such linguistic methods as metaphorical modeling, intertextual, contextual, structural, semantic and discursive analysis. It has been established that chrematonyms as secondary nominations are formed in two ways: they can be semantically derived from common names on the basis of metaphoricaland metonymic meanings, associative links or appear as a result of transonymization from other precedent names. The chrematonyms denoting weapons are of a hybrid type, which combines the features of proper names, nicknames and terms. Textual examples demonstrate pragmatic characteristics of chrematonyms to perform referential, emotive, persuasive, euphemistic, axiological, culturally differentiating and consolidating, and a number of other functions. The models of multiple metaphorical realizations and allusions to literary heritage and modern texts are characterized. The new precedent names and parodies, related to the situations of the Russian weapons usage, are analyzed from the point of view of their linguistic creativity. The main directions and trends of the development of precedent phenomena interactions, the ways of current intertextual domains extention are considered, recent examples of linguistic creativity on precedent resources production are described and the generation of new semantic memory cells and arcs of connections between them (in modern terms) in the Russian semiotic space is demonstrated.

Deep physics-aware stochastic seismic inversion

Seismic inversion allows the prediction of subsurface properties from seismic reflection data and is a key step in reservoir modeling and characterization. With the generalization of machine learning in geophysics, deep learning methods have been proposed as efficient seismic inversion methods. However, most of these methods lack a probabilistic approach to deal with the uncertainties inherent in the seismic inversion problem and/or rely on complete and representative training data, which often is partially or scarcely available. We have explored the ability of deep convolutional neural networks to extract meaningful and complex representations from spatially structured data, combined with geostatistical simulation, to efficiently invert poststack seismic data directly for high-resolution models of acoustic impedance. Our model incorporates physics constraints and sparse direct measurements while leveraging the use of imprecise but widely distributed indirect measurements as represented by the seismic data. The models generated with geostatistical simulation provide additional information with higher spatial resolution than the original seismic data and allow assessing uncertainty in the model predictions by generating multiple realizations of the subsurface properties. Our method can (1) provide an uncertainty assessment of the predictions, (2) model the complex and nonlinear relationship between data and model, (3) extend the seismic bandwidth at low and high ends of the frequency parameters spectrum, and (4) lessen the need for large, annotated training data. Our method is applied to a 1D synthetic example and a real 3D application example from a Brazilian reservoir. The predicted impedance models are compared with those obtained from a full iterative geostatistical seismic inversion. Our method allows retrieving similar models but has the advantage of generating alternative solutions in greater numbers, providing a larger exploration of the model parameter space in less time than the iterative geostatistical seismic inversion.

Uncertainty in Automated Well-Log Correlation Using Stochastic Dynamic Time Warping

Well-log correlation is used extensively to generate subsurface cross sections from sparse well data. This is commonly done by a subject matter expert such as stratigraphers and exploration geophysicists. Several methodologies exist for automating the procedure with varying success. Dynamic time warping (DTW) is a signal-processing technique where one signal is locally stretched and squeezed to maximize the similarity between a reference second signal. This is done by calculating a similarity cost matrix that is traversed to minimize the cumulative distance. The technique produces reasonable results when applied to the well correlation problem. The produced correlation, however, is deterministic, and thus, it does not allow for studying the associated uncertainty. This study presents an extension of traditional dynamic time warping to allow the generation of multiple realizations of correlations. To accomplish this, the cost matrix is traversed deterministically or probabilistically based on a local correlation metric, e.g., the local correlation coefficient. The resultant realizations show stability in the correlation markers where the signals are similar and instability where they are not. The methodology is applied to two adjacent wells. Multiple well-log types (gamma ray, sonic, and resistivity) are used to construct the similarity cost matrix between the two wells. The cost matrix is traversed multiple times to produce multiple realizations. The produced realizations are geologically acceptable. By generating a large number of realizations, the uncertainty in the solutions is quantified. While the application presented here relates to well-log correlation, the presented stochastic dynamic time warping methodology can be applied to other types of signals and data, such as seismic, chemostratigraphy data, and real-time drilling measurements.

Low-Complexity LMMSE Receiver Design for Practical-Pulse-Shaped MIMO-OTFS Systems

Orthogonal time frequency space modulation (OTFS) scheme establishes reliable communication in a rapidly time-varying wireless channel with a high Doppler spread. We design a low-complexity linear minimum mean squared error (LMMSE) receiver for practical-pulse-shaped multiple-input multiple-output (MIMO)-OTFS systems. The proposed receiver exploits the inherent channel sparsity and the channel-agnostic structure of matrices involved in the LMMSE receiver, and has only a log-linear complexity. It provides exactly the same solution, and hence the same bit error rate (BER), as that of the conventional LMMSE receiver with a cubic order of complexity. We also derive, by using the Taylor series expansion and the results from random matrix theory, a tight closed-form approximation for the post-processing signal-to-noise-plus-interference ratio (SINR) expression of the proposed receiver. This expression is derived by assuming imperfect receive channel state information. We show using extensive numerical investigations that the derived SINR expression, when averaged over multiple channel realizations, accurately characterizes the BER of a MIMO-OTFS system.

The Physical Processes Dominating the Impact of the Summer North Atlantic Oscillation on the Eastern Tibetan Plateau Summer Rainfall

Abstract The summer North Atlantic Oscillation (SNAO), an important climate signal in regulating the interannual variability of Tibetan Plateau (TP) summer rainfall, is closely related to a meridional precipitation dipole pattern between the southeastern and northeastern TP. In this study, based on diagnoses of observations and multiple realizations of the CESM2 historical simulation, we find that there are fundamental differences between the formation processes dominating the SNAO-related summer rainfall anomaly in the southeastern and northeastern TP. An atmospheric moisture budget analysis reveals that the anomalous vertical (horizontal) moisture advection makes the largest contribution to the southeastern (northeastern) TP summer rainfall anomaly. During the negative phase of SNAO, the increased precipitation in the southeastern TP is related to the anomalous ascending flows, which are driven by two processes according to the moist static energy budget. The first is the southward shift of the subtropical westerly jet stream, which produces positive anomalous zonal advection of the climatological moist enthalpy in the upper-middle troposphere over the southeastern TP. The second is related to the enhanced transport of anomalous warm moist air to the south of TP, which produces positive anomalous meridional advection of anomalous moist enthalpy into the lower troposphere over the southeastern TP under the control of climatological monsoonal meridional circulations. This positive moist enthalpy advection enhances the atmospheric moist static energy and facilitate enhanced local convection. For the northeastern TP, the decreased precipitation is dominated by negative anomalous horizontal moisture advections due to the SNAO-induced equivalent-barotropic anomalous cyclone near the eastern edge of the TP.

Risk analysis framework for the optimum remediation of a contaminated aquifer under uncertainty: application in Lake Karla aquifer, Thessaly, Greece

A risk analysis framework is proposed for the optimum remediation of a contaminated aquifer under hydrogeological uncertainty. The limited information and the spatial variation of hydraulic conductivity in a real-world large-scale aquifer create uncertain conditions for decision-making when remediation schemes ought to be accompanied by the minimum possibility of failure. The primary concern is focused on safeguarding public health when groundwater is used for urban drinking purposes from a contaminated aquifer. The proposed framework is based on the conjunctive use of stochastic simulation–optimization modelling followed up by a risk analysis application on remediation trade-offs. The framework includes three main steps/procedures: (i) the model formulation of multiple realizations of groundwater flow and contaminant transport, (ii) the optimal positioning and operation of the clean-up wells determined by the method of stochastic optimization, and (iii) the risk analysis of the optimum remediation strategies through a proposed decision model, so as the one with the minimum cost and risk of failure is chosen as the most appropriate. The proposed framework is tested for two scenarios of nitrogen fertilizer application in the cultivated areas. The strategic target is the groundwater nitrate concentration minimization in an area where exceedances of nitrate concentrations have been observed and water supply wells have been operating for the last twenty years satisfying domestic needs. The results demonstrate that, when decision-making is under hydrogeological uncertainty, the combined use of stochastic optimization and risk-based decision analysis can commend the remediation strategy with the minimum cost and the highest possibility of success.

A simulated annealing based stochastic long-term production scheduling of open-pit mines with stockpiling under grade uncertainty

ABSTRACT This research presents a new Simulated Annealing based stochastic optimisation algorithm to integrate geological uncertainty into the optimization process through multiple equiprobable simulated realisations of an orebody while considering stockpiling options and other relevant constraints. The stockpiling option is included, increasing the chances of processing high-grade and most certain ore blocks in early periods. The efficiency of the proposed algorithm in creating a single good enough production schedule that minimises the risk of deviation from production targets while maximising the net present value of the operation is demonstrated through three case studies, i.e. case A with 2448 blocks, B with 6,578 and C with 10,810 blocks. The comparison of results with the two-stage stochastic model reveals that the proposed methodology reduces the risk of production deviation to a minimal and provides a near-optimal solution with an optimality gap of 3.53, −0.87, and 8.19% for cases A, B, and C within a reasonable amount of time.

Investigation on intragranular and intergranular void growth and their competition in polycrystalline materials

In polycrystalline materials, void growth is strongly dependent on its surrounding microstructural environment such as grain morphologies, crystallographic orientations and void-grain boundary (GB) relative position. In this study, two kinds of void-GB relative positions are considered to investigate the orientation effect on void growth in FCC polycrystals: intragranular type, i.e., void located in interior of a grain; and intergranular type, i.e., void located at boundary of two adjacent grains. The analysis of orientation effect in intragranular (resp. intergranular) cases is conducted through a comparative study with single-crystal (resp. bi-crystal) counterparts. Moreover, the competition between the intragranular and intergranular void growth behaviors is statistically investigated for the first time. To this end, the representative volume element (RVE) of polycrystalline aggregates containing either intragranular or intergranular void is created by 3D Voronoi tessellation. Multiple realizations with different grain morphologies and crystallographic orientation permutations are implemented using crystal plasticity finite element (CPFE) simulations. We show that the mechanism for orientation effect on void growth in single crystals (resp. bi-crystals) is not preserved in intragranular (resp. intergranular) cases. According to the statistical analysis, both the intragranular and intergranular void growth follows a Gaussian distribution, whose profile is affected by both macroscopic stress triaxiality and Lode parameter. It is revealed that the statistical void growth distribution induced by random grain morphologies and crystallographic orientations is closely associated with the void-GB relative position (intragranular or intergranular). The present study may provide much richer insights into the mechanism of damage evolution in polycrystalline metals.

Characterizing spontaneous Ca2+ local transients in OPCs using computational modeling

Spontaneous Ca2+ local transients (SCaLTs) in isolated oligodendrocyte precursor cells are largely regulated by the following fluxes: store-operated Ca2+ entry (SOCE), Na+/Ca2+ exchange, Ca2+ pumping through Ca2+-ATPases, and Ca2+-induced Ca2+-release through ryanodine receptors and inositol-trisphosphate receptors. However, the relative contributions of these fluxes in mediating fast spiking and the slow baseline oscillations seen in SCaLTs remain incompletely understood. Here, we developed a stochastic spatiotemporal computational model to simulate SCaLTs in a homogeneous medium with ionic flow between the extracellular, cytoplasmic, and endoplasmic-reticulum compartments. By simulating the model and plotting both the histograms of SCaLTs obtained experimentally and from the model as well as the standard deviation of inter-SCaLT intervals against inter-SCaLT interval averages of multiple model and experimental realizations, we revealed the following: (1) SCaLTs exhibit very similar characteristics between the two data sets, (2) they are mostly random, (3) they encode information in their frequency, and (4) their slow baseline oscillations could be due to the stochastic slow clustering of inositol-trisphosphate receptors (modeled as an Ornstein-Uhlenbeck noise process). Bifurcation analysis of a deterministic temporal version of the model showed that the contribution of fluxes to SCaLTs depends on the parameter regime and that the combination of excitability, stochasticity, and mixed-mode oscillations are responsible for irregular spiking and doublets in SCaLTs. Additionally, our results demonstrated that blocking each flux reduces SCaLTs’ frequency and that the reverse (forward) mode of Na+/Ca2+ exchange decreases (increases) SCaLTs. Taken together, these results provide a quantitative framework for SCaLT formation in oligodendrocyte precursor cells.

Identification of hydraulic conductivity field of a karst aquifer by using transition probability geostatistics and discrete cosine transform with an ensemble method

AbstractKnowledge of the spatial distribution characteristics of hydraulic parameters is essential for the management and protection of karst groundwater resources. In this study, we propose a workflow integrating the transition probability geostatistics (T‐PROGS) and the discrete cosine transform (DCT) with the ensemble smoother with multiple data assimilation (ES‐MDA) method to map the hydraulic conductivity of karst aquifers that usually follow a multimodal distribution. The priori parameter ensemble is constructed from the T‐PROGS and transformed into an approximately Gaussian distributed coefficient field containing critical spatial structural features about the aquifer using DCT. The ES‐MDA method is employed to update the DCT coefficients by assimilating the measurements. In addition, a postprocessing process based on cumulative distribution function (CDF) mapping is used to address the problem of parameters gradually tending to a Gaussian distribution after updating using the ES‐MDA and the inappropriate selection of initial parameter values. In practice, the limited amount of available data makes it difficult to fully capture the spatial distribution of the parameters in the initial ensemble of a single realization. Therefore, we suggest a new strategy for structuring the initial ensemble by mixing samples from multiple realizations. We then apply the proposed approach to four single realization models and a combined multiple realizations model in a field hydraulic tomography investigation of a real karst aquifer. The computed results show that this method can effectively identify the characteristics of the spatial distribution of hydraulic conductivity in karst aquifers. Compared with an individual realization model, the uncertainty of the hydraulic conductivity estimated by the combined multiple realizations model is significantly reduced. Therefore, including more uncertainty of the aquifer in the initial ensemble is beneficial to improve the accuracy of the parameter estimation.