A forward and backward linkage agricultural production simulation model

The stochastic geostatistical inversion approach is widely used in subsurface inverse problems to estimate unknown parameter fields and corresponding uncertainty from noisy observations. However, the approach requires a large number of forward model runs to determine the Jacobian or sensitivity matrix, thus the computational and storage costs become prohibitive when the number of unknowns, m, and the number of observations, n increase. To overcome this challenge in large‐scale geostatistical inversion, the Principal Component Geostatistical Approach (PCGA) has recently been developed as a “matrix‐free” geostatistical inversion strategy that avoids the direct evaluation of the Jacobian matrix through the principal components (low‐rank approximation) of the prior covariance and the drift matrix with a finite difference approximation. As a result, the proposed method requires about K runs of the forward problem in each iteration independently of m and n, where K is the number of principal components and can be much less than m and n for large‐scale inverse problems. Furthermore, the PCGA is easily adaptable to different forward simulation models and various data types for which the adjoint‐state method may not be implemented suitably. In this paper, we apply the PCGA to representative subsurface inverse problems to illustrate its efficiency and scalability. The low‐rank approximation of the large‐dimensional dense prior covariance matrix is computed through a randomized eigen decomposition. A hydraulic tomography problem in which the number of observations is typically large is investigated first to validate the accuracy of the PCGA compared with the conventional geostatistical approach. Then the method is applied to a large‐scale hydraulic tomography with 3 million unknowns and it is shown that underlying subsurface structures are characterized successfully through an inversion that involves an affordable number of forward simulation runs. Lastly, we present a joint inversion of head and tracer test data using MODFLOW and MT3DMS as coupled black‐box forward simulation solvers. These applications demonstrate the advantages of the PCGA, i.e., the scalability to high‐dimensional inverse problems and the ability to utilize multiple forward models as black boxes.

Die cavity design of near flashless forging process using FEM-based backward simulation

The classification of single channel sand bodies within composite subaqueous distributary channels of a non-dense well area/zone is one of difficulties in geological research for oilfield development. In this study, the distribution of single channel sand bodies within composite subaqueous channels of the third member of the Qingshankou Formation in the Qian’an oilfield was analyzed by combining well-logging and seismic data-based forward simulation method. First, within the log curve in the high-dense well zone, four log-geology identification marks of single subaqueous distributary channel in Compound channel are established. Then, using the special seismic response characteristics of single channel composite positions, four forward simulation models of single channel combinations were developed. Finally, on the basis of the aforementioned forward simulation models, single subaqueous distributary channel sand bodies were identified within sparse well zones.KeywordsSeismic forward simulationWell-logging and seismic data combinationSingle subaqueous distributary channelQingshankou formationQian’an oilfieldSongliao basin

BackgroundThere are several situations in population biology research where simulating DNA sequences is useful. Simulation of biological populations under different evolutionary genetic models can be undertaken using backward or forward strategies. Backward simulations, also called coalescent-based simulations, are computationally efficient. The reason is that they are based on the history of lineages with surviving offspring in the current population. On the contrary, forward simulations are less efficient because the entire population is simulated from past to present. However, the coalescent framework imposes some limitations that forward simulation does not. Hence, there is an increasing interest in forward population genetic simulation and efficient new tools have been developed recently. Software tools that allow efficient simulation of large DNA fragments under complex evolutionary models will be very helpful when trying to better understand the trace left on the DNA by the different interacting evolutionary forces. Here I will introduce GenomePop, a forward simulation program that fulfills the above requirements. The use of the program is demonstrated by studying the impact of intracodon recombination on global and site-specific dN/dS estimation.ResultsI have developed algorithms and written software to efficiently simulate, forward in time, different Markovian nucleotide or codon models of DNA mutation. Such models can be combined with recombination, at inter and intra codon levels, fitness-based selection and complex demographic scenarios.ConclusionGenomePop has many interesting characteristics for simulating SNPs or DNA sequences under complex evolutionary and demographic models. These features make it unique with respect to other simulation tools. Namely, the possibility of forward simulation under General Time Reversible (GTR) mutation or GTR×MG94 codon models with intra-codon recombination, arbitrary, user-defined, migration patterns, diploid or haploid models, constant or variable population sizes, etc. It also allows simulation of fitness-based selection under different distributions of mutational effects. Under the 2-allele model it allows the simulation of recombination hot-spots, the definition of different frequencies in different populations, etc. GenomePop can also manage large DNA fragments. In addition, it has a scaling option to save computation time when simulating large sequences and population sizes under complex demographic and evolutionary situations. These and many other features are detailed in its web page [1].

An upper bound elemental technique approach to the process design of axisymmetric forging by forward and backward simulation

In this paper we propose a methodology which combines Forward and Backward Simulation in order to extend discrete-event simulation by the ability to calculate float times. In the case of Backward Simulation, the simulation starts at the virtual completion date of the construction project and runs backwards in time until the start date. To determine float times it is important that the task execution sequence is in the same order for forward and backward simulation. Consequently we present an extension to the simulation concept that controls the execution order of the tasks within the backward simulation. By combining the results of the forward and backward simulation, it is possible to determine float times for each task while taking into account resources. A comprehensive case study illustrates the application of this new approach. One result of this is the determination of detailed float times for each task using discrete-event simulation.

Lava flows have severe impacts on the Earth’s surface, communities, and natural habitats. Accurate modeling of lava flow paths is crucial for volcanic hazard assessment and risk mitigation. This research integrated remote sensing, geographic information systems, geomorphological techniques, and hydrologic analysis to numerically determine the optimal ground sample distance (GSD) of digital elevation models (DEMs) for delineating lava flow paths. Two volcanic regions were analyzed: the Afar Region of Ethiopia (Alu, Dalaffilla, and Erta Ale volcanoes) and the Reykjanes Peninsula of Iceland (Fagradalsfjall volcano). Forward and backward modeling methods were used to simulate potential lava flow paths. Remote sensing data, including multispectral and thermal images as well as DEMs, were utilized for analysis. A large-scale mean-shift image segmentation method was applied to identify lava objects and extract the main centerline for analysis. The forward model was applied to the fissures between the Alu and Dalaffilla volcanoes. The main lava centerline was derived from a 2008 ASTER image, while flow paths were simulated using the 2000 SRTM DEM. An optimal GSD of 35 m resulted in the lowest root mean square error (RMSE) of 146.89 m. This was validated using the 2017 Erta Ale eruption, confirming 35 m as a suitable GSD for delineating a major lava polygon. In the backward model, hydrologic analysis on resampled DEMs using TauDEM identified an optimal GSD of 38 m with an RMSE of 123.44 m. In the study of the Fagradalsfjall volcano, an optimal GSD of 71 m with an RMSE of 98.84 m was obtained, while the backward model yielded an optimal GSD of 33 m with an RMSE of 173.56 m. Statistical analyses were conducted to validate the results. This research contributed to volcanic hazard assessment by providing a framework that integrated geospatial technologies into lava flow modeling. Initiating simulation from the observed lava outlet, the forward model addressed a key limitation of the backward model. Results indicate that lava flow path accuracy was sensitive to the GSD of a DEM. The findings further demonstrate that the forward model may aid emergency response planning and risk mitigation strategies in volcanic regions.

The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounding Testbed-Interferometer (NAST-I), is the infrared component of a suite of airborne infrared and microwave spectrometers, developed for the Integrated Program Office (IPO), that has been flying on high-altitude aircraft as part of the risk reduction effort for NPOESS. It is a high spectral resolution (0.25 cm<SUP>-1</SUP>, unapodized) and high spatial resolution (~2 km, nadir) cross-track scanning (~ 45 km swath width, at 20 km aircraft altitude) Fourier Transform Spectrometer (FTS) observing within the 3.7 - 16.1 micron spectral range. In addition to characterizing the atmospheric thermal and moisture structure and providing information on radiatively active trace gases (e.g. O<SUB>3</SUB> &amp; CO) during field experiments, NAST-I measurements greatly contribute toward instrument and forward model pre-launch specification optimization (i.e., for the Cross-track Infrared Sounder, CrIS, to fly on NPOESS) and will enhance post-launch calibration/validation activities for CrIS as well as for other future advanced atmospheric spaceborne sensors (e.g., the EOS AIRS, CERES, MODIS, MOPITT, &amp; TES instruments). In this paper, we investigate some of the challenges associated with validating infrared spectral radiances obtained from remote sensing measurements and forward model simulations. Specifically, measured infrared spectral radiances are compared with radiance calculations performed using a Line by Line forward radiative transfer model based on nearly-coincident temperature and water vapor profiles observed with several independent in-situ, passive, and active measurement systems.

A comprehensive energy simulation model for energy-water-food nexus system analysis: A case study of the great Karun water resources system

We calculate the multicomponent responses of surface-hole transient electromagnetic method. The methods and models are unsuitable as geoelectric models of conductive surrounding rocks because they are based on regular local targets. We also propose a calculation and analysis scheme based on numerical simulations of the subsurface transient electromagnetic fields. In the modeling of the electromagnetic fields, the forward modeling simulations are performed by using the finite-difference time-domain method and the discrete image method, which combines the Gaver–Stehfest inverse Laplace transform with the Prony method to solve the initial electromagnetic fields. The precision in the iterative computations is ensured by using the transmission boundary conditions. For the response analysis, we customize geoelectric models consisting of near-borehole targets and conductive wall rocks and implement forward modeling simulations. The observed electric fields are converted into induced electromotive force responses using multicomponent observation devices. By comparing the transient electric fields and multicomponent responses under different conditions, we suggest that the multicomponent-induced electromotive force responses are related to the horizontal and vertical gradient variations of the transient electric field at different times. The characteristics of the response are determined by the varying the subsurface transient electromagnetic fields, i.e., diffusion, attenuation and distortion, under different conditions as well as the electromagnetic fields at the observation positions. The calculation and analysis scheme of the response consider the surrounding rocks and the anomalous field of the local targets. It therefore can account for the geological data better than conventional transient field response analysis of local targets.

The evolution of self-compatibility (SC) by the loss of self-incompatibility (SI) is regarded as one of the most frequent transitions in flowering plants. SI systems are generally characterized by specific interactions between the male and female specificity genes encoded at the S-locus. Recent empirical studies have revealed that the evolution of SC is often driven by male SC-conferring mutations at the S-locus rather than by female mutations. In this study, using a forward simulation model, we compared the fixation probabilities of male vs. female SC-conferring mutations at the S-locus. We explicitly considered the effects of pollen availability in the population and bias in the occurrence of SC-conferring mutations on the male and female specificity genes. We found that male SC-conferring mutations were indeed more likely to be fixed than were female SC-conferring mutations in a wide range of parameters. This pattern was particularly strong when pollen availability was relatively high. Under such a condition, even if the occurrence of mutations was biased strongly towards the female specificity gene, male SC-conferring mutations were much more often fixed. Our study demonstrates that fixation probabilities of those two types of mutation vary strongly depending on ecological and genetic conditions, although both types result in the same evolutionary consequence-the loss of SI.

How adaptive and non-adaptive processes interact to shape urban-rural clines remains a fundamental question in urban evolution. Here, we integrate whole-genome sequencing, demographic modelling and forward-in-time simulations to test how natural selection in urban and rural environments interacts with non-adaptive forces to maintain a pigmentation cline in eastern grey squirrels (Sciurus carolinensis). Coat colour polymorphism (grey versus melanic) is determined by a 24 bp deletion in the melanocortin-1 receptor gene (Mc1R). In Syracuse, New York (USA), squirrel melanism decreases from 50% in the city to less than 10% in rural forests. Our analyses revealed a strong urban bottleneck, yet urban-rural divergence at Mc1R was far greater than the genomic background, suggesting the pigmentation cline is maintained by selection. To estimate habitat-specific selection coefficients at Mc1R, we used forward simulation models using data on individual fitness and allele frequencies. Both models produce a surprising, yet consistent finding: strong selection against the melanic morph in rural forests and near neutrality in the city. We also detected elevated divergence between colour morphs at SMYD3-a gene implicated in adaptive thermogenesis-raising a possible link between pigmentation and thermal performance. Overall, our findings demonstrate that selection outside the city can be sufficient to maintain urban-rural clines amid urban bottlenecks, suggesting cities can preserve genetic diversity that would otherwise be lost in rural landscapes.

Flow simulation of multimillion grid cell models with hundreds of wells and decades long production history can be extremely time-consuming. This often limits the applicability of assisted history matching techniques. A pragmatic solution to this problem is grid coarsening which is now embedded in many commercial reservoir simulators. Instead of up-scaling geological models in external packages, grid cells are automatically amalgamated within the simulator while preserving flux distribution and reducing the total number of active cells. The resulting speedup can be significant, often only at small loss of accuracy. Both characteristics are essential elements of any inversion technique in a multimillion grid cell environment. For water-flood history matching, we have utilized a commercial finite-volume simulator and the streamline-based generalized travel time inversion whereby water-cut behavior is matched by adjusting inter-well permeabilities. To apply the assisted history matching technique on high resolution models, a flux reconstruction method is devised which makes full use of the benefits of automatic grid coarsening. It approximates fluxes in the original geological grid by recalculating transmissibility at the fine scale and redistributing coarse scale fluxes accordingly. The inversion is thus conducted on the fine scale grid, while the forward simulation model uses the coarse grid. While solving for an inexpensive coarse model, the calibration takes into account fine grid resolution. Upscaling is not required and the degree of coarsening can be adjusted during the matching process. The proposed method was successfully tested on a supergiant carbonate oilfield with about hundred wells and large-scale water injection. The history match improved dramatically at relatively low numerical cost, which also allowed for investigating multiple sensitivities. The results were verified against non-coarsened model. The significant increase in efficiency makes this a potential method of choice for cases, where previously assisted history matching techniques could not be deployed due to excessive run times.

This study summarizes 12 spectral indices for chlorophyll inversion, including traditional multi-spectral indices and newly published indices constructed based on “Red Edge” (680-750nm) and “Double-Peak” in red edge region. Among them, some are good candidates to resist Leaf Area Index (LAI) variations, but some are not. By using forward model simulations and in-situ measurements data, we systematically tested these indices and picked out the best one which (1) has the highest sensitivity to chlorophyll; (2) has the best resistance to LAI variation. Then predictive equations were developed from simulated data and validated using winter wheat ground measurement data collected in 2011, Yucheng Station at Chinese Academy of Sciences.

Technologies that increase the accuracy of soil-moisture monitoring, such as in-situ sensors, have been proposed as a key solution for increasing agricultural water productivity. However, quantifying how uncertainty in soil-moisture estimates lead to irrigation inefficiencies or economic losses has not been explicitly studied. We develop a framework that combines a crop simulation model with a rule-based irrigation decision-making algorithm to assess the impact of soil-moisture uncertainty on irrigation use and farm profits. We apply this modelling framework to a case study of irrigated maize production in Nebraska, United States, a region where improvements in agricultural water productivity are at the forefront of water-policy debates. We consider two main sources of uncertainty that result in a divergence between the farmers’ perception of soil-water content and the true water status, namely errors in the knowledge of soil texture and measurement of daily soil-water flux inflows and outflows. Even for very large errors in both soil-texture and water-flux measurements, impacts on water use and profits were marginal (11 ha-mm increase and $27 ha–1 decrease, respectively). In contrast, farmers’ choice of irrigation strategy had a much larger impact on water use and profits than uncertainty in soil-moisture information used to implement that strategy. Our findings show that near-optimal irrigation decisions can be made without perfect soil-moisture information. This conclusion suggests that providing farmers with improved irrigation scheduling recommendations – utilizing crop-water models and optimization techniques – would have a larger impact on water-use efficiency than simply providing farmers with technologies to more accurately monitor soil-moisture conditions.