Compositional data carry their relevant information in the relationships (logratios) between the compositional parts. It is shown how this source of information can be used in regression modeling, where the composition could either form the response, or the explanatory part, or even both. An essential step to set up a regression model is the way how the composition(s) enter the model. Here, balance coordinates will be constructed that support an interpretation of the regression coefficients and allow for testing hypotheses of subcompositional independence. Both classical least-squares regression and robust MM regression are treated, and they are compared within different regression models at a real data set from a geochemical mapping project.
相似文献In the field of mineral resources extraction, one main challenge is to meet production targets in terms of geometallurgical properties. These properties influence the processing of the ore and are often represented in resource modeling by coregionalized variables with a complex relationship between them. Valuable data are available about geometalurgical properties and their interaction with the beneficiation process given sensor technologies during production monitoring. The aim of this research is to update resource models as new observations become available. A popular method for updating is the ensemble Kalman filter. This method relies on Gaussian assumptions and uses a set of realizations of the simulated models to derive sample covariances that can propagate the uncertainty between real observations and simulated ones. Hence, the relationship among variables has a compositional nature, such that updating these models while keeping the compositional constraints is a practical requirement in order to improve the accuracy of the updated models. This paper presents an updating framework for compositional data based on ensemble Kalman filter which allows us to work with compositions that are transformed into a multivariate Gaussian space by log-ratio transformation and flow anamorphosis. This flow anamorphosis, transforms the distribution of the variables to joint normality while reasonably keeping the dependencies between components. Furthermore, the positiveness of those variables, after updating the simulated models, is satisfied. The method is implemented in a bauxite deposit, demonstrating the performance of the proposed approach.
相似文献Large floods are among the most hazardous natural phenomena, which in many cases cause enormous losses to the economy and lead to human casualties. Along with the use of modern instrumental data, the analysis of historical information on large past floods is widely practiced in the world. This allows obtaining qualitative and quantitative characteristics of historical floods and significantly expanding the observation series. The Selenga River is one of the largest rivers of Central Asia with catchment area equal to 447,060 km2, and also it is rather flood-prone river. The hydrological regime of the Selenga River is quite well studied in the twentieth century on the basis of gauging stations data, but there is still a lack of knowledge about past floods. In this paper, we present a list of 26 known floods within the Selenga River basin from 1730 to 1900, compiled from available historical documents (newspapers, scientific reports, diaries, memoirs, etc.). We estimated peak water levels for three catastrophic floods (1830, 1869 and 1897), the historical maximum of which was 850 cm. The reliability of our estimates is confirmed by a comparative analysis of the large 1971 flood. It was revealed that the largest floods can cause a rise of the Lake Baikal water level up to 200 cm. The inflow to Lake Baikal resulting from the largest floods in the Selenga River basin is comparable to the average annual inflow of water into the lake. We can conclude that the use of historical data for the analysis of floods in Eastern Siberia is quite acceptable, but some limitations must be taken into account.
相似文献Conditional facies modeling combines geological spatial patterns with different types of observed data, to build earth models for predictions of subsurface resources. Recently, researchers have used generative adversarial networks (GANs) for conditional facies modeling, where an unconditional GAN is first trained to learn the geological patterns using the original GAN’s loss function, then appropriate latent vectors are searched to generate facies models that are consistent with the observed conditioning data. A problem with this approach is that the time-consuming search process needs to be conducted for every new conditioning data. As an alternative, we improve GANs for conditional facies simulation (called GANSim) by introducing an extra condition-based loss function and adjusting the architecture of the generator to take the conditioning data as inputs, based on progressive growing of GANs. The condition-based loss function is defined as the inconsistency between the input conditioning value and the corresponding characteristics exhibited by the output facies model, and forces the generator to learn the ability of being consistent with the input conditioning data, together with the learning of geological patterns. Our input conditioning factors include global features (e.g., the mud facies proportion) alone, local features such as sparse well facies data alone, and joint combination of global features and well facies data. After training, we evaluate both the quality of generated facies models and the conditioning ability of the generators, by manual inspection and quantitative assessment. The trained generators are quite robust in generating high-quality facies models conditioned to various types of input conditioning information.
相似文献Here we present new data from a systematic Sr, Nd, O, C isotope and geochemical study of kimberlites of Devonian age Mirny field that are located in the southernmost part of the Siberian diamondiferous province. Major and trace element compositions of the Mirny field kimberlites show a significant compositional variability both between pipes and within one diatreme. They are enriched in incompatible trace elements with La/Yb ratios in the range of (65–300). Initial Nd isotope ratios calculated back to the time of the Mirny field kimberlite emplacement (t = 360 ma) are depleted relative to the chondritic uniform reservoir (CHUR) model being 4 up to 6 ɛNd(t) units, suggesting an asthenospheric source for incompatible elements in kimberlites. Initial Sr isotope ratios are significantly variable, being in the range 0.70387–0.70845, indicating a complex source history and a strong influence of post-magmatic alteration. Four samples have almost identical initial Nd and Sr isotope compositions that are similar to the prevalent mantle (PREMA) reservoir. We propose that the source of the proto-kimberlite melt of the Mirny field kimberlites is the same as that for the majority of ocean island basalts (OIB). The source of the Mirny field kimberlites must possess three main features: It should be enriched with incompatible elements, be depleted in the major elements (Si, Al, Fe and Ti) and heavy rare earth elements (REE) and it should retain the asthenospheric Nd isotope composition. A two-stage model of kimberlite melt formation can fulfil those requirements. The intrusion of small bodies of this proto-kimberlite melt into lithospheric mantle forms a veined heterogeneously enriched source through fractional crystallization and metasomatism of adjacent peridotites. Re-melting of this source shortly after it was metasomatically enriched produced the kimberlite melt. The chemistry, mineralogy and diamond grade of each particular kimberlite are strongly dependent on the character of the heterogeneous source part from which they melted and ascended.
相似文献Many coastal urban areas and many coastal facilities must be protected against pluvial and marine floods, as their location near the sea is necessary. As part of the development of a Probabilistic Flood Hazard Approach (PFHA), several flood phenomena have to be modelled at the same time (or with an offset time) to estimate the contribution of each one. Modelling the combination and the dependence of several flooding sources is a key issue in the context of a PFHA. As coastal zones in France are densely populated, marine flooding represents a natural hazard threatening the coastal populations and facilities in several areas along the shore. Indeed, marine flooding is the most important source of coastal lowlands inundations. It is mainly generated by storm action that makes sea level rise above the tide. Furthermore, when combined with rainfall, coastal flooding can be more consequent. While there are several approaches to analyse and characterize marine flooding hazard with either extreme sea levels or intense rainfall, only few studies combine these two phenomena in a PFHA framework. Thus this study aims to develop a method for the analysis of a combined action of rainfall and sea level. This analysis is performed on the city of Le Havre, a French urban city on the English Channel coast, as a case study. In this work, we have used deterministic materials for rainfall and sea level modelling and proposed a new approach for estimating the probabilities of flooding.
相似文献Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (Tcrit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of Tcrit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.
相似文献The connections between malaria incidence and climate variability have been studied in recent time using some mathematical and statistical models. Many of the statistical models in literature focused on time series approach based on Box–Jenkins methodology. However, fitting time series model based on the Box–Jenkins methodology may be challenging. Most malaria incidence data are count and are over-dispersed. In this study, negative binomial models were formulated for fitting malaria incidence in Akure—one of the epidemic cities in Nigeria. In particular, negative binomial models were formulated for each of the number of outpatient individuals, number of inpatient individuals and mortality count as a function of some climate variables. It was found that an increase in minimum temperature and relative humidity at lag 1 significantly increased the chance of malaria transmission and thereby leads to an increase in the number of inpatient and outpatient individuals, as well as the total number of malaria cases. The minimum temperature, rainfall amount and relative humidity of the study area have a significant impact on the increase of number of inpatient and outpatient individuals while mortality count depends on the total number of reported malaria cases. The findings from this study is to offer in-depth understanding on climate-malaria incidence linkages in Akure, Nigeria.
相似文献The mafic dykes (dolerites) during the Early Paleozoic are widely spread in Langao-Ziyang, southern Qiling Block, and the investigation on these dykes are very important. Previous studies have mainly focused on the Silurian mafic dykes; however, research on the Earlier Paleozoic mafic dykes is relatively weak at present. Therefore, the overall understanding of the mantle source and genetic dynamic setting during the Early Paleozoic in this area is lacking. To study the accurate age and origin of the Early Paleozoic mafic dykes in Ziyang, southern Shaanxi Province, the mafic dykes from dabacunand Qinmingzhai were selected and the petrology, zircon U–Pb chronology, geochemistry, and Sr–Nd–Hf isotopes were studied. Analysis indicates that the mafic dykes studied are mainly composed of dolerite, and they are the products of the Early Ordovician (475.8–480.7 Ma). Furthermore, the dolerites belong to alkaline rock series, and they are characterized by enrichment in LREE, Rb, Ba, Sr, Nb, (87Sr/86Sr)i = 0.7020–0.7050, εNd(t) = 3.0–4.0), εHf (t) = 4.5–12.1,176Hf/177Hf = 0.282681–0.282844. This suggests that the mafic dyke were derived from the partial melting of a depleted lithospheric mantle, and the genetic process is mainly controlled by the mantle plume based on the discussion of the genetic model. Furthermore, the genetic process experienced the separation and crystallization of olivine and clinopyroxene at the same time, with little crustal contamination.
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