Artificial Test Data Research Articles

Identification, characterization, and up-scaling of pore structure facies in the low permeability reservoirs: Insight into reservoir quality evaluation and sweet-spots analysis

The traditional pore structure classification scheme separates the qualitative pore origin classification from the quantitative pore structure parameter classification. This separation prevents a comprehensive understanding and evaluation of the pore structure and characteristics of multi-scale reservoirs. To overcome this limitation, we propose a new pore classification scheme that combines the “geological origin and structure parameters” of pore systems, introducing the term “pore structure facies” (PSF). By considering the genetic types of pores, the pore structure can be qualitatively divided into several categories, each representing different pore genesis. Within each category, the pore structure is further subdivided into subcategories based on pore structure parameters. To validate the effectiveness of the PSF scheme, we focus on the low permeability reservoirs of the Weixinan Depression in the Beibuwan Basin of the South China Sea. We identify and characterize PSF types using a multi-scale approach, including thin section, porosity-permeability, digital image analysis, mercury intrusion test, micro-CT imaging, and simulation. The results reveal that the study area can be classified into three main categories and six subcategories: intergranular homogeneous type (macroporous, mesoporous, and microporous), heterogeneous dissolution-pore type (moderate and strong dissolution), and cementation-induced few-pore type. Significant differences are observed in the two-dimensional and three-dimensional pore structure, petrophysical properties, and pore-scale flow of PSF, leading to significant variations in logging responses. Moreover, we successfully predict and verify the PSF profile using artificial neural network algorithms and field test data. The predicted PSF profile exhibits substantial differences in productivity, with the combination of PSF types serving as an indicator of productivity. We compare the quality of multi-scale reservoirs in the study area using the “point (single well) line (inter-well profile) plane (reservoir)" method based on PSF. Finally, to quantitatively characterize reservoir quality, we propose the Reservoir Sweet-spot Index (RSI), considering the reservoir thickness. By using the boundary of sedimentary facies, we generate a map of sweet-spots reservoirs that provides guidance for future energy exploration and development. The analytical scale of PSF and RSI can be flexibly adjusted, from the heterogeneity within the sand body to the distribution characteristics of a reservoir plane, offering valuable insights into reservoir quality estimation at different scales.

Differentially Private Block Coordinate Descent for Linear Regression on Vertically Partitioned Data

We present a differentially private extension of the block coordinate descent algorithm by means of objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, we derive a utility guarantee; a tolerance parameter indicates how much the differentially private regression may deviate from the analysis without differential privacy. The algorithm’s performance is compared with that of the standard block coordinate descent algorithm on both artificial test data and real-world data. We find that the algorithm is fast and able to generate practical predictions with single-digit privacy budgets, albeit with some accuracy loss.

An Improved Time-Varying Morphological Filtering and Its Application to Bearing Fault Diagnosis

Morphological filtering (MF) is a typical fault feature extraction technology, which has been widely used in rolling bearing fault diagnostics. The structural element (SE) length has an important influence on feature extraction and noise removal, and the filtering result obtained by the filtered signal under a single SE length or the weighted average of different filtered signals under partial SE lengths is limited and insufficient. To address this problem, a transient feature extraction method called improved time-varying morphological filtering (ITVMF) is proposed for bearing fault diagnosis, wherein the SE length is adaptively determined according to the inherent characteristics of vibration signals. Moreover, the autocorrelation envelop spectrum (AES) is employed to further eliminate fault-unrelated components. The main innovation of this method is to automatically extract bearing fault-related transient features using a novel time-varying SE (TVSE) based on autocorrelation while effectively eliminating the interference of random impulses and strong background noise on bearing fault features. The developed ITVMF-AES is investigated and validated on the simulation signals, accelerated bearing degradation data, and artificial experimental test data, and compared with three existing feature extraction methods. The results demonstrate that the developed methodology is more efficient in excavating transient impulse features and identifying bearing faults than the comparison methods, and has potential engineering application value.

Prediction of genomic breeding values based on pre-selected SNPs using ssGBLUP, WssGBLUP and BayesB for Edwardsiellosis resistance in Japanese flounder

BackgroundEdwardsiella tarda causes acute symptoms with ascites in Japanese flounder (Paralichthys olivaceus) and is a major problem for China’s aquaculture sector. Genomic selection (GS) has been widely adopted in breeding industries because it shortens generation intervals and results in the selection of individuals that have great breeding potential with high accuracy. Based on an artificial challenge test and re-sequenced data of 1099 flounders, the aims of this study were to estimate the genetic parameters of resistance to E. tarda in Japanese flounder and to evaluate the accuracy of single-step GBLUP (ssGBLUP), weighted ssGBLUP (WssGBLUP), and BayesB for improving resistance to E. tarda by using three subsets of pre-selected single nucleotide polymorphisms (SNPs). In addition, SNPs that are associated with this trait were identified using a single-SNP genome-wide association study (GWAS) and WssGBLUP.ResultsWe estimated a heritability of 0.13 ± 0.02 for resistance to E. tarda in Japanese flounder. One million SNPs at fixed intervals were selected from 4,978,724 SNPs that passed quality controls. GWAS identified significant SNPs on chromosomes 14 and 24. WssGBLUP revealed that the putative quantitative trait loci on chromosomes 1 and 14 contained SNPs that explained more than 1% of the genetic variance. Three 50 k-SNP subsets were pre-selected based on different criteria. Compared with pedigree-based prediction (ABLUP), the three genomic methods evaluated resulted in at least 7.7% greater accuracy of predictions. The accuracy of these genomic prediction methods was almost unchanged when pre-selected trait-related SNPs were used for prediction.ConclusionsResistance to E. tarda in Japanese flounder has a low heritability. GWAS and WssGBLUP revealed that the genetic architecture of this trait is polygenic. Genomic prediction of breeding values performed better than ABLUP. It is feasible to implement genomic selection to increase resistance to E. tarda in Japanese flounder with 50 k SNPs. Based on the criteria used here, pre-selection of SNPs was not beneficial and other criteria for pre-selection should be considered.

Comparing the ISO-recommended and the cumulative data-reduction algorithms in S-on-1 laser damage test by a reverse approach method.

We compare the ISO-recommended (the standard) data-reduction algorithm used to determine the surface laser-induced damage threshold of optical materials by the S-on-1 test with two newly suggested algorithms, both named "cumulative" algorithms/methods, a regular one and a limit-case one, intended to perform in some respects better than the standard one. To avoid additional errors due to real experiments, a simulated test is performed, named the reverse approach. This approach simulates the real damage experiments, by generating artificial test-data of damaged and non-damaged sites, based on an assumed, known damage threshold fluence of the target and on a given probability distribution function to induce the damage. In this work, a database of 12 sets of test-data containing both damaged and non-damaged sites was generated by using four different reverse techniques and by assuming three specific damage probability distribution functions. The same value for the threshold fluence was assumed, and a Gaussian fluence distribution on each irradiated site was considered, as usual for the S-on-1 test. Each of the test-data was independently processed by the standard and by the two cumulative data-reduction algorithms, the resulting fitted probability distributions were compared with the initially assumed probability distribution functions, and the quantities used to compare these algorithms were determined. These quantities characterize the accuracy and the precision in determining the damage threshold and the goodness of fit of the damage probability curves. The results indicate that the accuracy in determining the absolute damage threshold is best for the ISO-recommended method, the precision is best for the limit-case of the cumulative method, and the goodness of fit estimator (adjusted R-squared) is almost the same for all three algorithms.

Estimation of elevator passenger traffic based on the most likely elevator trip origin-destination matrices

We present a constraint programming formulation for the elevator trip origin-destination matrix estimation problem using a lexicographic bi-criteria optimization method where least squares minimization is applied to the measured counts and the minimum information or the maximum entropy approach to the whole matrix. An elevator trip consists of successive stops in one direction of travel with passengers inside the elevator. It can be defined as a directed network, where the nodes correspond to the stops on the trip and the arcs to the possible origins and destinations of the passengers. The goal is to estimate the most likely counts of passengers for the origin-destination pairs of every elevator trip occurring in a building that are consistent with the measured boarding and alighting counts and any prior information about the trip matrix. These counts are used to make passenger traffic forecasts which, in turn, are used in elevator dispatching to reduce uncertainties related to future passengers and therefore to improve passenger service level. Artificial test data was obtained by simulations of lunch hour traffic in a typical multi-story office building. This resulted in complex problem instances that enable robust performance and quality testing. The results show that the proposed approach outperforms previous alternatives in terms of quality, and can take an advantage of prior information. In addition, the proposed approach satisfies real time elevator group control requirements for estimating elevator trip origin-destination matrices. Practical application: The elevator trip origin-destination matrix estimation problem is important since it makes it possible to obtain complete information and statistics about the elevator passenger traffic. The statistics can be used to model future passengers which, when taken into account in the elevator group control, helps to improve passenger service level. Simulation experiments show that most of the problems occurring in reality can be solved within a reasonable time considering a real application, and the solving algorithms are relatively easy to implement using available constraint programming tools. Hence, this work is undoubtedly of interest to the building and elevator industry.

Investigating correlations between crack width, corrosion level and anchorage capacity

In assessing existing structures, inspection results need to be linked to the effects on load-carrying capacity; to provide such information, this study has investigated the correlation between splitting crack width, corrosion level and anchorage capacity. The study was based on 13 reinforced concrete beams that had been exposed to natural corrosion for 32 years, 11 beams with splitting cracks and 2 without. The crack pattern and widths were documented before undergoing structural testing of anchorage capacity. Thereafter, the reinforcement bars were extracted and their corrosion levels measured using two methods, gravimetric weight loss and 3D scanning. The corrosion level from the weight loss method was approximately twice as large; possible reasons are horizontal or subsurface corrosion pits, and the cleaning method. Further, for the same corrosion level, the specimens in this study had much larger crack widths and slightly lower bond capacity than the artificially corroded tests in the literature; a possible reason is that these specimens had been subjected to combined corrosion and freezing. However, the corrosion level and reduction in bond capacity related to crack width were both lower in the present than in previous studies in the literature. Thus, by formulating a damage indicator from the damage visible in the form of crack widths from artificial test data, the structural capacity is estimated to be on the safe side.

Numerical correlation of speleothem stable isotope records using a genetic algorithm

The correlation of geological records is commonly based on subjective and arbitrary interpretation by a researcher. Geological data are usually collected as a series on a depth scale, and then chronologies of the profiles are constructed. The correlation of geological records based on matching quality indicators requires an efficient method of searching for the best position. GenCorr is a software based on a genetic algorithm, for correlation of records with or without accompanying chronological control. Where their age information is available, points on both records may be moved in the range limited by the age uncertainties. GenCorr has been developed by the authors in Borland RAD Studio 2007 environment. The authors tested the impact of matched record length, noise level, sampling resolution, or additional linear components and hiatuses on the correlation results using artificial test data. Finally, the genetic algorithm was applied to stable isotope records obtained from two stalagmites collected from the Demianova Cave System (Low Tatras, Slovakia). The results show that the genetic algorithm correlation method is similar to that used in pattern recognition by the human eye without prior knowledge of the ages. The genetic algorithm is able to correlate in multidimensional space and is free from human biases, but we accept that there are also occasions where additional expert opinion may be also important. GenCorr can be run on every PC with MS Windows operating system, the package was not tested in Mac environment. GenCorr is available as a freeware from the authors. For downloading, please contact: dzeq@twarda.pan.pl.

Metalaxyl Resistance in Phytophthora infestans: Assessing Role of RPA190 Gene and Diversity Within Clonal Lineages.

Prior work has shown that the inheritance of resistance to metalaxyl, an oomycete-specific fungicide, is complex and may involve multiple genes. Recent research indicated that a single nucleotide polymorphism (SNP) in the gene encoding RPA190, the largest subunit of RNA polymerase I, confers resistance to metalaxyl (or mefenoxam) in some isolates of the potato late blight pathogen Phytophthora infestans. Using both DNA sequencing and high resolution melt assays for distinguishing RPA190 alleles, we show here that the SNP is absent from certain resistant isolates of P. infestans from North America, Europe, and Mexico. The SNP is present in some members of the US-23 and US-24 clonal lineages, but these tend to be fairly sensitive to the fungicide based on artificial media and field test data. Diversity in the level of sensitivity, RPA190 genotype, and RPA190 copy number was observed in these lineages but were uncorrelated. Controlled laboratory crosses demonstrated that RPA190 did not cosegregate with metalaxyl resistance from a Mexican and British isolate. We conclude that while metalaxyl may be used to control many contemporary strains of P. infestans, an assay based on RPA190 will not be sufficient to diagnose the sensitivity levels of isolates.

CONSTRAINING SOLAR FLARE DIFFERENTIAL EMISSION MEASURES WITH EVE AND RHESSI

Deriving a well-constrained differential emission measure (DEM) distribution for solar flares has historically been difficult, primarily because no single instrument is sensitive to the full range of coronal temperatures observed in flares, from $\lesssim$2 to $\gtrsim$50 MK. We present a new technique, combining extreme ultraviolet (EUV) spectra from the EUV Variability Experiment (EVE) onboard the Solar Dynamics Observatory with X-ray spectra from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI), to derive, for the first time, a self-consistent, well-constrained DEM for jointly-observed solar flares. EVE is sensitive to ~2-25 MK thermal plasma emission, and RHESSI to $\gtrsim$10 MK; together, the two instruments cover the full range of flare coronal plasma temperatures. We have validated the new technique on artificial test data, and apply it to two X-class flares from solar cycle 24 to determine the flare DEM and its temporal evolution; the constraints on the thermal emission derived from the EVE data also constrain the low-energy cutoff of the non-thermal electrons, a crucial parameter for flare energetics. The DEM analysis can also be used to predict the soft X-ray flux in the poorly-observed ~0.4-5 nm range, with important applications for geospace science.

An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets

Abstract. Spatiotemporal observations in Earth System sciences are often affected by numerous and/or systematically distributed gaps. This data fragmentation is inherited from instrument failures, sparse measurement protocols, or unfavourable conditions (e.g. clouds or vegetation thickness in case of remote-sensing data). Missing values are problematic as they may cause analytic biases and often inhibit advanced statistical analyses. Hence, gapfilling is an undesired but necessary task in Earth System sciences. State-of-the-art gapfilling algorithms based on Singular Spectrum Analysis (SSA) exploit the information contained in periodic temporal patterns to fill gaps in the observations. Here we propose an extension of this method in order to additionally consider the spatial processes and patterns underlying most geoscientific datasets. The latter has been made possible by including a recently developed 2-D-SSA approach. Using both artificial and real-world test data, we show that simultaneously exploiting spatial and temporal patterns improves the gapfilling substantially. We outperform conventional approaches particularly for large and systematically recurring gaps. The new method is reasonably fast and can be applied with a minimum of a priori assumptions regarding the structure of the data and the distribution of gaps. The algorithm is available as a ready-to-use open source software package.

Optimization of site exploration program for improved prediction of tunneling-induced ground settlement in clays

Excessive settlement caused by tunneling during construction often damages adjacent infrastructures and utilities. Such excessive settlement can also present a challenge in the maintenance of subways during their operation. Thus, it is important to be able to accurately predict tunneling-induced settlement. The uncertainties in geotechnical parameters, however, can lead to either an overestimation or an underestimation of the tunneling-induced settlement. Such uncertainties can arise from many sources such as spatial variability, measurement error, and model error; in this paper, the focus is on the geotechnical parameters characterization from site exploration. The goal here is to determine an optimal level of site exploration effort so that effective predictions of the tunneling-induced settlement in clays can be achieved. To this end, a Monte Carlo simulation-based numerical model of site exploration is first established to generate artificial test data. Then, a series of parametric analyses are performed to investigate the relationship between the level of site exploration effort and the accuracy of the tunneling-induced ground settlement prediction. Through the assumed different levels of site exploration effort, statistics of soil parameters are estimated using the maximum likelihood method and the tunneling-induced ground settlement is then analyzed using the probabilistic method, and finally the effect of site exploration effort is assessed. The knowledge generated from this series of analyses is then used to develop the proposed framework for selecting an optimal site exploration program for improved prediction of the tunneling-induced ground settlement in clays. Examples are presented to illustrate the proposed framework and demonstrate its effectiveness and significance.

HF‐measure: A new measurement for evaluating clusters in protein–protein interaction networks

Clustering of protein-protein interaction networks is one of the most prevalent methods for identifying protein complexes, detecting functional modules and predicting protein functions. In the past few years, many clustering methods have been proposed. However, it is still a challenging task to evaluate how well the protein clusters are identified. Even for two of the most popular measurements, F-measure and p-value, bias exists when evaluating the identified clusters. In this paper, we propose two new types of measurements to evaluate clusters more finely and distinctly. One is hF-measure(Tf) , a topology-free measurement and another is hF-measure(Tb) , a topology-based measurement. Unlike F-measure, the new measurements of hF-measure(Tf) and hF-measure(Tb) can discriminate between different types of errors. Both artificial test data and practical test data were used to evaluate the effectiveness of hF-measure(Tf) and hF-measure(Tb) . For the artificial test data, artificial errors were generated by replacing some cluster members with functionally similar or non-similar members. The practical test data was produced by seven clustering algorithms Markov Clustering, Molecular Complex Detection, HC-PIN, SPICI, CPM, Core-Attachment and RRW. The experimental results on artificial and practical test data both show that hF-measure(Tf) and hF-measure(Tb) evaluate clusters more accurately compared to F-measure. Especially, hF-measure(Tb) can capture the topology changes in clusters, which can also be used to the analysis of dynamic network.

Assessment of the influence of adaptive components in trainable surface inspection systems

In this paper, we present a framework for the classification of images in surface inspection tasks and address several key aspects of the processing chain from the original image to the final classification result. A major contribution of this paper is a quantitative assessment of how incorporating adaptivity into the feature calculation, the feature pre-processing, and into the classifiers themselves, influences the final image classification performance. Hereby, results achieved on a range of artificial and real-world test data from applications in printing, die-casting, metal processing and food production are presented.

Fourth-corner generation of plant functional response groups

Plant functional response groups (PFGs) are now widely established as a tool to investigate plant—environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474–478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631–640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length.

Inversion of time‐dependent nuclear well‐logging data using neural networks

ABSTRACTThe purpose of this work was to investigate a new and fast inversion methodology for the prediction of subsurface formation properties such as porosity, salinity and oil saturation, using time‐dependent nuclear well logging data. Although the ultimate aim is to apply the technique to real‐field data, an initial investigation as described in this paper, was first required; this has been carried out using simulation results from the time‐dependent radiation transport problem within a borehole. Simulated neutron and γ‐ray fluxes at two sodium iodide (NaI) detectors, one near and one far from a pulsed neutron source emitting at ∼14 MeV, were used for the investigation. A total of 67 energy groups from the BUGLE96 cross section library together with 567 property combinations were employed for the original flux response generation, achieved by solving numerically the time‐dependent Boltzmann radiation transport equation in its even parity form. Material property combinations (scenarios) and their correspondent teaching outputs (flux response at detectors) are used to train the Artificial Neural Networks (ANNs) and test data is used to assess the accuracy of the ANNs. The trained networks are then used to produce a surrogate model of the expensive, in terms of computational time and resources, forward model with which a simple inversion method is applied to calculate material properties from the time evolution of flux responses at the two detectors.The inversion technique uses a fast surrogate model comprising 8026 artificial neural networks, which consist of an input layer with three input units (neurons) for porosity, salinity and oil saturation; and two hidden layers and one output neuron representing the scalar photon or neutron flux prediction at the detector. This is the first time this technique has been applied to invert pulsed neutron logging tool information and the results produced are very promising. The next step in the procedure is to apply the methodology to real data.

Substructure system identification from coupled system test data

System identification in the context of linear structural dynamics and mechanical vibrations is addressed. More specifically, the focus is on large systems built up by components and the identification of its subsystems using test data of the complete system. A method based on reconstruction of the interface forces acting between the unknown subsystem and its neighbour is presented. Theoretical aspects regarding frequency domain load identification are illuminated. The causes and consequences of an ill-conditioned matrix inversion are emphasized. It is shown that the load identification and thus also the subsystem identification is sensitive to the existence of general anti-resonances in the frequency domain of interest. Different methods for validation of an identified subsystem are discussed. Two numerical examples with artificial test data are given to illustrate the procedure: a simple lumped parameter system and a slightly more complex distributed parameter system. The technique is also successfully applied to a physical test setup consisting of a plane frame structure built up from two components.

FOCUS: a deconvolution method based on algorithmic complexity

A new method for improving the resolution of images is presented. It is based on Occam's razor principle implemented using algorithmic complexity arguments. The performance of the method is illustrated using artificial and real test data.

Quantitative treatment of coarsely binned low-resolution recordings in molecular absorption spectroscopy

Optical multichannel detectors like photodiode arrays or CCD cameras combined with grating spectrometers are commonly used as detection systems in quantitative absorption spectroscopy. As a trade-off to broad spectral coverage, banded spectral features are sometimes recorded with insufficient spectral resolution and/or insufficiently fine detector binning. This renders the true physical spectrum of recorded intensities changed by instrumental and spectrum specific artefacts thus impeding comparability between results from different set-ups. In this work, it is demonstrated that in the case of a “well-behaved” – i.e. free of ro-vibronic structure – absorption band like the iodine monoxide IO(4 ← 0) transition, these effects can easily change the apparent peak absorption by up to 50%. Also deviations from the strict linearity (Beer–Lambert's law) between absorber concentration and apparent, i.e. pixelwise optical density occur. This can be critical in studies of chemical kinetics. It is shown that the observed non-linearity can cause errors of up to 50% in the determination of a second order rate coefficient for the IO self reaction. To overcome the problem, a consistent and rigorous integral approach for the treatment of intensity recordings is developed. Linearity between optical density and absorber concentration thereby is re-established. The method is validated using artificial test data as well as experimental data of the IO(4 ← 0) absorption transition, obtained in the context of I 2/O 3 photochemistry studies. The agreement is accurate to within ±2% (test data) and ±3% (experimental data) supporting the validity of the approach. Possible consequences for other spectroscopic work are indicated.

Unsupervised fuzzy learning and cluster seeking

This paper presents a new approach to unsupervised pattern classification. The classification scheme consists of two main stages. The first one is an unsupervised fuzzy learning procedure, which allows, using a similarity measure and a corresponding threshold, to seek clusters within a set of totally unlabeled samples. It provides, for each detected cluster, a good initial prototype as well as the membership degree of each sample. The second stage is an optimization procedure involving the fuzzy c-means (FCM) algorithm. Both procedures are repeated for different values of the similarity threshold, and three validity criteria are used to assess and rank the quality of all resulting partitions. The effectiveness of this approach is demonstrated, for different parameter values, on both artificial and real test data.