Two-dimensional Feature Space Research Articles

Monitoring soil salinity based on Sentinel-1/2 remote sensing parameters and two-dimensional space theory

Multi-spectral satellite remote sensing is widely used in soil salinity monitoring by virtue of its wide coverage, fast update and low cost, but it is susceptible to cloudy weather, limiting the accuracy and application conditions of the inversion, while SAR (synthetic aperture radar) is hardly affected by cloud with strong penetration ability and enables almost round-the-clock, climate-wide monitoring of soil salinity. Two-dimensional feature space model, being easily interpretive and widely used in soil salinity monitoring, requires only some easily extracted parameters and can reduce other influences on soil salinity inversion. Therefore, in order to take full advantages of the SAR and multi-spectral satellite, achieve monitoring of soil salinity in more climatic conditions and longer time, and further improve soil salinity monitoring accuracy, this paper combine SAR-Spectral data with two-dimensional space theory to construct two-dimensional models for monitoring soil salinity. In this study, two-dimensional space models are constructed using Sentinel-1 and Sentinel-2 feature parameters, and models are evaluated with R2 with measured soil salinity data of the Shahaoqu irrigation area in Hetao irrigation district, Inner Mongolia. The results show that it is feasible to use the parameters extracted from Sentinel-1 after H/a polarization for constructing two-dimensional feature space models for the soil salinity inversion(R2parameters of Sentinel-1 = 0.467,0.587,0.517), and the two-dimensional space models combining Sentinel-1/2 parameters are more accurate and stable in soil salinity monitoring than those using Sentinel-1 or Sentinel-2 alone((Rparameters of Sentinel-2 = 0.320,0.517,0.341),(R2parameters of Sentinel-1/2 = 0.507,0.545,0.562,0.512,0.557,0.568)).

Abstract 1003: Blood Pressure Metrics In Predicting Successful Thoracic Endovascular Aortic Repair

Blood pressure (BP) is a well-known risk factor for propagation of type B aortic dissections; however, there is limited data on its impact following surgical repair. While metrics such as BP variability have been associated with higher risk of cardiovascular events, such metrics has not been explored post TEVAR where management has largely focused on reducing systolic BP. By pairing unique BP metrics with preoperative aortic geometry, which has been shown to predict post-TEVAR outcomes, we can better understand what BP metrics are most useful. BP data collected in the first 24 hours after TEVAR for a cohort of 31 patients was used to calculate 24 unique BP metrics, including measures of centrality, variability, and stationarity. Preoperative CTA imaging data were used to create surface models for each aorta, which were then used to define aortic geometry as a unique point in the validated shape-size feature space. Logistic regression models with the BP and aortic geometry metrics were created to predict successful TEVAR outcomes using standard feature selection procedures and were validated using cross-validation (CV). Training and cross-validation accuracy were then compared between the models to determine the most generalizable model. A simple model using only preoperative aortic geometry in the two-dimensional shape-size feature space yielded a training accuracy of 77.4% and CV accuracy of 74.8%. An overfit model with all 24 BP metrics calculated from all available BP data (18.1 ± 6.0 BPs, range 5 - 29) had a 100% training accuracy but CV accuracy of 77.6%. Using the coefficients from this overfit model, we selected the most important features to train the next model. This resulted in a model using aortic geometry in addition to two systolic BP stationarity metrics and diastolic BP standard deviation. This model yielded a 96.8% training accuracy and 93.8% CV accuracy. Adding BP metrics creates a novel multi-dimensional aortic geometric-physiologic feature space and improves our ability to predict post-TEVAR outcomes. The BP variables that best contributed to this model were all measures of variability and stationarity, indicating that trends in BP must be evaluated to better identify patients at risk of poor outcomes.

Classification of Hippocampal Ripples: Convolutional Neural Network Learns Episode-Specific Changes.

The hippocampus is known to play an important role in memory by processing spatiotemporal information of episodic experiences. By recording synchronized multiple-unit firing events (ripple firings with 300 Hz-10 kHz) of hippocampal CA1 neurons in freely moving rats, we previously found an episode-dependent diversity in the waveform of ripple firings. In the present study, we hypothesized that changes in the diversity would depend on the type of episode experienced. If this hypothesis holds, we can identify the ripple waveforms associated with each episode. Thus, we first attempted to classify the ripple firings measured from rats into five categories: those experiencing any of the four episodes and those before experiencing any of the four episodes. In this paper, we construct a convolutional neural network (CNN) to classify the current stocks of ripple firings into these five categories and demonstrate that the CNN can successfully classify the ripple firings. We subsequently indicate partial ripple waveforms that the CNN focuses on for classification by applying gradient-weighted class activation mapping (Grad-CAM) to the CNN. The method of t-distributed stochastic neighbor embedding (t-SNE) maps ripple waveforms into a two-dimensional feature space. Analyzing the distribution of partial waveforms extracted by Grad-CAM in a t-SNE feature space suggests that the partial waveforms may be representative of each category.

Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals

Multipath interference in radar signals caused by sea, ground, and other environments poses significant challenges to the target detection, tracking, and classification capabilities of radar systems. Existing methods for radar signal identification require labeled samples and focus mainly on the classification of normal signals. However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. The method transforms radar signals into time–frequency images and is trained exclusively on normal samples. The autoencoder architecture is extended with a feature extractor network to capture latent sample features. CBAM attention is introduced to improve feature extraction. By learning the distribution of normal samples in high-dimensional image space and low-dimensional feature space, a two-dimensional feature space representing normal samples is constructed. A one-class SVM then learns the boundary of normal samples for anomaly detection. Extensive experiments on radar signal datasets validate the effectiveness of the proposed approach.

Distribution of local curvature values as a structural feature for off-line handwritten signature verification

In the paper, a new feature for describing a digital image of a handwritten signature based on the frequency distribution of the values of the local curvature of the signature contours, is proposed. The calculation of this feature on the binary image of a signature is described in detail. A normalized histogram of distributions of local curvature values for 40 bins is formed. The frequency values recorded as a 40-dimensional vector are called the local curvature code of the signature.During verification, the proximity of signature pairs is determined by correlation between curvature codes and LBP codes described by the authors in [23]. To perform the signature verification procedure, a two-dimensional feature space is constructed containing images of the proximity of signature pairs. When verifying a signature with N authentic signatures of the same person, N(N-1)/2 patterns of the proximity of pairs of genuine signatures and N images of pairs of proximity of the analyzed signature with genuine signatures are presented in the feature space. The Support Vector Machine (SVM) is used as a classifier.Experimental studies were carried out on digitized images of genuine and fake signatures from two databases. The accuracy of automatic verification of signatures on the publicly available CEDAR database was 99,77 % and on TUIT was 88,62 %.

Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data - Towards a quantitative understanding of fire impacts

Precise quantification of forest fire impacts is critical for management strategies in support of post-fire mitigation. In this regard, optical remote sensing imagery in combination with spectral unmixing has been widely used to measure fire severity by means of fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), charcoal (CH) and further ground components such as ash, bare soil and rocks. However, most unmixing analyses have made use of a single post-fire image without accounting for the pre-fire state. We aim to assess fire severity from Sentinel-2 data using a bi-temporal spectral unmixing analysis that provides a quantitative fire impact description and is oriented towards the process of change by including pre-fire and post-fire information. Unmixing was based on Random Forest Regression (RFR) modeling using synthetic training data from a bi-temporal spectral library. We describe fire severity as changes associated with the combustion of photosynthetic vegetation (PV–CH fraction) and dieback of photosynthetic vegetation (PV-NPV fraction). Unburned forest was mapped as stable photosynthetic vegetation (PV-PV fraction). We evaluated our approach on a forest fire that burned in a temperate forest region in eastern Germany in 2018. Independent validation was carried out based on reference fractions obtained from very high-resolution (VHR) imagery such as Plante Scope, SPOT6, orthophotos, aerial photos, and Google Earth. The results underline the effectiveness of our unmixing approach, with Root Mean Squared Errors (RMSE) of 0.072 for PV-CH, 0.09 for PV-NPV, and 0.08 for PV-PV fractions. Most of the errors were caused by spectral similarity between charcoal and shadow effects caused by trees, and the coloring of foliage and NPV in the late phenological season of the post-fire Sentinel-2 image. Based on the two-dimensional feature space of PV-CH and PV-NPV fractions, we calculated two metrics to characterize fire impacts: distance, an indicator of disturbance severity (sum of combustion and dieback), and angle, a measure of disturbance composition (gradient between combustion and dieback). Furthermore, we compared the fraction-based metrics with the difference Normalized Burn Ratio (dNBR). Since the dNBR is most sensitive to combustion and presence of charcoal, it does not fully characterize fire-related vegetation loss associated with dieback. The bi-temporal fraction-based indices provide more ecologically meaningful information on fire severity, particularly for regions that are less prone to severe wildfires such as Central Europe.

Unsupervised Feature Representation of Sleep EEG Data with Transient Deep Boltzmann Machine.

The supervised sleep staging methods are challenged by their strict requirements of a labelled and large dataset. This study considers an unsupervised dimensionality reduction method, the Deep Boltzmann Machine (DBM), trained to a transient state for binary classification of sleep stages. First, the joint time-frequency domain features from the polysomnographic recordings are extracted. Second, the extracted features are smoothed using 2 min rolling window to include contextual temporal information, and finally, they serve as an input for unsupervised training of DBM_transient. The results show that our method effectively separates the sleep stages in two-dimensional feature space with a large Fisher's discriminant value. The classification performance by the DBM_transient achieves a 96.1% F1 score, which is higher than DBM converged to an equilibrium state (95.2%), Principal Component Analysis (92.5%), Isometric Feature Mapping (95.9%), t-distributed Stochastic Neighbor Embedding (94.9%), and Uniform Manifold Approximation (95.0%) on the widely used sleep-EDF database. Additionally, Fisher's discriminant function demonstrates the superiority of the DBM_transient. The significance of the DBM transient lies in its ease of interpretability in two-dimensional space, and future multi-class implementation of the method may facilitate its usage in clinical applications.

Estimation psychophysiological state via nonlinear dynamic integral models

The method of experimental research “input-output”of the human oculo-motor system was developed and implemented using innovative eye-tracking technology for recording oculo-motor systemresponses to test visual stimuli. Stimuli are displayed on the monitor screen at different distances from the starting position. This formally corresponds to the action of step signals with different amplitudes at the input of the oculo-motor system. According to the empirical data of the “input-output”studies of the respondent's oculo-motor systemobtained with the aid of the TobiiProTX300 eye tracker, the transient functions of the first and diagonal intersections of the transient functions of the second and third orders of the oculo-motor systemwere determined. Experimental studies of the respondent's oculo-motor systemto identify the state of fatigue were carried out before the beginning (in the morning) and after the working day (in the evening). The obtained multidimensional transient functions are used as a source of primary data in the implementation of intelligent information technology for diagnosis and monitoring of the psychophysiological state of a person. Instrumental algorithmic and software tools for determining diagnostic features based on the identification data of the oculo-motor systemin the form of multidimensional transient functionsin the Python language have been developed.Training samples of data fortwo states of the respondent (“Normal”and “Fatigue”) were formed on the basis of the proposed heuristic features, which are determined using integral and differential transformations of the obtained multidimensional transient functionsof the oculo-motor system. Training samples of data are used to build classifiers of psychophysiological states of an individual using machine learning tools. The informativeness of individual features and all their possible combinations in pairs according to the indicator of the probability of correct recognition was studied using the method of complete search. The research results were obtained by evaluating the quality of recognition of states built by Bayesian classifiers in different spaces of the proposed features. An analysis of the stability of the correct recognitioninformativeness indicator of different feature spaces under the influence of different levels of additive noise on the features was carried out. Two-dimensional feature spaces with the maximum and most stable value of the correct recognitionindicator were found when solving the scientific and practical task of assessing the psychophysiological state (fatigue) of a person (0.9375). Thus, it seems appropriate to use the multidimensional transient functionsobtained from eye-tracking data in diagnostic studies in the fields of neuroscience and experimental psychology

Information Extraction from Industrial Sensor Data Using Time Series Meta-Features

In the smart manufacturing sector, analyzing time series data is essential for monitoring plants and machinery to prevent costly failures or shutdowns. In order to gain new insights and make better control decisions, new methods are needed for extracting information and interpreting sensor data from hundreds of systems. In this paper, we present an approach for visualizing and interpreting sensor data from TRIMET Aluminium SE Essen (TAE) using time series meta-features and principal component analysis (PCA). We describe our general approach of generating multiple two-dimensional feature spaces to identify salient and implausible sensor data. Using a set of 20 time series meta-features, we applied our approach to sensor data from TAE which were generated by thermocouples. Each step of the approach was integrated into a dashboard to ensure a user-friendly and approachable interaction in finding salient and implausible sensor data.

Extraction of Rocky Desertification Information in the Karst Area Based on the Red-NIR-SWIR Spectral Feature Space

The complex topography, severe surface fragmentation and landscape heterogeneity of the karst region of southwest China make it extremely difficult to extract information on rocky desertification in the region. In order to overcome the disadvantages of the surface parameter-based feature space approach, which is difficult to construct and apply, this study uses the reflectance of Landsat 8 Operational Land Imager (OLI) in the red (Red), near-infrared (NIR) and shortwave infrared (SWIR) bands as the feature variables, and establishes a two-dimensional SWIR-NIR, Red-NIR and SWIR-Red reflectance spectral feature space. The three models of perpendicular rocky desertification index 1 (PRDI1), perpendicular rocky desertification index 2 (PRDI2) and perpendicular rocky desertification index 3 (PRDI3) were also constructed based on the variation of the degree of rocky desertification in each spectral feature space. The accuracy of the rocky desertification extracted by these three index models was verified and compared with the karst rocky desertification index (KRDI) and rocky desertification difference index (RSDDI), which are constructed based on the surface parameter feature space. The results show that: (1) The waveband reflectance-based feature space model provides a new method for large-scale rocky desertification information extraction, characterized by easy data acquisition, simple index calculation and good stability, and is conducive to the monitoring and quantitative analysis of rocky desertification in karst areas. (2) The overall accuracy and Kappa coefficient of PRDI1 are 0.829 and 0.784, respectively, both higher than other index models, showing the best applicability, accuracy and effectiveness in rocky desertification information extraction. (3) According to the results extracted from PRDI1, the total area of rocky desertification in Huaxi District of Guizhou province is 320.44 km2, with the more serious grades of rocky desertification, such as severe and moderate, mainly distributed in the southwestern, western and southeastern areas of Huaxi District. This study provides important information on the total area and spatial distribution of different degrees of rocky desertification in the study area, and these results can be used to support the local government’s ecological and environmental management decisions. The method proposed in this study is a scientific and necessary complement to the characteristic spatial methods based on different surface parameters, and can provide important methodological support for the rapid and efficient monitoring of karstic rocky desertification over large areas.

Remote Sensing Monitoring of Soil Salinity in Weigan River–Kuqa River Delta Oasis Based on Two-Dimensional Feature Space

Soil salinization is a serious resource and ecological problem globally. The Weigan River–Kuqa River Delta Oasis is a key region in the arid and semi-arid regions of China with prominent soil salinization. The saline soils in the oasis are widely distributed over a large area, causing great harm to agricultural development and the environment. Remote sensing monitoring can provide a reference method for the management of regional salinization. We extracted the spectral indices and performed a correlation analysis using soil measurement data and Sentinel-2 remote sensing data. Then, two-dimensional feature space inversion models for soil salinity were constructed based on the preferred spectral indices, namely, the canopy response salinity index (CRSI), composite spectral response index (COSRI), normalized difference water index (NDWI), and green atmospherically resistant vegetation index (GARI). The soil salinity in a typical saline zone in the Weigan River–Kuqa River Delta Oasis was monitored and analyzed. We found that the inversion of the CRSI-COSRI model was optimal (R2 of 0.669), followed by the CRSI-NDWI (0.656) and CRSI-GARI (0.604) models. Therefore, a model based on the CRSI-COSRI feature space can effectively extract the soil salinization information for the study area. This is of great significance to understanding the salinization situation in the Weigan River–Kuqa River Delta Oasis, enriching salinization remote sensing monitoring methods, and solving the soil salinization problem in China.

An Enhanced Joint Indicator for Starter Failure Diagnostics in Auxiliary Power Unit

Degradation of the starter can lead to the failure of starting Auxiliary Power Units (APU) and the consequent safety hazards. To improve performance monitoring and malfunction prediction of APUs, in this paper, two indicators are developed based on the physics-based model of the APU starting process. The indicators quantify the health level of the starter and provide diagnostic information with no need for past measurements from the system. The health indicators are proposed to identify the degradation of the starter at both the system level and component level. An enhanced joint indicator is then developed to aggregate the two individual indicators to detect the starter failure within a two-dimensional feature space. Receiver operating characteristic (ROC) curves are adopted to evaluate the diagnostic performance of the three indicators and the optimal thresholds are determined based on the trade-off between the diagnostic reliability and the operating cost reduction. The enhanced joint indicator exhibits superior diagnostic performance and offers a significant improvement in overall maintenance costs.

Surface soil moisture retrieval through combining LST-VI feature space with soil porosity

ABSTRACT Surface soil moisture (SSM) is an important variable for controlling the energy exchange between earth’s surface and atmosphere. Accurate estimation of the distribution of SSM and its dynamic changes is increasingly essential in the study of hydrology, ecology, agriculture, geological hazard monitoring, and global climate change. This paper presents a new model for SSM retrieval (named, KSSM) through assimilating soil porosity with a two-dimensional feature space composed of land surface temperature and vegetation index (LST-VI). The KSSM model takes into account the physical properties of soil porosity, so it is suitable for SSM retrieval of various soil types in different regions. The KSSM model was validated using the measured data, and a comparison analysis with other data was conducted. Through the sensitivity analysis, it is concluded that the error of SSM retrieved by the KSSM model is less than 0.04 m3 m−3 and the KSSM model is robust. High accuracy of volumetric SSM estimation, error within 10% compared with the measured data, can be achieved by the KSSM model without the need for an in-situ SSM.

A novel remote sensing monitoring index of salinization based on three-dimensional feature space model and its application in the Yellow River Delta of China

Previous studies were mostly conducted based on two-dimensional feature space to monitor salinization, while studies on dense long-term salinization monitoring based on three-dimensional feature space have not been reported. Based on Landsat TM/ETM+/OLI images and three-dimensional feature space method, this study introduced six typical salinization surface parameters, including NDVI, salinity index, MSAVI, surface albedo, iron oxide index, wetness index to construct eight different feature space monitoring index. The optimal soil salinization monitoring index model was proposed base on field observed data and then the evolution process of salinization in Yellow River Delta (YRD) were analyzed and revealed during 1984–2022. The salinization monitoring index model of MSAVI-Albedo-IFe2O3 feature space had the highest accuracy with R2 = 0.93 and RMSE = 0.678g/kg. The spatial distribution of salinization in YRD showed an increasing trend from inland southwest to coastal northeast and the salinization intensity showed an increasing trend during 1984–2022 due to the implements of agricultural measures such as planting salt-tolerant crops, microbial remediation and fertility improvement. The rate of salinization deterioration in the northeast part was greater than others. Zones of salinization improvement were mainly located in cultivated land of the southwest parts.

Intelligence against complexity: Machine learning for nonuniform temperature-field measurements through laser absorption.

The effect of spatial nonuniformity of the temperature distribution was examined on the capability of machine-learning algorithms to provide accurate temperature prediction based on Laser Absorption Spectroscopy. First, sixteen machine learning models were trained as surrogate models of conventional physical methods to measure temperature from uniform temperature distributions (uniform-profile spectra). The best three of them, Gaussian Process Regression (GPR), VGG13, and Boosted Random Forest (BRF) were shown to work excellently on uniform profiles but their performance degraded tremendously on nonuniform-profile spectra. This indicated that directly using uniform-profile-targeted methods to nonuniform profiles was improper. However, after retraining models on nonuniform-profile data, the models of GPR and VGG13, which utilized all features of the spectra, not only showed good accuracy and sensitivity to spectral twins, but also showed excellent generalization performance on spectra of increased nonuniformity, which demonstrated that the negative effects of nonuniformity on temperature measurement could be overcome. In contrast, BRF, which utilized partial features, did not have good generalization performance, which implied the nonuniformity level had impact on regional features of spectra. By reducing the data dimensionality through T-SNE and LDA, the visualizations of the data in two-dimensional feature spaces demonstrated that two datasets of substantially different levels of non-uniformity shared very closely similar distributions in terms of both spectral appearance and spectrum-temperature mapping. Notably, datasets from uniform and nonuniform temperature distributions clustered in two different areas of the 2D spaces of the t-SNE and LDA features with very few samples overlapping.

Ідентифікація окуло-моторної системи людини на основі ряду Вольтерри: застосування в системі захисту інформації

The information technology of biometric identification of a person has re-ceived further development due to the use as a source of primary data of infor-mation models of the oculo-motor system (OMS) of the «input-output» type based on the Volterra series. Eye-tracking technology is used to build models. Experimental studies of the OMS of two respondents were carried out. Based on the data obtained with the Tobii Pro TX300 eye-tracker, the transient func-tions of the first, second and third orders of the OMS when applying the Volter-ra series model were determined. This makes it possible to increase the accura-cy of OMS modeling and, as a result, to increase the reliability of recognition in the space of the proposed heuristic features, which are determined using inte-gral and differential transformations of multidimensional transient functions of OMS, which greatly simplifies the identification of features and the practical implementation of the Bayesian classifier.A high variability of the transient functions of the second and third or-ders for two respondents was revealed. Thus, it seems appropriate to use multidimensional transient functions for biometric identification.A set of heuristic features are proposed, which are determined on the basis of multidimensional transient functions obtained from eye-tracking data. The informativeness of individual features and their combinations in pairswas investigated. Two-dimensional feature spaces with the maximum value of the probability of correct recognition indicator when solving the problem of biometric identification of a person were found (Pmax=0.974). The research results were obtained using the construction of Bayesian clas-sifiers in different spaces of the proposed features by means of machine learning based on the data of the formed trainingsamples

Incorporation of Data-Mined Knowledge into Black-Box SVM for Interpretability

The lack of interpretability often makes black-box models challenging to be applied in many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-mined knowledge into the black-box soft-margin SVM model to enhance accuracy and interpretability. The concept and incorporation mechanism of data-mined knowledge are successively developed, based on which a partially interpretable soft-margin SVM ( pTsm -SVM) optimization model is designed and then solved through reformulating the optimization problem as standard quadratic programming. An algorithm for mining linear positive (negative) class knowledge from general data sets is also proposed, which generates a linear two-dimensional discriminative rule with specificity (sensitivity) equal to 1 and the highest possible sensitivity (specificity) among all two-dimensional feature spaces. The knowledge-integrated pTsm -SVM works by achieving a good trade-off among the “large margin”, “high specificity”, and “high sensitivity”. Our experimental results on eight UCI datasets demonstrate the superiority of the proposed pTsm -SVM over the standard soft-margin SVM both in terms of accuracy and interpretability.

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model.

Soil salinity has been a major factor affecting agricultural production in the Keriya Oasis. It has a destructive effect on soil fertility and could destroy the soil structure of local land. Therefore, the timely monitoring of salt-affected areas is crucial to prevent land degradation and sustainable soil management. In this study, a typical salinized area in the Keriya Oasis was selected as a study area. Using Landsat 8 OLI optical data and ALOS PALSAR-2 SAR data, the optical remote sensing indexes NDVI, SAVI, NDSI, SI, were combined with the optimal radar polarized target decomposition feature component (VanZyl_vol_g) on the basis of feature space theory in order to construct an optical-radar two-dimensional feature space. The optical-radar salinity detection index (ORSDI) model was constructed to inverse the distribution of soil salinity in Keriya Oasis. The prediction ability of the ORSDI model was validated by a test on 40 measured salinity values. The test results show that the ORSDI model is highly correlated with soil surface salinity. The index ORSDI3 (R2 = 0.656) shows the highest correlation, and it is followed by indexes ORSDI1 (R2 = 0.642), ORSDI4 (R2 = 0.628), and ORSDI2 (R2 = 0.631). The results demonstrated the potential of the ORSDI model in the inversion of soil salinization in arid and semi-arid areas.

Data-driven technique estimates skin factor and average pressure during oil-flowing periods

We propose a data-driven workflow to address the problem of estimating skin factor, reservoir average pressure and productivity index during well production, in opposition to the conventional shut-in dependent approach. To that end, we demonstrate that bottom-hole pressure (BHP) and liquid flow-rate recorded during production form a two-dimensional feature space for regressions whose parameters are learned from past shut-ins. The proposed models learn how to map from BHP and flow-rate to skin factor, via non-linear mapping, and average pressure, via linear mapping, as they are trained using a few previous well shut-ins. Once trained, the regressors can be used during the production period to estimate skin factor and reservoir pressure. The theoretical groundings for the data-driven method are presented. We discuss the regression model limitations caused by the non-stationarity of reservoir and operational conditions and propose guidelines on model training so as to have the models adapt as the system evolves. The application of the method is demonstrated in field examples featuring non-stationary reservoir and operating conditions. The results suggest that regressors can be used to produce skin and reservoir pressure estimates during production, while a single shut-in can help update the function mapping inputs to prediction after changes in reservoir and operating conditions take place. Additionally, we show how the method can be used to detect anomalies in the wellbore. Practicing engineers involved in reservoir management and PDG monitoring for the purpose of formation damage control in oil producers in developed fields might find value in the content presented in this work.

Money supply impact on investment and GDP: statistical analysis

The question of how the changes in money supply influence investment and GDP have been studied intensively in recent history. However, not all aspects of this impact are sufficiently researched. In particular, the “new normality” (that has evolved recently) limits the use of well-known classical concepts and models in monetary policy, especially for emerging economies to which Ukraine belongs. Thus, the subject of this study was to assess the relationship between monetary aggregates, investment, and GDP by the world economic data analysis using mathematical statistics. As the information base for the study, the World Bank official statistics were taken (including broad money, gross capital formation, and GDP). More than 71% of all investigated countries showed a significant correlation between M3 and gross investment. The issue of how the strength of this relationship depends on the level of socio-economic development was investigated. Classification of countries was carried out using the “nearest neighbors” method in a two-dimensional feature space, namely, per capita income and correlation tightness. The analysis showed that 79% of all countries fall into the class with a proven high correlation. Moreover, their level of wealth and development was irrelevant. A cluster analysis of countries was fulfilled in the chosen feature space using the “mean shift” method. With the help of this method, all countries have been distributed into five clusters with different socio-economic conditions and an accuracy of 91%. Among them, there was a group of countries highly sensitive to change in monetization, up to extremely negative economic impacts. The study helped to conclude that, regardless of economic development, GDP benefits from an increase in the money supply. Although this factor is considered necessary, it is nevertheless not sufficient for economic growth, especially in the time of the fourth industrial revolution, when the government has to play a more active and complex role in accelerating national technological development.