An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes

This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes

Caractérisation de la dynamique de végétation des structures linéaires à l'aide du système de balayage laser aéroporté

Le réseau de structures linéaires est constitué de routes, de sentiers, de pipelines et de lignes sismiques aménagés dans une grande partie de la forêt boréale commerciale. Ces structures linéaires, fournissent un accès pour les opérations industrielles, récréatives, sylvicoles et de gestion des incendies, mais ont également des incidences économiques et environnementales qui impliquent à la fois les parties actives et non actives du réseau (e.g., les coûts d'entretien, érosion de la biodiversité, dégradation de l'habitat de la faune qui dépend de la forêt). Par conséquent, pour prévenir tous ces inconvénients, il est nécessaire de comprendre la dynamique des caractéristiques de végétation des structures linéaires et particulièrement des chemins forestiers. Les données de télédétection et la modélisation prédictive sont des outils utiles en fournissant des informations quantitatives précises et détaillées visant l’évaluation de l'état des structures linéaires (par exemple, la détérioration de la surface ou la dynamique des caractéristiques de végétation), et ce à plusieurs niveaux (paysage, région). Cependant, le potentiel des données de télédétection pour améliorer notre connaissance des caractéristiques de végétation à fine échelle sur les chemins forestiers n'a pas été entièrement exploré. Cette étude a examiné l'utilisation de données LiDAR aéroporté à haute résolution spatiale (1 m), de données climatiques et de terrain dans le but de fournir une meilleure compréhension de la dynamique de végétation des chemins forestiers: i) en développant un modèle prédictif pour l'estimation de la couverture végétale dérivée du modèle de hauteur de canopée (métrique de réponse), ii) en examinant les facteurs ayant un effet sur la couverture végétale en utilisant les mesures LiDAR (topographie: pente, TWI, ombrage et orientation), de l'imagerie optique Sentinel-2 (NDVI), des bases de données climatiques (ensoleillement et vitesse du vent) et de l'inventaire de terrain (largeur de l’ouverture du chemin et le temps depuis la construction ou entretien majeur). Nous avons évalué et comparé les performances des approches de régression par la méthode des moindres carrés et par apprentissage automatique couramment utilisées en modélisation écologique – régression linéaire multiple (mlr), méthode des splines de régression adaptative multivariée (mars), modèle additif généralisé (gam), méthode du plus proche voisin (knn), méthode d’arbres de régression boostés (gbm) et la méthode des forêts aléatoires (rf) –. Nous avons validé les résultats de nos approches en utilisant une métrique d'erreur – erreur quadratique moyenne (RMSE) – et une métrique de qualité d'ajustement – coefficient de détermination (R²) –. Les prédictions ont été testées par validation croisée et validées par rapport à un jeu de données indépendant. Nos résultats ont révélé que le modèle rf a montré les résultats les plus précis (validation croisée: R²=0.69, RMSE=18.69%, validation par un jeu de données indépendant: R²=0.62, RMSE=20.29%) et que les facteurs les plus informatifs étaient la largeur de l'ouverture du chemin qui avait l'effet négatif le plus marqué sur la couverture végétale, suggérant l'influence sous-jacente des perturbations antérieures, et le temps écoulé depuis la construction ou dernier entretien du chemin, qui avaient un effet positif sur l'augmentation de la couverture végétale. Les prédictions à long terme suggèrent qu'il faudra au moins 20 ans pour que les routes larges et étroites présentent respectivement ~50% et ~80% de couverture végétale. Cette étude a permis d'améliorer notre compréhension de la dynamique de végétation des chemins forestiers à fine échelle, tant sur le plan qualitatif que quantitatif. Les informations issues du modèle prédictif sont utiles pour la gestion à court et à long terme du réseau existant. De plus, le présent mémoire démontre que les modèles spatialement explicites utilisant des données LiDAR sont des outils fiables pour évaluer la dynamique de végétation des chemins forestiers et fournit des pistes pour de futures recherches et la possibilité d'intégrer cette approche quantitative à d'autres études de structures linéaires. Une meilleure connaissance des modèles de dynamique des caractéristiques de végétation sur les chemins forestiers, sur de grandes zones géographiques, peut aider à soutenir la gestion durable des forêts par la modulation de l'impact environnemental associé à l'empreinte linéaire

Balancing fracture and fatigue performance in asphalt pavements: A hybrid mechanistic and statistical modelling approach

The asphalt mix design and evaluation approaches are divided into two main categories as empirical and mechanistic-empirical (M-E) methods. The empirical methods are based on empirical observations of in-service pavement performance, and they do not take into account engineering properties or failure criteria. The M-E methods were introduced as a new generation for design and evaluation approaches that consider fundamental mixture properties such as material stiffness to determine the pavement\u27s structural response. However, the need for expensive and time-consuming performance-based laboratory tests and local calibration makes the M-E methods unsuitable for routing design. In addition, during the last few years, the asphalt paving industry has been consistently tried to improve pavement performance by introducing new types of materials in asphalt mixtures. Regardless of all the positive effects of innovative materials on mix performance, the M-E design and evaluation methods might not be able to fully capture the benefits that may be achieved through using these materials. It likely stems from the fact that the M-E methods only utilize mix stiffness to evaluate the performance with respect to different distresses. Therefore, a methodology needs to be developed within the framework of current design and evaluation approaches to consider the mixture performance and the impact of innovative materials on pavement performance.This dissertation research aimed to assess the mixture properties indices that can be implemented in performance-based design methods. The proposed endeavor will yield a more precise evaluation of the innovative materials impact on asphalt mixture performance through consideration of the viscoelastic nature of asphalt mixtures to determine mechanistic damage effect. Furthermore, several prediction models for a simplified viscoelastic continuum damage-based fatigue index (as crack initiation phase) and mixture fracture energy (as crack propagation phase) were developed to investigate asphalt mixture performance with respect to cracking. The models include the simultaneous impact of various mix variables that are available during the mix design process. Thus, they can be used as a predesign tool to investigate mixtures\u27 cracking properties without the need for any performance laboratory test data. Finally, a cracking balance design diagram (CBDD) was generated with a combination of prediction models for crack initiation and propagation. The CBDD helps toward better identification of cracking performance considering the simultaneous effects of both cracking phases in a single diagram

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Study of spin-scan imaging for outer planets missions

The constraints that are imposed on the Outer Planet Missions (OPM) imager design are of critical importance. Imager system modeling analyses define important parameters and systematic means for trade-offs applied to specific Jupiter orbiter missions. Possible image sequence plans for Jupiter missions are discussed in detail. Considered is a series of orbits that allow repeated near encounters with three of the Jovian satellites. The data handling involved in the image processing is discussed, and it is shown that only minimal processing is required for the majority of images for a Jupiter orbiter mission

Advances in Binders for Construction Materials

The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry

Simulation of NMR response from micro-CT images using artificial neural networks

The Nuclear Magnetic Resonance (NMR) log is amongst the functional techniques in petroleum investigation to segregating the reservoir and non-reservoir horizons precisely; furthermore, the NMR log provides an improved method to determine reservoir petrophysical parameters. Unfortunately, these data are usually sparse since acquiring NMR logs in producing cased wells is not possible and it is one of the most expensive tools in the logging industry thus its associated costs are the major limitation of its usage. Consequently, researchers have recently studied to virtually extract the NMR parameters via other routes. One such route, which we propose here is the possibility of estimating the T2 distribution curve and magnetization decay by establishing a relationship between micro-CT images and NMR parameters by means of artificial neural networks (ANN) and image analysis algorithms. Specifically, two ANN networks were designed, which numerically image features from micro-CT images as inputs, while the amplitude of the magnetization and relaxation time were output parameters. We assessed the procedure by taking the error rate and correlation coefficient into consideration and we conclude that the ANN model is capable of finding logical patterns between image features and NMR responses, and is thus able to reliably predict NMR response behavior. Furthermore, we quantitatively compared ANN and random walk (RW) NMR predictions, and we demonstrate that ANN readily outperforms RW in terms of accuracy

Trends and Prospects in Geotechnics

The Special Issue book presents some works considered innovative in the field of geotechnics and whose practical application may occur in the near future. This collection of twelve papers, in addition to their scientific merit, addresses some of the current and future challenges in geotechnics. The published papers cover a wide range of emerging topics with a specific focus on the research, design, construction, and performance of geotechnical works. These works are expected to inspire the development of geotechnics, contributing to the future construction of more resilient and sustainable geotechnical structures

FULL-WAVEFORM AND DISCRETE-RETURN LIDAR IN SALT MARSH ENVIRONMENTS: AN ASSESSMENT OF BIOPHYSICAL PARAMETERS, VERTICAL UNCERTATINTY, AND NONPARAMETRIC DEM CORRECTION

High-resolution and high-accuracy elevation data sets of coastal salt marsh environments are necessary to support restoration and other management initiatives, such as adaptation to sea level rise. Lidar (light detection and ranging) data may serve this need by enabling efficient acquisition of detailed elevation data from an airborne platform. However, previous research has revealed that lidar data tend to have lower vertical accuracy (i.e., greater uncertainty) in salt marshes than in other environments. The increase in vertical uncertainty in lidar data of salt marshes can be attributed primarily to low, dense-growing salt marsh vegetation. Unfortunately, this increased vertical uncertainty often renders lidar-derived digital elevation models (DEM) ineffective for analysis of topographic features controlling tidal inundation frequency and ecology. This study aims to address these challenges by providing a detailed assessment of the factors influencing lidar-derived elevation uncertainty in marshes. The information gained from this assessment is then used to: 1) test the ability to predict marsh vegetation biophysical parameters from lidar-derived metrics, and 2) develop a method for improving salt marsh DEM accuracy. Discrete-return and full-waveform lidar, along with RTK GNSS (Real-time Kinematic Global Navigation Satellite System) reference data, were acquired for four salt marsh systems characterized by four major taxa (Spartina alterniflora, Spartina patens, Distichlis spicata, and Salicornia spp.) on Cape Cod, Massachusetts. These data were used to: 1) develop an innovative combination of full-waveform lidar and field methods to assess the vertical distribution of aboveground biomass as well as its light blocking properties; 2) investigate lidar elevation bias and standard deviation using varying interpolation and filtering methods; 3) evaluate the effects of seasonality (temporal differences between peak growth and senescent conditions) using lidar data flown in summer and spring; 4) create new products, called Relative Uncertainty Surfaces (RUS), from lidar waveform-derived metrics and determine their utility; and 5) develop and test five nonparametric regression model algorithms (MARS - Multivariate Adaptive Regression, CART - Classification and Regression Trees, TreeNet, Random Forests, and GPSM - Generalized Path Seeker) with 13 predictor variables derived from both discrete and full waveform lidar sources in order to develop a method of improving lidar DEM quality. Results of this study indicate strong correlations for Spartina alterniflora (r \u3e 0.9) between vertical biomass (VB), the distribution of vegetation biomass by height, and vertical obscuration (VO), the measure of the vertical distribution of the ratio of vegetation to airspace. It was determined that simple, feature-based lidar waveform metrics, such as waveform width, can provide new information to estimate salt marsh vegetation biophysical parameters such as vegetation height. The results also clearly illustrate the importance of seasonality, species, and lidar interpolation and filtering methods on elevation uncertainty in salt marshes. Relative uncertainty surfaces generated from lidar waveform features were determined useful in qualitative/visual assessment of lidar elevation uncertainty and correlate well with vegetation height and presence of Spartina alterniflora. Finally, DEMs generated using full-waveform predictor models produced corrections (compared to ground based RTK GNSS elevations) with R2 values of up to 0.98 and slopes within 4% of a perfect 1:1 correlation. The findings from this research have strong potential to advance tidal marsh mapping, research and management initiatives