Design and implementation of a model predictive observer for AHRS

A GPS-aided Inertial Navigation System (GAINS) is used to determine the orientation? position and velocity of ground and aerial vehicles. The data measured by Inertial Navigation System (INS) and GPS are commonly integrated through an Extended Kalman Filter (EKF). Since the EKF requires linearized models and complete knowledge of predefined stochastic noises? the estimation performance of this filter is attenuated by unmodeled nonlinearity and bias uncertainties of MEMS inertial sensors. The Attitude Heading Reference System (AHRS) is applied based on the quaternion and Euler angles methods. A moving horizon-based estimator such as Model Predictive Observer (MPO) enables us to approximate and estimate linear systems affected by unknown uncertainties. The main objective of this research is to present a new MPO method based on the duality principle between controller and observer of dynamic systems and its implementation in AHRS mode of a low-cost INS aided by a GPS. Asymptotic stability of the proposed MPO is proven by applying Lyapunov’s direct method. The field test of a GAINS is performed by a ground vehicle to assess the long-time performance of the MPO method compared with the EKF. Both the EKF and MPO estimators are applied in AHRS mode of the MEMS GAINS for the purpose of real-time performance comparison. Furthermore? we use flight test data of the GAINS for evaluation of the estimation filters. The proposed MPO based on both the Euler angles and quaternion methods yields better estimation performances compared to the classic EKF.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

Analysis of Pore Pressure and Stress Distribution around a Wellbore Drilled in Chemically Active Elastoplastic Formations

Drilling in low-permeable reactive shale formations with water-based drilling mud presents significant challenges, particularly in high-pressure and high-temperature environments. In previous studies, several models were proposed to describe the thermodynamic behaviour of shale. Most shale formations under high pressure are expected to undergo plastic deformation. An innovative algorithm including work hardening is proposed in the framework of thermo-chemo-poroelasticity to investigate the effect of plasticity on stresses around the wellbore. For this purpose a finite-element model of coupled thermo-chemo-poro-elastoplasticity is developed. The governing equations are based on the concept of thermodynamics of irreversible processes in discontinuous systems. In order to solve the plastic problem, a single-step backward Euler algorithm containing a yield surface-correction scheme is used to integrate the plastic stress–strain relation. An initial stress method is employed to solve the non-linearity of the plastic equation. In addition, super convergent patch recovery is used to accurately evaluate the time-dependent stress tensor from nodal displacement. The results of this study reveal that thermal and chemical osmosis can significantly affect the fluid flow in low-permeable shale formations. When the salinity of drilling mud is higher than that of pore fluid, fluid is pulled out of the formation by chemical osmotic back flow. Similar results are observed when the temperature of drilling mud is lower than that of the formation fluid. It is found that linear elastic approaches to wellbore stability analysis appear to overestimate the tangential stress around the wellbore and produce more conservative stresses compared to the results of field observation. Therefore, the drilling mud properties obtained from the elastoplastic wellbore stability in shales provide a safer mud weight window and reduce drilling cost.     

The impact of the geologic setting on the Quaternary aquifer,El-Tur area,Southwest Sinai,Egypt

The groundwater extracted from the unconfined Quaternary aquifer is the main source of water supply in El-Tur area. The area is bounded from the east by the elevated basement complex of Southern Sinai and from the west by El-Qabaliyat Ridge. The wadis dissecting these highlands form effective watersheds of the Quaternary aquifer. These wadis form areas of focused recharge. Recharge also occurs directly via the Quaternary sediments covering El-Qaa Plain. Subsurface lateral groundwater flow from the fractured basement contributes significant recharge to the aquifer as well. The aquifer sediment facies affect the type and quality of groundwater. In the eastern part where the aquifer is composed mainly of gravel and coarse sand with fragments of weathered basement, the Na-Cl-SO₄ water dominates. In the west where the facies change is rapid and complex, many water types arise. The base exchange index (BEX) is positive in this part reflecting the role of clay minerals in changing the water types via cation exchange. In the east where clays are insignificant in the aquifer, the BEX is negative. In the western part next to El-Qabaliyat Ridge, the wells discharging from the calcareous sand zone have low groundwater salinities compared to the wells discharging from the alluvium. In general, the groundwater salinity increases in the direction of groundwater flow from the northeast to the southwest which reflects the dissolution of aquifer sediments. The concentration relationships between the major ions on one hand and chloride on the other reflect the dissolution of calcium carbonates, precipitation of K- and Mg-bearing minerals, and cation exchange of Ca for Na on clay minerals. The hydrochemical models support these reactions. In addition, they show that the effect of evaporation on the recharge water in the western catchment is about four times its effect on the eastern recharge water which reflects the rapid recharge through the wadis draining the fractured basement. Moreover, the contribution from the eastern catchment in sample No. 23 is more than four-folds the contribution from the western recharge area. The stable isotopes (²H and ¹⁸O) show that the Quaternary aquifer is recharging from recent rainfall. However, upward leakage of Paleogene groundwater (depleted in ¹⁸O) also occurs. The groundwater level map shows strong overpumping impact especially in the areas close to El-Tur city.     

Stochastic modeling of Iranian earthquakes and estimation of ground motion for future earthquakes in Greater Tehran

Strong-motion data from eight significant well-documented earthquakes in Iran have been simulated using a stochastic modeling technique for finite faults proposed by Beresnev and Atkinson [Bull Seismol Soc Am 87 (1997) 67–84; Seism Res Lett 69 (1998) 27–32]. The database consists of 61 three-component records from eight earthquakes of magnitude ranging from M 6.3 to M 7.4, recorded at hypocentral distances up to 200 km. The model predictions are in good agreement with available Iranian strong-motion data as evidenced by near-zero average of differences between logarithms of the observed and predicted values for all frequencies. The strength factor, sfact, a quantity that controls the high-frequency radiation from the source is determined, on an event-by-event basis, by fitting simulated to observed response spectra.     

Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

Geostatistical seismic inversion methods are routinely used in reservoir characterisation studies because of their potential to infer the spatial distribution of the petro‐elastic properties of interest (e.g., density, elastic, and acoustic impedance) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and a global spatial continuity model as estimated from the available well‐log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous, and, in many cases, non‐stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, the application of a single distribution function and a spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a three‐dimensional geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi‐distribution functions and spatial continuity models under non‐stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non‐stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques.     

Assessment of a spatial multi-criteria evaluation to site selection underground dams in the Alborz Province,Iran

Most part of Iran is arid and semi-arid; thus in most parts of the region, groundwater is the only source of water. This research presents a method based on a spatial multi-criterion evaluation (SMCE) for designing possible sites of underground dams and ranks them according to their suitability. The method was tested for siting underground dams in the Alborz Province, Iran. At first, screening algorithm was applied using exclusionary criteria, and thirty-one potential areas were recognized in the study area. In the next step, a suitable gorge or valley was recognized using the combination of basic maps and extensive field surveys (long axis of tank level) in each potential area. Subsequently, the analytical hierarchy process was used as a powerful tool for decision-making in the SMCE in order to evaluate different criteria for underground dam sites. SMCE techniques were then applied to combine the criteria, and obtain a suitability map in the study area. These sites were then compared and ranked according to their main criteria such as water, storage, axis and socio-economics. All these criteria were assessed through geographical information system modelling. This method shows passable results and could be used for site selection of underground dams in other regions of Iran.     

Mortality risk from heat stress expected to hit poorest nations the hardest

Climatic Change - Anthropogenic climate warming has increased the likelihood of extreme hot summers. To facilitate mitigation and adaptation planning, it is essential to quantify and synthesize...