Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by Metaheuristic Algorithms

Natural Resources Research - Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of...     

A Novel Combination of Gradient Boosted Tree and Optimized ANN Models for Forecasting Ground Vibration Due to Quarry Blasting

Natural Resources Research - An ensemble technique namely gradient boosted tree (GBTs) and several optimized neural network models were hybridized to predict peak particle velocity (PPV) caused by...     

Modeling of the Shale Volume in the Hendijan Oil Field Using Seismic Attributes and Artificial Neural Networks

Petrophysical properties have played an important and definitive role in the study of oil and gas reservoirs, necessitating that diverse kinds of information are used to infer these properties. In this study, the seismic data related to the Hendijan oil field were utilised, along with the available logs of 7 wells of this field, in order to use the extracted relationships between seismic attributes and the values of the shale volume in the wells to estimate the shale volume in wells intervals. After the overall survey of data, a seismic line was selected and seismic inversion methods (model-based, band limited and sparse spike inversion) were applied to it. Amongst all of these techniques, the model-based method presented the better results. By using seismic attributes and artificial neural networks, the shale volume was then estimated using three types of neural networks, namely the probabilistic neural network (PNN), multi-layer feed-forward network (MLFN) and radial basic function network (RBFN).     

Large-scale replenishment of groundwater depletion by long-lived extreme rainstorms in arid regions: case study of the 2019 floods in Iran

The Dena rainstorm in Iran in March and April 2019 caused about US$ 8.3?×?10⁹ damage in the country; however, it resulted in the replenishment of half of the dam reservoirs and 35% of ponds and lakes. Also, it increased the volume of groundwater stored in aquifers by 3.6?×?10⁹ m³. In arid and semiarid regions such as most parts of Iran, which usually face water scarcity, getting water from rainstorms is essential for replenishing water resources. This research aims to quantify the direct and indirect effects of the Dena rainstorm on the replenishment of Iran’s groundwater storage using the groundwater balance method and water-table fluctuation method. Studies showed that the main mechanisms for replenishment of groundwater storage due to the rainstorm included increases in precipitation recharge, surface runoff recharge, and artificial recharge, and reductions in irrigation withdrawal and evapotranspiration, while the contribution of each factor is estimated to be about 23, 28, 2, 15, and 32%, respectively.

     

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO,BAT and COA algorithms

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.     

A review of cyclone track shifts over the Great Lakes of North America: implications for storm surges

Natural Hazards - Cyclone tracks over the Great Lakes of North America shift, both East–West as well as North–South. The reasons for the shifts are various small-scale as well as...     

Stress Patterns in Northern Iraq and Surrounding Regions from Formal Stress Inversion of Earthquake Focal Mechanism Solutions

The collision zone between the Arabian and Eurasian plates is one of the most seismically active regions. Northern Iraq represents the northeastern part of the Arabian plate that has a suture zone with the Turkish and Iranian plates called the Bitlis–Zagros suture zone. The orientations of the principal stress axes can be estimated by the formal stress inversion of focal mechanism solutions. The waveform moment tensor inversion method was used to derive a focal mechanism solution of 65 earthquakes with magnitudes range from 3.5 to 5.66 in the study area. From focal mechanism solutions, the direction of slip and the orientations of the moment stress axes (P, N, and T) on the causative fault surface during an earthquake were determined. The dataset of the moment stress axes have been used to infer the regional principal stress axes (σ ₁, σ ₂, and σ ₃) by the formal stress inversion method. Two inversion methods, which are the new right dihedron and the rotational optimization methods, were used. The results show that six stress regime categories exist in the study area. However, the most common tectonic regimes are the strike-slip faulting (43.94 %), unspecified oblique faulting (27.27 %), and thrust faulting (13.64 %) regimes. In most cases, the strike-slip movement on the fault surfaces consists of left-lateral (sinistral) movement. The normal faulting is located in one small area and is due to a local tensional stress regime that develops in areas of strike-slip displacements as pull-apart basins. The directions of the horizontal stress axes show that the compressional stress regime at the Bitlis–Zagros suture zone has two directions. One is perpendicular to the suture zone near the Iraq–Iran border and the second is parallel in places as well as perpendicular in others to the suture zone near the Iraq–Turkey border. In addition, the principal stress axes in the Sinjar area near the Iraq–Syria border have a E–W direction. These results are compatible with the tectonic setting of the Arabian–Eurasian continental collision zone and the anticlockwise rotation of the Arabian plate that is evidently responsible for the strike-slip displacements on fault surfaces.     

Seismotectonics of the Bitlis–Zagros Fold and Thrust Belt in Northern Iraq and Surrounding Regions from Moment Tensor Analysis

Northern Iraq represents part of the convergent plate boundary between the Arabian and Eurasian plates. The collision zone between these two plates is manifested by the Bitlis–Zagros Fold and Thrust Belt. This belt is one of the most seismically active regions among the present active belts. This study intends to improve our knowledge on the seismotectonic activities in northern Iraq and the surrounding areas. To reach this goal, we used the waveform moment tensor inversion method to determine the focal depths, moment magnitudes, fault plane solutions, and directions of the principal stress axes of 25 events with magnitudes ≥3.5. The seismic data of these events were collected from 54 broadband stations which belong to the Kandilli Observatory and Earthquake Research Institute, the Incorporated Research Institutions for Seismology, the Observatories and Research Facilities for European Seismology, and the Iraqi Seismological Network. Computer Programs in Seismology, version 3.30 (Herrmann and Ammon 2004), was used for analysis. The results show that the focal depth of these events ranged from 15 to 25 km in general. The fault plane solutions show that the strike-slip mechanism is the most dominant mechanism in the study area, usually with a reverse component. The stress regime shows three major directions; north–south, northeast-southwest, and east–west. These directions are comparable with the tectonic regime in the region.     

Collapse capacity of soil‐structure systems under pulse‐like earthquakes

In this paper, a comprehensive study is carried out to examine the possibility of dynamic instability produced in soil‐structure systems using an ensemble of 50 pulse‐like records. A number of structural models with various vibration periods varying from 0.1 to 2 s are used in this study. The superstructure is simulated as a non‐linear SDOF oscillator with a two‐segment backbone curve having negative post‐yield stiffness. The soil is idealized based on the cone model concept widely used for practical purposes. The results of this investigation demonstrate that as the pulse period increases, the collapse relative lateral strength ratio decreases and probability of dynamic instability enhances. Moreover, soil flexibility makes the system dynamically more unstable, and as the non‐dimensional frequency increases, the collapse relative lateral strength ratio highly reduces. Additionally, the aspect ratio has insignificant effects on the collapse relative lateral strength ratio. Furthermore, comparison of the collapse relative lateral strength ratios resulting from pulse‐like motions with those obtained from studies under non‐pulse‐like motions (Miranda and Akkar; FEMA 440) for fixed‐base conditions shows that high‐velocity pulses exacerbate the dynamic instability problem and decrease the collapse relative lateral strength ratio. Copyright © 2014 John Wiley & Sons, Ltd.     

Inversion of surface gravity data for 3-D density modeling of geologic structures using total variation regularization

We develop an inversion procedure using the total variation (TV) regularization method as a stabilizing function to invert surface gravity data to retrieve 3-D density models of geologic structures with sharp boundaries. The developed inversion procedure combines several effective algorithms to solve the TV regularized problem. First, a matrix form of the gradient vector is designed using the Kronecker product to numerically approximate the 3-D TV function. The piecewise polynomial truncated singular value decomposition (PP-TSVD) algorithm is then used to solve the TV regularized inverse problem. To obtain a density model with depth resolution, we use a sensitivity-based depth weighting function. Finally, we apply the Genetic Algorithm (GA) to select the best combination of the PP-TSVD algorithm and the depth weighting function parameters. 3-D simulations conducted with synthetic data show that this approach produces sub-surface images in which the structures are well separated in terms of sharp boundaries, without the need of a priori detailed density model. The method applied to a real dataset from a micro-gravimetry survey of Gotvand Dam, southwestern Iran, clearly delineates subsurface cavities starting from a depth of 40 m within the area of the dam reservoir.