Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques

Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R²), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R²?=?0.930) in this study, its error (RMSE?=?7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R², and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment.

     

Temporal occurrence patterns and habitat use of fish in the Mangyeong estuary on the western coast of Korea in 2003, before dike enclosure

We documented the temporal occurrence patterns and habitat use of fish in the Mangyeong River estuary on the western coast of Korea from February to December 2003 based on monthly samples collected with a stow net. A total of 46 fish species were collected during the study period. Chelon haematocheilus, Konosirus punctatus, Neosalanx jordani, Sardinella zunasi, Synechogobius hasta, and Thryssa kammalensis were the predominate species, accounting for 84.4% of the collected fish. We classified the fish as brackish water (BF), coastal migratory (CMF), diadromous (DF), or freshwater (FF) fish. BF such as C. haematocheilus, S. hasta, and Coilia nasus were the most abundant and were collected almost year-round. As water temperatures increased in spring, adult CMF, such as K. punctatus, S. zunasi, T. kammalensis, T. adelae, Johnius grypotus, Miichthys miiuy, and Larimichthys polyactis, entered the estuary. In summer, large numbers of their juvenile offspring were caught, indicating a peak in fish abundance. The juveniles grew in the estuary before moving out to deeper waters for overwintering. Diadromous juveniles, such as those of Anguilla japonica (commonly known as glass eel), were collected in the spring during their upstream migration. FF, such as Carassius cuvieri and Acheilognathus rhombeus, were collected during the rainy season. In terms of the number of species, the number of individuals, and the biomass, the ratio of BF to all other fish type classifications in the Mangyeong Estuary (without a dam) was higher than that in the Geum River estuary (with a dam). The fish assemblage in the Mangyeong Estuary still maintained estuarine characteristics before dike enclosure, providing habitats for BF, nursery grounds for CMF, and migratory pathways for DF.     

Forecast of wave run-up on coastal structure using offshore wave forecast data

In this paper, first we introduce the wave run-up scale which describes the degree of wave run-up based on observed sea conditions near and on a coastal structure. Then, we introduce a simple method which can be used for daily forecast of wave run-up on a coastal structure. The method derives a multiple linear regression equation between wave run-up scale and offshore wind and wave parameters using long-term photographical observation of wave run-up and offshore wave forecasting model results. The derived regression equation then can be used for forecasting the run-up scale using the offshore wave forecasting model results. To test the implementation of the method, wave run-up scales were observed at four breakwaters in the East Coast of Korea for 9 consecutive months in 2008. The data for the first 6 months were used to derive multiple linear regression equations, which were then validated using the run-up scale data for the remaining 3 months and the corresponding offshore wave forecasting model results. A comparison with an engineering formula for wave run-up is also made. It is found that this method can be used for daily forecast and warning of wave run-up on a coastal structure with reasonable accuracy.     

Determination of the tectonic evolution of the Edremit Gulf based on seismic reflection studies

The Edremit Gulf, which developed during the Neogene-Quaternary, is a seismically active graben in NW Anatolia (Turkey) surrounded by the Sakarya continent. The sedimentary deposits in the gulf overlie the bedrock unconformably and can be separated into two parts as upper and lower deposits based on similarity of their seismic characteristics, and because the contact between them is clear. The lower deposits are characterized in the seismic profiles by the absence of well defined, continuous reflectors and are strongly disturbed by faults. A tectonic map and structural model of the Edremit Gulf was derived from interpreting 21 deep seismic profiles trending NE–SW and NW–SE within the gulf. Two fault systems were distinguished on the basis of this compilation. The NNW–SSE trending parallel faults are low-angle normal faults formed after compression. They controlled and deformed the lower basin deposits. A syncline and anticline with a broad fold-curvature length resulted in folds that developed parallel to basin boundaries in the lower basin deposits. The ENE–WSW trending high-angle faults have controlled and deformed the northern basin of the Edremit Gulf. The folds developed within the northern lower deposits originated from the listric geometry of the faults. These faults are normal faults associated with regional N–S extension in western Anatolia. The Edremit Gulf began to open under the control of low-angle NNW–SSE trending faults that developed after the compression of western Anatolia in an E–W direction in the early Neogene. Subsequently, regional N–S extensional stress and high-angle normal faults cut the previous structures, opened the northern basin, and controlled and deformed the lower basin deposits in the gulf. As a result, the Edremit Gulf has not been controlled by any strike-slip faults or the Northern Anatolian Fault. The basin developed in the two different tectonic regimes of western Anatolia as an Aegean type cross-graben from the Neogene to Holocene.     

Formation of the Upper Pleistocene terraces of Lake Van (Turkey)

Sedimentological and geomorphological studies of terraces around Lake Van (1647 m) provided a preliminary framework for lake‐level variations. The elevations of terraces and past lake level were measured with a differential global positioning system. A chronology is developed using ²³⁴U/²³⁰Th dating of travertines, ³⁹Ar/⁴⁰Ar dating of pyroclastites and ¹⁴C dating of organic matter. Facies and stratigraphic correlations identify four transgressions (C1′, C1″, C2′ and C2″), each followed by a regression which ended with low lake levels that caused river incision and terrace formation. Evidence of the oldest transgression (C1′) is found in the uppermost reaches of valleys up to 1755 m, an altitude higher than the present lake threshold (1736 m). This C1′ transgression may be related to pyroclastic flows which dammed an outlet located in the western part of the lake basin and which is dated to before 105 ka. After 100 ka, a second transgression (C1″) reached 1730/1735 m, possibly related to a younger ignimbrite flow, in association with high water inflow (warm and/or wetter conditions). The two younger transgressions reached 1700–1705 m. The first one (C2′) is dated to 26–24.5 cal. ka BP and the second one (C2″) to 21–20 cal. ka BP. Available data suggest that the long‐term lake‐level changes responded mainly to climate oscillations. Additional events such as river captures caused by volcanic falls filling valleys, tectonism, erosion and karstic diversion may have impacted these long‐term lake‐level changes. Copyright © 2010 John Wiley & Sons, Ltd.     

Source mechanism and stress analysis of 23 October 2011 Van Earthquake (Mw = 7.1) and aftershocks

On 23 October 2011 at 1341 local time, a strong earthquake (Mw?=?7.1) occurred east of Lake Van (KOERI; Kandilli Observatory and Earthquake Research Institute). The focal parameters of the main shock and 29 aftershocks with M?≥?4.0 were obtained from regional broadband seismic data from KOERI network by using Regional Moment Tensor Inversion Code (Dreger 2002). It is confirmed that the main shock had thrust faulting mechanism. The stress tensor analysis was completed using the focal mechanism solutions and the software developed by (Gephart Comp Geosci 16: 953-989, 1990). The maximum principal stress (P compressional) of the main shock is aligned in a N-S (NNW/SSE) direction and the tensional axis (T dilatation) is aligned in an E-W (ENE-WSW) direction. The b value is calculated as 0.96 using the maximum likelihood method (Utsu 1999).