Cretaceous Strike-Slip Dislocations in the Dalat Zone (Southeastern Vietnam)

Studies of ductile dislocations of the Jurassic terrigenous deposits and faulting kinematics of the Cretaceous injective structures in the Dalat zone show that the volcano-plutonic belt of southeastern Vietnam was formed under NE-striking sinistral dislocations affected by NNW transpression. This deformation took place in similar conditions as for the Cretaceous structure of the eastern margin of Asia. Due to the Indo-Eurasian collision, since the Cenozoic the Indo-Sinian Block has been displaced toward the southeast by a distance of more than 500 km from its primary position, causing transformation of the previously formed Cretaceous structures. Thus, the Cenozoic tectonogenesis in southeastern Vietnam should be studied with regard to the previous Cretaceous tectonic deformation.     

Computational validation of the Generalized Sutherland Equation for bubble–particle encounter efficiency in flotation

Bubble–particle encounter during flotation is governed by liquid flow relative to the rising bubble, which is a function of the adsorbed frothers, collectors, and other surfactants and surface contaminants. Due to surface contamination, the bubble surface in flotation has been considered as immobile (rigid). However, surface contamination can be swept to the backside of the rising bubble due to the relative liquid flow, leaving the front surface of the rising bubble mobile with a non-zero tangential component of the liquid velocity. The bubble with a mobile surface was considered by Sutherland who applied the potential flow condition and analyzed the bubble–particle encounter using a simplified particle motion equation without inertia. The Sutherland model was found to over-predict the encounter efficiency and has been improved by incorporating inertial forces which are amplified at the mobile surface with a non-zero tangential velocity component of the liquid phase. An analytical solution was obtained for the encounter efficiency using approximate equations and is called the Generalized Sutherland Equation (GSE). In this paper, the bubble–particle encounter interaction with the potential flow condition has been analyzed by solving the full motion equation for the particle employing a numerical computational approach. The GSE model was compared with the exact numerical results for the encounter efficiency. The comparison only shows good agreement between the GSE prediction and the numerical data for ultrafine particles (< 10 μm in diameter), the inertial forces of which are vanishingly small. For non-ultrafine particles, a significant deviation of the GSE model from the numerical data has been observed. Details of the numerical methodology and solutions for the (collision) angle of tangency and encounter efficiency are described.     

A study of the human-fatality rate in near-fault regions using the Victim Attribute Database

After the 1999 Chi-Chi earthquake, the Taiwanese government immediately issued new guidelines prohibiting the construction of structures for human occupancy within the Chelungpu fault zone. However, these guidelines were not based upon an in-depth hazard analysis of the near-fault regions. The positions of more than 80% of the 2,492 victims of the Chi-Chi earthquake were found by our research team. A Victim Attribute Database has been compiled that includes the GPS coordinates of the positioned victims as well as other attribute data associated with the victims. The human-fatality rates in the near-fault regions have been analyzed with regard to distances from the Chelungpu fault, the hanging-wall and footwall areas, as well as building type. The severity at the human-fatality rates in the near-fault regions is inversely proportional to distances from the causative fault, i.e., the closer the distance, the higher the human-fatality rate observed. The human-fatality rate for victims who lived in closer proximity to the hanging-wall areas is also significantly higher than those who lived in closer proximity to the footwall areas, especially in areas on either side of the fault and within 1,000 m of the fault surface trace. In terms of different building types, factors that include the capacity of the buildings to resistant strong shaking and the level of strong ground-motion greatly affected the human-fatality rates in the hanging-wall and footwall areas. Therefore simply prohibiting the construction of buildings within the active fault zone would be an insufficient method of reducing the number of potential victims; a nationwide effort should be undertaken to upgrade the capacity of buildings to resist strong shaking.     

A study of the durability of some shales,mudrocks and siltstones from Brazil

In spite of the considerable area covered by argillaceous sedimentary rocks (comprising shales, mudrocks and siltstones) in Brazil and the number of engineering works associated with these materials, little is known about their geotechnical, physical, chemical, mineralogical and textural characteristics and the effects of weathering processes on them. This work presents results obtained in a research project aiming primarily at identifying degradation processes responsible for the weathering of some of these rocks in Brazil. The rocks under investigation come from two different parts of the country, each one related to a specific engineering problem and to different geoenvironments. The project involved a comprehensive laboratory testing program for basic characterization (chemical and mineralogical composition, index properties, etc.) and evaluation of durability characteristics of these rocks. The present paper describes the testing program carried out and discusses the results obtained.     

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes,multilayer perceptron neural networks,and functional trees methods

The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.     

Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District,Yen Bai Province (Viet Nam)

Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management.     

Characteristic Features of REE and Pb–Zn–Ag Mineralizations in the Na Son Deposit,Northeastern Vietnam

The Na Son deposit is a small‐scale Pb–ZnPb–Zn–Ag deposit in northeast Vietnam and consists of biotite–chlorite schist, reddish altered rocks, quartz veins and syenite. The biotite–chlorite schist is intruded by syenite. Reddish altered rocks occur as an alteration halo between the biotite–allanite‐bearing quartz veins and the biotite–chlorite schist. Allanite occurs in the biotite–allanite‐bearing quartz veins and in the proximal reddish altered rocks. Rare earth element (REE) fluorocarbonate minerals occur along fractures or at rim of allanite crystals. The later horizontal aggregates of sulfide veins and veinlets cut the earlier reddish altered rocks. The earlier Pb–Zn veins consist of a large amount of galena and lesser amounts of sphalerite, pyrite and molybdenite. The later Cu veins cutting the Pb–Zn veins include chalcopyrite and lesser amounts of tetrahedrite and pyrite. The occurrences of two‐phase H₂O–CO₂ fluid inclusions in quartz from biotite–allanite‐bearing quartz veins and REE‐bearing fluorocarbonate minerals in allanite suggest the presence of CO₂ and F in the hydrothermal fluid. The oxygen isotopic ratios of the reddish altered rocks, biotite–chlorite schist, and syenite range from +13.9 to +14.9 ‰, +11.5 to +13.3 ‰, and +10.1 to +11.6 ‰, respectively. Assuming an isotopic equilibrium between quartz (+14.6 to +15.8 ‰) and biotite (+8.6 ‰) in the biotite–allanite‐bearing quartz vein, formation temperature was estimated to be 400°C. At 400°C, δ¹⁸O values of the hydrothermal fluid in equilibrium with quartz and biotite range from +10.5 to +11.7 ‰. These δ¹⁸O values are consistent with fluid that is derived from metamorphism. Assuming an isotopic equilibrium between galena (+1.5 to +1.7 ‰) and chalcopyrite (+3.4 ‰), the formation temperature was estimated to be approximately 300°C. The formation temperature of the Na Son deposit decreased with the progress of mineralization. Based on the geological data, occurrence of REE‐bearing minerals and oxygen isotopic ratios, the REE mineralization is thought to result from interaction between biotite–chlorite schist and REE‐, CO₂‐ and F‐bearing metamorphic fluid at 400°C under a rock‐dominant condition.     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.     

Mechanism of two rapid and long-runout landslides in the 16 April 2016 Kumamoto earthquake using a ring-shear apparatus and computer simulation (LS-RAPID)

Around hundred landslides were triggered by the Kumamoto earthquakes in April 2016, causing fatalities and serious damage to properties in Minamiaso village, Kumamoto Prefecture, Japan. The landslides included many rapid and long-runout landslides which were responsible for much of the damage. To understand the mechanism of these earthquake-triggered landslides, we carried out field investigations with an unmanned aerial vehicle to obtain DSM and took samples from two major landslides (Takanodai landslide and Aso-ohashi landslide) to measure parameters of the initiation and the motion of landslides. A series of ring-shear tests and computer simulations were conducted using a measured Kumamoto earthquake acceleration record from KNet station KMM005, 10 km west of Aso-ohashi landslide. The research results supported our assumed mechanism of sliding-surface liquefaction for the rapid and long-runout motion of these landslides.     

A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area

The objective of this study is to explore and compare the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques for the spatial prediction of landslides. The Luc Yen district in Yen Bai province (Vietnam) has been selected as a case study. LSSVM and MADT are effective machine learning techniques of classification applied in other fields but not in the field of landslide hazard assessment. For this, Landslide inventory map was first constructed with 95 landslide locations identified from aerial photos and verified from field investigations. These landslide locations were then divided randomly into two parts for training (70 % locations) and validation (30 % locations) processes. Secondly, landslide affecting factors such as slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to faults, distance to rivers, and rainfall were selected and applied for landslide susceptibility assessment. Subsequently, the LSSVM and MADT models were built to assess the landslide susceptibility in the study area using training dataset. Finally, receiver operating characteristic curve and statistical index-based evaluations techniques were employed to validate the predictive capability of these models. As a result, both the LSSVM and MADT models have high performance for spatial prediction of landslides in the study area. Out of these, the MADT model (AUC = 0.853) outperforms the LSSVM model (AUC = 0.803). From the landslide study of Luc Yen district in Yen Bai province (Vietnam), it can be conclude that the LSSVM and MADT models can be applied in other areas of world also for and spatial prediction. Landslide susceptibility maps obtained from this study may be helpful in planning, decision making for natural hazard management of the areas susceptible to landslide hazards.