Enhancement of Seismic Data Processing and Interpretation of Fracture Zones on the Upper Part of Granitic Basement in Cuu Long Basin,Vietnam

The fractured granite basement is the primary oil and gas reservoir in the Cuu Long Basin, Vietnam. Due to the complexity of this nonlayered unconventional target, combined with complicated fault and fracture systems, the seismic data quality near and within the basement section is very low. For this reason, it is important to apply improved seismic data processing workflows, filtering and migration techniques, as wells as attribute processing methods to enhance the imaging quality.Our studies show that applying different types of filters, including the f-k, Radon transform and Tau-P, improves signal to noise ratio, removing multiples, revealing basement’s top and its related fractured and fault zones. In addition, the application of multi-arrival-solution migration algorithms, such as Kirchhoff Migration and Controlled Beam Migration, provides improved imaging for identifying basement top and faults and fractures within the basement. Furthermore, the application of seismic attributes such as curvature, apparent dip, or energy gradient, is important in locating faults and fractures, whereas mapping of intensity and orientation of such structures assists the delineation of “sweet spots” and assists the planning of exploration.     

FEM × DEM modelling of cohesive granular materials: Numerical homogenisation and multi-scale simulations

The article presents a multi-scale modelling approach of cohesive granular materials, its numerical implementation and its results. At microscopic level, Discrete Element Method (DEM) is used to model dense grains packing. At the macroscopic level, the numerical solution is obtained by a Finite Element Method (FEM). In order to bridge the micro- and macro-scales, the concept of Representative Elementary Volume (REV) is applied, in which the average REV stress and the consistent tangent operators are obtained in each macroscopic integration point as the results of DEM’s simulation. In this way, the numerical constitutive law is determined through the detailed modelling of the microstructure, taking into account the nature of granular materials. We first elaborate the principle of the computation homogenisation (FEM × DEM), then demonstrate the features of our multiscale computation in terms of a biaxial compression test. Macroscopic strain location is observed and discussed.     

Lang Trang Cave (Northern Vietnam): Pleistocene Mammalian Locality with Gigantopithecus Finds

Doklady Earth Sciences - Speleological, geological and paleontological characteristics of the Lang Trang cave in northern Vietnam are presented. Primates Gigantopithecus blacki von Koenigswald,...     

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA) based on Deep Belief Network(DBN) with Back Propagation(BP) algorithm optimized by the Genetic Algorithm(GA).For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE) technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy, root mean square error(RMSE), and area under the receiver operatic characteristic curve(AUC) were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC = 0.989) and prediction accuracy(AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR(0.885), LMT(0.934), BLR(0.936), ADT(0.976), NBT(0.974), REPTree(0.811), ANFIS-BAT(0.944), ANFIS-CA(0.921), ANFIS-IWO(0.939), ANFIS-ICA(0.947), and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.     

Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach

Natural Hazards - Advances in the availability of multi-sensor, remote sensing-derived datasets, and machine learning algorithms can now provide an unprecedented possibility to predict flood events...