Ionosphere probing with simultaneous GNSS radio occultations

GPS Solutions - Radio occultation (RO) is a powerful technique for providing vertical profiles of refractivity, temperature, pressure, and water vapor of the neutral atmosphere and electron density...     

Seismic ground-roll noise attenuation using deep learning

We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Modeling undertow due to random waves

A numerical model of undertow due to random waves is developed. The model includes three sub-models: (i) a model for multi-directional and multi-frequency random wave transformation, (ii) a surface roller evolution model, and (iii) a model for calculating the vertical distribution and the mean value of the undertow velocity. The calculation of wave trough level is performed based on a theory for the wave asymmetry. The model was successfully validated against small- and large-scale laboratory experiments. Thus, the model is expected to provide reliable input for the modeling of sediment transport and morphological change due to waves and currents.     

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.     

越南西北部地区Sinh Quyen岩组副片麻岩的锆石年龄及其地质意义

在大地构造位置上, 越南西北部地区是古特提斯造山带东段重要的组成部分, 并记录了印支陆块和华南板块碰撞拼合历史.其构造归属是厘定该地区古特提斯板块缝合带位置的关键.报道了出露的基底岩石Sinh Quyen岩组副片麻岩的碎屑锆石年龄, 探讨其沉积物源和归属问题.该岩组主要由长英质副片麻岩和混合岩等岩石类型构成, 被认为是越南西北部中元古代—古元古代基底岩石.采用LA-ICP-MS锆石U-Pb定年方法分析了3个长英质副片麻岩样品的116粒碎屑锆石.123个分析点锆石年龄的统计结果显示, 碎屑锆石形成时代主要集中在约1.8 Ga左右, 有少量约2.2~3.0 Ga中太古代碎屑锆石, 暗示Sinh Quyen副片麻岩沉积物源主要为早古元古代基底岩石.基底岩石在形成时间上与华南板块古老基底相似, 推断Sinh Quyen岩组在构造归属上可能来自华南板块.部分碎屑锆石边部记录约250 Ma变质增生作用, 可能与华南板块—印支板块的印支期拼合有关, 记录了古特提斯造山作用.      

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.     

越南西北部Posen花岗岩锆石U-Pb年龄和Hf同位素组成特征

本文报道出露于越南西北部的Posen花岗岩岩体锆石U-Pb年龄和Hf同位素组成特征,讨论岩石成因和该地区新元古代岩浆作用的大地构造意义.分析3个花岗岩样品获得723Ma至760Ma的锆石U-Pb年龄,表明花岗岩形成于新元古代.该花岗岩的锆石Hf同位素组成有较大的变化范围,ε_(Hf)(t)值变化范围为-16.1至+3.4,单阶段Hf模式年龄为1186～1945Ma,暗示Posen花岗岩有着复杂的源区物源组成.在误差范围内,锆石两阶段Hf同位素模式年龄值主要集中在2.0～2.1Ga,与两阶段Nd同位素模式年龄值2.1～2.2Ga一致,说明花岗岩体主要由古元古代地壳物质部分熔融形成的.部分锆石颗粒具有正ε_(Hf)(t)值,可能指示花岗岩岩浆形成过程中存在壳-幔混合相互作用.在越南西北部发育新元古代岩浆作用可能与扬子板块广泛发育的、伴随Rodinia超大陆裂解过程的岩浆活动存在成因的联系,也揭示越南西北部地体可能与扬子板块具有亲缘关系,因此,可以推断马江断裂带应该代表印支板块和华南板块之间的古特提斯缝合带在越南西北部的延伸.     

Topography of the Moho and earth crust structure beneath the East Vietnam Sea from 3D inversion of gravity field data

The Moho depth, crustal thickness and fault systems of the East Vietnam Sea (EVS) are determined by 3D interpretation of satellite gravity. The Moho depth is calculated by 3D Parker inversion from residual gravity anomaly that is obtained by removing the gravity effects of seafloor and Pre-Cenozoic sediment basement topographies from the free air anomaly. The 3D inversion solution is constrained by power density spectrum of gravity anomaly and seismic data. The calculated Moho depths in the EVS vary from 30–31 km near the coast to 9 km in the Central Basin. A map of the lithosphere extension factor in the Cenozoic is constructed from Moho and Pre-Cenozoic sediment basement depths. The fault systems constructed by the maximum horizontal gradient approach include NE-SW, NW-SE, and N-S oriented faults. Based on the interpretation results, the EVS is sub-divided into five structural zones which demonstrated the different characteristics of the crustal structure.     

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.