Conjugate ruptures and seismotectonic implications of the 2019 Mindanao earthquake sequence inferred from Sentinel-1 InSAR data

In 2019, four strong earthquakes of Mw>6.4 occurred successively in Mindanao, Philippines. Based on the reports from the USGS and PHIVOLCS, these earthquakes were dominated by strike-slip ruptures. Whether these earthquakes are temporally and spatially related remained unknown. We characterized the coseismic displacement fields during the earthquake sequence using an InSAR technique with Sentinel-1 SAR data. The InSAR deformation measurements convincingly reveal that the four earthquakes produced distinct coseismic displacement patterns. We estimated the source parameters of the earthquakes with a two-step inversion strategy. The optimal model suggests that the earthquake sequence resulted from the reactivation of a conjugate fault structure that involves two nearly vertical left-lateral strike-slip faults and two high-angle right-lateral strike-slip faults. We calculated Coulomb stress changes from the earthquake sequence, suggesting that the previous strong earthquakes had significant stress-encouraging effects on the following events. The regional velocities based on the GPS analysis suggest that the formation of this conjugate structure is mainly due to the westward movement of the subducting Philippine Sea Plate. This earthquake sequence provides a seismotectonic background for subsequent strong earthquakes and helps to better understand the formation mechanisms and seismotectonic implications of conjugate structure rupturing.     

基于SBAS时序分析的大同地面沉降与地下水活动研究

大同盆地是汾渭盆地北端一个地面沉降较严重的区域,地下水开采是该区域地面沉降发生的一个重要原因。然而地下水活动与地面沉降在空间和时间的相关性却鲜有研究。为了掌握该地区地下水活动与地面沉降的内在联系,该文基于Envisat ASAR数据,利用短基线集(small baseline subset,SBAS)-In SAR技术对大同盆地地面沉降形变特征进行监测;同时利用地下水位监测数据,研究地面沉降中心与地下水位漏斗在空间和时间上的对应关系,定量分析2处地下水位波动与地表形变的关系。研究表明,地下水开采是大同盆地水源地地面沉降的主要原因,但并非所有的地下水位漏斗都存在地面沉降。该研究成果对指导该地区地下水开采及控制地面沉降有一定参考价值。     

Indoor localization for pedestrians with real-time capability using multi-sensor smartphones

ABSTRACT

The localization of persons or objects usually refers to a position determined in a spatial reference system. Outdoors, this is usually accomplished with Global Navigation Satellite Systems (GNSS). However, the automatic positioning of people in GNSS-free environments, especially inside of buildings (indoors) poses a huge challenge. Indoors, satellite signals are attenuated, shielded or reflected by building components (e.g. walls or ceilings). For selected applications, the automatic indoor positioning is possible based on different technologies (e.g. WiFi, RFID, or UWB). However, a standard solution is still not available. Many indoor positioning systems are only suitable for specific applications or are deployed under certain conditions, e.g. additional infrastructures or sensor technologies. Smartphones, as popular cost-effective multi-sensor systems, is a promising indoor localization platform for the mass-market and is increasingly coming into focus. Today’s devices are equipped with a variety of sensors that can be used for indoor positioning. In this contribution, an approach to smartphone-based pedestrian indoor localization is presented. The novelty of this approach refers to a holistic, real-time pedestrian localization inside of buildings based on multi-sensor smartphones and easy-to-install local positioning systems. For this purpose, the barometric altitude is estimated in order to derive the floor on which the user is located. The 2D position is determined subsequently using the principle of pedestrian dead reckoning based on user's movements extracted from the smartphone sensors. In order to minimize the strong error accumulation in the localization caused by various sensor errors, additional information is integrated into the position estimation. The building model is used to identify permissible (e.g. rooms, passageways) and impermissible (e.g. walls) building areas for the pedestrian. Several technologies contributing to higher precision and robustness are also included. For the fusion of different linear and non-linear data, an advanced algorithm based on the Sequential Monte Carlo method is presented.     

略论珠穆朗玛峰重力值的推估

仅根据邻近点的重力与高程资料,采用了4种有关公式,有效地推估了珠穆朗玛峰顶上的重力值,该值为(976970±7)×10^-5m·s^-2。这一结果为精确推求珠峰大地水准面和正高提供了必要的数据     

定位参数抗差估计的无偏性

根据抗差估计中误差的最基本假设—对称分布 ,考虑到ρ函数的条件及把反对称估计作为抗差估计计算迭代的初始值 ,验证了定位参数的抗差估计值也一定是反对称估计 ,满足无偏性条件 ,得到定位参数的抗差估计是无偏估计。     

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Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.     

K 近邻非参数回归概率预报技术及其应用

针对参数回归技术制作概率预报存在拟合好、但预报结果不稳定的现象, 提出了用K近邻非参数回归技术制作概率预报的新途径。K 近邻非参数回归技术包括历史样本数据库、近邻子集生成和优化以及预报量估计4 个主要部分。利用该技术进行了单要素概率预报(主要包括云量和降水)和多维联合概率预报(降水、总云量、风速和气温)试验, 并对试验结果进行了检验。实例研究结果表明:该文所给出的计算方案预报稳定性好, 准确率较高,具有良好的业务应用价值。     

内蒙古中西部宝音图群云母片岩的变质P-T轨迹与锆石年代学研究

位于内蒙古中西部地区的宝音图群,主要由云母片岩、斜长角闪岩、石英岩和大理岩等组成。其中云母片岩中发育典型的巴罗式递增变质带,目前对于该递增变质带的研究非常薄弱。本文对宝音图群的云母片岩开展了系统的岩相学、矿物化学、相平衡模拟以及锆石U-Pb年代学的研究。岩相学研究结果显示,三个云母片岩样品具有不同的峰期矿物组合,分别为石榴子石+白云母+黑云母+斜长石+石英,十字石+白云母+黑云母+斜长石+钛铁矿+石英,石榴子石+蓝晶石+白云母+黑云母+斜长石+钛铁矿+石英。此外,样品LS93和LS07存在晚期红柱石的叠加。相平衡模拟研究限定三个云母片岩的峰期温度压力分别为：577℃、0.73 GPa, 620℃、0.78 GPa和670℃、0.8 GPa,具有递增型特征,其峰期温度压力指示的地热梯度为18～20℃/km,为典型的中压相系;此外,三个样品的P-T轨迹均显示早期升温升压直至峰期阶段,随后降温降压的顺时针型,反映地壳加厚和后期抬升的演化过程。云母片岩中的锆石发育典型的变质边结构,变质边的Th/U=0.004～0.02,锆石U-Pb年代学结果显示该变质年龄为398±6 Ma,代表峰期或近峰期阶...     

Spatial Breakdown Point of Variogram Estimators

In the context of robust statistics, the breakdown point of an estimator is an important feature of reliability. It measures the highest fraction of contamination in the data that an estimator can support before being destroyed. In geostatistics, variogram estimators are based on measurements taken at various spatial locations. The classical notion of breakdown point needs to be extended to a spatial one, depending on the construction of most unfavorable configurations of perturbation. Explicit upper and lower bounds are available for the spatial breakdown point in the regular unidimensional case. The difficulties arising in the multidimensional case are presented on an easy example in IR² , as well as some simulations on irregular grids. In order to study the global effects of perturbations on variogram estimators, further simulations are carried out on data located on a regular or irregular bidimensional grid. Results show that if variogram estimation is performed with a 50% classical breakdown point scale estimator, the number of initial data likely to be contaminated before destruction of the estimator is roughly 30% on average. Theoretical results confirm the previous statement on data in IR^d , d 1.