定点形变站网运行管理与产品服务平台设计与实现

定点形变站网以地壳形变作为观测对象,通过连续观测地壳水平变形和垂直变形,获取地壳应力应变的时变信息,为地震中短期和短临预测提供基础数据。在地震台站改革新形势下,为进一步提升定点形变站网产品产出自动化水平和定点形变产品在地震行业中的应用,2023年,中国地震台网中心联合湖北省地震局建立了基于B-S架构的定点形变站网运行管理与产品服务平台,该平台是定点形变观测的综合性业务平台,集成了原有的多个系统功能,实现了统一的在线数据处理、质量评估、产品发布及对外服务功能。平台部署至今,已逐步发挥成效,推动提升了站网运行管理规范化、信息化水平及产出服务能力。     

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.     

Debris-flow-dominated sediment transport through a channel network after wildfire

Field studies that investigate sediment transport between debris-flow-producing headwaters and rivers are uncommon, particularly in forested settings, where debris flows are infrequent and opportunities for collecting data are limited. This study quantifies the volume and composition of sediment deposited in the arterial channel network of a 14-km² catchment (Washington Creek) that connects small, burned and debris-flow-producing headwaters (<1 km²) with the Ovens River in SE Australia. We construct a sediment budget by combining new data on deposition with a sediment delivery model for post-fire debris flows. Data on deposits were plotted alongside the slope–area curve to examine links between processes, catchment morphometry and geomorphic process domains. The results show that large deposits are concentrated in the proximity of three major channel junctions, which correspond to breaks in channel slope. Hyperconcentrated flows are more prominent towards the catchment outlet, where the slope–area curve indicates a transition from debris flow to fluvial domains. This shift corresponds to a change in efficiency of the flow, determined from the ratio of median grain size to channel slope. Our sediment budget suggests a total sediment efflux from Washington Creek catchment of 61 × 10³ m³. There are similar contributions from hillslopes (43 ± 14 × 10³ m³), first to third stream order channel (35 ± 12 × 10³ m³) and the arterial fourth to fifth stream order channel (31 ± 17 × 10³ m³) to the total volume of erosion. Deposition (39 ± 17 × 10³ m³) within the arterial channel was higher than erosion (31 ± 17 × 10³ m³), which means a net sediment gain of about 8 × 10³ m³ in the arterial channel. The ratio of total deposition to total erosion was 0.44. For fines <63 μm, this ratio was much smaller (0.11), which means that fines are preferentially exported. This has important implications for suspended sediment and water quality in downstream rivers. © 2019 John Wiley & Sons, Ltd.     

Surficial fractures in the Navajo sandstone,south-western USA: the roles of thermal cycles,rainstorms, granular disintegration,and iterative cracking

Deep (> 5 m) sheeting fractures in the Navajo sandstone are evident at numerous sites in southern Utah and derive from tectonic stresses. Strong diurnal thermal cycles are, however, the likely triggers for shallow (< 0.3 m) sheeting fractures. Data from subsurface thermal sensors reveal that large temperature differences between sensors at 2 and 15 cm depth on clear summer afternoons are as great as those that trigger sheeting fractures in exposed California granite. Extensive polygonal patterns in the Navajo sandstone are composed of surface-perpendicular fractures and were produced by contractile stresses. Numerous studies have shown that porewater diminishes the tensile strength of sandstone. Based on our thermal records, we propose that cooling during monsoonal rainstorms triggers polygonal fracturing of temporarily weakened rock. On steep outcrops, polygonal patterns are rectilinear and orthogonal, with T-vertices. Lower-angle slopes host hexagonal patterns (defined by the dominance of Y-vertices). Intermediate patterns with rectangles and hexagons of similar scale are common. We posit that outcropping fractures are advancing downward by iterative steps, and that hexagons on sandstone surfaces (like prismatic columns of basalt) have evolved from ancestral orthogonal polygons of similar scale. In lava flows, fractures elongate intermittently as they follow a steep thermal gradient (the source of stress) as it rapidly moves through the rock mass. In our model, a steep, surficial thermal gradient descends through unfractured sandstone, but at the slow pace of granular disintegration. Through time, as the friable rock on stable slopes erodes, iterative cracking advances into new space. Hexagonal patterns form as new fractures, imperfectly guided by the older ones, propagate in new directions, and vertices drift into a configuration that minimizes the ratio of fracture length to polygon area. © 2020 John Wiley & Sons, Ltd.     

基于表层岩土渗透对三峡井网井水位降雨影响的精准分析

为研究三峡井网表层岩土渗透对井水位降雨的影响,采取井区表层岩土垂向渗透性测试方法试验,测得表层岩土垂向渗透性,并建立数学模型,用于降雨渗入补给分析。在此模型基础上,通过三峡井网8口井水位、气象三要素的对比观测资料对井水位日动态、月动态、年动态的影响进行精准分析与验证。结果表明:这种影响的特征是相当复杂的,同一个降雨过程在不同井上产生的影响特征不同,这一方面可能与各井的水文地质条件不同有关,另一方面可能还与各井点的降雨过程的差异也有关。     

地震信息网络数据的动态存储方法研究

为了提高地震信息网络数据存储的效率和稳定性,本文提出一种地震信息网络数据的动态储存方法。首先,设计和分析地震信息网络数据的动态存储系统中的硬件部分,基于Hadoop的分布式集群大数据动态存储系统,整合多台存储服务器,为一个集群系统,并平衡存储服务器负载情况,获取性能指标的比例值,通过加权公式获取综合性能参数,根据综合负载指标和服务器综合指标参数获取最佳存储服务器。对本文系统、高速数据存储系统和网络编码云存储系统进行大数据动态存储对比实验。实验结果表明:地震信息网络数据的动态存储耗时最短,存储效率最快;具有较高存储稳定性,且不会出现过载问题。     

基于GIS的白龙江流域舟曲—武都段的滑坡危险性评价

本研究以围绕着白龙江流域的甘肃省南部的宕昌县、舟曲县和武都区部分地区为研究区,根据全国滑坡编目中得到的272个历史滑坡数据以及选取的高程、坡度、坡向、平面曲率、剖面曲率、归一化植被指数（NDVI）、降雨、岩性、距道路距离和距河流距离10种影响因子,利用三种具有代表性的定量方法：信息量模型、以及基于频率比模型的逻辑回归模型和人工神经网络模型对研究区内滑坡灾害危险性进行评价。三种评价结果均显示研究区内滑坡灾害的极高和高危险区主要沿白龙江河谷地区呈带状分布。从危险性分区图可看出,人工神经网络模型得到的分区图较为合理,既表现出沿河谷地区集中分布的趋势,也呈现出对滑坡历史数据较为独立的特征,这一研究结果与前人研究结果一致。根据受试者工作特征曲线（ROC曲线）对三种模型的精度进行检验,检验得到的AUC值分别为0.818、0.829和0.837,说明三种评价结果均具有较高的可靠性,基于频率比模型的人工神经网络模型相比其他两个模型具有更好的评价精度,能更好地进行滑坡危险性的预测和评价,其中高程、降雨、岩性以及距道路距离对评价结果影响更大,这四种影响因子重要性值占比为52.1%。为该地区的城市扩建与灾害预防预测提供了参考。     

Erosion probability model of base-soil particle migration into a granular filter under local flow

To avoid soil erosion, lters often are installed in the downstream cross section of dams. However, the probability of piping failure and the lter effect both are related to the soil pore network. Previous erosion probability models for a base soil- lter system do not consider the effect of local ow. Therefore, in this study, an improved erosion probability model is established, in which the deviation between the main ow direction of the soil and the local ow direction in the lter was considered based on a previous model. The improved model was validated by numerical simulation. The erosion probability was found to affect the reasonable evaluation of lter effects and the optimal selection of lter thickness. The controlling con- striction size in a granular lter was increased to Dc* Dc5 with a lter thickness of 59 Dm.     

基于遗传优化神经网络的高速公路路基沉降量预测

控制路基沉降是公路工程中的一个关键技术问题,而路基沉降与其影响因素之间存在着线性、非线性关系。当输入自变量较多时,用传统神经网络建模容易出现过拟合现象,导致网络模型预测精度较低。针对此问题,本文用遗传算法对神经网络模型的权值和阈值进行优化,同时讨论遗传参数的设定对输出结果的影响。通过对成南高速的实测数据进行仿真,试验结果表明:优化后的BP神经网络具有较高的预测精度,预测效果明显优于传统神经网络模型的输出结果,该预测方法可作为高速公路路基长期沉降预测的一种有效辅助手段。     

Post-wildfire sediment cascades: A modeling framework linking debris flow generation and network-scale sediment routing

Wildfires represent one of the largest disturbances in watersheds of the Intermountain West. Yet, we lack models capable of predicting post-wildfire impacts on downstream ecosystems and infrastructure. Here we present a novel modeling framework that links new and existing models to simulate the post-wildfire sediment cascade, including spatially explicit predictions of debris flows, storage of debris flow sediment within valleys, delivery of debris flow sediment to active channels, and the downstream routing of sediment through river networks. We apply the model to sediment dynamics in Clear Creek watershed following the 2010 Twitchell Canyon Fire in the Tushar Mountains of southern Utah. The debris flow generation model performed well, correctly predicting 19 out of 20 debris flows from the largest catchments, with only four false positives and two false negatives at observed rainfall intensities. In total, the model predicts the occurrence of 160 post-wildfire debris flows across the Clear Creek watershed, generating more than 650 000 m³ of sediment. Our new storage and delivery model predicts the vast majority of this sediment is stored within valleys, and only 13% is delivered to the river network. The sediment routing model identifies numerous sediment bottlenecks within the network, which alter transport dynamics and may be hotspots for aggradation and aquatic habitat alteration. The volume of sediment exported from the watershed after seven years of simulation totals 17% of that delivered, or 2% of the total generated debris flow sediment. In the case of the Twitchell Canyon Fire, this highlights that significant post-wildfire sediment volumes can be stored in valleys (87%) and within the stream network (11%). Finally, we discuss useful insights that can be gleaned from the model framework, as well as the limitations and need for more monitoring and theory development in order to better constrain essential inputs, process rates, and morphodynamics. © 2019 John Wiley & Sons, Ltd.