Classification of seismic signals at Villarrica volcano (Chile) using neural networks and genetic algorithms

Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The described methodology can be used to classify more seismic signals to improve the study of the activity of this volcano or to extend the study to other active volcanoes of the region.     

我国风云极轨气象卫星及应用进展

我国风云极轨气象卫星数据,目前已经被应用在天气预报、气候预测、自然灾害、环境监测、科学研究等多个重要领域,为我国国民经济建设、国防建设、防灾减灾和全球许多国家的经济发展做出了重要贡献.本文介绍了我国风云极轨气象卫星的发展历程,重点阐述了目前风云极轨气象卫星在卫星数据预处理、定量产品反演、卫星资料同化等方面的应用研究进展...     

Feasibility of borehole ambient noise interferometry for permanent reservoir monitoring

The analysis of seismic ambient noise acquired during temporary or permanent microseismic monitoring campaigns (e.g., improved/enhanced oil recovery monitoring, surveillance of induced seismicity) is potentially well suited for time‐lapse studies based on seismic interferometry. No additional data acquisition required, ambient noise processing can be automatized to a high degree, and seismic interferometry is very sensitive to small medium changes. Thus there is an opportunity for detection and monitoring of velocity variations in a reservoir at negligible additional cost and effort. Data and results are presented from an ambient noise interferometry study applied to two wells in a producing oil field in Romania. Borehole microseismic monitoring on three component geophones was performed for four weeks, concurrent with a water‐flooding phase for improved oil recovery from a reservoir in ca. 1 km depth. Both low‐frequency (2 Hz–50 Hz) P‐ and S‐waves propagating through the vertical borehole arrays were reconstructed from ambient noise by the virtual source method. The obtained interferograms clearly indicate an origin of the ambient seismic energy from above the arrays, thus suggesting surface activities as sources. It is shown that ambient noise from time periods as short as 30 seconds is sufficient to obtain robust interferograms. Sonic log data confirm that the vertical and horizontal components comprise first arrivals of P‐wave and S‐waves, respectively. The consistency and high quality of the interferograms throughout the entire observation period further indicate that the high‐frequency part (up to 100 Hz) represents the scattered wave field. The temporal variation of apparent velocities based on first‐arrival times partly correlates with the water injection rate and occurrence of microseismic events. It is concluded that borehole ambient noise interferometry in production settings is a potentially useful method for permanent reservoir monitoring due to its high sensitivity and robustness.     

Chemical characteristics of the crater lakes of Popocatetetl, El Chichon, and Nevado de Toluca volcanoes, Mexico

Three crater lakes from Mexican volcanoes were sampled and analyzed at various dates to determine their chemical characteristics. Strong differences were observed in the chemistry among the three lakes: Nevado de Toluca, considered as dormant, El Chichón at a post-eruptive stage, and Popocatépetl at a pre-eruptive stage. Not surprisingly, no influence of volcanic activity was found at the Nevado de Toluca volcano, while the other volcanoes showed a correlation between the changing level of activity and the evolution of chemical trends. Low pHs (<3.0) were measured in the water from the active volcanoes, while a pH of 5.6 was measured at the Nevado de Toluca Sun lake. Changes with time were observed at Popocatépetl and El Chichón. Concentrations of volcanic-gas derived species like Cl⁻, SO₄²⁻ and F⁻ decreased irregularly at El Chichón from 1983 until 1997. Major cations concentrations also diminished at El Chichón. A 100% increase in the SO₄²⁻ content was measured at Popocatépetl between 1985 and 1994. An increase in the Mg/Cl ratio between 1992 (Mg/Cl=0.085) and 1994 (Mg/Cl=0.177) was observed at Popocatépetl, before the disappearance of the crater lake in 1994. It is concluded that chemical analysis of crater lakes may provide a useful additional tool for active-volcano monitoring.     

无人机红外传感器改制及数据获取研究

针对云雾复杂气候条件下无人机可见光影像难以获取的问题,文章在分析了地质灾害应急遥感监测伴随的复杂气候条件的基础上,根据目前无人机传感器的构造原理,通过对各种红外滤片的分析和比较,研究基于红外滤片元器件的无人机传感器红外改制技术和红外影像数据增强方法,对航摄传感器进行红外改装。实验结果表明,无人机红外传感器在多云雾条件下能够获取高精度清晰影像,有效解决云雾条件下地质灾害应急数据获取难题,为地质灾害应急决策提供参考。     

关于省级地理国情监测的思考

以湖北省为例,探讨湖北省地理国情监测工作指导思想、开展原则及目标,提出本省地理国情监测工作定位及服务对象,结合本省实际情况构想本省地理国情监测工作的主要任务,为其他省份开展地理国情监测工作提供参考。     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

RADARSAT数字图像积雪覆盖下地物解译方法

采用加拿大RADARSAT雷达数字图像,以新疆阿勒泰地区阿勒泰市、布尔津、吉木乃、哈巴河、福海5个县(市)为典型试验区,研究了积雪覆盖下雷达遥感图像地物解译的基本原理和几何纠正方法,比较分析了标准模式的地理参考高分辨率合成孔径雷达图像与经过亮度增强、边缘增强、图像增强、斑点压缩和纹理分析处理的图像上地物目视解译的特点,建立了积雪覆盖下雷达图像上耕地、水系、交通道路、居民地等与抗灾救灾密切相关的地物的解译标志。结果表明,可弥补可见光及近红外遥感积雪监测的缺陷,为雪灾发生区的救援决策提供科学依据。     

滑坡GPS监测数据处理方法探讨

介绍了GPS在滑坡监测中数据处理的两种常用方法,分析了某滑坡监测实例,得出了在滑坡GPS监测中单基线法优于网平差法的结论.     

Analytical model of well leakage pressure perturbations in a closed aquifer system

Deep saline aquifers are commonly used for disposal and storage of various surface fluids. The target injection zone must be hydraulically isolated from overlying zones in order to ensure containment of the injected fluids. Improperly plugged nonoperational abandoned wells that penetrate the injection zone are the main potential leakage pathways. Leakage through such wells may cause an observable pressure signal in a zone overlying the injection zone; such a signal can be used to detect the leakage. In this paper we develop an analytical model to evaluate the pressure change induced by leakage through a well in a multilayer system. Unlike previous analytical models on the topic, our model uses a closed system, which may significantly affect the strength and behavior of the pressure signal induced by leakage. The analytical model is first presented for a two-layer system centered at the leaky well location. We evaluate the leakage-induced pressure change using the Laplace transform of Duhamel’s superposition integral, yielding the solution in the Laplace domain. We then derive a late-time asymptotic solution using the final value theorem, which suggests that the leakage rate becomes constant after sufficient time. We then obtain the multilayer solution by extending the two-layer solution and presenting it in matrix form in the Laplace domain. We apply the solution to three examples. In the first example, we apply the analytical model to a two-layer system, investigating its behavior and comparing the results with a numerical solution. In order to demonstrate behavior and potential applications of the multilayer analytical model, we present two multilayer examples: one with identical layers and another, replicating a CO₂ storage site, with dissimilar layers. The leakage-induced pressure change does not necessarily decrease as the distance increases from the injection zone toward the surface.