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1.
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.  相似文献   

2.
In this paper, we suggest a technique for forecasting seismic events based on the very low and low frequency (VLF and LF) signals in the 10 to 50 Hz band using the neural network approach, specifically, the error back-propagation method (EBPM). In this method, the solution of the problem has two main stages: training and recognition (forecasting). The training set is constructed from the combined data, including the amplitudes and phases of the VLF/LF signals measured in the monitoring of the Kuril-Kamchatka region and the corresponding parameters of regional seismicity. Training the neural network establishes the internal relationship between the characteristic changes in the VLF/LF signals a few days before a seismic event and the corresponding level of seismicity. The trained neural network is then applied in a prognostic mode for automated detection of the anomalous changes in the signal which are associated with seismic activity exceeding the assumed threshold level. By the example of several time intervals in 2004, 2005, 2006, and 2007, we demonstrate the efficiency of the neural network approach in the short-term forecasting of earthquakes with magnitudes starting from M ≥ 5.5 from the nighttime variations in the amplitudes and phases of the LF signals on one radio path. We also discuss the results of the simultaneous analysis of the VLF/LF data measured on two partially overlapping paths aimed at revealing the correlations between the nighttime variations in the amplitude of the signal and seismic activity.  相似文献   

3.
The seismic activity of the Nevado del Ruiz volcano was monitored during August–September 1985 using a three-component portable seismograph station placed on the upper part of the volcano. The objective was to investigate the frequency content of the seismic signals and the possible sources of the volcanic tremor. The seismicity showed a wide spectrum of signals, especially at the beginning of September. Some relevant patterns from the collected records, which have been analyzed by spectrum analysis, are presented. For the purpose of analysis, the records have been divided into several categories such as long-period events, tremor, cyclic tremor episodes, and strong seismic activity on September 8, 1985.The origin of the seismic signals must be considered in relation to the dynamical and acoustical properties of fluids and the shape and dimensions of the volcano's conduits.The main results of the present experiment and analysis show that the sources of the seismic signals are within the volcanic edifice. The signal characteristics indicate that the sources lie in fluid-phase interactions rather than in brittle fracturing of solid components.  相似文献   

4.
The continuous background seismic activity contains information on the internal state of a volcanic system. Here, we report the influence of major regional tectonic earthquakes (M > 5 in most cases) on such state, reflected as changes in the spectral and dynamical parameters of the volcano continuous seismic data. Although changes do not always occur, analysis of five cases of earthquake-induced variations in the signals recorded at Popocatépetl volcano in central México reveal significant fluctuations following the tectonic earthquakes. External visible volcanic activity, such as small to moderate explosions and ash emissions, were related to those fluctuations. We briefly discuss possible causes of the variations. We conclude that recognition of fluctuations in the dynamical parameters in volcano monitoring seismic signals after tectonic earthquakes, even those located in the far field, hundreds of kilometers away, may provide an additional criterion for eruption forecasting, and for decision making in the definition of volcanic alert levels.  相似文献   

5.
长白山天池火山地震类型及火山活动性的初步研究   总被引：3，自引：0，他引：3  
2002年以来，长白山天池火山区出现了地震活动增强、地形变加剧和多种地球化学异常等现象，火山口附近发生的多次有感地震在社会上产生了较大影响。本文利用2002年以来的流动地震观测资料，采用频谱分析、时频分析和多台站资料对比的方法，对火山区地震事件的类型进行了分析；对火山活动的危险性进行了初步研究。结果表明，目前天池火山区出现的大量地震活动仍然属于火山构造地震，少量台站地震记录中表现出的低频特征主要是由于局部介质影响造成的，排除了长周期地震引起的可能。尽管长白山天池火山地震活动明最增强，震群活动较为频繁，但仍属于岩浆活动的早期阶段，短期内发生火山喷发的危险性较小。  相似文献   

6.
Merapi volcano located in central Java, Indonesia, is one of the most active stratovolcanoes in the world. Many Earth scientists have conducted studies on this volcano using various methods. The geological features around Merapi are very attractive to be investigated because they have been formed by a complex tectonic process and volcanic activities since tens of millions of years ago. The southern mountain range, Kendeng basin and Opak active fault located around the study area resulted from these processes. DOMERAPI project was conducted to understand deep magma sources of the Merapi volcano comprehensively. The DOMERAPI network was running from October 2013 to mid-April 2015 by deploying 46 broad-band seismometers around the volcano. Several steps, i.e., earthquake event identification, arrival time picking of P and S waves, hypocenter determination and hypocenter relocation, were carried out in this study. We used Geiger’s method (Geiger 1912) for hypocenter determination and double-difference method for hypocenter relocation. The relocation result will be used to carry out seismic tomographic imaging of structures beneath the Merapi volcano and its surroundings. For the hypocenter determination, the DOMERAPI data were processed simultaneously with those from the Agency for Meteorology, Climatology and Geophysics (BMKG) seismic network in order to minimize the azimuthal gap. We found that the majority of earthquakes occurred outside the DOMERAPI network. There are 464 and 399 earthquakes obtained before and after hypocenter relocation, respectively. The hypocenter relocation result successfully detects some tectonic features, such as a nearly vertical cluster of events indicating a subduction-related backthrust to the south of central Java and a cluster of events to the east of Opak fault suggesting that the fault has an eastward dip.  相似文献   

7.
We present a review of the principal methods used for the seismic detection and identification of active underwater volcanism, based on our experience in French Polynesia. In particular, we descrobe the 5-year activity in the Tahiti-Mehetia area, during which more than 32000 earthquakes were detected by the Polynesian network. We discuss the use of the following three types of seismic waves: conventional (mostly body waves), seismic tremor, andT waves propagated in the low-velocity acoustic channel of the ocean. For each of these waves, we discuss the principal characteristics of the signals, their spectral content, the type of information they provide on the activity of the volcano, and the various limitations faced by their use in detection or monitoring of underwater volcanic edifices. We present a review of the principal swarms monitored by the Polynesian network, and discuss their characterization as either volcanic or tectonic.  相似文献   

8.
In the last 9 years, the amount and the quality of geophysical and volcanological observations of Stromboli's' activity have undergone a marked increase. This new information highlighted that the landslides on the Sciara del Fuoco flank are tightly linked to the volcanic activity. Actually, at the beginning of the December 28, 2002, effusive eruption, the seismic monitoring network was less dense than now, and therefore it is not known if there was an increase in the landslide rate before the eruption. Despite this, it is known that a big landslide occurred 2 days after the beginning of the eruption which caused a tsunami (December 30, 2002). More recently, the effusive eruption in February 2007 was preceded by an increase in landslides on the Sciara del Fuoco flank, which were recorded by the seismological monitoring system that had been improved after the 2002–2003 crisis. These episodes led us to believe that monitoring the Sciara del Fuoco flank instability is an important topic, and that landslides might be significant short-term precursors of effusive eruptions at the Stromboli volcano. To automatically detect landslide signals, we have developed a specialized neural algorithm. This can distinguish between landslides and the other types of seismic signals usually recorded at the Stromboli volcano (i.e., explosion quakes and volcanic tremor). The discrimination results show an average performance of 98.67 %. According to the experience of the crisis of 2007, to identify changes that can be considered as precursors of effusive eruptions, we set up an automatic decision-making method based on the neural network responses. This method can operate on a continuous data stream. It calculates a landslide percentage index (LPI) that depends on the number of records that are classified by the net as landslides over a given time interval. We tested the method on February 27, 2007, including the beginning of the effusive phase. The index showed an increase as early as at 09:00 UTC on that day and reached its maximum value (100 %) at 12:00, about 40 min before the onset of the eruption. After the beginning of the effusive phase, the index remains high due to the blocks that roll down along the slope from the front of the lava flow. On the basis of these tests, we propose a decision-making method that is able to recognize a trend in the LPI similar to that of 2007 eruption, allowing the identification of precursors of effusive phases at the Stromboli volcano.  相似文献   

9.
INTRODUCTIONThe Changbaishan volcano is located in Jilin Province , along the border of China and NorthKorea .It isthelargest nature reservein China .Changbaishan belongstothe northeastern Asian activebelt in the eastern margin of the Euro-Asia plate . The Changbaishan volcano is a gigantic ,polygenetic ,central volcano,and has been active since Holocene .The early eruption started in thePliocene andformedthe basaltic shield. Duringthe middle and late Pleistocene ,the volcanic cone …  相似文献   

10.
The determination of signal properties like frequency, amplitude, “signature” of the sequence of arrivals of P- and S-phases and the subsequent computation of hypocenter parameters, are the steps of a procedure in the analysis of swarms of high-frequency seismic events from Nevado del Ruiz volcano, which could be useful in other volcanic areas. The use of this procedure on Ruiz data led to the determination of various seismic source volumes, which together with hypocenters of non-swarmed events and the identification of geological features such as faults which cross the area, alignment of domes and volcanic edifices, and the record of low-frequency seismic events not centered in the area of the active vent, suggest the existence of a magma chamber of a rather complex geometry. This hypothesis would suggest that magma, emplaced in zones of weakness (faults) that affect the area, may act as the source of seismic activity. This demands a revision of the methodology used in the deformation measurements, and in the analysis of released energy and event occurrence too. The relations between hypocenters (sources), location and relative energy of the swarms, as well as the general volcanic activity (tremor, ash and gas emission, deformation) could turn out to be a possible means to assist in the identification of premonitory activity and thus be relevant to the process of monitoring and forecasting.  相似文献   

11.
陈天  易远元 《地震学报》2021,43(4):474-482
本文以提高地震数据的成像质量为目标,提出一种智能的卷积神经网络降噪框架,从带有噪声的地震数据中自适应地学习地震信号。为了加速网络训练和避免训练时出现梯度消失现象,我们在网络中加入残差学习和批标准化的方法,并采用了ReLU激活函数和Adam优化算法优化网络。此外,Marmousi和F3数据集被用来对网络进行训练和测试,经过充分训练的网络不仅能在学习中保留地震数据特征,而且能去除随机噪声。首先充分地训练网络,从中提取出随机噪声,并保留学习到的地震数据特征,之后通过重建地震数据估算测试集中的波形特征。合成记录和实际数据的处理结果显示了深度卷积神经网络在随机噪声压制任务中的潜力,并通过实验验证表明了深度卷积神经网络框架有很好的去噪效果。   相似文献   

12.
In this paper we present densely sampled fumarole temperature data, recorded continuously at a high-temperature fumarole of Mt. Merapi volcano (Indonesia). These temperature time series are correlated with continuous records of rainfall and seismic waveform data collected at the Indonesian–German multi-parameter monitoring network. The correlation analysis of fumarole temperature and precipitation data shows a clear influence of tropical rain events on fumarole temperature. In addition, there is some evidence that rainfall may influence seismicity rates, indicating interaction of meteoric water with the volcanic system. Knowledge about such interactions is important, as lava dome instabilities caused by heavy-precipitation events may result in pyroclastic flows. Apart from the strong external influences on fumarole temperature and seismicity rate, which may conceal smaller signals caused by volcanic degassing processes, the analysis of fumarole temperature and seismic data indicates a statistically significant correlation between a certain type of seismic activity and an increase in fumarole temperature. This certain type of seismic activity consists of a seismic cluster of several high-frequency transients and an ultra-long-period signal (<0.002 Hz), which are best observed using a broadband seismometer deployed at a distance of 600 m from the active lava dome. The corresponding change in fumarole temperature starts a few minutes after the ultra-long-period signal and simultaneously with the high-frequency seismic cluster. The change in fumarole temperature, an increase of 5 °C on average, resembles a smoothed step. Fifty-four occurrences of simultaneous high-frequency seismic cluster, ultra-long period signal and increase of fumarole temperature have been identified in the data set from August 2000 to January 2001. The observed signals appear to correspond to degassing processes in the summit region of Mt. Merapi.  相似文献   

13.
腾冲火山地区微震观测(Ⅰ)   总被引：4，自引：0，他引：4  
本文综述了腾冲火山地区微震观测台网的建立与所取得的具有研究价值的地震记录。内容包括观测系统性能、台站布局、地震记录。由观测资料表明腾冲火山地区存在微震活动。讨论了台网需要努力的目标。  相似文献   

14.
Simplified methods have been practiced by researchers to assess nonlinear liquefaction potential of soil. Derived from several field and laboratory tests, various simplified procedures such as stress-based, strain-based, Chinese criteria, etc. have been developed by utilizing case studies and undisturbed soil specimens. In order to address the collective knowledge built up in conventional liquefaction engineering, an alternative general regression neural network model is proposed in this paper.To meet this objective, a total of 620 sets of data including 12 soil and seismic parameters are introduced into the model. The data includes the results of field tests from the two major earthquakes that took place in Turkey and Taiwan in 1999 and some of the desired input parameters are obtained from correlations existing in the literature.The proposed GRNN model was developed in four phases, mainly: identification phase, collection phase, implementation phase, and verification phase. An iterative procedure was followed to maximize the accuracy of the proposed model. The case records were divided randomly into testing, training, and validation datasets.Generating a model that takes into account of 12 soil and seismic parameters is not feasible by using simplified techniques; however, the proposed GRNN model effectively explored the complex relationship between the introduced soil and seismic input parameters and validated the liquefaction decision obtained by simplified methods. The proposed GRNN model predicted well the occurrence/nonoccurrence of soil liquefaction in these sites. The model provides a viable tool to geotechnical engineers in assessing seismic condition in sites susceptible to liquefaction.  相似文献   

15.
During the hour preceding the March 9, 1998, eruption of the Piton de la Fournaise volcano, the deformation network of the Observatory recorded some large deformations in the summit area. A broadband seismic station of the GEOSCOPE global network, RER, is located about 8 km away from the summit of the volcano. Signals from that station may be interpreted as tilt changes. The combination of the above two kinds of signals allows, by using a tensile fault model, to constrain the geometry as well as some characteristics (volume, propagation velocity) of the dyke that intruded the summit area during the hour preceding the beginning of eruptive activity.  相似文献   

16.
Volcanoes generate a broad range of seismo-volcanic and infrasonic signals, whose features and variations are often closely related to volcanic activity. The study of these signals is hence very useful in the monitoring and investigation of volcano dynamics. The analysis of seismo-volcanic and infrasonic signals requires specifically developed techniques due to their unique characteristics, which are generally quite distinct compared with tectonic and volcano-tectonic earthquakes. In this work, we describe analysis methods used to detect and locate seismo-volcanic and infrasonic signals at Mt. Etna. Volcanic tremor sources are located using a method based on spatial seismic amplitude distribution, assuming propagation in a homogeneous medium. The tremor source is found by calculating the goodness of the linear regression fit (R 2) of the log-linearized equation of the seismic amplitude decay with distance. The location method for long-period events is based on the joint computation of semblance and R 2 values, and the location method of very long-period events is based on the application of radial semblance. Infrasonic events and tremor are located by semblance–brightness- and semblance-based methods, respectively. The techniques described here can also be applied to other volcanoes and do not require particular network geometries (such as arrays) but rather simple sparse networks. Using the source locations of all the considered signals, we were able to reconstruct the shallow plumbing system (above sea level) during 2011.  相似文献   

17.
Continuous seismic monitoring at Martinique since the 1902 eruption of the Montagne Pelée volcano did not detect local earthquakes for the first 70 years. For the only eruption which occurred in this time span in 1929 the seismograph was 20 km away and of a standard type, not particularly suited for the detection of small-scale local seismicity. Improvement of the monitoring array over the last 15 years with the installation of sensors on the volcano itself allowed the detection of signals of local origin which were interpreted as being due to surface sources, such as rockfalls and landslides. Since December 1985 seismic sources in the volcano itself, i.e. small earthquakes at shallow depth, were identified and located with the aid of a temporary upgrading of the array close to these weak sources. Such an onset of local seismicity could not have been detected with previous seismic equipment; such episodes of seismicity in the volcano might have occurred in the past, apparently quiescent history of the volcano as the reinterpretation of seismograms of some events in 1976 would indicate, without evolving to more important volcanic phenomena. For seismographs on volcanoes the constant upgrading of observation capabilities is certainly perferred to a strict continuity of standard observations.  相似文献   

18.
Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.  相似文献   

19.
针对随机地震反演中存在的两个主要问题,随机实现含有噪声和难以从大量随机实现中挖掘有效信息,提出了一种基于神经网络的随机地震反演方法.通过对多组随机实现及其正演地震数据的计算,构建了基于序贯高斯模拟的训练集.这也为应用神经网络求解地球物理反问题,提供了一种有效建立训练集的方法.较之传统的神经网络反演,这种训练集不仅保证了学习样本具有多样性,同时还引入了空间相关性.数值模拟结果表明,该方法只需要通过单层前馈神经网络,就可以比较有效的解决一个500个阻抗参数的反演问题.  相似文献   

20.
腾冲火山区微震观测(Ⅱ)   总被引：9，自引：3，他引：6  
论述了腾冲火山地区流动数字化台网的建设与观测过程。根据腾冲台网记录的各种微震图像,将其进行分类分析。主要有包络型事件、微构造破裂事件、汽爆事件、高频地震和小震群。由震相分析判断热海台记录到岩浆熔融体上的反射ＳxＳ波,腾冲地震台记录到Ｓ波不发育图像。表明腾冲火山区南部存在岩浆熔融体,估计距地表５～８ｋｍ。腾冲台网观测表胆腾冲火山区周边地区地震活动水平明显高于火山区,火山区南部的马鞍山热海一带是腾冲火  相似文献   

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