Aftershocks series monitoring of the September 18, 2004 M = 4.6 earthquake at the western Pyrenees: A case of reservoir-triggered seismicity?

On September 18, 2004, a 4.6 mbLg earthquake was widely felt in the region around Pamplona, at the western Pyrenees. Preliminary locations reported an epicenter less than 20 km ESE of Pamplona and close to the Itoiz reservoir, which started impounding in January 2004. The area apparently lacks of significant seismic activity in recent times. After the main shock, which was preceded by series of foreshocks reaching magnitudes of 3.3 mbLg, a dense temporal network of 13 seismic stations was deployed there to monitor the aftershocks series and to constrain the hypocentral pattern. Aftershock determinations obtained with a double-difference algorithm define a narrow epicentral zone of less than 10 km², ESE–WNW oriented. The events are mainly concentrated between 3 and 9 km depth. Focal solutions were computed for the main event and 12 aftershocks including the highest secondary one of 3.8 mbLg. They show mainly normal faulting with some strike-slip component and one of the nodal planes oriented NW–SE and dipping to the NE. Cross-correlation techniques applied to detect and associate events with similar waveforms, provided up to 33 families relating the 67% of the 326 relocated aftershocks. Families show event clusters grouped by periods and migrating from NW to SE. Interestingly, the narrow epicentral zone inferred here is located less than 4 km away from the 111-m high Itoiz dam. These hypocentral results, and the correlation observed between fluctuations of the reservoir water level and the seismic activity, favour the explanation of this foreshock–aftershock series as a rapid response case of reservoir-triggered seismicity, burst by the first impoundment of the Itoiz reservoir. The region is folded and affected by shallow dipping thrusts, and the Itoiz reservoir is located on the hangingwall of a low angle southward verging thrust, which might be a case sensible to water level fluctuations. However, continued seismic monitoring in the coming years is mandatory in this area to infer more reliable seismotectonic and hazard assessments.     

Application of back-propagation networks in debris flow prediction

Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

基于GIS与ANN耦合技术的地裂缝灾情非线性模拟预测系统——以山西榆次地裂缝灾害为例

现代地裂缝在世界许多国家普遍存在 ,已成为当今世界范围内的主要地质灾害之一。本文在详尽分析了山西榆次地裂缝的各个致灾因子的基础上 ,利用GIS技术建立了地质学意义上的专题层 ;然后采用人工神经网络技术构建出了地裂缝灾害活动性的评价模型 ,并建立了地裂缝活动性的评价系统 ,对榆次地裂缝进行了灾害活动性评价 ,为榆次市城建和国土规划等部门的正确决策提供了重要的科学依据     

喀什"奇异震群"与构造地震的对比研究

20 0 1年 11月 14、 15日新疆喀什台记录到 86次振动事件 (“奇异震群”事件 )。为识别其性质 ,应用波谱分析、偏振分析等方法 ,对该事件和同范围构造地震的地震波特征进行了对比研究 ,发现 :(1)喀什“震群”的全波列衰减比构造地震的全波列衰减快很多 ;(2 )与构造地震的速度谱相比 ,“震群”的频谱相对较为“集中”,而构造地震的则较为“发散”;(3)“震群”位移谱的拐角频率和卓越频率均较构造地震的相应量高出一倍多 ,且离散较小。通过对喀什“奇异震群”这些特征进行分析研究 ,认为这一事件极有可能是人工事件     

Classification of artificial (man-made) ground

The legacy inherited from anthropogenic processes needs to be addressed in order to provide reliable and up-to-date ground information relevant to development and regeneration in the urban environment. The legacy includes voids as well as anthropogenic deposits (artificial ground). Their characteristics derive from former quarrying and mining activities industrial processes creating derelict ground, variably consolidated made ground, and contaminated groundwater and soils. All need to be systematically assessed to inform the planning process and provide the basis for engineering solutions. Site-specific investigation needs to be conducted on the back of good quality geoscientific data. This comes from ‘field’ survey, remotely sensed data interpretation, historical maps, soil geochemical sampling, and geotechnical investigation. Three-dimensional and, in the future, four dimensional, characterization of superficial deposits is required to reach an understanding of the potential spatial lithological variability of artificial ground and the geometry of important surfaces, i.e. the boundary conditions. The classification scheme for artificial ground outlined in this paper and adopted by the British Geological Survey, will help in achieving this understanding.     

Application of visual,statistical and artificial neural network methods in the differentiation of water from the exploited aquifers in Kuwait

The ability of artificial neural network to differentiate water samples from the two aquifers of Kuwait on the basis of their major ion chemistry has been demonstrated. The major ion concentration distribution in the groundwater of the Kuwait Group and the Dammam Formation aquifers of Kuwait appears very similar. Cross-plots, supported by the discriminant function analysis of the data, however, suggest that there are some subtle differences in the overall composition of the water from the two aquifers that make it possible to differentiate the water from the two aquifers in almost 80% of the cases. An artificial neural network improved the differentiation capability to 90% of the cases. It is also possible to estimate the fraction of Kuwait Group water in the flow stream of dually completed wells with the help of an artificial neural network developed for this purpose. Electronic Publication     

福建省干旱概况及夏旱期间人工增雨条件分析

对福建省干旱的成因和干旱的环流形势进行了探讨,着重分析了夏旱期间人工增雨作业的天气形势、云状、回波特征及云顶温度等条件,结果表明福建省干旱的形成与大型环流形势、地形、地貌及土壤植被条件有关;平均而言,闽东南沿海干旱多于内陆地区;西太平洋副热带高压是夏季致旱的主要天气系统;夏季,西风槽前型、台风外围影响型、副热带辐合带型是进行人工增雨作业的有利天气形势,Sc、Cb、Cu云都有一定的人工增雨作业条件,其中降水时长1～5 h,过程雨量1.0～5.0 mm的云是旱季作业比较适合的作业云.这些结果为开展人工增雨作业提供理论依据,达到解除或缓解干旱的目的.     

月降水量的神经网络混合预报模型研究

以均生函数表征预报量自身周期变化，结合500hPa月平均高度场和月平均海温场预报因子，采用神经网络方法建立了一种新的短期气候预报模型。分别以广西桂北、桂中和桂南6月降水量作为预报对象进行预报试验，结果表明，这种新的预报方法比均生函数回归预报模型及高度场、海温场预报因子的回归预报模型，具有更好的物理基础和预报能力。     

震后震型早期判定的人工神经网络方法

应用人工神经网络的方法，利用３０次强震震后１天和２天内的地震资料作为学习样本，对广西及其邻区发生的４次地震的震型作了早期预测判定，结果表明应用效果较好，正确率达７５％。该方法值得进一步研究。