Microseismic activity in the Hellenic Volcanic Arc, Greece, with emphasis on the seismotectonic setting of the Santorini–Amorgos zone

The volcanic arc of the Hellenic subduction zone with its four volcanic centers is of major relevance when evaluating the seismovolcanic hazard for the Aegean region. We present results from a 22-station temporary seismic network (CYCNET) in the central Hellenic Volcanic Arc (HVA). CYCNET recordings allow to analyze the level and spatio-temporal evolution of microseismic activity in this region for the first time. A total of 2175 events recorded between September 2002 and July 2004 are analyzed using statistical methods, cluster analysis and relative relocation techniques. We identify distinct regions with significantly varying spatio-temporal behavior of microseismicity. A large portion of the seismic activity within the upper crust is associated with the presence of islands representing horst structures that were generated during the major Oligocene extensional phase. In contrast, the central part of the Cyclades metamorphic core complex remains aseismic considering our magnitude threshold of 1.8 except one spot where events occur swarm-like and with highly similar waveforms.The highest activity in the study area was identified along the SW–NE striking Santorini–Amorgos zone. Within this zone the submarine Columbo volcano exhibits strong temporal variations of seismic activity on a high background level. This activity is interpreted to be directly linked to the magma reservoir and therein the migration of magma and fluids towards the surface. NE of Columbo where no volcanic activity has yet been reported we observe a similar seismicity pattern with small-scaled activity spots that might represent local pathways of upward migrating fluids or even developing volcanic activity within this zone of crustal weakness. In contrast, the Santorini and Milos volcanic complexes do not show significant temporal variations and low to moderate background activity, respectively. Relating our results to the distribution of historical earthquakes and the GPS-derived horizontal velocity field we conclude that the Santorini–Amorgos zone is presently in the state of right-lateral transtension reflecting a major structural boundary of the volcanic arc subdividing it into a seismically and volcanically quiet western and an active eastern part.     

Recent fold growth and drainage development: The Janauri and Chandigarh anticlines in the Siwalik foothills, northwest India

The active growth of a fault-and-thrust belt in frontal zones of Himalaya is a prominent topographical feature, extending 2500 km from Assam to Pakistan. In this paper, kinematical analysis of frontal anticlines and spatial mapping of active faults based on geomorphological features such as drainage pattern development, fault scarps and uplifted Quaternary alluvial fans are presented. We analyse the geomorphic and hydrographic expressions of the Chandigarh and the Janauri active anticlines in the NW India Siwaliks. To investigate the morphological scenario during the folding process, we used spatial imagery, geomorphometric parameters extracted from digital elevation models and fieldwork. Folding between the Beas and Sutlej Rivers gives clear geomorphological evidence of recent fold growth, presumably driven by movements of blind thrust faults. Structural style within the Janauri and Chandigarh anticlines is highly variable (fault-propagation folds, pop-up structures and transfer faults). The approach presented here involves analysis of topography and drainage incision of selected landforms to detect growth of active anticlines and transfer faults. Landforms that indicate active folding above a southwest-dipping frontal thrust and a northeast-dipping back-thrust are described. Along-strike differences in ridge morphology are measured to describe the interaction of river channel patterns with folds and thrust faults and to define history of anticline growth. The evolution of the apparently continuous Janauri ridge has occurred by the coalescence of independent segments growing towards each other. By contrast, systematic drainage basin asymmetry shows that the Chandigarh anticline ridge has propagated laterally from NW to SE.     

Researching European 'alternative' food networks: some methodological considerations

Recent European literature on 'alternative' food networks (AFNs) draws heavily upon an apparently accessible and diverse body of non-conventional food networks in the agro-food sector and whilst researchers frequently refer to individual examples of farmers markets, box schemes, producer cooperatives and community-supported agriculture projects, less attention is given to the methodological processes that facilitate the identification and examination of these networks. From the preliminary stages of a research project focusing on examples of AFNs, ² this paper examines the process of operationalizing AFNs research and reviews the difficulties associated with identifying, comparing and characterizing AFNs.     

Rainfall‐induced landslide hazard assessment using artificial neural networks

In Japan, landslides triggered by heavy rainfall tend to occur during the annual rainy season from early June until the middle of July; these landslides constitute a major hazard causing significant property damage and loss of life. This paper proposes the use of back propagation neural networks (BPNN) to predict the probability of landslide occurrence for a scenario of heavy rainfall in the Minamata area of southern Kyushu Island, Japan. All of the landslides were detected from aerial photographs taken in 1999, 2001 and 2003, and a geospatial database of lithology, topography, soil characteristics, land use and precipitation was constructed using geographical information systems (GIS). The training sample consists of 602 cells that include landslide activity and 1600 cells in stable areas. Using the trained BPNN with 49 input nodes, three hidden layers, and one output node, 239 589 cells were processed to produce a map of landslide probability for a maximum daily precipitation of 329 mm and a maximum cumulative precipitation of 581 mm for an incessant, intense rainfall event in the future. The resultant hazard map was classified into four hazard levels; it can be referenced for land‐use planning and decision‐making for community development. Copyright © 2005 John Wiley & Sons, Ltd.     

Spatial structures of stream and hillslope drainage networks following gully erosion after wildfire

The drainage networks of catchment areas burned by wildfire were analysed at several scales. The smallest scale (1–1000 m²) representative of hillslopes, and the small scale (1000 m² to 1 km²), representative of small catchments, were characterized by the analysis of field measurements. The large scale (1–1000 km²), representative of perennial stream networks, was derived from a 30‐m digital elevation model and analysed by computer analysis. Scaling laws used to describe large‐scale drainage networks could be extrapolated to the small scale but could not describe the smallest scale of drainage structures observed in the hillslope region. The hillslope drainage network appears to have a second‐order effect that reduces the number of order 1 and order 2 streams predicted by the large‐scale channel structure. This network comprises two spatial patterns of rills with width‐to‐depth ratios typically less than 10. One pattern is parallel rills draining nearly planar hillslope surfaces, and the other pattern is three to six converging rills draining the critical source area uphill from an order 1 channel head. The magnitude of this critical area depends on infiltration, hillslope roughness and critical shear stress for erosion of sediment, all of which can be substantially altered by wildfire. Order 1 and 2 streams were found to constitute the interface region, which is altered by a disturbance, like wildfire, from subtle unchannelized drainages in unburned catchments to incised drainages. These drainages are characterized by gullies also with width‐to‐depth ratios typically less than 10 in burned catchments. The regions (hillslope, interface and channel) had different drainage network structures to collect and transfer water and sediment. Copyright © 2005 John Wiley & Sons, Ltd.     

Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning

The evaluation of surface water resources is a necessary input to solving water management problems. Neural network models have been trained to predict monthly runoff for the Tirso basin, located in Sardinia (Italy) at the S. Chiara section. Monthly time series data were available for 69 years and are characterized by non-stationarity and seasonal irregularity, which is typical of a Mediterranean weather regime. This paper investigates the effects of data preprocessing on model performance using continuous and discrete wavelet transforms and data partitioning. The results showed that networks trained with pre-processed data performed better than networks trained on undecomposed, noisy raw signals. In particular, the best results were obtained using the data partitioning technique.     

火山碎屑岩层孔隙度的计算方法

火山碎屑岩岩性复杂,因此骨架参数难以选择,影响孔隙度的计算效果。针对大庆油田海拉尔盆地的火山碎屑岩储层,对计算孔隙度的一般性方法进行研究;针对火山碎屑岩的成分,根据岩心分析资料计算其理论骨架参数值;采用3层BP神经网络进行训练学习,通过编程处理,实现人工神经网络计算火山碎屑岩储层孔隙度,其结果与岩心分析孔隙度比较,平均误差小于2%,能满足储量计算要求。     

Correction of soil parameters in calculation of embankment settlement using a BP network back-analysis model

Finite element method (FEM) have been widely used for the calculation of settlement of embankment on soft soils in the last decade. However, due to the complexity of construction, spatial inhomogeneity of soils, as well as sensitivity of numerical results to the variation of soil parameters, large discrepancy typically exists between numerical outputs and field observations. This paper presents a novel method, combining FEM and an improved back-propagation (BP) neural network, for correction of soil parameters in numerical prediction of embankment settlement. Duncan–Chang hyperbolic soil model is adopted with the sensitivity of eight constitutive parameters numerically investigated. The soil parameters with large sensitivity are identified, and together with the representative settlements, are used for the training of the improved BP neural network which, once established, generates correction factors of soil parameters for subsequent more accurate FEM forward predictions. It is demonstrated that the proposed numerical back-analysis framework is very efficient in practical engineering applications to calculate highway settlement.     

A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis

The creep strain is proportional to the logarithm of the time under load, and is proportional to the stress and the temperature. At higher temperatures the creep rate falls off less rapidly with time, and the creep strain is proportional to a fractional power of time, with exponent increasing as the temperature increases and reaching a value ∼1/3 at temperatures, of about 0.5 T _m. At these temperatures the creep increases with stress according to a power greater than unity and possibly exponentially increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) such as by experimental methods and multivariate regression analysis etc. These methods are cumbersome and time consuming. In conjunction with statistics and conventional mathematical methods, a hybrid method can be developed that may prove a step forward in modeling geotechnical problems. In the present investigation, Artificial Neural Network (ANN) technique and Co-active neuro-fuzzy inference system (CANFIS) backed Genetic algorithm technique have been used for the prediction of creep strain and energy of Jog (B), and a comparative study has made between the two models.