Poroelastic relaxation and aftershocks of the 2001 Bhuj earthquake, India

We analyse aftershocks of the 26 January 2001 Bhuj earthquake, India, that were recorded for 10 weeks following the mainshock. We calculate undrained or instantaneous pore pressure and change in Coulomb stress due to the earthquake and their poroelastic relaxation in the following 10 weeks period. Almost all aftershocks occurred in the region of coseismic dilatation. In the subsequent period, pore pressure increased through relaxation in the dilatation region which further modified coseismic Coulomb stress. Maximum increase in pore pressure is estimated to be about 0.7 MPa in 60 days time following the mainshock. Correlation between the zones of increased pore pressure and postseismic Coulomb stress with that of aftershocks, suggests a definite role of fluid diffusion in their delayed triggering.     

A simulation of earthquake induced undrained pore pressure changes with bearing on some soil liquefaction observations following the 2001 Bhuj earthquake

The Bhuj earthquake of January 26th, 2001, induced wide spread liquefaction within the Kachch peninsula. It has been pointed out that inundation due to soil liquefaction was short lived in some parts than in others in the affected region. Several geological, seismological and hydrological factors would have cumulatively contributed to these observed changes. We simulate in this article, undrained or short-term change in pore pressure in a poroelastic half space, in response to a simplified model of the Bhuj earthquake source. We find that the regions of relatively shorter lived inundation due to soil liquefaction may fall in the region where pore pressure responsible for soil liquefaction attributable to strong ground shaking was counteracted by pore pressure changes due to undrained poroelastic effect and vice versa.     

Study of the epicentral trends and depth sections for aftershocks of the 26th january 2001, Bhuj earthquake in Western India

The Geological Survey of India (GSI) established a twelve-station temporary microearthquake (MEQ) network to monitor the aftershocks in the epicenter area of the Bhuj earthquake (M _w7.5) of 26th January 2001. The main shock occurred in the Kutch rift basin with the epicenter to the north of Bhachao village, at an estimated depth of 25 km (IMD). About 3000 aftershocks (M _d ≥ 1.0), were recorded by the GSI network over a monitoring period of about two and half months from 29th January 2001 to 15th April 2001. About 800 aftershocks (M _d ≥ 2.0) are located in this study. The epicenters are clustered in an area 60 km × 30 km, between 23.3‡N and 23.6‡N and 70‡E and 70.6‡E. The main shock epicenter is also located within this zone. Two major aftershock trends are observed; one in the NE direction and other in the NW direction. Out of these two trends, the NE trend was more pronounced with depth. The major NE-SW trend is parallel to the Anjar-Rapar lineament. The other trend along NW-SE is parallel to the Bhachao lineament. The aftershocks at a shallower depth (<10km) are aligned only along the NW-SE direction. The depth slice at 10 km to 20 km shows both the NE-SW trend and the NW-SE trend. At greater depth (20 km–38 km) the NE-SW trend becomes more predominant. This observation suggests that the major rupture of the main shock took place at a depth level more than 20 km; it propagated along the NE-SW direction, and a conjugate rupture followed the NW-SE direction. A N-S depth section of the aftershocks shows that some aftershocks are clustered at shallower depth ≤ 10 km, but intense activity is observed at 15–38 km depth. There is almost an aseismic layer at 10–15 km depth. The activity is sparse below 38 km. The estimated depth of the main shock at 25 km is consistent with the cluster of maximum number of the aftershocks at 20–38 km. A NW-SE depth section of the aftershocks, perpendicular to the major NE-SW trend, indicates a SE dipping plane and a NE-SW depth section across the NW-SE trend shows a SW dipping plane. The epicentral map of the stronger aftershocksM ≥ 4.0 shows a prominent NE trend. Stronger aftershocks have followed the major rupture trend of the main shock. The depth section of these stronger aftershocks reveals that it occurred in the depth range of 20 to 38 km, and corroborates with a south dipping seismogenic plane.     

Source parameters of the 2001 Mw 7.7 Bhuj earthquake,Gujarat, India,aftershock sequence

We present the estimated source parameters from SH-wave spectral modeling of selected 463 aftershocks (2002–06) of the 26 January 2001 Bhuj earthquake, the well-recorded largest continental intraplate earthquake. The estimated seismic moment (M_o), corner frequency (f_c), source radius (r) and stress drop (Δσ) for aftershocks of moment magnitude 1.7 to 5.6 range from 3.55×10¹¹ to 2.84×10¹⁷ N-m, 1.3 to 11.83 Hz, 107 to 1515 m and 0.13 to 26.7 MPa, respectively, while the errors in f_c and Δσ are found to be 1.1 Hz and 1.1 MPa, respectively. We also notice that the near surface attenuation factor (k) values vary from 0.02 to 0.03. Our estimates reveal that the stress drop values show more scatter (Mo^{0.5 to 1} is proportional to Δσ) toward the larger M_o values (≥10^14.5 N-m), while they show a more systematic nature (Mo³ is proportional to Δσ) for smaller M_o values (<10^14.5 N-m), which can be explained as a consequence of a nearly constant rupture radius for smaller aftershocks in the region. The large stress drops (= 10 MPa) associated with events on the north Wagad fault (at 15–30 km depth) and Gedi fault (at 3–15 km depth) can be attributed to the large stress developed at hypocentral depths as a result of high fluid pressure and the presence of mafic intrusive bodies beneath these two fault zones.     

Modification of the liquefaction potential index and liquefaction susceptibility mapping for a liquefaction-prone area (Inegol,Turkey)

Although some liquefaction assessment methods were proposed to evaluate the liquefaction potential of sandy soils, the conventional method based on the standard penetration test (SPT) has been commonly used in most countries and in Turkey. However, it alone is not a sufficient tool for the evaluation of liquefaction potential. The liquefaction potential index was proposed to quantify the severity of liquefaction. Nevertheless, the liquefaction potential index and the severity categories do not answer the question: "Which areas will not liquify?" Besides, the categories do not include a "moderate" category; on the other hand, the "high" and "low" categories are included. This situation is also contrary to the nature of classification schemes. In this study, the liquefaction potential index and the liquefaction potential categories were modified by considering the existing form of the categories based on the liquefaction potential index. While the category of low was omitted, the categories of moderate and "non-liquefied" were adopted. A factor of safety of 1.2 was assumed as the lowest value for the liquefaction potential category of non-liquefied. In addition, the town of Inegol in the Marmara region became the case study for checking the performance of the liquefaction potential categories suggested in this study.     

The Bhuj, India, earthquake: lessons learned for earthquake safety of dams on alluvium

The Bhuj, India, earthquake of 26 January 2001, M_s 7.9, caused dams built on alluvium to sustain damage ranging from cosmetic to severe. Major damage was caused almost entirely by soil liquefaction in the alluvium. The critical factor was the level of earthquake ground motion.

The Bhuj earthquake showed that peak horizontal accelerations (PHAs)≤0.2 g were generally safe. PHAs>0.2 g were hazardous, when unconsolidated granular foundation soils were water saturated. N values of <20 are indicative of susceptibility to soil liquefaction. The Bhuj experience showed that alluvial foundation soils, subject to a PHA>0.2 g, must be evaluated over the full area beneath a new dam and all soils deemed susceptible to liquefaction must be either removed or treated. For remediating an old dam, reliable options are removal and replacement of liquefiable alluvium beneath upstream and downstream portions of the dam, combined with building berms designed to provide stability for the dam should there be a strength loss in soils beneath the dam.     

Mapping the liquefaction induced soil moisture changes using remote sensing technique: an attempt to map the earthquake induced liquefaction around Bhuj, Gujarat, India

The Bhuj earthquake (Mw = 7.9) occurred in the western part of India on 26th January 2001 and resulted in the loss of 20,000 lives and caused extensive damage to property. Soil liquefaction related ground failures such as lateral spreading caused significant damage to bridges, dams and other civil engineering structures in entire Kachchh peninsula. The Bhuj area is a part of large sedimentary basin filled with Jurassic, Tertiary and Quaternary deposits. This work pertains to mapping the areas that showed sudden increase in soil moisture after the seismic event, using remote sensing technique. Multi-spectral, spatial and temporal data sets from Indian Remote Sensing Satellite are used to derive the Liquefaction Sensitivity Index (LSeI). The basic concept behind LSeI is that the near infrared and shortwave infrared regions of electromagnetic spectrum are highly absorbed by soil moisture. Thus, the LSeI is herein used to identify the areas with increase in soil moisture after the seismic event. The LSeI map of Bhuj is then correlated with field-based observation on Cyclic Stress Ratio (CSR) and Cyclic Resistance Ratio (CRR), depth to water table, soil density and Liquefaction Severity Index (LSI). The derived LSeI values are in agreement with liquefaction susceptible criteria and observed LSI (R ² = 0.97). The results of the study indicate that the LSeI after calibration with LSI can be used as a quick tool to map the liquefied areas. On the basis of LSeI, LSI, CRR, CSR and saturation, the unconsolidated sediments of the Bhuj area are classified into three susceptibility classes.     

Aftershocks of 26th january 2001 Bhuj earthquake and seismotectonics of the Kutch region

The 26th January 2001 Bhuj earthquake was followed by intense aftershock activity. Aftershock data from United States Geological Survey (USGS) utilized in this study encompasses three months period from 26th January to 26th April 2001. Epicenters of the aftershock are plotted on a map depicting active faults. All the aftershocks of magnitude > 5 and 70% of those ranging between magnitude 3 and 5 are confined to an area resembling a horseshoe pattern with a pointed end towards NE. The other 20% of magnitude 3 to 5 are enclosed within an almost parallel boundary. Only 10% are found to be beyond this limiting boundary. 50% of the recorded after-shocks took place within the first week of the main event and this study reveals that the basic characteristic pattern of aftershock activity can be determined on the basis of the data of only one week. Four major NW-SE trending active faults are mapped in the Kutch region. They define the western limit of Cambay structure and also mark the western limit of Dharangadhra and Wadhwan basins along the SE continuation in Saurashtra. These faults separate the Kutch region into two geologically different blocks. On the SW side the mapped horseshoe pattern gets characteristically truncated along the western most fault, which is characterized by a strike-slip movement in the south and vertical movement in the north. The present study has revealed that the epicenter of the 26th January earthquake is located in the vicinity of the Bhachau township, close to the intersection with the Kutch mainland fault. Furthermore, it has been noticed that most of the epicenters of the aftershock are confined in the intersectional area of the Kutch mainland fault and the NW-SE faults.     

A media-based assessment of damage and ground motions from the january 26th, 2001<Emphasis Type="Italic">M</Emphasis> 7.6 Bhuj,India earthquake

We compiled available news and internet accounts of damage and other effects from the 26th January, 2001, Bhuj earthquake, and interpreted them to obtain modified Mercalli intensities at over 200 locations throughout the Indian subcontinent. These values are used to map the intensity distribution using a simple mathematical interpolation method. The maps reveal several interesting features. Within the Kachchh region, the most heavily damaged villages are concentrated towards the western edge of the inferred fault, consistent with western directivity. Significant sedimentinduced amplification is also suggested at a number of locations around the Gulf of Kachchh to the south of the epicenter. Away from the Kachchh region intensities were clearly amplified significantly in areas that are along rivers, within deltas, or on coastal alluvium such as mud flats and salt pans. In addition we use fault rupture parameters inferred from teleseismic data to predict shaking intensity at distances of 0–1000 km. We then convert the predicted hard rock ground motion parameters to MMI using a relationship (derived from internet-based intensity surveys) that assigns MMI based on the average effects in a region. The predicted MMIs are typically lower by 1–2 units than those estimated from news accounts. This discrepancy is generally consistent with the expected effect of sediment response, but it could also reflect other factors such as a tendency for media accounts to focus on the most dramatic damage, rather than the average effects. Our modeling results also suggest, however, that the Bhuj earthquake generated more high-frequency shaking than is expected for earthquakes of similar magnitude in California, and may therefore have been especially damaging.     

Pre-seismic, co-seismic and post-seismic displacements associated with the Bhuj 2001 earthquake derived from recent and historic geodetic data

The 26th January 2001 Bhuj earthquake occurred in the Kachchh Rift Basin which has a long history of major earthquakes. Great Triangulation Survey points (GTS) were first installed in the area in 1856–60 and some of these were measured using Global Positioning System (GPS) in the months of February and July 2001. Despite uncertainties associated with repairs and possible reconstruction of points in the past century, the re-measurements reveal pre-seismic, co-seismic and post-seismic deformation related to Bhuj earthquake. More than 25 Μ-strain contraction north of the epicenter appears to have occurred in the past 140 years corresponding to a linear convergence rate of approximately 10 mm/yr across the Rann of Kachchh. Motion of a single point at Jamnagar 150 km south of the epicenter in the 4 years prior to the earthquake, and GTS-GPS displacements in Kathiawar suggests that pre-seismic strain south of the epicenter was small and differs insignificantly from that measured elsewhere in India. Of the 20 points measured within 150 km of the epicenter, 12 were made at existing GTS points which revealed epicentral displacements of up to 1 m, and strain changes exceeding 30 Μ-strain. Observed displacements are consistent with reverse co-seismic slip. Re-measurements in July 2001 of one GTS point (Hathria) and eight new points established in February reveal post-seismic deformation consistent with continued slip on the Bhuj rupture zone.     

Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea

The likelihood ratio, logistic regression, and artificial neural networks models are applied and verified for analysis of landslide susceptibility in Youngin, Korea, using the geographic information system. From a spatial database containing such data as landslide location, topography, soil, forest, geology, and land use, the 14 landslide-related factors were calculated or extracted. Using these factors, landslide susceptibility indexes were calculated by likelihood ratio, logistic regression, and artificial neural network models. Before the calculation, the study area was divided into two sides (west and east) of equal area, for verification of the models. Thus, the west side was used to assess the landslide susceptibility, and the east side was used to verify the derived susceptibility. The results of the landslide susceptibility analysis were verified using success and prediction rates. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.     

Tectonics and crustal structures related to Bhuj earthquake of January 26, 2001: based on gravity and magnetic surveys constrained from seismic and seismological studies

Gravity and magnetic data of the Kachchh basin and surrounding regions have delineated major E–W and NW–SE oriented lineaments and faults, which are even extending up to plate boundaries in the north Arabian Sea and western boundary of the Indian plate, respectively. The epicentral zone of Bhuj earthquake and its aftershocks is located over the junction of Rann of Kachchh and median uplifts viz. Kachchh mainland and Wagad uplifts, which are separated by thrust faults. Gravity data with constraints from the results of the seismic studies along a profile suggest that the basement is uplifted towards the north along thrust faults dipping 40–60° south. Similarly gravity and magnetic modeling along a profile across Wagad uplift suggest south dipping (50–60°) basement contacts separating rocks of high susceptibility and density towards the north. One of these contacts coincides with the fault plane of the Bhuj earthquake as inferred from seismological studies and its projection on the surface coincides with the E–W oriented north Wagad thrust fault. A circular gravity high in contact with the fault in northern part of the Wagad uplift along with high amplitude magnetic anomaly suggests plug type mafic intrusive in this region. Several such gravity anomalies are observed over the island belt in the Rann of Kachchh indicating their association with mafic intrusions. The contact of these intrusives with the country rock demarcates shallow crustal inhomogeneities, which provides excellent sites for the accumulation of regional stress. A regional gravity anomaly map based on the concept of isostasy presents two centers of gravity lows of −11 to −13 mGal (10⁻⁵ m/s²) representing mass deficiency in the epicentral region. Their best-fit model constrained from the receiver function analysis and seismic refraction studies suggest crustal root of 7–8 km (deep crustal inhomogeneity) under them for a standard density contrast of −400 kg/m³. It is, therefore, suggested that significant amount of stress get concentrated in this region due to (a) buoyant crustal root, (b) regional stress due to plate tectonic forces, and (c) mafic intrusives as stress concentrators and the same might be responsible for the frequent and large magnitude earthquakes in this region including the Bhuj earthquake of January 26, 2001.     

Evaluation of ground motion characteristics,effects of local geology and liquefaction susceptibility: the case of Itea,Corinth Gulf (Greece)

In the present paper we analyze the effect of local geology on ground motion by means of numerical calculations (numerical models) using total (TS) and effective stress (ES) methods. These numerical calculations have been applied to the site of Itea, Corinth Gulf, which was chosen based on liquefaction susceptibility criteria and field inspection. Data from seismic refraction experiments and cone penetration test N-values as well as selected records of ground motion in nearby areas were used to construct the input file for the numerical model. By means of␣dynamic analysis such characteristics of ground motion as acceleration time histories, response spectra, and amplification function were evaluated. A one-dimensional soil amplification effect was clearly shown. Liquefaction probability at the Itea site was predicted based on the safety factor and the calculation of the induced settlement at the test site. Results of the TS and ES modeling lead us to conclude: (1) the presence of soft soil at Itea caused significant amplification (almost 2.5-fold higher magnitude) of the underlying bedrock motion and, therefore, can contribute to damage; (2) the area of Itea is highly susceptible to liquefaction due to presence of silty sand deposits at depths between 2.48 m (the position of the water table) and 12 m that demonstrate the rapid growth of the excess pore water pressure (EPWP) ratio with an increase in peak ground acceleration values.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

Crustal and lithospheric thinning beneath the seismogenic Kachchh rift zone,Gujarat (India): Its implications toward the generation of the 2001 Bhuj earthquake sequence

A high-resolution passive seismic experiment in the Kachchh rift zone of the western India has produced an excellent dataset of several thousands teleseismic events. From this network, 500 good teleseismic events recorded at 14 mobile broadband sites are used to estimate receiver functions (for the 30–310° back-azimuth ranges), which show a positive phase at 4.5–6.1 s delay time and a strong negative phase at 8.0–11.0 s. These phases have been modeled by a velocity increase at Moho (i.e. 34–43 km) and a velocity decrease at 62–92 km depth. The estimation of crustal and lithospheric thicknesses using the inversion of stacked radial receiver functions led to the delineation of a marked thinning of 3–7 km in crustal thickness and 6–14 km in lithospheric thickness beneath the central rift zone relative to the surrounding un-rifted parts of the Kachchh rift zone. On an average, the Kachchh region is characterized by a thin lithosphere of 75.9 ± 5.9 km. The marked velocity decrease associated with the lithosphere–asthenoshere boundary (LAB), observed over an area of 120 km × 80 km, and the isotropic study of xenoliths from Kachchh provides evidence for local asthenospheric updoming with pockets of partial melts of CO₂ rich lherzolite beneath the Kachchh seismic zone that might have caused by rifting episode (at 88 Ma) and the associated Deccan thermal-plume interaction (at 65 Ma) episodes. Thus, the coincidence of the area of the major aftershock activity and the Moho as well as asthenospheric upwarping beneath the central Kachchh rift zone suggests that these pockets of CO₂-rich lherzolite partial melts could perhaps provide a high input of volatiles containing CO₂ into the lower crust, which might contribute significantly in the seismo-genesis of continued aftershock activity in the region. It is also inferred that large stresses in the denser and stronger lower crust (at 14–34 km depths) induced by ongoing Banni upliftment, crustal intrusive, marked lateral variation in crustal thickness and related sub-crustal thermal anomaly play a key role in nucleating the lower crustal earthquakes beneath the Kachchh seismic zone.     

Soil liquefaction during the Arequipa Mw 8.4, June 23, 2001 earthquake, southern coastal Peru

The Arequipa June 23, 2001, earthquake with a moment magnitude of Mw 8.4 struck southern Peru, northern Chile and western Bolivia. This shallow (29 km deep) interplate event, occurring in the coupled zone of the Nazca subduction next to the southeast of the subducting Nazca ridge, triggered very localized but widely outspread soil liquefaction. Although sand blows and lateral spreading of river banks and road bridge abutments were observed 390 km away from the epicenter in the southeast direction (nearing the town of Tacna, close to the Chile border), liquefaction features were only observed in major river valleys and delta and coastal plains in the meizoseismal area. This was strongly controlled by the aridity along the coastal strip of Southern Peru. From the sand blow distribution along the coastal area, a first relationship of isolated sand blow diameter versus epicentral distance for a single event is ever proposed. The most significant outcome from this liquefaction field reconnaissance is that energy propagation during the main June 23, 2001, event is further supported by the distribution and size of the isolated sand blows in the meizoseismal area. The sand blows are larger to the southeast of the epicenter than its northwestern equivalents. This can be stated in other words as well. The area affected by liquefaction to the northwest is less spread out than to the southeast. Implications of these results in future paleoliquefaction investigations for earthquake magnitude and epicentral determinations are extremely important. In cases of highly asymmetrical distribution of liquefaction features such as this one, where rupture propagation tends to be mono-directional, it can be reliably determined an epicentral distance (between earthquake and liquefaction evidence) and an earthquake magnitude only if the largest sand blow is found. Therefore, magnitude estimation using this uneven liquefaction occurrence will surely lead to underrating if only the shortest side of the meizoseismal area is unluckily studied, which can eventually be the only part exhibiting liquefaction evidence, depending on the earthquake location and the distribution of liquefaction-prone environments.     

Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy

In this article, the results of a study aimed to assess the landslide susceptibility in the Calaggio Torrent basin (Campanian Apennines, southern Italy) are presented. The landslide susceptibility has been assessed using two bivariate-statistics-based methods in a GIS environment. In the first method, widely used in the existing literature, weighting values (Wi) have been calculated for each class of the selected causal factors (lithology, land-use, slope angle and aspect) taking into account the landslide density (detachment zones + landslide body) within each class. In the second method, which is a modification of the first method, only the landslide detachment zone (LDZ) density has been taken into account to calculate the weighting values. This latter method is probably characterized by a major geomorphological coherence. In fact, differently from the landslide bodies, LDZ must necessarily occur in geoenvironmental classes prone to failure. Thus, the calculated Wi seem to be more reliable in estimating the propensity of a given class to generate failure. The thematic maps have been reclassified on the basis of the calculated Wi and then overlaid, with the purpose to produce landslide susceptibility maps. The used methods converge both in indicating that most part of the study area is characterized by a high–very high landslide susceptibility and in the location and extent of the low-susceptible areas. However, an increase of both the high–very high and moderate–high susceptible areas occurs in using the second method. Both the produced susceptibility maps have been compared with the geomorphological map, highlighting an excellent coherence which is higher using method-2. In both methods, the percentage of each susceptibility class affected by landslides increases with the degree of susceptibility, as expected. However, the percentage at issue in the lowest susceptibility class obtained using method-2, even if low, is higher than that obtained using method-1. This suggests that method-2, notwithstanding its major geomorphological coherence, probably still needs further refinements.     

Temporal change in the structural fabric during pre- and post-26 January 2001 Bhuj earthquake,using remote sensing data

The Bhuj region, Kutch, India, is included in the highly seismic zonation map of India. The Kutch is a rift basin and so far has experienced three major earthquakes that are due to reverse fault mechanism, which in turn have been ascribed to compressive stresses. Origin of these stresses is considered to be due to north–south convergence of the Indian Plate with the Tibetan plate, and this has placed the entire Indian Plate under the compressive stress regime. Analysis of the stress pattern in the Bhuj region, therefore, has been carried out by extracting lineaments with the help of remote sensing data for the pre- and post-earthquake periods of 26 January 2001 earthquake. For this purpose, the area has been segmented into four sectors. The lineament frequency and the percent frequency from each sector and also for the whole area have been worked out. Resolution of stress on the principle of triaxial ellipsoid has been worked out for each sector and also for the whole area. There results a temporal change in the stress pattern in each sector and also for the whole area. However, the direction of horizontal maximum compressive stress for the whole area appears to be in N 10°E in the pre-earthquake period that has changed to N 10°W in the post-earthquake period. Thus, temporal change in the horizontal maximum compressive stress direction as N 23°E, inferred by Gowd et al. (J Geophy Res 97:11879–11888, 1992) to N 10°E prior to and N 10°W in the post-earthquake period, as inferred from lineament analysis and near parallelism of the lineament maxima with that of the North Kathiawar Fault and the Chambal Jamnagar Lineament along with the longer axis of the isoseismals of the Bhuj 2001 earthquake indicates a modification in the structural fabric of the region as well as a possibility of development of a major plane of weakness.     

基于MATLAB的BP神经网络在砂土液化评价中的应用

本文运用MATLAB的神经网络工具箱(NNT)建立砂土液化BP网络预测模型,并以南京地铁1#线玄武门站-南京站区间隧道砂土液化评价为例,阐述了基于MATLAB的BP网络应用于砂土液化分析的可行性和应用价值.     

Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)

Landslide susceptibility mapping is one of the most critical issues in Turkey. At present, geotechnical models appear to be useful only in areas of limited extent, because it is difficult to collect geotechnical data with appropriate resolution over larger regions. In addition, many of the physical variables that are necessary for running these models are not usually available, and their acquisition is often very costly. Conversely, statistical approaches are currently pursued to assess landslide hazard over large regions. However, these approaches cannot effectively model complicated landslide hazard problems, since there is a non-linear relationship between nature-based problems and their triggering factors. Most of the statistical methods are distribution-based and cannot handle multisource data that are commonly collected from nature. In this respect, logistic regression and neural networks provide the potential to overcome drawbacks and to satisfy more rigorous landslide susceptibility mapping requirements. In the Hendek region of Turkey, a segment of natural gas pipeline was damaged due to landslide. Re-routing of the pipeline is planned but it requires preparation of landslide susceptibility map. For this purpose, logistic regression analysis and neural networks are applied to prepare landslide susceptibility map of the problematic segment of the pipeline. At the end, comparative analysis is conducted on the strengths and weaknesses of both techniques. Based on the higher percentages of landslide bodies predicted in very high and high landslide susceptibility zones, and compatibility between field observations and the important factors obtained in the analyses, the result found by neural network is more realistic.