H. R. Wason

Effect of surface wave inversion non-uniqueness on 1D seismic ground response analysis

Surface wave methods are increasingly being used for geotechnical site characterization. The methodology is based on the dispersive characteristic of Rayleigh waves in vertically heterogeneous medium. Experimental dispersion curve is inverted to obtain one-dimensional shear-wave velocity profile by inverse problem solution. Uncertainty associated with this surface wave inversion has drawn much attention. Inverse problem solution can provide different equivalent shear-wave velocity profiles, which may lead to different seismic site response analysis. In this study, a neighborhood algorithm has been used for inversion of dispersion curve to get a set of equivalent shear-wave velocity profiles. These equivalent velocity profiles are then used for 1D ground response analysis for different input motion record of the same earthquake at different epicentral distances. Results show significant variation in amplification spectrum in terms of maximum amplification as well as peak frequency. The extent of this uncertainty largely depends on the characteristics of the ground motion records at different epicentral distances. A linear variation is observed between mean coefficients of variation of amplification spectrum and epicentral distance of ground motion records. A gradual increase in mean value of peak frequency and peak amplification with the epicentral distance is also observed.

Unbiased Estimation of Moment Magnitude from Body‐ and Surface‐Wave Magnitudes

Abstract For regression of variables having measurement errors, general orthogonal regression (GOR) is the most appropriate statistical procedure that yields a linear relation between the true values of the dependent ( y t ) and independent ( x t ) variables. However, the GOR procedure to obtain unbiased estimate of the dependent variable for a given error‐affected value of the predictor variable is not well addressed in the literature. In the conventional GOR approach, the error‐affected value of the predictor variable is substituted as such in the GOR relation, yielding biased estimates of y t . In another approach, the orthogonal projections of the given points on the GOR line are used to first estimate x t and then y t . In this study, a procedure making use of true points on the GOR line is proposed to obtain improved estimates of y t . The proposed GOR procedure is applied to the magnitude conversion problem between m b to M w and M s to M w , using real data set. The absolute average differences of the estimates obtained and their standard deviations are compared, indicating that the proposed GOR procedure provides improved estimates of the dependent variable ( y t ) compared with the conventional GOR approach. The improved unified magnitudes obtained using the proposed GOR procedure will result in more realistic seismic hazard for a given catalog and seismotectonic environment.

Liquefaction identification using class-based sensor independent approach based on single pixel classification after 2001 Bhuj, India earthquake

A strong earthquake with magnitude 7.7 that shook the Indian Province of Gujarat on the morning of January 26, 2001 caused wide spread destruction and casualties. Earthquake-induced ground failures, including liquefaction and lateral spreading, were observed in many areas. Optical remote sensing offers an excellent opportunity to understand the post-earthquake effects both qualitatively and quantitatively. The impact of using conventional indices from Landsat-7 temporal images for the liquefaction is empirically investigated and compared with class-based sensor independent (CBSI) indices, while applying possibilistic fuzzy classification as a soft computing approach via supervised classification. Five spectral indices, namely simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) are investigated to identify liquefaction using temporal multi-spectral images. A soft-computing based fuzzy algorithm, which is independent of statistical distribution data assumption, is used to extract a single land cover class from remote sensing multi-spectral images. The result indicates that appropriately used indices can incorporate temporal variations, while extracting liquefaction with soft computing techniques for coarser spatial resolution with temporal remote sensing data. It is found that CBSI-NDVI with temporal data was good for extraction liquefaction while CBSI-TNDVI with temporal data was good for extraction water bodies.

Earthquake-induced built-up damage identification using IRS-P6 data: a comparative study using fuzzy-based classifiers

The 8 October 2005 earthquake caused widespread destruction in both the state of Jammu and Kashmir of India and Northern Pakistan. Due to poor accessibility in the hazardous and difficult mountainous terrain, a proper and comprehensive ground-based survey was not possible. However, with the help of remote sensing data and its analysis techniques, it is feasible to undertake both earthquake-related damage identification and assessment. This study attempts to document and identify built-up damaged (BD) areas using spectral indices taking temporal multispectral images from IRS-P6 LISS-IV. Five spectral indices have been used to identify BD areas using supervised possibilistic c-means (PCM) and noise cluster (NC) classifiers, to analyse the satellite data. The result indicates that Class Based Sensor Independent (CBSI) based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices provide the best results for identifying BD areas, while Simple Ratio (SR) index gives the best results for built-up undamaged area identification. Further, it observed that PCM classifier performed better in comparison to NC classifier.

Study of soft classification approaches for identification of earthquake-induced liquefied soil

The existence of mixed pixels led to the development of several approaches for soft (or fuzzy) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. There exist many different potential techniques for sub-pixel mapping from remotely sensed imagery to identify specific class. The fuzzy-based possibilistic c-means (PCM), noise cluster (NC) and noise cluster with entropy (NCE) classifiers were applied to identify the Bhuj, India (2001), earthquake induced soil liquefaction and compared as soft computing approaches via supervised classification. The soil liquefaction identification was empirically investigated and compared with class-based sensor-independent (CBSI) spectral band ratio using Landsat-7 temporal images. It has been found that CBSI-based temporal indices yield the better results for identification of liquefied soil areas while it was easily separated with pre-earthquake existing water body in that area. The NCE classifier performed better for conventional temporal indices, while NC classifier performed better for soil liquefaction and PCM classifier performed better for water body identification with CBSI temporal indices.

Identification of seismically susceptible areas in western Himalaya using pattern recognition

Seismicity in the western Himalayas is highly variable. Several historical and instrumentally recorded devastating earthquakes originated in the western Himalayas which are part of the Alpine–Himalayan belt. Earthquakes cause tremendous loss of life and to the built environment. The amount of loss in terms of life and infrastructure has been rising continuously due to significant increase in population and infrastructure. This study is an attempt to identify seismically susceptible areas in western Himalaya, using pattern recognition technique. An area between latitude 29∘–36∘N and longitude 73∘–80∘E was considered for this study. Pattern recognition starts with identification, selection and extraction of features from seismotectonic data. These features are then subjected to discriminant analysis and the study area was classified into three categories, viz., Area A: most susceptible area, Area B: moderately susceptible area, and Area C: least susceptible area. Results show that almost the entire states of Himachal Pradesh and Uttarakhand and a portion of Jammu & Kashmir are classified as Area A, while most of Jammu & Kashmir is classified as Area B and the Indo-Gangetic plains are classified as Area C.

Regression Relations for Magnitude Conversion for the Indian Region

Earthquake catalogs contain data about time of occurrence, hypocentral coordinates, earthquake magnitude, and other earthquake information. The data is derived from the observations of different seismic wave types, generally from heterogeneous networks of seismometers in space–time domain. Earthquake catalogs provide useful inputs for studies dealing with seismicity, seismotectonics, internal structure of the earth, and seismic hazard assessment. A homogeneous earthquake catalog is of critical importance for the study of earthquake occurrence patterns in space and time, seismic hazard estimates, land-use planning, seismic microzonation, and other seismological applications. These studies require well-defined and consistently determined earthquake magnitudes. Historical seismic records usually do not fulfill this criterion.

Earthquake-induced built-up damage identification using fuzzy approach

A strong earthquake with magnitude Mw 7.6 shook the Kashmir (Himalayan region) on 8 October 2005, causing wide spread destruction and casualties. The earthquake destroyed approximately 400,000 houses and over 86,000 people lost their lives. The difficult mountainous terrain, with poor accessibility, of the earthquake affected region covering parts of Pakistan and India has become a natural hindrance for any comprehensive ground survey. In such a situation, remotely sensed imagery data from satellites have become an important tool to assess damage due to natural disasters. This work is an attempt to document and identify built-up damage (BD) areas by spectral indices using temporal (pre- and post-earthquake) multispectral images from IRS P6 LISS-IV data. Five spectral indices have been used to identify BD areas damage using supervised Noise Cluster (NC) classifier. The result indicates that Class Based Sensor Independent (CBSI) based Transformed Normalized Difference Vegetation Index (TNDVI) temporal indices data generate better output for BD areas and simple ratio (SR) provide the best results for identifying built-up undamaged (BUD) areas with less entropy as well as membership range.