Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps

Evaluation of maps generated from different conceptual models or data processing approaches at spatial level has importance in many geoenvironmental applications. This paper addresses the spatial comparison of different landslide susceptibility zonation (LSZ) raster maps of the same area derived from various procedures.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.     

Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment

The objective of this paper is to evaluate the importance of geomorphological expert knowledge in the generation of landslide susceptibility maps, using GIS supported indirect bivariate statistical analysis. For a test area in the Alpago region in Italy a dataset was generated at scale 1:5,000. Detailed geomorphological maps were generated, with legends at different levels of complexity. Other factor maps, that were considered relevant for the assessment of landslide susceptibility, were also collected, such as lithology, structural geology, surficial materials, slope classes, land use, distance from streams, roads and houses. The weights of evidence method was used to generate statistically derived weights for all classes of the factor maps. On the basis of these weights, the most relevant maps were selected for the combination into landslide susceptibility maps. Six different combinations of factor maps were evaluated, with varying geomorphological input. Success rates were used to classify the weight maps into three qualitative landslide susceptibility classes. The resulting six maps were compared with a direct susceptibility map, which was made by direct assignment of susceptibility classes in the field. The analysis indicated that the use of detailed geomorphological information in the bivariate statistical analysis raised the overall accuracy of the final susceptibility map considerably. However, even with the use of a detailed geomorphological factor map, the difference with the separately prepared direct susceptibility map is still significant, due to the generalisations that are inherent to the bivariate statistical analysis technique.     

The anisotropy of magnetic susceptibility in biotite, muscovite and chlorite single crystals

The anisotropy of magnetic susceptibility (AMS) of single crystals of biotite, muscovite and chlorite has been measured in order to provide accurate values of the magnetic anisotropy properties for these common rock-forming minerals. The low-field AMS and the high-field paramagnetic susceptibility are defined. For the high-field values, it is necessary to combine the paramagnetic deviatoric tensor obtained from the high-field torque magnetometer with the paramagnetic bulk susceptibility measured from magnetization curves of the crystals. This leads to the full paramagnetic susceptibility ellipsoid due to the anisotropic distribution of iron cations in the silicate lattice. The ellipsoid of paramagnetic susceptibility, which was obtained for the three phyllosilicates, is highly oblate in shape and the minimum susceptibility direction is subparallel to the crystallographic c-axes. The anisotropy of the susceptibility within the basal plane of the biotite has been evaluated and found to be isotropic within the accuracy of the instrumental measurements. The degree of anisotropy of biotite and chlorite is compatible with previously reported values while for muscovite the smaller than previously published values. The shape of the chlorite AMS ellipsoid for all the samples is near-perfect oblate in contrast with a wide distribution of oblate and prolate values reported in earlier studies. Reliable values are important for deriving models of the magnetic anisotropy where it reflects mineral fabrics and deformation of rocks.     

Validation of Landslide Susceptibility Maps; Examples and Applications from a Case Study in Northern Spain

A procedure for validating landslide susceptibility maps wasapplied in a study area in northern Spain and the results obtained compared. Validationwas used to carry out sensitivity analysis for individual variables and combinationsof variables. The validity of different map-making methods was tested, as well as theutility of different types of Favourability Functions. The results obtained show thatvalidation is essential to determine the predictive value of susceptibility maps. Italso helps to better select the most suitable function and significant variables, thus improving the efficiency of the mapping process. Validation based on a temporal strategy makes it possible to derive hazard maps from susceptibility maps.     

Recovering magnetic susceptibility from electromagnetic data over a one-dimensional earth

While the inversion of electromagnetic data to recover electrical conductivity has received much attention, the inversion of those data to recover magnetic susceptibility has not been fully studied. In this paper we invert frequency-domain electromagnetic (EM) data from a horizontal coplanar system to recover a 1-D distribution of magnetic susceptibility under the assumption that the electrical conductivity is known. The inversion is carried out by dividing the earth into layers of constant susceptibility and minimizing an objective function of the susceptibility subject to fitting the data. An adjoint Green's function solution is used in the calculation of sensitivities, and it is apparent that the sensitivity problem is driven by three sources. One of the sources is the scaled electric field in the layer of interest, and the other two, related to effective magnetic charges, are located at the upper and lower boundaries of the layer. These charges give rise to a frequency-independent term in the sensitivities. Because different frequencies penetrate to different depths in the earth, the EM data contain inherent information about the depth distribution of susceptibility. This contrasts with static field measurements, which can be reproduced by a surface layer of magnetization. We illustrate the effectiveness of the inversion algorithm on synthetic and field data and show also the importance of knowing the background conductivity. In practical circumstances, where there is no a priori information about conductivity distribution, a simultaneous inversion of EM data to recover both electrical conductivity and susceptibility will be required.     

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.     

Magnetic susceptibility of ultrahigh pressure eclogite: The role of retrogression

Retrograde metamorphism played the dominant role in changing the low-field rock magnetic properties and density of 198 specimens of variably retrograded eclogites from the main borehole of the Chinese Continental Scientific Drilling Project (CCSD) and from surface outcrops in the Donghai area in the southern part of the Sulu UHP belt, China. Bulk magnetic susceptibility (κ) of unretrogressed UHP eclogite is controlled by whole-rock chemical composition and ranges from 397 to 2312 μSI with principal magnetic susceptibility carrying minerals paramagnetic garnet, omphacite, rutile and phengite. Partially retrograded eclogites show large variations in magnetic susceptibility between 804 and 24,277 μSI, with high mean magnetic susceptibility values of 4372 ± 4149 μSI caused by appreciable amounts of Fe-Ti oxide minerals such as magnetite, ilmenite and/or titanohematite produced by retrograde metamorphic reactions. Completely retrograded eclogites have lower susceptibilities of 1094 ± 600 μSI and amphibolite facies mineral assemblages lacking high magnetic susceptibility minerals. Jelínek's corrected anisotropy (P_j) of eclogites ranges from 1.001 to 1.540, and shows a positive correlation with low-field magnetic susceptibility (κ). Arithmetic mean bulk density (ρ) shows a steady decrease from 3.54 ± 0.11 g/cm³ (fresh eclogite) to 2.98 ± 0.06 g/cm³ (completely retrograded eclogite). Retrograde metamorphic changes in mineral composition during exhumation appear to be the major factor causing variations in low field magnetic susceptibility and anisotropy. Retrograde processes must be taken into account when interpreting magnetic surveys and geophysical well logs in UHP metamorphic terranes, and petrophysical properties such as density and low-field magnetic susceptibility could provide a means for semi-quantifying the degree of retrogression of eclogite during exhumation.