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1.
Chun-Hung Wu  Su-Chin Chen   《Geomorphology》2009,112(3-4):190-204
This work provides a landslide susceptibility assessment model for rainfall-induced landslides in Central Taiwan based on the analytical hierarchy process method. The model considers rainfall and six site factors, including slope, geology, vegetation, soil moisture, road development and historical landslides. The rainfall factor consists of 10-day antecedent rainfall and total rainfall during a rainfall event. Landslide susceptibility values are calculated for both before and after the beginning of a rainfall event. The 175 landslide cases with detailed field surveys are used to determine a landslide-susceptibility threshold value of 9.0. When a landslide susceptibility assessment value exceeds the threshold value, slope failure is likely to occur. Three zones with different landslide susceptibility levels (below, slightly above, and far above the threshold) are identified. The 9149 landslides caused by Typhoon Toraji in Central Taiwan are utilized to validate the study's result. Approximately, 0.2%, 0.4% and 15.3% of the typhoon-caused landslides are located in the three landslide susceptibility zones, respectively. Three villages with 6.6%, 0.4% and 4.9% of the landslides respectively are used to validate the accuracy of the landslide susceptibility map and analyze the main causes of landslides. The landslide susceptibility assessment model can be used to evaluate susceptibility relative to accumulated rainfall, and is useful as an early warning and landslide monitoring tool.  相似文献   

2.
Spatially and temporally distributed modeling of landslide susceptibility   总被引：8，自引：1，他引：8  
Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km2 Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.  相似文献   

3.
Comparing models of debris-flow susceptibility in the alpine environment   总被引：12，自引：3，他引：9  
Debris-flows are widespread in Val di Fassa (Trento Province, Eastern Italian Alps) where they constitute one of the most dangerous gravity-induced surface processes. From a large set of environmental characteristics and a detailed inventory of debris flows, we developed five models to predict location of debris-flow source areas. The models differ in approach (statistical vs. physically-based) and type of terrain unit of reference (slope unit vs. grid cell). In the statistical models, a mix of several environmental factors classified areas with different debris-flow susceptibility; however, the factors that exert a strong discriminant power reduce to conditions of high slope-gradient, pasture or no vegetation cover, availability of detrital material, and active erosional processes. Since slope and land use are also used in the physically-based approach, all model results are largely controlled by the same leading variables.Overlaying susceptibility maps produced by the different methods (statistical vs. physically-based) for the same terrain unit of reference (grid cell) reveals a large difference, nearly 25% spatial mismatch. The spatial discrepancy exceeds 30% for susceptibility maps generated by the same method (discriminant analysis) but different terrain units (slope unit vs. grid cell). The size of the terrain unit also led to different susceptibility maps (almost 20% spatial mismatch). Maps based on different statistical tools (discriminant analysis vs. logistic regression) differed least (less than 10%). Hence, method and terrain unit proved to be equally important in mapping susceptibility.Model performance was evaluated from the percentages of terrain units that each model correctly classifies, the number of debris-flow falling within the area classified as unstable by each model, and through the metric of ROC curves. Although all techniques implemented yielded results essentially comparable; the discriminant model based on the partition of the study area into small slope units may constitute the most suitable approach to regional debris-flow assessment in the Alpine environment.  相似文献   

4.
A landslide susceptibility analysis is performed by means of Artificial Neural Network (ANN) and Cluster Analysis (CA). This kind of analysis is aimed at using ANNs to model the complex non linear relationships between mass movements and conditioning factors for susceptibility zonation, in order to identify unstable areas. The proposed method adopts CA to improve the selection of training, validation, and test records from data, managed within a Geographic Information System (GIS). In particular, we introduce a domain-specific distance measure in cluster formation. Clustering is used in data pre-processing to select non landslide records and is performed on the whole dataset, excluding the test set landslides. Susceptibility analysis is carried out by means of ANNs on the so-generated data and compared with the common strategy to select random non-landslide samples from pixels without landslides. The proposed method has been applied in the Brembilla Municipality, a landslide-prone area in the Southern Alps, Italy. The results show significant differences between the two sampling methods: the classification of the test set, previously separated and excluded from the training data, is always better when the non-landslide patterns are obtained using the proposed cluster sampling. The case study validates that, by means of a domain-specific distance measure in cluster formation, it is possible to introduce expert knowledge into the black-box modelling method, implemented by ANNs, to improve the predictive capability and the robustness of the models obtained.  相似文献   

5.
Geological disasters not only cause economic losses and ecological destruction, but also seriously threaten human survival. Selecting an appropriate method to evaluate susceptibility to geological disasters is an important part of geological disaster research. The aims of this study are to explore the accuracy and reliability of multi-regression methods for geological disaster susceptibility evaluation, including Logistic Regression (LR), Spatial Autoregression (SAR), Geographical Weighted Regression (GWR), and Support Vector Regression (SVR), all of which have been widely discussed in the literature. In this study, we selected Yunnan Province of China as the research site and collected data on typical geological disaster events and the associated hazards that occurred within the study area to construct a corresponding index system for geological disaster assessment. Four methods were used to model and evaluate geological disaster susceptibility. The predictive capabilities of the methods were verified using the receiver operating characteristic (ROC) curve and the success rate curve. Lastly, spatial accuracy validation was introduced to improve the results of the evaluation, which was demonstrated by the spatial receiver operating characteristic (SROC) curve and the spatial success rate (SSR) curve. The results suggest that: 1) these methods are all valid with respect to the SROC and SSR curves, and the spatial accuracy validation method improved their modelling results and accuracy, such that the area under the curve (AUC) values of the ROC curves increased by about 3%–13% and the AUC of the success rate curve values increased by 15%–20%; 2) the evaluation accuracies of LR, SAR, GWR, and SVR were 0.8325, 0.8393, 0.8370 and 0.8539, which proved the four statistical regression methods all have good evaluation capability for geological disaster susceptibility evaluation and the evaluation results of SVR are more reasonable than others; 3) according to the evaluation results of SVR, the central-southern Yunnan Province are the highest susceptibility areas and the lowest susceptibility is mainly located in the central and northern parts of the study area.  相似文献   

6.
Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km2) and the other for model extrapolation (approximately 4600 km2). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.  相似文献   

7.
Comparison of satellite and air photo based landslide susceptibility maps   总被引：4，自引：1，他引：4  
Landslide susceptibility maps can be prepared in a variety of ways. Many geoscientists favour the use of an overlay model approach in which several map layers are combined by some arithmetic rules to determine the potential for sliding in an area or region. The resulting susceptibility maps, although based on a subjective weighting of relevant factors, can often be of high accuracy and utility. In order to obtain the relevant input data for this type of analysis, remotely sensed data are often used. To date, susceptibility mapping, just as the mapping of historic and individual landslides, has tended to require higher-resolution imagery. This has somewhat limited the application of landslide susceptibility mapping. While high-resolution air photo or satellite imagery is superior to lower resolution imagery for the purpose of mapping of historic and individual landslides, such higher levels of resolution may not be required for the development of landslide susceptibility maps. In order to determine if medium-resolution satellite imagery, such as SPOT or ASTER, could provide the needed data for landslide susceptibility mapping, a comparison was undertaken of landslide susceptibility model output resulting from the use of stereo NAPP aerial photography versus the use of data obtained from stereo SPOT imagery. The test area selected for this study consisted of two watersheds, Pena Canyon and Big Rock Canyon, situated west of Santa Monica, California, USA, along the Pacific Coast Highway. Both watersheds have a long and well-documented history of landslide activity and sufficient geologic variability and complexity to provide a good test site. The specific overlay model used in this evaluation required input data consistent with the needs of many other models of this type. The model output derived from the two different data sources and presented here in the form of susceptibility maps were virtually identical. Statistical and difference analysis confirmed that both methods of obtaining input data provide similar results and successfully identified landslide prone areas. These results suggest that satellite imagery, in this instance, SPOT images, could potentially be used in lieu of conventional air photos, to evaluate landslide susceptibility. In many situations, especially in the case of remote locations and/or developing countries, this capability should result in substantial savings in terms of time, financial resources, and overall viability.  相似文献   

8.
An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.  相似文献   

9.
Experiments involving the gradual drying out of controlled mixtures of soil and organic lake sediment during storage at room temperature show that this leads to a loss of magnetic susceptibility and isothermal remanence greatly in excess of the initial values for the sediment components of the mixtures. We conclude that during storage in the moist state, soil-derived, fine-grained, ferrimagnetic iron oxides (magnetite and/or maghemite) are transformed to paramagnetic and/or imperfect antiferrimagnetic minerals. The imperfect anti-ferromagnetic component of the initial mixtures, which probably includes goethite, appears to survive and may even increase during storage. The experimental results compare well with the previously documented effects of storing wet sediment from the site, Peckforton Mere, Cheshire, U.K., over a comparable time interval. We conclude that transformation of fine grained ferrimagnets during storage diagenesis may be responsible for many of the examples of loss of magnetic susceptibility and remanence attributed by other authors solely to the oxidation of an iron sulphide such as greigite. Only where greigite is positively identified is it valid to infer a contribution from it to the magnetic properties of lake sediments: loss of susceptibility or remanence during storage is not alone a sufficient basis for such an inference. Early drying of samples will normally avoid the effects of storage diagenesis; and recent sediment samples so treated will, where greigite formation, bacterial magnetite and magnetite dissolution are insignificant, provide a valid basis for source identification on the basis of magnetic properties.  相似文献   

10.
Sediments and soils often contain superparamagnetic (SP) magnetite or maghemite grains that cause a frequency dependence of low-field susceptibility X fd which does not exceed 15 per cent/decade of frequency. Present models predict very different volume distributions for samples with the largest observed frequency dependence of susceptibility. While Stephensons' (1971) power-law model predicts most grains to be smaller than the stable single domain (SSD) threshold, the phenomenological model of >Dearing et al . (1996) suggests that most grains are between 10 and 25 nm in diameter. Finally, the recent calculations of Eyre (1997) indicate very broad volume distributions. This study reviews the nature of the superparamagnetic–stable single domain (SP–SSD) transition. The change of AC susceptibilities with grain size (or temperature) at the SP–SSD boundary is more gradual than commonly assumed. When distributions of particle coercivities and volumes are also considered, X fd values are much smaller than those calculated by Eyre (1997). Nonetheless, X fd can be larger than 15 per cent, and a larger frequency dependence has indeed been measured for some samples. The question whether the observed limited X fd of soils and sediments is a result of a broad distribution or of a bimodal distribution, where SP and SSD grains are restricted to a certain relative abundance, can potentially be answered by susceptibility determinations at more than two frequencies and by measurements of the temperature dependence of susceptibility.  相似文献   

11.
X. Yao  L.G. Tham  F.C. Dai 《Geomorphology》2008,101(4):572-582
The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.  相似文献   

12.
GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.  相似文献   

13.
GIS and ANN model for landslide susceptibility mapping   总被引：4，自引：0，他引：4  
1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a…  相似文献   

14.
GIS and ANN model for landslide susceptibility mapping   总被引：1，自引：0，他引：1  
XU Zeng-wang 《地理学报》2001,11(3):374-381
Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.  相似文献   

15.
开展广泛的磁化率特征研究是目前中国南方红色风化壳环境磁学研究的一个重要内容。通过对昆明西山4个峨眉山玄武岩风化壳剖面进行系统的磁化率测定发现,所有剖面磁化率值都呈现自剖面底部向顶部增高的趋势,这一现象与以往流行的玄武岩风化壳磁化率值随风化成土过程的加深而减小的模式有较大出入。磁化率与频率磁化率的对比分析结果进一步表明风化过程中形成的超顺磁颗粒可能是本区玄武岩风化壳(至少顶部60 cm区域)磁化率增强的主要贡献者。  相似文献   

16.
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.  相似文献   

17.
焦方谦  赵新生  陈川 《干旱区地理》2013,36(6):1111-1124
利用空间统计方法进行泥石流易发性定量评价的本质是度量影响因子（地形、地貌、岩性等）和响应因子（泥石流）之间的空间关系,最后给出所有影响因子综合作用结果,得到泥石流易发性评价结果。选取高程、坡度、坡向、地形起伏度、岩性、降雨量与水系距离7个因素作为泥石流影响因子,采用证据权模型,对研究区内泥石流进行灾害易发性评价,使用自然间断法将研究区内泥石流易发程度分级,得到研究区泥石流易发后概率图,获得了较高的置信度,方法简单易行,可以在其它灾害易发性评价中推广。  相似文献   

18.
This study assessed gully erosion susceptibility in Southern Gombe State, Nigeria. The objectives of the study were to: (1) prepare gully inventory of Southern Gombe State, (2) apply the Analytical Hierarchy Process to assess the contribution of gully erosion predisposing factors, and (3) produce a gully erosion susceptibility map of Southern Gombe State. Based on geomorphological study involving interpretation of Google Earth images and field surveys, 127 gullies were identified and 13 gully erosion predisposing factors assumed to influence gully erosion susceptibility were selected. Identified gullies were randomly split into training (89 or 70 per cent) and validation (38 or 30 per cent) datasets. The contribution of each gully erosion predisposing factor was obtained using the Analytical Hierarchy Process. The results indicated that slope (0.130), stream density (0.121), and distance from stream (0.121) play crucial roles in gully erosion susceptibility. By overlaying the gully erosion susceptibility factor maps, a gully erosion susceptibility map was created. A natural break method was then used to classify gully erosion areas into relatively safe (6.04 km2), less susceptible (3332.46 km2), moderately susceptible (1811.49 km2), highly susceptible (1146.35 km2), and extremely susceptible (1726.77 km2) categories. Field verification confirmed that the map accurately classified 92.11 per cent of the validation datasets, signifying the Analytical Hierarchy Process as a reliable method for gully erosion susceptibility assessment. The created gully erosion susceptibility map can assist land planners to identify critical gully erosion areas where prevention and mitigation actions should be implemented.  相似文献   

19.
Magnetic susceptibility variations in the Chinese loess/palaeosol sequences have been used extensively for palaeoclimatic interpretations. The magnetic signal of these sequences must be divided into lithogenic and pedogenic components because the palaeoclimatic record is primarily reflected in the pedogenic component. In this paper we compare two methods for separating the pedogenic and lithogenic components of the magnetic susceptibility signal: the citrate-bicarbonate-dithionite (CBD) extraction procedure, and a mixing analysis. Both methods yield good estimates of the pedogenic component, especially for the palaeosols. The CBD procedure underestimates the lithogenic component and overestimates the pedogenic component. The magnitude of this effect is moderately high in loess layers but almost negligible in palaeosols. The mixing model overestimates the lithogenic component and underestimates the pedogenic component. Both methods can be adjusted to yield better estimates of both components. The lithogenic susceptibility, as determined by either method, suggests that palaeoclimatic interpretations based only on total susceptibility will be in error and that a single estimate of the average lithogenic susceptibility is not an accurate basis for adjusting the total susceptibility. A long-term decline in lithogenic susceptibility with depth in the section suggests more intense or prolonged periods of weathering associated with the formation of the older palaeosols.
The CBD procedure provides the most comprehensive information on the magnitude of the components and magnetic mineralogy of loess and palaeosols. However, the mixing analysis provides a sensitive, rapid, and easily applied alternative to the CBD procedure. A combination of the two approaches provides the most powerful and perhaps the most accurate way of separating the magnetic susceptibility components.  相似文献   

20.
Summary. Susceptibility, thermo-remanent magnetization (TRM) and isothermal remanent magnetization (IRM) anisotropy ellipsoids have been determined for several rock samples. The results indicate that the ellipsoid of initial susceptibility is less anisotropic than the TRM and low field IRM ellipsoids which are found experimentally to be of identical shape. This suggests that palaeomagnetic data for anisotropic rocks may be corrected by using the anisotropy ellipsoid determined from magnetically non-destructive low field IRM measurements. Such IRM measurements can also be used to obtain anisotropy axes of samples which are inherently anisotropic but which have a susceptibility which is too weak to be accurately measured. The results for a series of artificial anisotropic samples containing magnetite particles of different sizes (in the range 0.2–90 μm) were very similar to those for the rocks. In contrast, a comparison of the susceptibility and IRM ellipsoids for anisotropic samples containing particles from a magnetic tape gave very different results in accordance with theory. Such results imply that susceptibility and IRM ellipsoids could be used to determine whether anisotropic rocks contain uniaxial single-domain particles (magnetization confined to the easy axis) or whether the particles are essentially multidomain.  相似文献   

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