scholarly journals Magnetic Susceptibility Mapping of Rocks and Depth Estimation of Anomalies: A Case Study of Igarra and Its Environs

2019 ◽  
pp. 1-11
Author(s):  
V. N. Nwugha ◽  
D. O. Ikoro ◽  
C. N. Okeke-Oguegbe ◽  
A. C. Ezebunanwa
Keyword(s):  
Magnetic Susceptibility ◽  
Mineral Exploration ◽  
Depth Estimation ◽  
Susceptibility Mapping ◽  
Susceptibility Map ◽  
Magnetic Minerals ◽  
Strike Direction ◽  
Southwest Nigeria ◽  
Exploration Exploitation

Magnetic Susceptibility Mapping and Depth Estimation of Anomalies were carried out on Igarra and its environs, Southwest Nigeria. This was to assist in mineral exploration in the area. The study area is located within the Igarra schist belt which is underlain by rocks of Precambrian basement complex. The Total Magnetic Field over the study area was obtained by digitizing the aeromagnetic map of Auchi (Sheet 226) acquired from the Nigerian Geologic Survey Agency (NGSA). A total of 19 (nineteen) magnetic anomalies were identified on the map; 5 magnetic highs and 14 lows. 8 anomalies have a NW-SE strike direction, 4 in the NE-SW and 7 in the E-W direction. The amplitude of the anomalies and strength of the total field were used to determine the susceptibility values for each of the anomalies. The Susceptibility values were used to generate a Magnetic Susceptibility map of the study area on SURFER 13 software. TMI plots on the anomalies were carried out on MICROSOFT EXCEL 2010. Depth estimates of the anomalies were got using three methods: The Half Width rule, Hannel rule and Tirburg rule. The Susceptibility map shows a noticeable pattern of increase in magnetic minerals from the Southwestern to the Northeastern part of the map. The Depth of the basement anomalies were relatively shallow ranging from 0.8 km to 2.6 km. The results of this work provide a preliminary guide to those that engage in mineral exploration / exploitation in the area.

Wildfire susceptibility mapping via machine learning: the case study of Liguria Region, Italy

2020 ◽  
Author(s):  
Paolo Fiorucci ◽  
Mirko D'Andrea ◽  
Andrea Trucchia ◽  
Marj Tonini
Keyword(s):  
Machine Learning ◽  
Natural Hazards ◽  
Predisposing Factors ◽  
Susceptibility Mapping ◽  
Burned Area ◽  
Susceptibility Map ◽  
Susceptibility Maps ◽  
Vegetation Heterogeneity ◽  
Wildfire Susceptibility

<p>Risk and susceptibility analyses for  natural hazards are of great importance for the sake of  civil protection, land use planning  and risk reduction programs. Susceptibility maps are based on the assumption that future events are expected to occur under similar conditions as the observed ones. Each unit area is assessed in term of relative spatial likelihood, evaluating the potential to experience a particular hazard in the future based solely on the intrinsic local characteristics. These concept is well-consolidated in the research area related with the risk assessment, especially for landslides. Nevertheless, the need exist for developing new quantitative and robust methods allowing to elaborate susceptibility  maps and to apply this tool to the study of other natural hazards.  In  the presented work, such  task is pursued for the specific  case of wildfires in Italy. The  two main approaches for such studies are the adoption  of physically based models and the data driven methods. In  the presented work, the latter  approach is  pursued, using  Machine Learning techniques in order to learn  from and make prediction  on the available information (i.e. the observed burned area and the predisposing factors) . Italy is severely affected by wildfires due to the high topographic and vegetation heterogeneity of its territory  and  to  its   meteorological conditions. The present study has as its main objective the  elaboration of a wildfire susceptibility map for Liguria region (Italy) by making use of Random Forest, an ensemble ML algorithm based on decision trees. The quantitative evaluation of susceptibility is carried out considering two different aspects: the location of past  wildfire occurrences, in terms of burned area, and the related anthropogenic and geo-environmental  predisposing factors that may favor fire spread. Different implementation of the model  were performed and compared. In  particular,  the effect of  a pixel's  neighboring land cover (including the type of vegetation and no-burnable area) on the output susceptibility map is investigated. In order to assess the  performance  of the model, the spatial-cross validation has been carried  out, trying  out different  number of folders. Susceptibility maps for the two fire seasons (the  summer  and  the winter  one) were finally computed  and validated. The  resulting  maps show  higher susceptibility zones , developing closer to the coast in summer and along the interior part of  the region in winter. Such zones matched well with the testing burned area, thus  proving the  overall  good performance of the proposed method.</p><p><strong>REFERENCE</strong></p><p> Tonini M., D’Andrea M., Biondi G., Degli Esposti S.; Fiorucci P., A machine learning based approach for wildfire susceptibility mapping. The case study of Liguria region in Italy. <em>Geosciences</em> (2020, submitted)</p><p><br><br></p>

Mapping of apparent magnetic susceptibility and the identification of fractures: A case study from the Eye-Dashwa Lakes pluton, Atikokan, Ontario

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. B147-B156 ◽  
Author(s):  
Madeline D. Lee ◽  
William A. Morris ◽  
Hernan A. Ugalde
Keyword(s):  
Magnetic Susceptibility ◽  
Forward Modeling ◽  
Local Scale ◽  
Magnetic Data ◽  
Susceptibility Map ◽  
Magnetic Minerals ◽  
Data Set ◽  
Susceptibility Data ◽  
Apparent Susceptibility

In situ magnetic-susceptibility measurements are only possible on outcrops, which are often limited by overburden and water bodies. An alternative approach is to derive an apparent susceptibility map from total-magnetic-intensity (TMI) surveys, which was done in this study for the Eye-Dashwa Lakes pluton near Atikokan, Ontario. Susceptibility logs of cores directly link alteration to systematic changes in the amount and composition of magnetic minerals. The surficial distribution of alteration zones was originally estimated from a limited number of in situ magnetic-susceptibility measurements. Here, through forward modeling of the TMI data set, susceptibility data are used to validate the apparent susceptibility data set. The modeling accounts for the bathymetric surface of all lakes that cover the area. A two-step process of bulk and local-scale modeling was used to estimate apparent susceptibility patterns. Bulk magnetic susceptibility is used as an indicator of overall alteration content, and local-scale apparent magnetic-susceptibility values are computed using a forward-modeling routine. The new apparent magnetic data set indicates northwest and northeast linears, which are the same as those seen in previous studies.

Evidence of tephra reworking in loess based on 2D magnetic susceptibility mapping: A case study from Rocourt, Belgium

2016 ◽  
Vol 394 ◽  
pp. 123-132 ◽  
Author(s):  
G. Jouannic ◽  
A.V. Walter-Simonnet ◽  
G. Bossuet ◽  
J.P. Simonnet ◽  
A. Jacotot
Keyword(s):  
Magnetic Susceptibility ◽  
Susceptibility Mapping

scholarly journals ANALISIS SUSEPTIBILITAS MAGNETIK TANAH LAPISAN ATAS SEBAGAI INDIKATOR BENCANA LONGSOR DI BUKIT SULA KECAMATAN TALAWI KOTA SAWAHLUNTO

2018 ◽  
Vol 15 (2) ◽  
pp. 112
Author(s):  
Arif Budiman ◽  
Dwi Puryanti ◽  
Febri Naldi
Keyword(s):  
Magnetic Susceptibility ◽  
High Frequency ◽  
Low Frequency ◽  
Magnetic Minerals ◽  
Average Value ◽  
Rain Drop ◽  
Space Range ◽  
Test Result ◽  
Scanning Electron

Landslide is a disaster that can harm properties and souls. Losses due to landslide can be minimized if there are known signs of landslide.. In this research, the landslide indicator is known through the analysis of the magnetic susceptibility of topsoil. This research is a case study conducted at Bukit Sula, Talawi District, Sawahlunto City.Soil samples were taken from two locations in Sula Hill, which are vegetated location (location A) and unvegetated location (location B). This research’s samples took with downward vertical  of each 100 m was taken with a space range of 5 m, so that is obtained 21 sampling points at each of these locations. Measurement of magnetic susceptibility value using Bartington Magnetic Susceptibility Meter measured at two frequencies, namely low frequency of 0.465 kHz (χLF) and high frequency of 4.65 kHz (χHF). At location A the obtained average value of χLF is 804.05×10-8 m3kg-1while the average value of χHF is 804.25×10-8 m3kg-1. At location B the obtained average value of χLF is 9.85×10-8 m3kg-1, while the average value of χHF is 9.64×10-8 m3kg-1. XRF test result showed that magnetic minerals in samples at both locations a hematit (Fe2O3). Based on the comparison of susceptibility value and concentration of hematite and quartz minerals between sample of location A and location B, it can be said that location B has been eroded. Based on the presence of superparamagnetic grain, the samples taken from location B have finer grains than the samples at location A. Scanning Electron Microscope (SEM) also shows that sample B has finer grains than the sample B.  These are because location B is an area without vegetation, causing rain drop directly into the soil and can decrease the level of soil grain attachment. Therefore, location B more likely occurred landslide than location A.

scholarly journals Integration of an InSAR and ANN for Sinkhole Susceptibility Mapping: A Case Study from Kirikkale-Delice (Turkey)

2021 ◽  
Vol 10 (3) ◽  
pp. 119
Author(s):  
Hakan A. Nefeslioglu ◽  
Beste Tavus ◽  
Melahat Er ◽  
Gamze Ertugrul ◽  
Aybuke Ozdemir ◽  
...  
Keyword(s):  
High Speed ◽  
Fundamental Problem ◽  
Vertical Displacement ◽  
Deformation Monitoring ◽  
Susceptibility Mapping ◽  
Susceptibility Map ◽  
Engineering Structures ◽  
Conditioning Factors ◽  
High Deformation ◽  
Settlement Site

Suitable route determination for linear engineering structures is a fundamental problem in engineering geology. Rapid evaluation of alternative routes is essential, and novel approaches are indispensable. This study aims to integrate various InSAR (Interferometric Synthetic Aperture Radar) techniques for sinkhole susceptibility mapping in the Kirikkale-Delice Region of Turkey, in which sinkhole formations have been observed in evaporitic units and a high-speed train railway route has been planned. Nine months (2019–2020) of ground deformations were determined using data from the European Space Agency’s (ESA) Sentinel-1A/1B satellites. A sinkhole inventory was prepared manually using satellite optical imagery and employed in an ANN (Artificial Neural Network) model with topographic conditioning factors derived from InSAR digital elevation models (DEMs) and morphological lineaments. The results indicate that high deformation areas on the vertical displacement map and sinkhole-prone areas on the sinkhole susceptibility map (SSM) almost coincide. InSAR techniques are useful for long-term deformation monitoring and can be successfully associated in sinkhole susceptibility mapping using an ANN. Continuous monitoring is recommended for existing sinkholes and highly susceptible areas, and SSMs should be updated with new results. Up-to-date SSMs are crucial for the route selection, planning, and construction of important transportation elements, as well as settlement site selection, in such regions.

scholarly journals Application of a Novel Hybrid Method for Flood Susceptibility Mapping with Satellite Images: A Case Study of Seoul, Korea

2021 ◽  
Vol 13 (14) ◽  
pp. 2786
Author(s):  
Roya Narimani ◽  
Changhyun Jun ◽  
Saqib Shahzad ◽  
Jeill Oh ◽  
Kyoohong Park
Keyword(s):  
High Risk ◽  
Hybrid Method ◽  
Satellite Images ◽  
Controlling Factors ◽  
Susceptibility Mapping ◽  
Low Risk ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
Flood Maps

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.

scholarly journals A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

2021 ◽  
Vol 10 (2) ◽  
pp. 93
Author(s):  
Wei Xie ◽  
Xiaoshuang Li ◽  
Wenbin Jian ◽  
Yang Yang ◽  
Hongwei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Assessment Model ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Area Index

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

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