The use of spontaneous potential (SP) anomalies is well known in the geophysical literatures because of its effectiveness and significance in solving many complex problems in mineral exploration. The inverse problem of self-potential data interpretation is generally ill-posed and nonlinear. Methods based on derivative analysis usually fail to reach the optimal solution (global minimum) and trapped in a local minimum. A new simple heuristic solution to SP anomalies due to 2D inclined sheet of infinite horizontal length is investigated in this study to solve these problems. This method is based on utilizing whale optimization algorithm (WOA) as an effective heuristic solution to the inverse problem of self-potential field due to a 2D inclined sheet. In this context, the WOA was applied first to synthetic example, where the effect of the random noise was examined and the method revealed good results using proper MATLAB code. The technique was then applied on several real field profiles from different localities aiming to determine the parameters of mineralized zones or the associated shear zones. The inversion parameters revealed that WOA detected accurately the unknown parameters and showed a good validation when compared with the published inversion methods.
In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results. 相似文献
Natural Hazards - Analysis of long-term land use and land cover (LULC) changes requires up-to-date remotely sensed data to assess their effects on erosion. This is a particularly important... 相似文献
The relationship between aquifer hydraulic conductivity and aquifer resistivity, either measured on the ground surface by
vertical electrical sounding (VES) or from resistivity logs, or measured in core samples have been published for different
types of aquifers in different locations. Generally, these relationships are empirical and semi-empirical, and confined in
few locations. This relation has a positive correlation in some studies and negative in others. So far, there is no potentially
physical law controlling this relation, which is not completely understood. Electric current follows the path of least resistance,
as does water. Within and around pores, the model of conduction of electricity is ionic and thus the resistivity of the medium
is controlled more by porosity and water conductivity than by the resistivity of the rock matrix. Thus, at the pore level,
the electrical path is similar to the hydraulic path and the resistivity should reflect hydraulic conductivity. We tried in
this paper to study the effect of degree of groundwater saturation in the relation between hydraulic conductivity and bulk
resistivity via a simple numerical analysis of Archie’s second law and a simplified Kozeny-Carmen equation. The study reached
three characteristic non-linear relations between hydraulic conductivity and resistivity depending on the degree of saturation.
These relations are: (1) An inverse power relation in fully saturated aquifers and when porosity equals water saturation,
(2) An inverse polynomial relation in unsaturated aquifers, when water saturation is higher than 50%, higher than porosity,
and (3) A direct polynomial relation in poorly saturated aquifers, when water saturation is lower than 50%, lower than porosity.
These results are supported by some field scale relationships. 相似文献