DC resistivity method is commonly used for acquiring subsurface resistivity data in environmental and engineering investigations. However, its interpretation is hampered by a variety of factors of which the non-linear nature of the process and bias effects on the data; mainly due to the noise; are the most problem, which limit the model resolution to a large extent. The ability to invert resistivity data successfully depends on many factors such as the uniqueness of the model as well as the robustness of the inversion algorithm. Hereafter we are investigating this problem using three different 1-Diversion algorithms. The three algorithms have been applied to a numerical model of 4 layers to determine the optimum solution produced by each of them. A random noise of 5, 10 and 20 percents has been added to the model in a forward step to determine the most stable, robustness algorithm. Those algorithms have been applied to the field data set measured at Hamam Faraum hot spring area, Egypt, aiming to make a refinement to the previous 1-D inversion done before for the same data set (E1-Qady et. al, 1998). We could conclude that the algorithm of Meju, (1992), is the most effective one in this study. The resulted 1-D geoelectrical cross section could elucidate the subsurface structure and explain the origin of the hot water in the area.
|ジャーナル||Memoirs of the Graduate School of Engineering, Kyushu University|
|出版ステータス||出版済み - 12 1 2000|
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