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Abstract

Several 2-D “minimum structure” inversion algorithms (de Groot-Hedlin and Constable, 1990) are available for interpretation of profile oriented resistivity data (e.g. Oldenburg and Li, 1994; Loke, 1995). For very large data sets these algorithms are intensive to run and the models are often hard to interpret in terms of a layered geological model which is the most suitable model type in sedimentary environments. As an alternative to the 2-D “minimum structure” inversion algorithm, we have developed the 1-D Laterally Constrained Inversion (1-D LCI) algorithm. The LCI algorithm is parameterized and uses a series of laterally constrained 1-D models in the inversion. The 1-D LCI algorithm has for some years been used for some years by routine to invert multi electrode data (Wisén and Auken, this conference) as well as Pulled Array Continues Electric Sounding (PACES) data (e.g. Sørensen, 1996). The aim of this abstract and presentation is first to present a statistic study of the ability of the LCI algorithm to recognize a geological model, second to present the LCI algorithm itself. PACES data are used to map the upper 25 – 30 m of the geology for a detailed, regional mapping of protective clay caps of aquifers (Christensen and Sørensen, 1998). With the resolution capabilities and the equivalence problem of resistivity data in mind it is important to know not only the geological/hydrogeophysical model but also the probability of the model. One approach to analyze the quality of an inverted model section is to generate data over some simplified models, invert them and compare the inversion results to the original model. The decisive factor doing this is how representative the forward model is. A more thorough approach is to generate data from a large number of stochastic model sections which reflect the expected geological environment in a given area of investigation. Subsequently, the data must be submitted to the same data processing and inversion process as field data. Based on a comparison between the original model and the inverted model, it is possible to calculate a statistical measure for the quality of the inverted model.

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/content/papers/10.3997/2214-4609.201406195
2002-09-08
2020-11-27
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201406195
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