Resistivity imaging of Aluto-Langano geothermal field using 3-D magnetotelluric inversion
Abstract Magnetotelluric (MT) method is a widely used geophysical method in geothermal exploration. It is used to image subsurface resistivity structures from shallow depths up to several kilometers of depth. Resistivity imaging using MT method in high-enthalpy geothermal systems is an effective tool to identify conductive clay layers that cover the geothermal systems and to detect a potential reservoir. A resistivity model is vital for deciding the location of pilot and production sites at the early stages of a geothermal project. In this study, a 3-D resistivity model of Aluto-Langano geothermal field was constructed to map structures related to a geothermal resource. The inversion program, ModEM was used to recover the 3-D resistivity model of the study area. The 3-D inversion result revealed the three main resistivity structures: a high-resistivity surface layer related to unaltered volcanic rocks at shallow depth, underlain by a conductive zone associated with the presence of conductive clay minerals, predominantly smectite. Beneath the conductive layer, the resistivity increases gradually to higher values related to the formation of high-temperature alteration minerals such as chlorite and epidote. The resistivity model recovered from 3-D inversion in Aluto-Langano corresponds very well to the conceptual model for high-enthalpy volcanic geothermal systems. The conductive clay cap is overlying the resistive propylitic upflow zone as confirmed by the geothermal wells in the area. Highlights Resistivity distribution in high-enthalpy geothermal systems is governed by hydrothermal alteration. The information on the Earth's resistivity obtained by magnetotelluric (MT) method has been valuable in detecting hydrothermal alteration zones in such systems. The resistivity model recovered from 3-D inversion in Aluto-Langano corresponds very well to the conceptual model for high-enthalpy volcanic geothermal systems and geological information.
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- DOI : http://dx.doi.org/10.1016/j.jafrearsci.2017.12.017
- Elsevier : 저널 > 논문
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