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IUGG XXIV General Assembly July 2-13, 2007 Perugia, Italy<br />

(S) - <strong>IASPEI</strong> - International Association of Seismology and Physics of the Earth's<br />

Interior<br />

JSS007 Oral Presentation 1953<br />

Artificial neural network analysis of MT and geothermal data to delineate<br />

the 3-D structure of Tattapani geothermal Region, Central India<br />

Dr. Saurabh Verma<br />

Geophysical Exploration and Modeling National Geophysical research Institute, India IAGA<br />

Artificial neural network modeling employing magneto-telluric and heat flow data is done to derive the<br />

3-D structure of the Tattapani geothermal region, Sarguja district, M.P., India. The region represents<br />

one of the most prominent geothermal fields in central India that comprises several hot springs with<br />

temperatures ranging between 50 to 98 0C. The geothermal activity is governed by the major ENE-<br />

WSW trending Tattapani fault and cross faults oriented in NE-SW direction. A number of 300-350 m<br />

deep boreholes drilled within an area of 4 x 0.5 sq km yield temperatures in the range 1000C to 2100C<br />

that is also corroborated by the geochemically computed base temperature. Analysis of MT data reveals<br />

the 3-D depth-wise distribution of resistivity from which the temperatures are derived employing the<br />

borehole data. The ANN algorithm to generate the 3-D structure of the geothermal field utilizes these<br />

data sets along with the measured heat flow data. Correlation with the local structural and the regional<br />

tectonic framework highlights the factors that control the dynamics of geothermal fluids in the region.<br />

Keywords: tattapani geothermal field, 3d geothermal structure, ann analysis mt and heat flow

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