A closely-spaced magnetotelluric study of the Ahuachapán-Chipilapa geothermal field, El Salvador

Romo, J. M.; Flores, Carlos; Vega, Raymundo; Vázquez, Rogelio; Flores, Marco A. Pérez; Treviño, Enrique; Esparza, Francisco J.; Quijano, Julio E.; García, Víctor H.

doi:10.1016/s0375-6505(97)00014-x

Cited by 26 publications

(11 citation statements)

References 27 publications

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“…The conductor C 3 corresponds to the reservoir currently being exploited S e a published models. Figure 4(b) shows model B obtained by Romo et al (1997), and in Fig. 4(c) model C is shown, as obtained by an approximate imaging technique valid only for low resistivity contrasts (Romo et al, 1997).…”

Section: Ahuachapán El Salvadormentioning

confidence: 99%

“…Figure 4(b) shows model B obtained by Romo et al (1997), and in Fig. 4(c) model C is shown, as obtained by an approximate imaging technique valid only for low resistivity contrasts (Romo et al, 1997). Every technique has its own limitations, but GAs/RGAs have the advantage that they do not involve approximations that are associated commonly with other methods.…”

Section: Ahuachapán El Salvadormentioning

confidence: 99%

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Application of 2-D inversion with genetic algorithms to magnetotelluric data from geothermal areas

Pérez-Flores

Schultz

2014

Earth Planet Sp

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We apply a modified genetic algorithm, the "recombinant genetic analogue" (RGA) to the inversion of magnetotelluric (MT) data from two different geothermal areas, one in El Salvador and another in Japan. An accurate 2-D forward modelling algorithm suitable for very heterogeneous models forms the core of the inverse solver. The forward solution makes use of a gridding algorithm that depends on both model structure and frequency. The RGA represents model parameters as parallel sets of bit strings, and differs from conventional genetic algorithms in the ways in which the bit strings are manipulated in order to increase the probability of convergence to a global minimum objective function model. A synthetic data set was generated from a chessboard model, and the RGA was shown capable of reconstructing the model to an acceptable tolerance. The algorithm was applied to MT data from Ahuachapán geothermal area in El Salvador and compared with other interpretations. Data from the geothermal area of Minamikayabe in Japan served as a second test case. The RGA is highly adaptable and well suited to non-linear hypothesis testing as well as to inverse modelling.

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Section: Ahuachapán El Salvadormentioning

confidence: 99%

Section: Ahuachapán El Salvadormentioning

confidence: 99%

Application of 2-D inversion with genetic algorithms to magnetotelluric data from geothermal areas

Pérez-Flores

Schultz

2014

Earth Planet Sp

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“…Correlation between the low resistivity and high-temperature zones leads to the conclusion that the influence of clay minerals resulting from hydrothermal alteration processes is not the main contributing factor to enhanced deep conductivities (Romo et al, 1997). The spatial distribution of the deep conductivity anomalies and of the productive wells in the Ahuachapan geothermal area (Fig.…”

Section: Defining Drilling Targetsmentioning

confidence: 99%

“…One is that the low resistivity is a manifestation of the geothermal reservoir itself, which contains hot water in which there are dissolved chemicals. The other is that the low resistivity zone is regarded as a clay alteration halo (Romo et al, 1997). overlying the high-temperature reservoir. Accordingly, in the former, drilling targets are the low resistivity objects, whereas in the latter they are the moderately resistive layers underlying the very conductive ones.…”

Section: Defining Drilling Targetsmentioning

confidence: 99%

Electromagnetic sounding of geothermal zones

Spichak

Manzella

2009

Journal of Applied Geophysics

139

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“…In particular, it is a major geophysical tool in geothermal exploration (e.g., Romo et al, 1997;Uchida et al, 2000Uchida et al, , 2002Fiordelisi et al, 2000;Maneja et al, 2001). The resistivity structure of a geothermal reservoir is often characterised by a combination of a low-resistivity clay-rich cap layer on top and a relatively high-resistivity reservoir zone beneath.…”

Section: Introductionmentioning

confidence: 99%

Stable 3D Inversion of MT Data and its Application to Geothermal Exploration

Uchida¹,

Sasaki²

2006

Exploration Geophysics

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A stable inversion technique has been developed for threedimensional (3D) interpretation of magnetotelluric (MT) data. The inversion method was based on the Gauss-Newton (linearised least-squares) method with smoothness regularisation. Static shifts were also treated as unknown parameters in the inversion. The forward modelling was done by using the staggered-grid finite difference method. The Akaike Bayesian information criterion (ABIC) was applied to the search for the optimum trade-off parameters while the minimising the data misfit, model roughness, and static shifts. The inversion method was then applied to a large volume MT dataset obtained in the Ogiri geothermal area, southwestern Japan. The inversion was performed stably, resulting in a good fit between the observed and computed apparent resistivities and phases. The 3D resistivity structure recovered was generally similar to that obtained by a two-dimensional (2D) inversion in previous work, although the deeper portion of the 3D model seemed to be more realistic than that of the 2D one. The 3D model was also in a good agreement with the geological interpretation of the geothermal reservoirs. These results indicated the necessity for 3D interpretation in geothermal exploration and in other applications in complicated geological environments.

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A closely-spaced magnetotelluric study of the Ahuachapán-Chipilapa geothermal field, El Salvador

Cited by 26 publications

References 27 publications

Application of 2-D inversion with genetic algorithms to magnetotelluric data from geothermal areas

Application of 2-D inversion with genetic algorithms to magnetotelluric data from geothermal areas

Electromagnetic sounding of geothermal zones

Stable 3D Inversion of MT Data and its Application to Geothermal Exploration

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