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The Larderello-Travale geothermal area. The 86 MT sites are located in the study area around Travale and were acquired during three different MT campaigns in 1992 (black-labeled sites), 2004 (blue-labeled sites), and 2006–07 (red-labeled sites). Only a subset of 51 sites belonging to the 2004 dataset were selected for 3D inversion because of their low level of noise, similarity of the period range, and the regular spatial distribution of the site locations. The dashed box represents the central area of the 3D model mesh.
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The geoelectrical features of the Travale geothermal field (Italy), one of the most productive geothermal fields in the world, have been investigated by means of three-dimensional (3D) magnetotelluric (MT) data inversion. This study presents the first resistivity model of the Travale geothermal field derived from derivative-based 3D MT inversion. W...
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The magnetotelluric method is a geophysical method commonly used to map subsurface resistivity. The subsurface’s true resistivity is generated by inversion of the magnetotelluric data. Inversions carried out using conventional methods such as linear and global approaches have several limitations including the need for an initial model, models trapp...
Citations
... The resulting algorithms for estimating the depth of the Curie point location in the section Lu et al., 2022) allow us to characterize the thermal state of the subsurface and indirectly make a qualitative assessment of the electrical resistivity of blocks in the geological section, using the correlation between the depth of the Curie point and the value of the heat flux. This follows from the fact that in the active regions of the Earth with an anomalous heat flux, MT methods usually determine the crustal zones of higher electrical conductivity caused by the partial melting of the rocks from the penetrating magmatic intrusions (Pace et al., 2022). ...
... Due to the penetration depth, among the electromagnetic methods, magnetotelluric (MT) has been widely used both for the characterization of volcanoes worldwide (e.g. Bertrand et al., 2022;Pace et al., 2022), and in particular in the CVZ (e.g. Díaz et al., 2015;Comeau et al., 2016;Mancini et al., 2019), as well as for studies in the Andean subduction zone (e.g. ...
... Геотермальные ресурсы разделяют на гидротермальные, предполагающие использование подвижных теплоносителей -природного пара, термальных вод и пароводных смесей [1], а также петротермальные, использующие энергию горячих твердых пород и их расплавов, которая может быть извлечена в их теплообмене с техногенным подвижным теплоносителем [2][3][4]. Поскольку в первом случае носителем тепла являются флюиды, то для поиска их источников и каналов транспортировки к поверхности используют методы электромагнитного (чаще всего магнитотеллурического) зондирования, наиболее чувствительные к типу и объему флюидов [5][6][7][8][9][10][11][12][13]. В то же время применение петротермальной энергетики основывается на том тепле, которое можно получать не только из подземных вод, но и из сухих твердых пород (так называемые «инженерные» системы, или HDR (англ.: Hot Dry Rocks)). ...
... вставку на рис. 1), и измерений в скважинах в работах [7][8][9][10][11][12] были построены двумерные модели удельного сопротивления, пористости, проницаемости, температуры, теплопроводности и удельной теплоемкости (рис. 2). ...
The purpose of the study is identification of locations promising for geothermal resource exploration based on the joint analysis of sections of petro- and thermophysical properties of rocks. Having conducted magnetotelluric sounding in the geothermal area under investigation and having built 2-D models of electrical resistivity, porosity, permeability, temperature, thermal conductivity and specific heat capacity, the authors performed a joint cluster analysis of sections of enumerated parameters. A cluster cross-section is constructed taking into account all the discussed parameters, which is essentially a petro- and thermophysical “passport” of the site under examination. Each cluster of the latter is characterized by its own set of property ranges. Two promising locations for drilling exploratory boreholes have been identified in the Soultz-sous-Forêts (France) geothermal area. One of them corresponds to the already developed reservoir of petrothermal energy, while another is located at the depths of 2–3 km in another part of the section and has a potential for future exploration works. The information base created on this basis is a convenient tool for interactive selection of sites promising for exploration of different deposits and building of a “prospectivity map” of surface sites for drilling exploration boreholes.
... We set an error floor as a portion of the absolute value of the impedance components (|Zij|) instead of the mean of the non-diagonal components (|Zxy·Z xy| 1/2 ), because the mean value might have underestimated one component of the impedance tensor relative to the others [58,59]. For the Zxx and Zyy components, the error limit was set to a minimum value of 15% and for the Zxy and Zyx components to 8% (error floor). ...
The geoelectric properties of the geothermal system associated with the Tolhuaca volcano were investigated by three-dimensional (3D) inversion of magnetotelluric (MT) data. This study presents the first resistivity model of the Tolhuaca volcano derived from 3D MT inversion to have a better understanding of its magmatic and hydrothermal system. We selected data from 54 MT stations for 3D inversion. We performed a series of 3D MT inversion tests by changing the type of data to be inverted, as well as the starting model to obtain a model in agreement with the geology. The final 3D MT model presents a conductive body (<20 Ωm) located 2 km below the summit of Tolhuaca volcano, inferred as a shallow magmatic storage compartment. We also distinguish a ~300 m thick layer of high conductivity (<10 Ωm) corresponding to argillic hydrothermal alteration. The MT model includes two resistive bodies (~200 Ωm) in the upper crust below the laterally displaced argillic alteration layer to the west beneath the extinct Tolhuaca, which would correspond to a shallow reservoir (~1000 m from the surface) and a deep reservoir (>1800 m from the surface) that had so far not been identified by previous resistivity models. The result of this study provides new insights into the complexity of the Tolhuaca geothermal system.
... The first case study is a TDEM sounding acquired for groundwater prospection over a known stratigraphy (Pace et al., 2019a). The second case study is located in the Travale geothermal area (central Italy), where TDEM soundings were acquired for the characterization of the uppermost part of the geothermal reservoir (Pace et al., 2022). The final results are quantitatively compared to finally show a thorough overview of the algorithms' performances. ...
... This is the place where geothermal exploration is said to be born in 1913 (Arias et al., 2010) and one of the most productive geothermal areas of the world (Bertani et al., 2005;Manzella et al., 2019). The Travale area has been investigated by numerous geophysical surveys over the last decades, but the electromagnetic methods have been of pivotal importance to detect the Figure 4 The curves of the objective function evolution with the iterations for the GWO (pink), PSO (blue) and GA (black) algorithms for the synthetic example A Comparative Analysis of Three Computational-Intelligence Metaheuristic Methods resistivity distribution of the deep features of the geothermal system (Manzella, 2004;Manzella et al., 2010;Muñoz, 2014;Pace, 2020;Pace et al., 2022;. The TDEM soundings were acquired in Travale to be jointly interpreted along with magnetotelluric soundings and hence to improve the characterization of the geothermal field (Pace, 2020;Pace et al., 2022). ...
... The Travale area has been investigated by numerous geophysical surveys over the last decades, but the electromagnetic methods have been of pivotal importance to detect the Figure 4 The curves of the objective function evolution with the iterations for the GWO (pink), PSO (blue) and GA (black) algorithms for the synthetic example A Comparative Analysis of Three Computational-Intelligence Metaheuristic Methods resistivity distribution of the deep features of the geothermal system (Manzella, 2004;Manzella et al., 2010;Muñoz, 2014;Pace, 2020;Pace et al., 2022;. The TDEM soundings were acquired in Travale to be jointly interpreted along with magnetotelluric soundings and hence to improve the characterization of the geothermal field (Pace, 2020;Pace et al., 2022). The eight TDEM sites (a1, b2, b6, e1, g1, k1, k4, and k5) were located on different geological settings and are shown in Fig. 9. ...
We focus on the performances of three nature-inspired metaheuristic methods for the optimization of time-domain electromagnetic (TDEM) data: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO) algorithms. While GA and PSO have been used in a ple-thora of geophysical applications, GWO has received little attention in the literature so far, despite promising outcomes. This study directly and quantitatively compares GA, PSO and GWO applied to TDEM data. To date, these three algorithms have only been compared in pairs. The methods were first applied to a synthetic example of noise-corrupted data and then to two field surveys carried out in Italy. Real data from the first survey refer to a TDEM sounding acquired for groundwater prospection over a known stratigraphy. The data set from the second survey deals with the characterization of a geothermal reservoir. The resulting resistivity models are quantitatively compared to provide a thorough overview of the performances of the algorithms. The comparative analysis reveals that PSO and GWO perform better than GA. GA yields the highest data misfit and an ineffective minimization of the objective function. PSO and GWO provide similar outcomes in terms of both resistivity distribution and data misfits, thus providing compelling evidence that both the emerging GWO and the established PSO are highly valid tools for stochastic inverse modeling in geophysics.
The paper presents the techniques and results of the joint interpretation of AMTS and RMT-C tensor data in the area of the Karelian Isthmus near the village of Yablonovka, promising the discovery of primary diamond sources. Earlier, according to drilling data, fluid-cataclastic breccias, fluidolites, were identified here, which, according to modern geological concepts, can be primary diamond sources. The site is located in the marginal part of the Pash-Ladoga rift zone and is characterized by a complex three-dimensional structure. Under these conditions, in the presence of a system of multidirectional faults, the joint interpretation of the AMTS and RMT-C tensor data was performed within a 3D model of the medium. To reduce the inversion execution time, an approach and a procedure for transforming the RMT-C data obtained in the intermediate zone of a controlled source to sounding curves corresponding to the plane wave model are proposed. The transformed RMT-C apparent resistivity curves were also used to correct the static shifts of the AMTS curves. A joint 3D inversion was performed taking into account the relief. The resulting model is in good agreement with the geological data obtained from the wells previously drilled near the work site. The geoelectric sections show zones of relatively increased resistivity, which may be associated with fluidolites. The developed approaches to the joint 3D interpretation of AMTS and RMT-C tensor data and the obtained information about the structure and properties of the basement rocks, rocks hosting fluidolites, and overlying sedimentary deposits, can be used in prospecting works for diamonds on the Karelian Isthmus and adjacent territories.
This paper discusses the results of magnetic variation soundings at two sites in the eastern Arctic, in the Chaun Bay region, at the Pevek and Valkarkai weather stations, by using the ModEM program to perform a three–dimensional (3D) inversion of tippers. The inversion has produced a geoelectric model of the region in a subsurface area of 300 × 300 × 200 km. The moduli of tippers at both sites have values between 0.2 and 0.85, reaching the maximum ones in a period of 1000 s. At the Pevek weather station, the real induction arrow in the Parkinson convention is oriented to the west throughout the range of periods. At the second site, its azimuth changes from 30° to the NE to –30° to the NW as the period of variations increases. Throughout the range of depths, conductive inhomogeneities are located to the west and north of the Chaun folded zone. They extend as a narrow strip under the western and northern coastal parts of the zone at depths of 8–12 km. As the depth increases, they are split into blocks, which reach their maximum size in the horizontal plane at depths ranging between 20 and 30 km. The most prominent among them is the conductive block beneath the southern part of the Chaun Bay. The roots of these blocks are distinguishable at depths of up to 100 km. The Chaun folded zone is represented by a high–resistance block down to a depth of 150 km with an electrical resistivity of more than 1000 Ω⋅m. Comparison of the obtained geoelectric model with the geophysical studies previously conducted here reveals a correlation between the location of conductive formations and the location of weakly positive anomalies in the gravity field in the Bouguer and isostatic reductions in the coastal water area.
Japanese geothermal power plants are expected to be significant renewable energy sources owing to the abundance of geothermal sources in Japan. The plant capacity factor in geothermal power plants is low. One reason is frequent sudden pressure drops in production well corresponding to the change of subsurface condition. To obtain a stable steam quantity, it is necessary to observe the subsurface conditions in real-time and perform appropriate operations. The use of a model to predict steam enthalpy in real-time has potential to monitor changes in subsurface conditions and contribute to the composition of plant operational strategies. However, training a model requires a large amount of data. The purpose of this study is to evaluate the effectiveness of transferring the knowledge of a pretrained model for predicting steam enthalpy in one plant to another plant with limited data. This study proposes a methodology based on the combination of the temporal fusion transformer (TFT) architecture and transfer learning (TL). This approach represents a novel way to share knowledge from a pretrained model based on historical data from a plant, which helps reduce the need for large amounts of data when dealing with a new plant. A pretrained TFT model (PM) enables the prediction of rapid steam enthalpy decreases in the source plant. Transfer learning using a PM was confirmed to enhance the performance of steam enthalpy prediction in another plant compared to using a model without pretraining. The effectiveness of transfer techniques has the potential to contribute to improving the operational efficiency of geothermal power plants. The transfer learning strategies proposed in this study heavily rely on the similarity of the source data. In the future, we aim to compute data correlations between plants and the effectiveness of transfer learning.
