Ability of the magnetotelluric method to image a deep conductor: Exploration of a supercritical geothermal system

Ishizu, Keiichi; Ogawa, Yasuo; Mogi, Toru; Yamaya, Yusuke; Uchida, Toshihiro

doi:10.1016/j.geothermics.2021.102205

Cited by 9 publications

(2 citation statements)

References 47 publications

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“…Therefore C 1 is an important feature in the presented model. It is known that the inverse solution of MT data is non‐unique, meaning that many models can fit the observed data equally well (Constable et al., 1987; Ishizu et al., 2021). We calculate the MT responses for models where the resistivity values of C 1 are replaced by values between 1 and 100 Ωm (1, 3, 10, 30, and 100 Ωm) to determine how well the resistivity values are constrained by the data (Figure S7 in Supporting Information S1).…”

Section: Resultsmentioning

confidence: 99%

Estimation of Spatial Distribution and Fluid Fraction of a Potential Supercritical Geothermal Reservoir by Magnetotelluric Data: A Case Study From Yuzawa Geothermal Field, NE Japan

et al. 2022

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Fluids within the Earth's crust may exist under supercritical conditions (i.e., >374°C and >22.1 MPa for pure water). Supercritical geothermal reservoirs at depths of 2–10 km below the surface in northeastern (NE) Japan mainly consist of magmatic fluids that exsolved from the melt during the course of fractional crystallization. Supercritical geothermal reservoirs have received attention as next‐generation geothermal resources because they can offer significantly more energy than that obtained from conventional geothermal reservoirs found at temperatures <350°C. However, the spatial distribution and fluid fraction of supercritical geothermal reservoirs, which are required for their resource assessment, are poorly understood. Here, the magnetotelluric (MT) method with electrical resistivity imaging is used in the Yuzawa geothermal field, NE Japan, to collect data on the fluid fraction and spatial distribution of a supercritical geothermal reservoir. The collected MT data reveal a potential supercritical geothermal reservoir (>400°C) with dimensions of 3 km (width) × 5 km (length) at a depth of 2.5–6.0 km below the surface. The estimated fluid fraction of the reservoir is 0.1%–4.2% with salinity values of 5–10 wt%. The melt is also imaged below the reservoir, and based on the resistivity model; we develop a mechanism for the evolution of the supercritical geothermal reservoir, wherein upwelling supercritical fluids supplied from the melt are trapped under less permeable silica sealing and accumulate there.

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Section: Resultsmentioning

confidence: 99%

Estimation of Spatial Distribution and Fluid Fraction of a Potential Supercritical Geothermal Reservoir by Magnetotelluric Data: A Case Study From Yuzawa Geothermal Field, NE Japan

et al. 2022

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“…The development of electromagnetic (EM) geophysical methods in the 1950s has played a crucial role in mapping the lateral and vertical 2 variations in subsurface resistivity. These methods, such as natural source audio magnetotelluric (AMT) and controlled source (CSAMT), have found wide-range of applications in metallic mineral exploration [5][6][7][8][9], groundwater studies [10], and geothermal system investigations [11][12][13][14]. Induced polarization (IP) also has a longstanding history in geophysics, initially employed in the field of mining geophysics to delineate and localize ore bodies [15,16].…”

Section: Introductionmentioning

confidence: 99%

Application of Audio-Magnetotelluric and Dual-Frequency Induced Polarization Joint Inversion in a Pb-Zn Deposit Exploration in Inner Mongolia, China

Fahad,

Liu,

Chen

et al. 2024

Preprint

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Models of subsurface structures are important for successful deposit exploration but are challenged by the need to integrate data from different geophysical methods. In the present study, we evaluated a method of joint inversion in which audio-magnetotelluric (AMT) and dual frequency induced polarization (DFIP) data sets are inverted simultaneously to produce a consistent 2D resistivity model to show a clear image of subsurface structures. To achieve the objectives, we conducted AMT and DFIP surveys first on the same survey line within the Dongjun lead-zinc deposit in inner Mongolia by developing 31 AMT survey sites with an interstation separation of 40 m on a 1440 m survey track and operated in fifty-three frequencies in the range 1–10,400 Hz to record the resistivity distribution of subsurface to depths exceeding 800 m. The same survey setup up was applied to the DFIP method by utilizing the SQ-3C model with a pole-dipole array configuration and conducted measurements at frequencies of 4 Hz and 4/13 Hz. We first separately inverted AMT and DFIP measurements. The two-dimensional (2D) model obtained from AMT data revealed distinct low resistivity anomalies in the middle of the 2D inversion model. In contrast, the DFIP inversion model showed a high resistive body in the same region. In response to the discrepancies observed in the separate 2D inversion models, we implemented a joint inversion for both the AMT and DFIP datasets. The joint inversion resistivity model shows surficial conducting bodies and a high conductive body along the profile with relatively high Percent Frequency Effect (PEF) indicating high chargeability. The final joint inversion resistivity model clearly images the large silica alteration zone and the Pb-Zn mineralization. This study demonstrates the feasibility of a joint inversion methodology and highlights the value of integrating geophysical methods through joint inversion for enhanced characterization and exploration of lead-zinc ores.

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Robust inversion of 1D magnetotelluric data using the Huber loss function

Desifatma,

Djaja,

Pratomo

et al. 2024

Comput Geosci

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In this study, a robust 1D inversion was performed on magnetotelluric (MT) data by utilizing the Huber loss function to avoid misleading interpretation of results, which is caused by the presence of outliers in the data. The MT method utilizes the ratio between the electric field (E) and the magnetic field (H), which are perpendicular to each other, to obtain impedance values (Z). These values are used to extract subsurface information in the form of electrical resistivity variations with depth. In the study, forward modeling of the 1D MT responses was conducted by recursively calculating Z values. Robust inversion was performed using the Huber loss function as an objective function to minimize the difference between the calculated and observed data. The Huber loss function combines the squared loss and the absolute loss to anticipate the presence of outliers in MT observation data. The robust inversion was performed on synthetic data with additional noise and field data. The inversion process utilized 50 layers with a thickness of 500 m, a hyperparameter $$\varvec{\delta }= 5\times {10}^{-2}$$ δ = 5 × 10 - 2 , $$\lambda =0.01$$ λ = 0.01 , and $${\lambda }_{t}=100$$ λ t = 100 . The number of iterations used was 500 for inversions that used synthetic data and 3000 for inversions that used field data. The robust inversion scheme using the Huber loss function successfully overcame the presence of outliers and accurately estimated the actual model parameters, both for synthetic data and field data that contained outliers. The misfit was relatively small and close to zero, indicating that the inversion code works well.

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Ability of the magnetotelluric method to image a deep conductor: Exploration of a supercritical geothermal system

Cited by 9 publications

References 47 publications

Estimation of Spatial Distribution and Fluid Fraction of a Potential Supercritical Geothermal Reservoir by Magnetotelluric Data: A Case Study From Yuzawa Geothermal Field, NE Japan

Estimation of Spatial Distribution and Fluid Fraction of a Potential Supercritical Geothermal Reservoir by Magnetotelluric Data: A Case Study From Yuzawa Geothermal Field, NE Japan

Application of Audio-Magnetotelluric and Dual-Frequency Induced Polarization Joint Inversion in a Pb-Zn Deposit Exploration in Inner Mongolia, China

Robust inversion of 1D magnetotelluric data using the Huber loss function

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