Delineating geothermal system through 3D geomagnetic and gravity data inversion on Blawan Ijen geothermal area, East Java

The concurrent development of the Steamboat Hills geothermal area for power production and the adjacent alluvial aquifers for drinking water in Washoe County, Nevada, necessitates a good understanding of the hydrogeologic connection between these water resources. The problem is that adequate characterization of the subsurface geologic structure is not possible with existing geologic data. This need prompted us to construct a detailed 3D representation of the subsurface geologic structure based on 2.75D forward modeling of 11 gravity and aeromagnetic profiles constrained by geologic data and physical (density, magnetic susceptibility, remanent magnetic) properties. Potential-fields modeling results provided greater definition of the alluvial basins, and when combined with well-log data, yield an overall basin volume surrounding Steamboat Hills that is 64% greater than the volume derived from well-log data alone. A representation of the geothermal reservoir, consisting of altered granodiorite and metamorphic rocks, illustrates that the flow of thermal water is fault controlled. The model also suggests that thermal water may upflow along an unexplored fault flanking western Steamboat Hills. North-trending faults that conduct thermal water from the geothermal system to the alluvial aquifer appear to be zones of altered volcanics that produce subtle aeromagnetic anomalies.

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Gravity data inversion based genetic algorithm and generalized least squares

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems ◽

10.1109/icicisys.2009.5358049 ◽

2009 ◽

Author(s):

Ning Qiu ◽

Qingsheng Liu ◽

Quanye Gao

Keyword(s):

Genetic Algorithm ◽

Least Squares ◽

Gravity Data ◽

Generalized Least Squares ◽

Data Inversion

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Geomagnetic Survey to Explore High-Temperature Geothermal System in Blawan-Ijen, East Java, Indonesia

E3S Web of Conferences ◽

10.1051/e3sconf/20183102003 ◽

2018 ◽

Vol 31 ◽

pp. 02003 ◽

Cited By ~ 3

Author(s):

Yunus Daud ◽

Syamsu Rosid ◽

Fikri Fahmi ◽

Faris Maulana Yunus ◽

Reza Muflihendri

Keyword(s):

High Temperature ◽

Magnetic Anomaly ◽

Volcanic Rocks ◽

Hydrothermal Activity ◽

Geothermal System ◽

Geothermal Area ◽

Variation Reduction ◽

Geomagnetic Survey ◽

Surface Alteration ◽

Steep Topography

Ijen geothermal area is high-temperature geothermal system located in Bondowoso regency, East Java. It is categorized as caldera-hosted geothermal system which is covered by quaternary andesitic volcanic rocks with steep topography at the surrounding. Several surface thermal manifestations are found, such as altered rocks near Mt. Kukusan and a group of Blawan hotsprings in the northern part of the caldera. Geomagnetic survey was conducted at 72 stations which is distributed inside the caldera to delineate the existence of hydrothermal activity. Magnetic anomaly was obtained by reducing total magnetic measured on the field by IGRF and diurnal variation. Reduction to pole (RTP) method was applied with geomagnetic inclination of about -32°. In general, the result shows that high magnetic anomaly is distributed at the boundary of study area, while low magnetic anomaly is observed in the centre. The low anomaly indicates demagnetized rock that probably caused by hydrothermal activity. It has a good correlation with surface alteration observed close to Mt. Kukusan as well as high temperature reservoir drilled in the centre of caldera. Accordingly, the low magnetic anomaly also presents the possibility of geothermal reservoir in Ijen geothermal area.

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Disjoint interval bound constraints using the alternating direction method of multipliers (ADMM) for geologically constrained inversion: Application to gravity data

Geophysics ◽

10.1190/geo2019-0633.1 ◽

2020 ◽

pp. 1-45

Author(s):

Vitaliy Ogarko ◽

Jérémie Giraud ◽

Roland Martin ◽

Mark Jessell

Keyword(s):

Gravity Data ◽

Alternating Direction Method ◽

Rock Properties ◽

Model Parameters ◽

Method Of Multipliers ◽

Geological Modeling ◽

Bound Constraints ◽

Data Inversion ◽

Alternating Direction ◽

Geological Information

To reduce uncertainties in reconstructed images, geological information must be introduced in a numerically robust and stable way during the geophysical data inversion procedure. In the context of potential (gravity) data inversion, it is important to bound the physical properties by providing probabilistic information on the number of lithologies and ranges of values of possibly existing related rock properties (densities). For this purpose, we introduce a generalization of bounding constraints for geophysical inversion based on the alternating direction method of multipliers (ADMM). The flexibility of the proposed technique enables us to take into account petrophysical information as well as probabilistic geological modeling, when it is available. The algorithm introduces a priori knowledge in terms of physically acceptable bounds of model parameters based on the nature of the modeled lithofacies in the region under study. Instead of introducing only one interval of geologically acceptable values for each parameter representing a set of rock properties, we define sets of disjoint intervals using the available geological information. Different sets of intervals are tested, such as quasi-discrete (or narrow) intervals as well as wider intervals provided by geological information obtained from probabilistic geological modeling. Narrower intervals can be used as soft constraints encouraging quasi-discrete inversions. The algorithm is first applied to a synthetic 2D case for proof-of-concept validation and then to the 3D inversion of gravity data collected in the Yerrida basin (Western Australia). Numerical convergence tests show the robustness and stability of the bound constraints we apply, which is not always trivial for constrained inversions. This technique can be a more reliable uncertainty reduction method as well as an alternative to other petrophysically or geologically constrained inversions based on more classical “clustering” or Gaussian-mixture approaches.

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