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dc.contributor.authorKasiaheng, Muthia
dc.date.accessioned2025-08-11T08:22:37Z
dc.date.available2025-08-11T08:22:37Z
dc.date.issued2025-08-11
dc.identifier.urihttps://library.universitaspertamina.ac.id//xmlui/handle/123456789/14435
dc.description.abstractAccurate delineation of geothermal reservoir zones containing hot fluids is crucial for optimizing exploration and production strategies. This study applies the Low Frequency Passive Seismic (LFPS) method, integrating Vertical-to-Horizontal Spectral Ratio (V/H) and vertical component Power Spectral Density (PSD-Z) anal yses, to characterize fluid-related anomalies in Field N of the SN Geothermal Field. Three-component microtremor data from 128 acquisition points were processed us ing a modified hvsrpy workflow, incorporating bandpass filtering (1–10 Hz), Fast Fourier Transform (FFT), and a Short-Term Average/Long-Term Average (STA/LTA)-based window rejection algorithm to enhance spectral stability. Re sults reveal a clear differentiation between proven, potential, and non-potential zones. Proven reservoir zones exhibit strong, consistent fundamental peaks in the low-frequency range (1–3 Hz) with harmonic responses, indicative of wave–fluid interactions in hydraulically connected, fluid-saturated porous media. Potential zones display similar patterns but with moderate amplitudes, suggesting lower sat uration or weaker connectivity, while non-potential zones lack significant low-fre quency anomalies. Spatial correlation between high V/H and PSD-Z values aligns with mapped fault structures, underscoring structural control on geothermal fluid migration. This integrated LFPS approach demonstrates high reliability and cost effectiveness in delineating geothermal reservoirs for guiding drilling targets and further geophysical surveys.en_US
dc.subjectGeothermal, microtremor, LFPS, V/H Ratio, PSD-Z, reservoir, spec tral anomaly.en_US
dc.titleIdentification of a Geothermal Reservoir Zone Using the Low-Frequency Passive Seismic (LFPS) Method in the SN Field.en_US
dc.typeThesisen_US


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