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The use of PhaseNet and GaMMA in microseismic monitoring in geothermal fields Ambara Putra, I Putu Raditya
Jurnal Ilmiah MTG Vol 14, No 2 (2023): Jurnal Ilmiah MTG Volume 14 No.2 Tahun 2023
Publisher : Jurusan Teknik Geologi Fakultas Teknologi Mineral UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/jmtg.v14i2.11436

Due to its potential to decrease greenhouse gas emissions and decrease dependence on fossil fuels, geothermal energy has recently attracted more attention as a renewable and sustainable form of power generation. For evaluating the reservoir's integrity and understanding the underlying geomechanical processes in geothermal fields, microseismic monitoring is crucial. To accurately analyze and understand these microseismic occurrences, it is necessary to accurately identify their phases. Therefore, in this study, we used the PhaseNet-GaMMA combination to identify the arrival times of P and S and associate them to determine the microseismic events of these phases. PhaseNet-GaMMA succeeded in detecting a greater number of phases and events compared to catalog data, where the identification match rate was 85%. Even so, the time required for automatic detection of PhaseNet-GaMMA is relatively short and simple, so it is very good if used as an initial stage in the process of identifying phases and microevents in geothermal fields.

Implementation of CO2 Source-Sinks Match Database Development. Case Study: West Java Tony, Brian; Nugraha, Fanata Yudha; Al Hakim, Muhamad Firdaus; Putra, I Putu Raditya Ambara; Chandra, Steven
Journal of Petroleum and Geothermal Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/jpgt.v5i2.13432

Carbon capture and storage (CCS) is widely recognized as a significant technology in mitigating carbon dioxide (CO2) emissions from major industrial facilities, such as power plants and refineries. CCS involves the capture of concentrated CO2 streams from point sources, followed by subsequent safe and secure storage in appropriate geological reservoirs. We developed spatial database system using Geographic Information System (GIS) tools to facilitate source-sink matching between CO2 emitter and CO2 storage to foster the implementation of CCS/CCUS technologies in Indonesia. In this study, we proposed workflow approach to determine the location of CO2 sinks/storage candidates given limited data available. Additionally, this method spatially characterizes and represents probable clusters where opportunities for CCS/CCUS implementation are present. We consider the existing pipeline route and Right of Ways (ROW) to minimize the potential cost related to transportation of CO2 using pipeline. The priority of available storage is classified based on the storage capacity, distance, and other technical criteria to determine the optimal location of potential CO2 injection. We applied the workflow to Coal Fired Power Plant in West Java as the CO2 source, and we obtained 6 depleted fields that are connected to the existing ROW with CO2 storage capacity of 42.03 MMT.

The use of PhaseNet for Event Identification of Microearthquake Monitoring in Geothermal Field Al Hakim, Muhamad Firdaus; Ambara Putra, I Putu Raditya
Journal of Petroleum and Geothermal Technology Vol. 6 No. 1 (2025): May
Publisher : Universitas Pembangunan Nasional "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/jpgt.v6i1.13437

Geothermal energy is a sustainable energy source that requires continuous microseismic monitoring to assess reservoir integrity and geomechanical behavior. Traditional phase identification methods are challenged by noisy environments and complex waveforms, especially in geothermal fields. This study explores the efficacy of PhaseNet, a deep learning neural network model, in detecting P and S wave arrival times for micro-earthquake events. The PhaseNet model was retrained using local seismic data from a geothermal field and tested for its performance in identifying seismic phases. The results were validated against a manual seismic catalog, with additional clustering and association analysis conducted using GaMMA and hypocenter locations determined with NonLinLoc. The findings demonstrate that PhaseNet, combined with GaMMA, provides robust phase detection capabilities, essential for early-stage monitoring in geothermal development.

IMAGING DISPERSION CURVE OF DISPERSIVE WAVES USING SHORT-TIME FOURIER TRANSFORM: 2025 MYANMAR EARTHQUAKE M 7.7 Kurniawan, Muhammad Fachrul Rozi; Putra, I Putu Raditya Ambara; Pratama, Yudha Agung
JGE (Jurnal Geofisika Eksplorasi) Vol. 11 No. 3 (2025)
Publisher : Engineering Faculty Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jge.v11i3.490

Understanding of Earth's subsurface is crucial for mitigating geological hazards, particularly earthquakes. A key parameter for subsurface characterization is the surface wave dispersion curve, which strongly reflects shear wave velocity (Vs) at various depths. This study presents an extraction of dispersion curves from earthquake signals using the Short-Time Fourier Transform (STFT). The STFT method enables the analysis of non-stationary signals like earthquake signals by dividing them into small segment, assumed-stationary segments, then applying the Fourier Transform to each segment. This process generates a time-frequency spectrogram that represents the evolution of frequencies over time. Myanmar earthquake M 7.7 is one of the greatest earthquakes that have damaging impacts. We used three inline stations for evaluating the waveform at CHTO (Chiang Mai, Thailand), KAPI (Sulawesi, Indonesia), and WRAB (Tennant Creek, NT, Australia). Waveform for KAPI and WRAB stations categorized teleseismic event represented good penetration waves to image deeper subsurface layes. Surface waves clearly seen at KAPI and WRAB classified by very low frequency and high amplitude in wave group train. The spectrogram, energy peaks at each frequency can be identified, which directly correlate with the group velocity of the surface waves. STFT successfully extract dispersion curve of surface waves at KAPI and WRAB station. However, the dispersion curve could not be extracted at CHTO station because its too close to the epicentre resulted in significant interference of waves phase caused inseparable frequency spectrum on each wave phases. Remarks on the study is stations nearer to the epicenter exhibit a higher frequency and broader range of dominant frequency, while those farther away show a lower frequency and narrow frequency range. The advantage of the STFT method lies in its ability to enable the identification of dispersion modes with good time-frequency resolution.

Determining the Best Zone for Waste Storage Ponds: Integrating DEM analysis and Satellite Gravity data in the Prospect Area of Ungaran Geothermal Mining Working Area, Semarang, Indonesia. Humairoh, Wahyuni Annisa -; Mardiati, Dani; Ambara Putra, I Putu Raditya
Journal of Applied Sciences, Management and Engineering Technology Vol 6, No 2 (2025)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jasmet.2025.v6i2.8194

The Ungaran Geothermal Mining Working Area, Mount Ungaran, has geothermal prospects around the Gedongsongo and Nglimut areas, which have the potential to develop as Indonesian geothermal exploration projects. The challenges in developing geothermal exploration projects in Indonesia are the PLTP sector, which generates geothermal waste in the form of brine and geothermal mud. If discharged into the environment, this waste can pose a threat to human health and ecosystems. This study aimed to specify the most suitable zone for waste storage ponds in the Ungaran geothermal prospect area. The method integrates data analysis of Digital Elevation Model (DEM) imagery, Landsat imagery, and air gravity data, which produces integrated maps, such as Maps of Fault and Fractures density and Maps of Land Cover. Second vertical derivative (SVD) analysis from air gravity data is also used to ensure the presence of a structure. There are five parameters for determining the pond-making zone: Not a residential area with a slope of less than 15%, distance from the fault is more than 200 m, distance from the road is more than 100 m, and distance from areas of geothermal manifestations such as hot springs and fumaroles is more than 200 m. Based on the interpretation of the integrated maps resulting from the analysis, several zones are suitable for creating waste storage ponds in the Nglimut and Gedongsongo prospect areas. The Nglimut area has potential zones, in contrast. In the Gedongsongo area, there are no potential zones. The Nglimut prospect has two possible zones; the best zone is N2, where all five parameters are perfectly satisfied. The northern area of N1 has one geothermal manifestation (hot spring). The best-to-fair zones are N2 and N1.

The use of PhaseNet and GaMMA in microseismic monitoring in geothermal fields Ambara Putra, I Putu Raditya
Jurnal Ilmiah MTG Vol 14 No 2 (2023): Jurnal Ilmiah MTG Volume 14 No.2 Tahun 2023
Publisher : Jurusan Teknik Geologi Fakultas Teknologi Mineral UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/jmtg.v14i2.11436

Due to its potential to decrease greenhouse gas emissions and decrease dependence on fossil fuels, geothermal energy has recently attracted more attention as a renewable and sustainable form of power generation. For evaluating the reservoir's integrity and understanding the underlying geomechanical processes in geothermal fields, microseismic monitoring is crucial. To accurately analyze and understand these microseismic occurrences, it is necessary to accurately identify their phases. Therefore, in this study, we used the PhaseNet-GaMMA combination to identify the arrival times of P and S and associate them to determine the microseismic events of these phases. PhaseNet-GaMMA succeeded in detecting a greater number of phases and events compared to catalog data, where the identification match rate was 85%. Even so, the time required for automatic detection of PhaseNet-GaMMA is relatively short and simple, so it is very good if used as an initial stage in the process of identifying phases and microevents in geothermal fields.

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