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Location of the study area highlighting the surrounding area of the RG4 well (signed as a black triangle). (A) São Miguel DEM; (B) orthophoto map of the RG4 surrounding area in 2006, before the thermal anomaly; and (C) UAV RGB image after the thermal anomaly, 2020 (Source: CIVISA). The red line corresponds to the area with the highest concentration of anomalies and visible alteration of vegetation.
Source publication
Current-day volcanic activity in the Azores archipelago is characterized by seismic events and secondary manifestations of volcanism. Remote sensing techniques have been widely employed to monitor deformation in volcanic systems, map lava flows, or detect high-temperature gas emissions. However, using satellite imagery, it is still challenging to i...
Contexts in source publication
Context 1
... February 2010, the only existing gas emission and thermal anomaly in the area were circumscribed to Caldeiras da Ribeira Grande fumarole (highlighted as a star in Figure 1). Gas and temperature measurements began periodically in the area after February 2010, and a new fumarolic field (with maximum temperatures around 100 • C and hydrothermal compositions) developed in the area after May 2010 ( Figure 2). Since early 2012, the spatial distribution of the main gas emissions (essentially CO 2 ) and anomalous thermal areas remained quite stable [41]. ...
Context 2
... selected study site (Figure 4) was chosen from the total geothermal exploration area (Figure 1) and aimed at minimizing the influence of the different land uses and altimetry. + 2σ) presented in green, above average temperatures + 2σ in orange, and above average + 3σ in red, and (C) framing of the area near the RG4 well, where it was not possible to identify thermal anomalies; (D) the ASTER nighttime thermal infrared image from 24 June 2010, (E) normal temperatures presented in green, above average temperatures + 2σ in orange, and above average + 3σ in red, and (F) framing of the area near the RG4 well, where it was not possible to identify thermal anomalies; (G) the ASTER nighttime thermal infrared image from 21 September 2010, (H) normal temperatures presented in green, above average temperatures + 2σ in orange, and above average + 3σ in red, and (I) framing of the area near the RG4 well, where it was not possible to identify thermal anomalies; (J) the ASTER nighttime thermal infrared image from 14 August 2011, (K) normal temperatures presented in green, above average temperatures + 2σ in orange, and above average + 3σ in red, and (L) framing of the area near the RG4 well, where it was possible to identify thermal anomalies; (M) the ASTER nighttime thermal infrared image from 09 August 2012, (N) normal temperatures presented in green, above average temperatures + 2σ in orange, and above average + 3σ in red, and (O) framing of the area near the RG4 well, where it was possible to identify thermal anomalies. ...
Citations
... In this regard, together with MODIS, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), aboard the Terra satellite marked a transformative phase in the thermal remote sensing of volcanic activity. With five TIR (8-12 μm) bands, and a spatial resolution of 90 m ASTER has been instrumental in detecting early (or precursory) signs of volcanic activity (Pieri and Abrams, 2005;Reath et al., 2016), track the evolution of volcanic unrest Pailot-Bonnétat et al., 2023), assess the progression of effusive episodes Genzano et al., 2021;Ramsey and Flynn, 2020;Ramsey et al., 2023), quantify the thermal energy sourced by volcanic and hydrothermal targets (Mia et al., 2018;Mannini et al., 2019;Ramsey et al., 2023), locate thermal anomalies in volcanic regions, fumarolic fields, geothermal areas, and hydrothermal systems (Genzano et al., 2021, Taryn et al., 2018, Uchôa et al., 2023, Hellman and Ramsey, 2004, Vaughan et al., 2012a, Vaughan et al., 2020, Chalik et al., 2019, Braddock et al., 2017, Hilman et al., 2020, Caputo et al., 2019, and references therein), and creating comprehensive multidecadal database of volcanic thermal behaviour (Reath et al., 2019a, Urai and Pieri, 2011a, 2011b [https://gbank.gsj.jp/vsidb/image/Agun g/volinfo.html]). ...
Detecting early signs of impending eruptions and monitoring the evolution of volcanic phenomena are fundamental objectives of applied volcanology, both essential for timely assessment of associated hazards. Thermal remote sensing proves to be a cost-effective, yet reliable, information source for these purposes, especially for the hundreds of volcanoes still lacking conventional ground-based monitoring networks. In this work, we present an innovative and effective single band TIR-based (11.45 μm) algorithm (TIRVolcH), capable of detecting thermal anomalies in a broad range of volcanic settings, from low-temperature hydrothermal systems to high-temperature effusive events. Based on the processing of Visible Infrared Imaging Radiometer Suite (VIIRS) scenes, the algorithm offers an unprecedented trade-off between spatial (375 m) and temporal resolution (multiple acquisitions per day), having the potential to detect thermal anomalies for pixel-integrated temperatures as low as 0.5 K above the background, while maintaining a false positive rate of ∼1.8 %. The analysis of decadal time series of VIIRS data (2012−2023), acquired at three different volcanoes, reveals how the algorithm can: (i) detect hydrothermal crises at fumarolic fields (Vulcano, Italy), (ii) unveil thermal unrest preceding dome extrusions and explosive eruptions (Agung, Indonesia), and (iii) spatially trace lava flows extent and quantify their advancement rate, as well as track their long-term cooling behaviour (La Palma, Spain).
We envisage that the algorithm will prove instrumental for detecting early signs of volcanic activity and following the evolution of eruptive phenomena, providing a useful tool for hazard management and risk reduction applications. Furthermore, the compilation of statistically robust multidecadal thermal datasets will provide novel insights and new perspectives into volcano monitoring, laying the ground for forthcoming higher-resolution TIR missions.
... Geothermal resources are distributed in convergent plate margins, rifts, oceanic island hotspots, and largescale extensional tectonic activity areas (Simmons, 2020) and various nondestructive geophysical exploration methods have been used to identify geothermal resources (Hudson et al., 2022;Susilawati et al., 2023;Uchôa et al., 2023;Yasin et al., 2023). With obvious advantages in deep exploration, geophysical technology can effectively detect deep hidden fault structures (Di et al., 2020), obtain important information such as strata and burial depth, and provide important information for the location selection of geothermal wells. ...
Integrated geophysical technology is a necessary and effective means for geothermal exploration. However, integration of geophysical technology for large‐scale surveys with those for geothermal reservoir localization is still in development. This study used the controlled source audio‐frequency magnetotelluric method technology for large‐scale exploration to obtain underground electrical structure information and micromotion detection technology to obtain underground wave velocity structure information. The combination of two detection technologies was used for local identification of geothermal reservoirs. Further, auxiliary correction and inversion constraint were implemented through the audio magnetotelluric sounding technology for maximum authenticity restoration of the near‐ and transition‐field data. Through these technology improvements, a geothermal geological model was established for the Binhai County of Jiangsu Province in China and potential geothermal well locations were identified. On this basis, a geothermal well was drilled nearly 3000 m deep, with a daily water volume of over 2000 m³/day and a geothermal water temperature of 51°C at the well head. It is found that predictions using the above integrated geophysical exploration technology are in good agreement with the well geological formation data. This integrated geophysical technology can be effectively applied for geothermal exploration with high precision and reliability.
Volcanic CO2 diffuse degassing can impact infrastructure, soils, vegetation, microbiota, fauna, and human health. These impacts include acidification of soils, leading to sparse or absent vegetation and changes in microbiota types. Most of the study sites in this review are areas of quiescent volcanism, where soil CO2 emissions is a permanent and silent hazard. Lethal indoor and outdoor CO2 concentrations measured in different regions of the world (Azores, Aeolian and Canary Islands, Colli Albani, Methana, Massif Central, Mammoth Mountain, Nyiragongo, Nyamulagira, and Rotorua volcanoes) are associated with the asphyxia and death of humans and other fauna (e.g., birds, reptiles, cows, elephants, and dogs). To address the hazard posed by volcanic CO2 diffuse degassing, we suggest mitigation measures including mandatory CO2 hazard maps for land-use planning, “gas-resistant” construction codes, ventilation mechanisms, monitoring and early warning systems, along with educational campaigns to reduce the gas exposure risks.
This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post‐2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.
