Monitoring microseismicity of the Hengill Geothermal Field in Iceland

Deep learning forecasting of large induced earthquakes via precursory signals

Precursory phenomena to earthquakes have always attracted researchers' attention. Among the most investigated precursors, foreshocks play a key role. However, their prompt identification with respect to background seismicity still remains an issue. The task is worsened when dealing with low-magnitude earthquakes. Despite that, seismology and, in particular real-time seismology, can nowadays benefit from the use of Artificial Intelligence (AI) to face the challenge of effective precursory signals discrimination. Here, we propose a deep learning method named PreD-Net (precursor detection network) to address precursory signal identification of induced earthquakes. PreD-Net has been trained on data related to three different induced seismicity areas, namely The Geysers, located in California, USA, Cooper Basin, Australia, Hengill in Iceland. The network shows a suitable model generalization, providing considerable results on samples that were not used during the network training phase of all the sites. Tests on related samples of induced large events, with the addition of data collected from the Basel catalogue, Switzerland, assess the possibility of building a real-time warning strategy to be used to avoid adverse consequences during field operations.

Steam caps in geothermal reservoirs can be monitored using seismic noise interferometry

Harvesting geothermal energy often leads to a pressure drop in reservoirs, decreasing their profitability and promoting the formation of steam caps. While steam caps are valuable energy resources, they also alter the reservoir thermodynamics. Accurately measuring the steam fraction in reservoirs is essential for both operational and economic perspectives. However, steam content estimations are very limited both in space and time since current methods rely on direct measurements within production wells. Besides, these estimations normally present large uncertainties. Here, we present a pioneering method for indirectly sampling the steam content in the subsurface using the ever-present seismic background noise. We observe a consistent annual velocity drop in the Hengill geothermal field (Iceland) and establish a correlation between the velocity drop and steam buildup using in-situ borehole data. This application opens new avenues to track the evolution of any gas reservoir in the crust with a surface-based and cost-effective method.

Hyperthermophile diversity microbes in the Calientes geothermal field, Tacna, Peru

Hyperthermophile microorganisms have been discovered worldwide, and several studies regarding biodiversity and the potential biotechnological applications have been reported. In this work, we describe for the first time the diversity of hyperthermophile communities in the Calientes Geothermal Field (CGF) located 4400 m above sea level in Tacna Region, Perú. Three hot springs were monitored and showed a temperature around 84 to 88 °C, for the microbiome analyzed was taken by sampling of sediment and water (pH 7.3-7.6). The hyperthermophile diversity was determined by PCR, DGGE, and DNA sequencing. The sediments analyzed showed a greater diversity than water samples. Sediments showed a more abundant population of bacteria than archaea, with the presence of at least 9 and 5 phylotypes, respectively. Most interestingly, in some taxa of bacteria (Bacillus) and archaea (Haloarcula and Halalkalicoccus), any of operational taxonomic units (OTUs) have not been observed before in hyperthermophile environments. Our results provide insight in the hyperthermophile diversity and reveal the possibility to develop new biotechnological applications based on the kind of environments.