Search Results (34)

Phase arrival times and polarities provide essential kinematic constraints for and dynamic insights into seismic sources, respectively. This information serves as fundamental data in seismological study. For microseismic events with smaller magnitudes, reliable phase picking and polarity determination are even more challenging but play a crucial role in source location and focal mechanism inversion. This study innovatively proposes a deep learning model suitable for simultaneous phase picking and polarity determination with continuous microseismic waveforms. Building upon the Earthquake Transformer (EQT) model, we implemented structural improvements through four distinct decoders specifically designed for three tasks of P-wave picking, S-wave picking, and P-wave first-motion polarity determination and named the model EQT-Plus (EQTP). Notably, the polarity determination task was decomposed into two independent decoders to enhance the learning of polarity characteristics. Through training on a northern California dataset and testing on microseismic events (Md < 3) in the Geysers region, the results demonstrate that the EQTP model achieves superior performance in both phase picking and polarity determination compared to the PhaseNet+ model. It not only provides accurate phase picking but also shows higher consistency with manual picking results in polarity determination. We further validated the good generalization ability of the model with the DiTing dataset from China. This study not only advances the adaptation of the Transformer model in seismology but also reliably delivers fundamental information essential for refined microseismic inversion, offering an alternative and advanced tool for the seismological community. Full article

Abandoned production and monitoring wells in depleted oil and gas fields can readily serve as primary leakage pathways for stored CO₂. The temperature, pressure conditions around the wellbore bottom, and CO₂ concentration influence the phase behavior of CO₂ during leakage. This study establishes a 3D wellbore–reservoir coupled model using CO₂ injection data from 1 December 2009, in the DAS area, eastern Cranfield oilfield, Mississippi, USA, to analyze the dynamic evolution of CO₂ leakage along wellbores. Simulations are conducted using the collaboration of ECO2M and ECO2N v2.0 modules. The study examines leakage regimes under varying distances from the injection well and different reservoir temperatures. The results indicate that CO₂ phase changes occur primarily in wells near the injection point or under high-pressure and high CO₂ saturation conditions, usually with a short leakage period due to ice formation at the wellhead. In areas with low CO₂ saturation, prolonged leakage periods lead to significant pressure drops at the bottom, as well as the temperature as a result of the Joule–Thomson effect. Lower reservoir temperatures facilitate smoother and more gradual leakage. These findings provide a theoretical foundation for ensuring the safe implementation of CCUS projects and offer insights into the mechanical explanation of CO₂ geyser phenomena. Full article

Geothermal energy has emerged as a cornerstone in renewable energy, delivering reliable, low-emission baseload electricity and heating solutions. This review bridges the current knowledge gap by addressing challenges and opportunities for engineers and scientists, especially those transitioning from other professions. It examines deep and shallow geothermal systems and explores the advanced technologies and skills required across various climates and environments. Transferable expertise in drilling, completion, subsurface evaluation, and hydrological assessment is required for geothermal development but must be adapted to meet the demands of high-temperature, high-pressure environments; abrasive rocks; and complex downhole conditions. Emerging technologies like Enhanced Geothermal Systems (EGSs) and closed-loop systems enable sustainable energy extraction from impermeable and dry formations. Shallow systems utilize near-surface thermal gradients, hydrology, and soil conditions for efficient heat pump operations. Sustainable practices, including reinjection, machine learning-driven fracture modeling, and the use of corrosion-resistant alloys, enhance well integrity and long-term performance. Case studies like Utah FORGE and the Geysers in California, US, demonstrate hydraulic stimulation, machine learning, and reservoir management, while Cornell University has advanced integrated hybrid geothermal systems. Government incentives, such as tax credits under the Inflation Reduction Act, and academic initiatives, such as adopting geothermal energy at Cornell and Colorado Mesa Universities, are accelerating geothermal integration. These advancements, combined with transferable expertise, position geothermal energy as a major contributor to the global transition to renewable energy. Full article

Consecutive oscillatory eruptions of a mixture of gas and liquid in urban stormwater systems, commonly referred to as sewer geysers, are investigated using transient three-dimensional (3D) computational fluid dynamics (CFD) models. This study provides a detailed mechanistic understanding of geyser formation under partially filled dropshaft conditions, an area not previously explored in depth. The maximum geyser eruption velocities were observed to reach 14.58 m/s under fully filled initial conditions ($h_w/h_d$ = 1) and reduced to 5.17 m/s and 3.02 m/s for partially filled conditions ($h_w/h_d$ = 0.5 and 0.23, respectively). The pressure gradients along the horizontal pipe drove slug formation and correlated directly with the air ingress rates and dropshaft configurations. The influence of the dropshaft diameter was also assessed, showing a 116% increase in eruption velocity when the dropshaft to horizontal pipe diameter ratio ($D_d/D_t$) was reduced from 1.0 to 0.5. It was found that the strength of the geyser (as represented by the eruption velocity from the top of the dropshaft) increased with an increase in the initial water depth in the dropshaft and a reduction in the dropshaft diameter. Additionally, the Kelvin–Helmholtz instability criteria were satisfied during transitions from stratified to slug flow, and they were responsible for the jump and transition of the flow during the initial rise and fallback of the water in the dropshaft. The present study shows that, under an initially lower water depth in the dropshaft, immediate spillage is not guaranteed. However, the subsequent mixing of air from the horizontal pipe generated a less dense mixture, causing a change in pressure distribution along the tunnel, which drove the entire geyser mechanism. This study underscores the critical role of the initial conditions and geometric parameters in influencing geyser dynamics, offering practical guidelines for urban drainage infrastructure. Full article

This research paper explores biogas production in an underground temperature-controlled fixed dome digester and compares it with a similar uncontrolled digester. Two underground fixed-dome digesters, one fitted with a solar heating system and a stirrer and the other one with an identical stirrer only, were batch-fed with cow dung slurry collected from the University of Fort Hare farm and mixed with water in a ratio of 1:1. The solar heating system consisted of a solar geyser, pex-al-pex tubing, an electric ball valve, a water circulation pump, an Arduino aided temperature control system, and a heat exchanger located at the centre of the digester. Both the digesters were intermittently stirred for 10 min every 4 h. The digester without a heating system was used as a control. Biogas production in the two digesters was compared to assess the effect of solar heating on biogas production. The total solids, volatile solids, and the chemical oxygen demand of the cow dung used as substrate were determined before and after digestion. These were compared together with the cumulative biogas produced and the methane content for the controlled and uncontrolled digesters. It was observed that the temperature control system kept the slurry temperature in the controlled digester within the required range for 82.76% of the retention period, showing an efficiency of 82.76%. Some maximum temperature gradients of 7.0 °C were observed in both the controlled and uncontrolled digesters, showing that the stirrer speed of 30 rpm was not fast enough to create the needed vortex for a uniform mix in the slurry. It was further observed that the heat from the solar geyser and the ground insulation were sufficient to keep the digester temperature within the required temperature range without any additional heat source even at night. Biogas yield was observed to depend on the pH with a strong coefficient of determination of 0.788 and 0.755 for the controlled and uncontrolled digesters, respectively. The cumulative biogas was 26.77 m³ and 18.05 m³ for controlled and uncontrolled digesters, respectively, which was an increase of 33%. The methane content increased by 14% while carbon dioxide decreased by 10% from the uncontrolled to the controlled scenario. The percentage removal of the TS, VS, and COD was 66.26%, 76.81%, and 74.69%, respectively, compared to 47.01%, 60.37%, and 57.86% for the uncontrolled situation. Thus, the percentage removal of TS, VS, and COD increased by 19.25%, 16.44%, and 16.89%, respectively. Full article

The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations. Full article

The present study describes the isolation of an extremely thermophilic bacterium from El Tatio, a geyser field in the high planes of Northern Chile. The thermophile bacterium named Thermus thermophilus strain ET-1 showed 99% identity with T. thermophilus SGO.5JP 17-16 (GenBank accession No. CP002777) by 16S rDNA gene analysis. Morphologically, the cells were non-sporeforming Gram-negative rods that formed colonies with yellow pigmentation. This strain is able to proliferate between 55 and 80 °C with a pH range of 6–10, presenting an optimum growth rate at 80 °C and pH 8. The bacterium produces an extracellular protease activity. Characterization of this activity in a concentrated enzyme preparation revealed that extracellular protease had an optimal enzymatic activity at 80 °C at pH 10, a high thermostability with a half-life at 80 °C of 10 h, indicating that this enzyme can be classified as an alkaline protease. The proteolytic enzyme exhibits great stability towards chelators, divalent ions, organic solvents, and detergents. The enzyme was inhibited by phenylmethylsulfonyl fluoride (PMSF), implying that it was a serine protease. The high thermal and pH stability and the resistance to chelators/detergents suggest that the protease activity from this T. thermophilus. strain could be of interest in biotechnological applications. Full article

Solar energy is one of the most promising forms of alternative energy because it has no adverse effects on the environment and is entirely free. Converting solar energy into thermal energy is the most common and straightforward method; the efficiency of solar thermal conversion is approximately 70 percent. The intermittent nature of solar energy availability affects the performance of solar water heaters (SWH), which lowers the usefulness of solar energy in residential and commercial settings, particularly for water heating. Even at low temperatures, the performance of a collector can be improved by using low-density polyethylene (LDPE) glazing instead of traditional glass because it is less expensive and lighter than glass. Using a comprehensive experimental-simulative study, the Glass Solar water heater (glass SWH) and the low-density polyethylene solar water heater (LDPE SWH) are analyzed, examined, and compared in this work. These solar water heaters have galvanized iron (GI) as their absorber material. The SWHs were operated in a closed loop at a constant mass flow rate of 0.013 kg/s, and a 4E analysis (which stands for energy, exergy, economics, and efficiency recovery ratio) was carried out. This analysis included a look at the dynamic time, uncertainty, weight reduction, carbon footprint, and series connection. An LDPE SWH has an energy efficiency that is 5.57% and an exergy efficiency that is 3.2% higher than a glass SWH. The weight of the LDPE SWH is 32.56% lower than that of the glass SWH. Compared to the price of a conventional geyser, installing our SWH results in a cost savings of 40.9%, and monthly energy costs are reduced by an average of 25.5%. Compared to October, September has the quickest dynamic time to reach the desired temperature, while October has the most significant dynamic time. The efficiency recovery ratio (ERR) of a glass SWH is 0.0239% lower than that of an LDPE SWH. LDPE SWHs had a carbon credit worth INR 294.44 more than glass SWHs. The findings of these tests demonstrate that the LDPE SWH is a practical replacement for traditional means of heating water, such as SWHs and geysers. Full article

This study aimed to optimize hydroponic systems for simultaneous wastewater treatment/nutrient recovery and plant growth. Various hydroponic systems (geyser pump, full flow, ebb and flow, nutrient film techniques, aeroponics, misting) were constructed using 160 mm PVC waste pipes supported on a 200 L reservoir. Secondary wastewater was used to cultivate rice (Oryza sativa), ivy (Hedera helix), tomatoes (Solanum lycopersicum), and wheatgrass (Triticum aestivum). Parameters such as plant height, biomass, retention time, temperature, conductivity, pH, dissolved oxygen, ammonia, nitrite, nitrate, total phosphorus, COD, BOD, TDS, TSS, and TS were monitored. Results indicated minor variations in pH, EC, and TDS over time in systems with and without plants, with no significant differences. Turbidity decreased significantly (p ≤ 0.001) in all systems, while TOC levels reduced significantly (p ≤ 0.05) only in the presence of plants. BOD and COD levels exhibited similar reductions with and without plants. Ammonium levels decreased in plant systems, while nitrite levels remained unchanged. Nitrate levels increased significantly in plant systems, and phosphate levels showed no significant difference. Additionally, significant (p ≤ 0.001) plant length (12.84–46.75%) and biomass (31.90–57.86%) increases were observed in all hydroponic systems, accompanied by higher levels of dissolved oxygen (36.26–53.65%), compared to the control (4.59%). The hydroponic system that created a moist atmosphere, either through misting or aeroponics, thus allowing maximum access to oxygen, showed the greatest growth. This study confirmed the importance of oxygen availability to the rhizosphere for plant growth and wastewater treatment. It also identified limitations and investigated the impact of dissolved oxygen concentration on plant–microorganism interactions. Optimal oxygen availability was achieved when plant roots were exposed to a moist atmosphere created by the hydroponic system through aeroponics or misting. The findings have practical implications for hydroponic system design in urban vertical farms, benefiting wastewater treatment, mitigating eutrophication, and reducing food miles. Full article

In recent years, building energy consumption has increased every day due to population growth and an increased human desire for a healthy and pleasant lifestyle, and this is responsible for a crisis of energy shortages worldwide. Therefore, use of solar water heating (SWH) systems in buildings for hot water demand is the prime need of the hour to maintain sustainability. The novelty of this work was in developing a phase change material (stearic acid)-filled U-tube based evacuated tube solar collector (collector A). In addition, another collector B, left without energy storage material, was considered a reference unit for comparing the energy and exergy outputs. The study’s main aim was to examine the energy, exergy, enviro- and exergoeconomic analysis of newly developed water heating systems. The findings of study revealed that the maximum daily energy outputs of collector A were found to be 85.86% (simultaneous mode) and 84.27% (midday charging mode) at a high mass flow rate (0.5 LPM), and exergy outputs were 19.41% and 21.35%, respectively, at a low flow rate. The thermal output of collector A was higher than that of collector B. The per liter cost of hot water produced from collector A with PCMs was found to be INR 0.1261 and INR 0.1276, respectively, under both modes, which is less compared with the electric geyser (0.325 INR). The levelized energy cost, net present worth, and the payback time of the developed collector A obtained were 4.61 INR/kWh, INR 49710, and 4.49 years (simultaneous), and 4.67 INR/kWh, INR 48130, and 4.64 years (mid-day charging), respectively. Furthermore, the amount of CO₂ mitigation from the energy and exergy perspective for collector A was found to be 24.30 and 23.76 tCO₂/lifetime and 5.31, 5.58 tCO₂/lifetime, respectively. Full article

Transient flows in stormwater systems can lead to damaging and dangerous operational conditions, as exemplified by geysering events created by the uncontrolled release of entrapped air pockets. Extreme rain and associated rapid inflows may result in air pocket entrapment, which causes significant changes in flow conditions and potentially geysering. Stormwater geysers have been studied in different experimental and numerical modeling studies, as well as from limited data gathered within real systems. However, there is still no complete understanding of geysering events, as stormwater system geometries vary considerably. Most past studies involved releasing air from an intermediate shaft, in which a significant fraction of the entrapped air may bypass the release. This work advances the understanding of geysering by considering uncontrolled air release through an upstream shaft or manhole. In such cases, the entire air pocket is released upon reaching the shaft, worsening the occurrence of geysering. Pressure and water level measurements were performed for various combinations of initial water pressure, trapped air pocket volume, and vertical shaft geometries, indicating the higher severity of these geysering events. The results obtained also corroborate previous studies in that the measured pressure heads were lower than the grade elevation. Future studies should include larger-scale testing and the representation of this geometry using CFD. Full article

Geyser geothermal fields are scenic volcanic landforms that often contain tens to hundreds of thermal spot vents that erupt boiling water or contain bubbling mud pools. The fields are potentially hazardous sites due to boiling water temperatures and changes in vent locations and eruption dynamics, which are poorly understood. Here we report on the rapid and profound changes that can affect such a geyser field and ultimately lead to a dangerous, unanticipated eruption. We studied the Geyser Valley, Kamchatka Peninsula, which is a field of geysers and other thermal features and boiling pools. Using high-resolution tri-stereo satellite data and unmanned aerial systems (UAS) with optical and thermal infrared cameras in 2018 and 2019, we were able to identify a newly emerging explosion site. Structure-from-motion analysis of data acquired before and after the explosion reveals morphological and thermal details of the new vent. The explosion site produced an aureole zone of more than 150 m³ of explosively redeposited gravel and clay, a slightly elliptical crater with a diameter of 7.5 m and a crater rim 0.30 m high. However, comparison with archives of photogrammetric data suggests that this site was thermally active years earlier and contained a crater that was obscured and covered by landslides and river sediments. The results allow us to develop a conceptual model and highlight the hazard potential of thermal features buried by landslides and clastic deposits. Sudden explosions may occur at similar sites elsewhere, highlighting the need for careful assessment and monitoring of geomorphological and hydrological changes at geyser sites in other regions. Full article

Water injection in geothermal areas is the preferential strategy to sustain the natural production of geothermal resources. In this context, monitoring microearthquakes is a fundamental tool to track changes in the reservoirs in terms of soil composition, response to injections, and resource exploitation with space and time. Therefore, refined source characterization is crucial to better estimate the size, source mechanism, and rupture process of microearthquakes, as they are possibly related to industrial activities, and to identify any potential variation in the background seismicity. Standard approaches for source parameter estimation are ordinarily based on the modelling of Fourier displacement spectra and its characteristic parameters: the low-frequency spectral level and corner frequency. Here, we apply an innovative time domain technique that uses the curves of P-wave amplitude vs. time along the seismogram. This methodology allows estimation of seismic moment, source radius, and stress release from the plateau level and the corner time of the average logarithm of P-wave displacement versus time with the assumption of a triangular moment rate function, uniform rupture speed, and a constant/frequency-independent Q-factor. In the current paper, this time domain methodology is implemented on a selected catalog of microearthquakes consisting of 83 events with a moment magnitude ranging between 1.0 and 1.5 that occurred during a 7-year period (2007–2014) of fluid extraction/injection around Prati-9 and Prati-29 wells at The Geysers geothermal field. The results show that the time domain technique provides accurate seismic moment (moment magnitude) and rupture duration/radius estimates of microearthquakes down to the explored limit (M 1) while accounting for the anelastic attenuation effect in the radiated high-frequency wavefield. The retrieved source radius vs. moment scaling is consistent with a self-similar, constant stress drop scaling model, which proves an appropriate attenuation correction and the validity of the assumed, triangular moment rate function for microearthquake ruptures. Two alternative mechanical models are proposed to explain the observed difference (about two orders of magnitude) in the retrieved average stress release estimates between the time and frequency domain methods. We argue that the two quantities may not refer to the same physical quantity representing the stress release of earthquake ruptures. Either the smaller stress release values from the time domain method may indicate a larger fracture area (by a factor of 20) radiating the observed P-waveforms than the one estimated from the corner frequencies, or the frequency domain estimate is a proxy for dynamic stress release while the time domain is more representative of the static release. The latter is associated with a much lower dynamic friction value than static friction value at the fault during the rupture process. Full article

We used all-atom Molecular Dynamics (MD) computer simulations to study the formation of pre-polymers between the four nucleotides in RNA (AMP, UMP, CMP, GMP) in the presence of different substrates that could have been present in a prebiotic environment. Pre-polymers are $C{3}^{\prime }$–$C{5}^{\prime }$ hydrogen-bonded nucleotides that have been suggested to be the precursors of phosphodiester-bonded RNA polymers. We simulated wet–dry cycles by successively removing water molecules from the simulations, from ~60 to 3 water molecules per nucleotide. The nine substrates in this study include three clay minerals, one mica, one phosphate mineral, one silica, and two metal oxides. The substrates differ in their surface charge and ability to form hydrogen bonds with the nucleotides. From the MD simulations, we quantify the interactions between different nucleotides, and between nucleotides and substrates. For comparison, we included graphite as an inert substrate, which is not charged and cannot form hydrogen bonds. We also simulated the dehydration of a nucleotide-only system, which mimics the drying of small droplets. The number of hydrogen bonds between nucleotides and nucleotides and substrates was found to increase significantly when water molecules were removed from the systems. The largest number of $C{3}^{\prime }$–$C{5}^{\prime }$ hydrogen bonds between nucleotides occurred in the graphite and nucleotide-only systems. While the surface of the substrates led to an organization and periodic arrangement of the nucleotides, none of the substrates was found to be a catalyst for pre-polymer formation, neither at full hydration, nor when dehydrated. While confinement and dehydration seem to be the main drivers for hydrogen bond formation, substrate interactions reduced the interactions between nucleotides in all cases. Our findings suggest that small supersaturated water droplets that could have been produced by geysers or springs on the primitive Earth may play an important role in non-enzymatic RNA polymerization. Full article

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F₁ score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain. Full article