Based on the observation of the geochemical characteristics of 19 hot springs in the Litang Fault Zone (LFZ) from 2010 to 2019, the major elements, trace elements, and stable isotopes were investigated, and a conceptual model of ground fluid circulation in the LFZ was established. The main hydrochemical type of hot spring water samples is HCO₃⁻-Na⁺. The δ²H values range from −157.6‰ to −123.4‰ and δ¹⁸O values range from −24.5‰ to −15.4‰. The hot spring water in the Litang fault zone is mainly recharged by infiltrating precipitation, with a recharge elevation of 4062~6018 m. Hydrochemical types of Litang hot springs are mainly controlled by the circulation of groundwater in a deep fault system, and are related to the rock lithology of thermal reservoir and water–rock reaction areas. Hot springs in the Litang fault zone attribute to three different heat sources, belonging to three geothermal systems. The flow direction of groundwater in the LFZ is roughly from northwest to southeast along the Litang fault. The deeper the circulation depth of hot spring water on the fault, the higher the thermal reservoir temperature and the stronger the seismic activity of the segment, which is closely related to the increase in pore fluid pressure, rock weakening, and deep fluid upwelling. This study is helpful for further study on regional hydrogeological environments and provides a scientific basis for revealing geothermal fluid movement in fault zones.

This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six hot springs, located within an active fault zone along the southeastern coast of China, were carefully chosen as hydrochemical monitoring sites for an extended period of two and a half years. Using this data, a prediction model integrating six algorithms was developed to forecast M ≥ 5 earthquakes in Taiwan. The model's performance was validated against recorded earthquake events, and the factors influencing its predictive capability were analyzed. Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre‐earthquake anomalies from post‐earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods.

Hydrogeochemical characterization studies are regarded as an important method for determining the origin of hot springs. The major elements, trace elements, and stable isotopes of four groups of hot spring water samples and two groups of gas samples collected from the intersection of the Altyn Tagh fault zone and the East Kunlun fault belt were investigated in this study. The hot spring water temperature ranged between 6 °C and 14 °C. The water chemistry types of the hot springs were Na·Mg-Cl, Mg·Na-Cl·HCO₃, Na-Cl·SO₄, and Na-Cl·HCO₃. The δD values ranged from −50.00% to −68.60%, while the δ¹⁸O values ranged from −6.90% to −8.60%. The hot spring water was recharged mainly by infiltrating precipitation, with a recharge elevation of 3390~3676 m. The heat storage temperature ranged from 66.7 to 164.9 °C. The circulation depth was estimated to range between 1043 and 2679 m. The strontium isotopic composition of the water samples in response to the main weathering sources comprised carbonate and sulfate. CO₂ was the main component in the hot spring gas in the study region, and its content was over 95%. The ³He/⁴He-R/Ra relationship diagram revealed that the mantle-sourced helium from the Yitunbulake spring was 3.06%. In comparison, that from the Aiken spring was 7.38%, which indicated an intrusion of mantle-source material mixed into the hot springs in the study region. The crustal marine limestone contributed significantly to the carbon inventory of the hot spring gas samples (>75%). The dissolution of the marl aquifer resulted in the release of CO₂. Carbon was primarily obtained through metamorphism and hydrothermal reactions in the basement lithologies. The Yitunbulake and Aiken hot springs are found near the intersection of the Arjin and East Kunlun fractures, where the water–rock response is relatively strong and the depth of circulation and thermal storage temperature are both high. This causes relatively high ambient pressure to be released from the deep fluid, resulting in microseismic activity in this region. The continuous observation of Aiken spring water chemistry allows for the monitoring of fracture activity in the region. The results of the study could serve as a foundation for further exploration of the relationship between geothermal water and deep faults, shallow geological formations, hydrogeological conditions, and geothermal resource development in the region.

Studying the hydrogeochemical characteristics of hot springs provides essential geochemical information for monitoring earthquake precursors and understanding the relationship between fluids, fractures, and earthquakes. This paper investigates the hydrogeochemical characteristics of hot springs along the Tingri–Nyima Rift (TNR) in southern Tibet, a seismically active zone at the collision front of the Indian and Asian-European plates. The major elements, hydrogen, and oxygen isotopes of seven thermal springs were analyzed from July 2019 to September 2021. The findings indicate that Mount Everest’s meteoric water, which has a recharge elevation of roughly 7.5–8.4 km, is the main source of recharge for the hot springs. The water samples have two main hydrochemical types: HCO₃-Na and Cl-Na. The temperature of the geothermal reservoir is between 46.5 and 225.4 °C, while the circulation depth is between 1.2 and 5.0 km based on silica-enthalpy mixing models and traditional geothermometers. Furthermore, continuous measurements of major anions and cations at the Yundong Spring (T06) near Mount Everest reveal short-term (8 days) seismic precursor anomalies of hydrochemical compositions before an M_L4.7 earthquake 64.36 km away from T06. Our study suggests that seismicity in the northern section of the TNR is controlled by both hydrothermal activity and tectonic activity, while seismicity in the southern section is mainly influenced by tectonic activity. In addition to magnitude and distance from the epicenter, geological forces from deep, large fissures also affect how hot springs react to seismic occurrences. A fluid circulation model is established in order to explain the process of groundwater circulation migration. The continuous hydrochemical monitoring of hot springs near Everest is critical for studying the coupling between hot springs, fractures, and earthquakes, as well as monitoring information on earthquake precursory anomalies near Everest.