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doi: 10.26599/JGSE.2025.9280044
Abstract:
2025, 13(1): 1-4.
doi: 10.26599/JGSE.2025.9280034
Abstract:
To fulfill the new requirements of geological work in the modern era and explore the latest advancements, the cutting-edge directions and trends of hydrogeology science, as well as the key scientific issues and major demands in hydrogeology, those contribute to the development of an ecological civilization and effective natural resource management, the national hydrogeological academic symposium took place from October 25th to 27th, 2024. Organized by the China Geological Society and collaboratively hosted by the Hydrogeology Professional Committee, the event featured an insightful speech by Mr. Wang Min, former Deputy Minister of the Ministry of Natural Resources. His perspectives offer valuable insights for current geological endeavours, and we have compiled the content of this speech into this article.
To fulfill the new requirements of geological work in the modern era and explore the latest advancements, the cutting-edge directions and trends of hydrogeology science, as well as the key scientific issues and major demands in hydrogeology, those contribute to the development of an ecological civilization and effective natural resource management, the national hydrogeological academic symposium took place from October 25th to 27th, 2024. Organized by the China Geological Society and collaboratively hosted by the Hydrogeology Professional Committee, the event featured an insightful speech by Mr. Wang Min, former Deputy Minister of the Ministry of Natural Resources. His perspectives offer valuable insights for current geological endeavours, and we have compiled the content of this speech into this article.
2025, 13(1): 5-21.
doi: 10.26599/JGSE.2025.9280035
Abstract:
This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite mission. The primary objective is to develop accurate predictive models for groundwater level changes by leveraging the unique capabilities of GRACE satellite data in conjunction with advanced machine learning algorithms. Three widely-used machine learning models, namely DT, SVM and RF, were employed to analyze and model the relationship between GRACE satellite data and groundwater fluctuations in South Khorasan Province, Iran. The study utilized 151 months of GRACE data spanning from 2002 to 2017, which were correlated with piezometer well data available in the study area. The JPL model was selected based on its strong correlation (R2 = 0.9368) with the observed data. The machine learning models were trained and validated using a 70/30 split of the data, and their performance was evaluated using various statistical metrics, including RMSE, R2 and NSE. The results demonstrated the suitability of machine learning approaches for modeling groundwater fluctuations using GRACE satellite data. The DT model exhibited the best performance during the calibration stage, with an R2 value of 0.95, RMSE of 0.655, and NSE of 0.96. The SVM and RF models achieved R2 values of 0.79 and 0.65, and NSE values of 0.86 and 0.71, respectively. For the prediction stage, the DT model maintained its high efficiency, with an RMSE of 1.48, R2 of 0.87, and NSE of 0.90, indicating its robustness in predicting future groundwater fluctuations using GRACE data. The study highlights the potential of machine learning techniques, particularly Decision Trees, in conjunction with GRACE satellite data, for accurate prediction and monitoring of groundwater fluctuations in arid and semi-arid regions. The findings demonstrate the effectiveness of the DT model in capturing the complex relationships between GRACE data and groundwater dynamics, providing reliable predictions and insights for sustainable groundwater management strategies.
This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite mission. The primary objective is to develop accurate predictive models for groundwater level changes by leveraging the unique capabilities of GRACE satellite data in conjunction with advanced machine learning algorithms. Three widely-used machine learning models, namely DT, SVM and RF, were employed to analyze and model the relationship between GRACE satellite data and groundwater fluctuations in South Khorasan Province, Iran. The study utilized 151 months of GRACE data spanning from 2002 to 2017, which were correlated with piezometer well data available in the study area. The JPL model was selected based on its strong correlation (R2 = 0.9368) with the observed data. The machine learning models were trained and validated using a 70/30 split of the data, and their performance was evaluated using various statistical metrics, including RMSE, R2 and NSE. The results demonstrated the suitability of machine learning approaches for modeling groundwater fluctuations using GRACE satellite data. The DT model exhibited the best performance during the calibration stage, with an R2 value of 0.95, RMSE of 0.655, and NSE of 0.96. The SVM and RF models achieved R2 values of 0.79 and 0.65, and NSE values of 0.86 and 0.71, respectively. For the prediction stage, the DT model maintained its high efficiency, with an RMSE of 1.48, R2 of 0.87, and NSE of 0.90, indicating its robustness in predicting future groundwater fluctuations using GRACE data. The study highlights the potential of machine learning techniques, particularly Decision Trees, in conjunction with GRACE satellite data, for accurate prediction and monitoring of groundwater fluctuations in arid and semi-arid regions. The findings demonstrate the effectiveness of the DT model in capturing the complex relationships between GRACE data and groundwater dynamics, providing reliable predictions and insights for sustainable groundwater management strategies.
2025, 13(1): 22-33.
doi: 10.26599/JGSE.2025.9280036
Abstract:
The karst geothermal reservoir in Xiong'an New Area is a representative example of an ancient buried hill geothermal system. However, published heat flow data are predominantly derived from the Cenozoic sedimentary cap. Due to the limited depth of borehole exploration, heat flow measurements and analyses of the Archean crystalline basement in the study area are rare. Further investigation of the heat flow and temperature field characteristics within the Archean crystalline basement beneath the karst geothermal reservoir is necessary to understand the vertical distribution of heat flow and improve the geothermal genetic mechanism in the area. The D01 deep geothermal scientific drilling parameter well was implemented in the Niutuozhen geothermal field of Xiong'an New Area. The well exposed the entire Gaoyuzhaung Formation karst geotheremal reservoir of the Jixian system and drilled 1,723.67 m into the Archean crystalline basement, providing the necessary conditions for determining its heat flow. This study involved borehole temperature measurements and thermophysical property testing of core samples from the D01 well to analyze the vertical distribution of heat flow. The findings revealed distinct segmentation in the geothermal gradient and rock thermophysical properties. The geothermal reservoir of Gaoyuzhuang Formation is dominated by convection, with significant temperature inversions corresponding to karst fracture developments. In contrast, the Archean crystalline basement exhibits conductive heat transfer. After 233 days of static equilibrium, the average geothermal gradients of the Gaoyuzhuang Formation and the Archean crystalline basement were determined to be 1.5°C/km and 18.3°C/km, respectively. These values adjusted to −0.8°C/km and 18.2°C/km after 551 days, with the longer static time curve approaching steady-state conditions. The average thermal conductivity of dolomite in Gaoyuzhuang Formation was measured as 4.37±0.82 W/(K·m), and that of Archean gneiss as 2.41±0.40 W/(K·m). The average radioactive heat generation rate were 0.30±0.32 μW/m3 for dolomite and 1.32±0.69 μW/m3 for gneiss. Using the temperature curve after 551 days and thermal conductivity data, the Archean heat flow at the D01 well was calculated as (43.9±7.0) mW/m2, While the heat flow for the Neogene sedimentary cap was estimated at 88.6mW/m2. The heat flow of Neogene sedimentary caprock is significantly higher than that of Archean crystalline basement at the D01 well, with an excess of 44.7 mW/m2 accounting for approximately 50% of the total heat flow in the Neogene sedimentary caprock. This is primarily attributed to lateral thermal convection within the high-porosity and high-permeability karst dolomite layer, and vertical thermal convection facilitated by the Niudong fault, which collectively contribute to the heat supply of the Neogene sedimentary caprock. Thermal convection in karst fissure and fault zone contribute approximately 50% of the heat flow in the Neogene sedimentary caprock. This study quantitatively revealed the vertical distribution of heat flow, providing empirical evidence for the genetic mechanism of the convection-conduction geothermal system in sedimentary basins.
The karst geothermal reservoir in Xiong'an New Area is a representative example of an ancient buried hill geothermal system. However, published heat flow data are predominantly derived from the Cenozoic sedimentary cap. Due to the limited depth of borehole exploration, heat flow measurements and analyses of the Archean crystalline basement in the study area are rare. Further investigation of the heat flow and temperature field characteristics within the Archean crystalline basement beneath the karst geothermal reservoir is necessary to understand the vertical distribution of heat flow and improve the geothermal genetic mechanism in the area. The D01 deep geothermal scientific drilling parameter well was implemented in the Niutuozhen geothermal field of Xiong'an New Area. The well exposed the entire Gaoyuzhaung Formation karst geotheremal reservoir of the Jixian system and drilled 1,723.67 m into the Archean crystalline basement, providing the necessary conditions for determining its heat flow. This study involved borehole temperature measurements and thermophysical property testing of core samples from the D01 well to analyze the vertical distribution of heat flow. The findings revealed distinct segmentation in the geothermal gradient and rock thermophysical properties. The geothermal reservoir of Gaoyuzhuang Formation is dominated by convection, with significant temperature inversions corresponding to karst fracture developments. In contrast, the Archean crystalline basement exhibits conductive heat transfer. After 233 days of static equilibrium, the average geothermal gradients of the Gaoyuzhuang Formation and the Archean crystalline basement were determined to be 1.5°C/km and 18.3°C/km, respectively. These values adjusted to −0.8°C/km and 18.2°C/km after 551 days, with the longer static time curve approaching steady-state conditions. The average thermal conductivity of dolomite in Gaoyuzhuang Formation was measured as 4.37±0.82 W/(K·m), and that of Archean gneiss as 2.41±0.40 W/(K·m). The average radioactive heat generation rate were 0.30±0.32 μW/m3 for dolomite and 1.32±0.69 μW/m3 for gneiss. Using the temperature curve after 551 days and thermal conductivity data, the Archean heat flow at the D01 well was calculated as (43.9±7.0) mW/m2, While the heat flow for the Neogene sedimentary cap was estimated at 88.6mW/m2. The heat flow of Neogene sedimentary caprock is significantly higher than that of Archean crystalline basement at the D01 well, with an excess of 44.7 mW/m2 accounting for approximately 50% of the total heat flow in the Neogene sedimentary caprock. This is primarily attributed to lateral thermal convection within the high-porosity and high-permeability karst dolomite layer, and vertical thermal convection facilitated by the Niudong fault, which collectively contribute to the heat supply of the Neogene sedimentary caprock. Thermal convection in karst fissure and fault zone contribute approximately 50% of the heat flow in the Neogene sedimentary caprock. This study quantitatively revealed the vertical distribution of heat flow, providing empirical evidence for the genetic mechanism of the convection-conduction geothermal system in sedimentary basins.
2025, 13(1): 34-46.
doi: 10.26599/JGSE.2025.9280037
Abstract:
Climate change significantly affects environment, ecosystems, communities, and economies. These impacts often result in quick and gradual changes in water resources, environmental conditions, and weather patterns. A geographical study was conducted in Arizona State, USA, to examine monthly precipitation concentration rates over time. This analysis used a high-resolution 0.50×0.50 grid for monthly precipitation data from 1961 to 2022, Provided by the Climatic Research Unit. The study aimed to analyze climatic changes affected the first and last five years of each decade, as well as the entire decade, during the specified period. GIS was used to meet the objectives of this study. Arizona experienced 51–568 mm, 67–560 mm, 63–622 mm, and 52–590 mm of rainfall in the sixth, seventh, eighth, and ninth decades of the second millennium, respectively. Both the first and second five year periods of each decade showed acceptable rainfall amounts despite fluctuations. However, rainfall decreased in the first and second decades of the third millennium. and in the first two years of the third decade. Rainfall amounts dropped to 42–472 mm, 55–469 mm, and 74–498 mm, respectively, indicating a downward trend in precipitation. The central part of the state received the highest rainfall, while the eastern and western regions (spanning north to south) had significantly less. Over the decades of the third millennium, the average annual rainfall every five years was relatively low, showing a declining trend due to severe climate changes, generally ranging between 35 mm and 498 mm. The central regions consistently received more rainfall than the eastern and western outskirts. Arizona is currently experiencing a decrease in rainfall due to climate change, a situation that could deteriorate further. This highlights the need to optimize the use of existing rainfall and explore alternative water sources.
Climate change significantly affects environment, ecosystems, communities, and economies. These impacts often result in quick and gradual changes in water resources, environmental conditions, and weather patterns. A geographical study was conducted in Arizona State, USA, to examine monthly precipitation concentration rates over time. This analysis used a high-resolution 0.50×0.50 grid for monthly precipitation data from 1961 to 2022, Provided by the Climatic Research Unit. The study aimed to analyze climatic changes affected the first and last five years of each decade, as well as the entire decade, during the specified period. GIS was used to meet the objectives of this study. Arizona experienced 51–568 mm, 67–560 mm, 63–622 mm, and 52–590 mm of rainfall in the sixth, seventh, eighth, and ninth decades of the second millennium, respectively. Both the first and second five year periods of each decade showed acceptable rainfall amounts despite fluctuations. However, rainfall decreased in the first and second decades of the third millennium. and in the first two years of the third decade. Rainfall amounts dropped to 42–472 mm, 55–469 mm, and 74–498 mm, respectively, indicating a downward trend in precipitation. The central part of the state received the highest rainfall, while the eastern and western regions (spanning north to south) had significantly less. Over the decades of the third millennium, the average annual rainfall every five years was relatively low, showing a declining trend due to severe climate changes, generally ranging between 35 mm and 498 mm. The central regions consistently received more rainfall than the eastern and western outskirts. Arizona is currently experiencing a decrease in rainfall due to climate change, a situation that could deteriorate further. This highlights the need to optimize the use of existing rainfall and explore alternative water sources.
2025, 13(1): 47-61.
doi: 10.26599/JGSE.2025.9280038
Abstract:
Xiong'an New Area boasts abundant geothermal resources, with widespread Jixianian geothermal reservoirs serving as key targets for exploration and development. Zoning geothermal resources helps characterize their distribution and attributes, offering critical guidance for their sustainable exploitation and utilization. This study integrates data from drilling and production tests across 21 geothermal wells to analyze the Jixianian strata, including depth, thickness, temperature, single-well water yield, Groundwater Level Depth (GWD), and Total Dissolved Solids (TDS). Employing fuzzy mathematics, a zoning analysis was performed, yielding quantitative evaluation scores and delineating favorable zones for development. Key findings include: (1) Geothermal reservoirs in the Rongcheng and Niutuozhen uplifts exhibit shallow burial depths, substantial thicknesses, high productivity, and relatively low temperatures, making them highly suitable for large-scale geothermal exploitation; (2) Zones with high resource potential but uncertain conditions require further exploration to mitigate development risks; (3) Areas near the Rongcheng fault or Jixianian strata buried deeper than 4,000 m are recommended for deferred exploitation; (4) Comprehensive evaluation reveals that the Jixianian carbonate geothermal reservoirs in Xiong'an New Area manifest geothermal resources of 5,370.31×1016 J, geothermal fluid reserves of 101.17×108 m3, and recoverable fluid reserves of 93.41×104 m3/d under balanced extraction and reinjection. Recoverable geothermal heat amounts to 9.36×1016 J/a, equivalent to 319.4×104 t/a of standard coal. This study provides valuable insights into the exploration and sustainable exploitation of deep geothermal reservoirs in Xiong'an New Area, enhancing resource utilization and contributing to the development of a green and sustainable Xiong'an New Area.
Xiong'an New Area boasts abundant geothermal resources, with widespread Jixianian geothermal reservoirs serving as key targets for exploration and development. Zoning geothermal resources helps characterize their distribution and attributes, offering critical guidance for their sustainable exploitation and utilization. This study integrates data from drilling and production tests across 21 geothermal wells to analyze the Jixianian strata, including depth, thickness, temperature, single-well water yield, Groundwater Level Depth (GWD), and Total Dissolved Solids (TDS). Employing fuzzy mathematics, a zoning analysis was performed, yielding quantitative evaluation scores and delineating favorable zones for development. Key findings include: (1) Geothermal reservoirs in the Rongcheng and Niutuozhen uplifts exhibit shallow burial depths, substantial thicknesses, high productivity, and relatively low temperatures, making them highly suitable for large-scale geothermal exploitation; (2) Zones with high resource potential but uncertain conditions require further exploration to mitigate development risks; (3) Areas near the Rongcheng fault or Jixianian strata buried deeper than 4,000 m are recommended for deferred exploitation; (4) Comprehensive evaluation reveals that the Jixianian carbonate geothermal reservoirs in Xiong'an New Area manifest geothermal resources of 5,370.31×1016 J, geothermal fluid reserves of 101.17×108 m3, and recoverable fluid reserves of 93.41×104 m3/d under balanced extraction and reinjection. Recoverable geothermal heat amounts to 9.36×1016 J/a, equivalent to 319.4×104 t/a of standard coal. This study provides valuable insights into the exploration and sustainable exploitation of deep geothermal reservoirs in Xiong'an New Area, enhancing resource utilization and contributing to the development of a green and sustainable Xiong'an New Area.
2025, 13(1): 62-73.
doi: 10.26599/JGSE.2025.9280039
Abstract:
Effective management of water resources, especially groundwater, is crucial and requires a precise understanding of aquifer characteristics, imposed stresses, and the groundwater balance. Simulation-optimization models plays a vital role in guiding planners toword sustainable long-term aquifer exploitation. This study simulated monthly water table variations in the Kashan Plain over a ten-year period from 2008 to 2019 across 125 stress periods using the GMS model. The model was calibrated for both steady-state and transient conditions for the 2008–2016 period and validated for the 2016–2019 period. Results indicated a 4.4 m decline in groundwater levels over the 10-year study period. Given the plain's location in a arid climatic zone with limited effective precipitation for aquifer recharge, the study focused on groundwater extraction management. A modified two-point hedging policy was employed as a solution to mitigate critical groundwater depletion, reducing the annual drawdown rate from 0.44 m to 0.31 m and conserving 255 million cubic meters (mcm) of water annually. Although this approach slightly decreased reliability (i.e. the number of months meeting full water demands), it effectively minimized the risk of severe droughts and irreparable damages. This policy offers managers a dynamical and intelligent tool for regulating groundwater extraction, balancing aquifer sustainability with agricultural and urban water requirements.
Effective management of water resources, especially groundwater, is crucial and requires a precise understanding of aquifer characteristics, imposed stresses, and the groundwater balance. Simulation-optimization models plays a vital role in guiding planners toword sustainable long-term aquifer exploitation. This study simulated monthly water table variations in the Kashan Plain over a ten-year period from 2008 to 2019 across 125 stress periods using the GMS model. The model was calibrated for both steady-state and transient conditions for the 2008–2016 period and validated for the 2016–2019 period. Results indicated a 4.4 m decline in groundwater levels over the 10-year study period. Given the plain's location in a arid climatic zone with limited effective precipitation for aquifer recharge, the study focused on groundwater extraction management. A modified two-point hedging policy was employed as a solution to mitigate critical groundwater depletion, reducing the annual drawdown rate from 0.44 m to 0.31 m and conserving 255 million cubic meters (mcm) of water annually. Although this approach slightly decreased reliability (i.e. the number of months meeting full water demands), it effectively minimized the risk of severe droughts and irreparable damages. This policy offers managers a dynamical and intelligent tool for regulating groundwater extraction, balancing aquifer sustainability with agricultural and urban water requirements.
2025, 13(1): 74-89.
doi: 10.26599/JGSE.2025.9280040
Abstract:
The North China Plain is vital hub for agricultural production and urban development. However, decades of excessive groundwater extraction have resulted on significant land subsidence, posing severe threats to the region's socio-economic stability and sustainable development. The relationship between land deformation and groundwater storage Anomalies in this region remains insufficiently understood, and the driving factors behind land subsidence require further exploration. This study employs downscaled GRACE and SBAS InSAR technologies to monitor and analyze land subsidence and groundwater storage Anomalies in four representative cities of the North China Plain: Beijing, Tianjin, Cangzhou, and Hengshui. Using geodetector methods, the study investigates the driving factors of land subsidence, incorporating both natural environmental and human activity factors. The results indicate that: (1) Groundwater storage in the North China Plain generally exhibited an overall declining trend from 2002 to 2022, with the rate of decrease weakening from southwest to northeast, showing a clear spatial clustering pattern. (2) While, land subsidence rates in the main urban areas of each city were relatively low, severe subsidence persisted in the surrounding suburban and rural areas. (3) The temporal trends of land subsidence were consistent with changes in groundwater storage across all cities. (4) Groundwater storage Anomalies emerged as the most significant factor influencing the spatial distribution of land subsidence, with a q-value of 0.387, followed by factors such as DEM, evapotranspiration, and rainfall. Seasonal characteristics were evident in land deformation corresponding to groundwater storage Anomalies: During the spring and summer irrigation periods, land subsidence occurred due to groundwater depletion, while in autumn and winter, the surface uplifted with increased groundwater storage. In Cangzhou and Hengshui, excessive deep groundwater extraction caused a lagged response in land subsidence relative to groundwater storage Anomalies. Furthermore, interaction among various factors significantly amplified their influence on land subsidence. The interaction between groundwater storage Anomalies and rainfall had the strongest combined effect, underscoring its critical role in shaping land subsidence in the study area. The findings offer valuable insights for the scientific prevention and management of land subsidence in the North China Plain.
The North China Plain is vital hub for agricultural production and urban development. However, decades of excessive groundwater extraction have resulted on significant land subsidence, posing severe threats to the region's socio-economic stability and sustainable development. The relationship between land deformation and groundwater storage Anomalies in this region remains insufficiently understood, and the driving factors behind land subsidence require further exploration. This study employs downscaled GRACE and SBAS InSAR technologies to monitor and analyze land subsidence and groundwater storage Anomalies in four representative cities of the North China Plain: Beijing, Tianjin, Cangzhou, and Hengshui. Using geodetector methods, the study investigates the driving factors of land subsidence, incorporating both natural environmental and human activity factors. The results indicate that: (1) Groundwater storage in the North China Plain generally exhibited an overall declining trend from 2002 to 2022, with the rate of decrease weakening from southwest to northeast, showing a clear spatial clustering pattern. (2) While, land subsidence rates in the main urban areas of each city were relatively low, severe subsidence persisted in the surrounding suburban and rural areas. (3) The temporal trends of land subsidence were consistent with changes in groundwater storage across all cities. (4) Groundwater storage Anomalies emerged as the most significant factor influencing the spatial distribution of land subsidence, with a q-value of 0.387, followed by factors such as DEM, evapotranspiration, and rainfall. Seasonal characteristics were evident in land deformation corresponding to groundwater storage Anomalies: During the spring and summer irrigation periods, land subsidence occurred due to groundwater depletion, while in autumn and winter, the surface uplifted with increased groundwater storage. In Cangzhou and Hengshui, excessive deep groundwater extraction caused a lagged response in land subsidence relative to groundwater storage Anomalies. Furthermore, interaction among various factors significantly amplified their influence on land subsidence. The interaction between groundwater storage Anomalies and rainfall had the strongest combined effect, underscoring its critical role in shaping land subsidence in the study area. The findings offer valuable insights for the scientific prevention and management of land subsidence in the North China Plain.
2025, 13(1): 90-100.
doi: 10.26599/JGSE.2025.9280041
Abstract:
With the rapid development of molecular biology technology, especially the application of metagenomics, many challenges in groundwater microbial research have been overcome. Metagenomics has enabled the exploration of the diversity of unculturable microorganisms in groundwater. This paper reviews macro genomics 16S rRNA and metagenomics sequencing data, highlighting recent applications of metagenomics in investigating groundwater microbial communities. It also examines the relationship between microbial diversity and environmental factors, the identification of functional microbial groups, the role of microorganisms in groundwater pollution remediation, and their contribution to the hydrogeochemical cycle. Finally, it provide insights into future research directions in groundwater microbiology.
With the rapid development of molecular biology technology, especially the application of metagenomics, many challenges in groundwater microbial research have been overcome. Metagenomics has enabled the exploration of the diversity of unculturable microorganisms in groundwater. This paper reviews macro genomics 16S rRNA and metagenomics sequencing data, highlighting recent applications of metagenomics in investigating groundwater microbial communities. It also examines the relationship between microbial diversity and environmental factors, the identification of functional microbial groups, the role of microorganisms in groundwater pollution remediation, and their contribution to the hydrogeochemical cycle. Finally, it provide insights into future research directions in groundwater microbiology.
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Editor-in-chief1.7
Impact Factor(2023)
2.8
CiteScore 2023
Editor-in-ChiefHOU Chun-tang
Sponsors
Institute of Hydrogeology and Environmental Geology (IHEG), CAGS
China Chapter, International Association of Hydrogeologists (IAH-CC)
Commission on Hydrogeology, Geological Society of China(GSC-CH)
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