Subsurface sedimentary structure identification using deep learning: A review
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
[HTML][HTML] Soil moisture measuring techniques and factors affecting the moisture dynamics: A comprehensive review
The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that
regulates important land surface processes. It affects critical land–atmospheric phenomena …
regulates important land surface processes. It affects critical land–atmospheric phenomena …
Data‐worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework
Reliable characterization of subsurface structures is essential for earth sciences and related
applications. Data assimilation‐based identification frameworks can reasonably estimate …
applications. Data assimilation‐based identification frameworks can reasonably estimate …
[HTML][HTML] Flash flood susceptibility assessment and zonation by integrating analytic hierarchy process and frequency ratio model with diverse spatial data
Flash floods are the most dangerous kinds of floods because they combine the destructive
power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of …
power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of …
A deep learning-based data-driven approach for predicting mining water inrush from coal seam floor using micro-seismic monitoring data
H Yin, G Zhang, Q Wu, S Yin… - … on Geoscience and …, 2023 - ieeexplore.ieee.org
Microseismic monitoring during mining operations generates spatiotemporal data that could
indicate strata fractures and deformations leading to water inrush anomalies. However …
indicate strata fractures and deformations leading to water inrush anomalies. However …
Enhancing waste management and prediction of water quality in the sustainable urban environment using optimized algorithm of least square support vector machine …
Urban groundwater influences a wide range of processes in the natural world, including
climatic, geological, geomorphic, biogeochemical, ecotoxicological, hydrological, and …
climatic, geological, geomorphic, biogeochemical, ecotoxicological, hydrological, and …
[HTML][HTML] Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling
Landslides are a recurrent environmental hazard in hilly regions and affect the
socioeconomic development in Pakistan. The current study area is the tourism and hydro …
socioeconomic development in Pakistan. The current study area is the tourism and hydro …
[HTML][HTML] A hybrid physics-informed data-driven neural network for CO2 storage in depleted shale reservoirs
To reduce CO 2 emissions in response to global climate change, shale reservoirs could be
ideal candidates for long-term carbon geo-sequestration involving multi-scale transport …
ideal candidates for long-term carbon geo-sequestration involving multi-scale transport …
Application of robust deep learning models to predict mine water inflow: Implication for groundwater environment management
Traditional mine water inflow prediction is characterized by a high degree of uncertainty in
model parameters and complex mechanisms involved in the water inflow process. Data …
model parameters and complex mechanisms involved in the water inflow process. Data …
Integration of deep learning and information theory for designing monitoring networks in heterogeneous aquifer systems
Groundwater monitoring networks are direct sources of information for revealing subsurface
system dynamic processes. However, designing such networks is difficult due to …
system dynamic processes. However, designing such networks is difficult due to …