相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Three-dimensional geotechnical-layer mapping in Seoul using borehole database and deep neural network-based model
Han-Saem Kim et al.
ENGINEERING GEOLOGY (2022)
Combination of Machine Learning and Kriging for Spatial Estimation of Geological Attributes
Gamze Erdogan Erten et al.
NATURAL RESOURCES RESEARCH (2022)
A Combination of Geostatistical Methods and Principal Components Analysis for Detection of Mineralized Zones in Seafloor Hydrothermal Systems
Vitor Ribeiro deSa et al.
NATURAL RESOURCES RESEARCH (2021)
Microbial sulfate reduction plays an important role at the initial stage of subseafloor sulfide mineralization
Tatsuo Nozaki et al.
GEOLOGY (2021)
Support vector machine and artificial neural network modelling of orogenic gold prospectivity mapping in the Swayze greenstone belt, Ontario, Canada
Francisca Maepa et al.
ORE GEOLOGY REVIEWS (2021)
Subseafloor sulphide deposit formed by pumice replacement mineralisation
Tatsuo Nozaki et al.
SCIENTIFIC REPORTS (2021)
3D geostatistical modeling of metal contents and lithofacies for mineralization mechanism determination of a seafloor hydrothermal deposit in the middle Okinawa Trough, Izena Hole
Vitor Ribeiro de Sa et al.
ORE GEOLOGY REVIEWS (2021)
Petrofacies prediction and 3-D geological model in tight gas sandstone reservoirs by integration of well logs and geostatistical modeling
Binfeng Cao et al.
MARINE AND PETROLEUM GEOLOGY (2020)
Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction
Panagiotis Tziachris et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2020)
Numerical Simulation-Based Clarification of a Fluid-Flow System in a Seafloor Hydrothermal Vent Area in the Middle Okinawa Trough
S. A. Tomita et al.
GEOPHYSICAL RESEARCH LETTERS (2020)
Artificial intelligence models to generate visualized bedrock level: a case study in Sweden
Abbas Abbaszadeh Shahri et al.
MODELING EARTH SYSTEMS AND ENVIRONMENT (2020)
Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals
A. P. Sergeev et al.
CATENA (2019)
Distribution and health risk assessment of arsenic and selected heavy metals in Groundwater of Chandigarh, India
Khaiwal Ravindra et al.
ENVIRONMENTAL POLLUTION (2019)
A new method for interpolating linear features in aeromagnetic data
Tomas Naprstek et al.
GEOPHYSICS (2019)
A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content
Lin Chen et al.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2019)
Internal Structure of a Seafloor Massive Sulfide Deposit by Electrical Resistivity Tomography, Okinawa Trough
K. Ishizu et al.
GEOPHYSICAL RESEARCH LETTERS (2019)
An artificial neural network based model to predict spatial soil type distribution using piezocone penetration test data (CPTu)
Abdolvahed Ghaderi et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2019)
High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging
D. A. Tarasov et al.
APPLIED GEOCHEMISTRY (2018)
Resistivity-Based Temperature Estimation of the Kakkonda Geothermal Field, Japan, Using a Neural Network and Neural Kriging
Kazuya Ishitsuka et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2018)
The tremendous potential of deep-sea mud as a source of rare-earth elements
Yutaro Takaya et al.
SCIENTIFIC REPORTS (2018)
Comparison of machine learning methods for copper ore grade estimation
Bahram Jafrasteh et al.
COMPUTATIONAL GEOSCIENCES (2018)
Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging
Ying-Qiang Song et al.
SUSTAINABILITY (2017)
Depth profiles of resistivity and spectral IP for active modern submarine hydrothermal deposits: a case study from the Iheya North Knoll and the Iheya Minor Ridge in Okinawa Trough, Japan
Shogo Komori et al.
EARTH PLANETS AND SPACE (2017)
Geostatistics-based regional characterization of groundwater chemistry in a sedimentary rock area with faulted setting
Lei Lu et al.
ENVIRONMENTAL EARTH SCIENCES (2016)
An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping
Brandon Heung et al.
GEODERMA (2016)
A case study of the modeling of a hydrothermal reservoir: Khankala deposit of geothermal waters
A. Farkhutdinov et al.
GEOTHERMICS (2016)
Rapid growth of mineral deposits at artificial seafloor hydrothermal vents
Tatsuo Nozaki et al.
SCIENTIFIC REPORTS (2016)
Estimating Spatial Precipitation Using Regression Kriging and Artificial Neural Network Residual Kriging (RKNNRK) Hybrid Approach
Youngmin Seo et al.
WATER RESOURCES MANAGEMENT (2015)
Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach
Qi-quan Li et al.
CATENA (2013)
Representation Learning: A Review and New Perspectives
Yoshua Bengio et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)
Geostatistics applied to cross-well reflection seismic for imaging carbonate aquifers
Jorge Parra et al.
JOURNAL OF APPLIED GEOPHYSICS (2013)
Spatial statistics to estimate peat thickness using airborne radiometric data
A. Keaney et al.
SPATIAL STATISTICS (2013)
Neural modelling of the spatial distribution of air pollutants
H. Pfeiffer et al.
ATMOSPHERIC ENVIRONMENT (2009)
General regression neural network residual estimation for ore grade prediction of limestone deposit
S. Chatterjee et al.
TRANSACTIONS OF THE INSTITUTIONS OF MINING AND METALLURGY SECTION A-MINING TECHNOLOGY (2007)
New indices for characterizing spatial models of ore deposits by the use of a sensitivity vector and an influence factor
Katsuaki Koike et al.
MATHEMATICAL GEOLOGY (2006)
Spatial correlation structures of fracture systems for deriving a scaling law and modeling fracture distributions
Katsuaki Koike et al.
COMPUTERS & GEOSCIENCES (2006)
Comparative evaluation of neural network learning algorithms for ore grade estimation
B. Samanta et al.
MATHEMATICAL GEOLOGY (2006)
Environmental data mining and modeling based on machine learning algorithms and geostatistics
M Kanevski et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2004)
Submarine hydrothermal mineralization in the Okinawa Trough, SW of Japan: an overview
GP Glasby et al.
ORE GEOLOGY REVIEWS (2003)
Subsea-floor replacement in volcanic-hosted massive sulfide deposits
MG Doyle et al.
ORE GEOLOGY REVIEWS (2003)
Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis
K Koike et al.
MATHEMATICAL GEOLOGY (2001)
分享论文
