Results 131 to 140 of about 3,202 (214)

Resolving Micrometre‐Scale Zircon Growth in Silicic Systems by 7 μm LA‐MC‐ICP‐MS U‐Pb Dating: Linking Petrology and Geochronology in Silicic Volcanic and Granitic Rocks from Victoria, Australia

Geostandards and Geoanalytical Research, EarlyView.
Key Points 7 μm spot‐diameter LA‐MC‐ICP‐MS analyses yield accurate zircon U‐Pb ages. 1013 Ω amplifier resistors improve low‐signal Pb measurements. Saturn correction protocols minimise down‐hole fractionation effects. We present a systematic evaluation of high spatial‐resolution zircon U‐Pb dating using a Thermo Scientific Neoma multi‐collector ICP‐MS ...
Cristiano Lana, John D. Clemens
wiley   +1 more source

Log-Based Identification and Distribution Characteristics of Diagenetic Facies in Low-Permeability Dolomite Reservoirs. [PDF]

ACS Omega
Yang Z, Zhou J, Meng Z, Cheng W, Feng M, Dang Z, Chen J.   +6 more
europepmc   +1 more source

Imaging Point Source Groundwater Discharges in a Confined Coastal Aquifer Using Electrical Resistivity

Groundwater, EarlyView.
Marine electrical resistivity tomography (ERT) is applied to characterize point‐source submarine groundwater discharge (PSGD) along NW Yucatán. ERT observations and forward modeling constrain conduit detectability under varying hydrogeological conditions.
Mariana Gómez‐Nicolás, Armando Huitzilt‐Rodriguez, Enrique Iñiguez, Roger Pacheco‐Castro, Marco Pérez‐Flores, Paulo Salles, Fiona Whitaker   +6 more
wiley   +1 more source

Integrating remote sensing and multi-criteria decision analysis for groundwater zoning in the Eastern Desert of Egypt. [PDF]

Sci Rep
Ezz H.
europepmc   +1 more source

Predicting Groundwater Hydrochemical Facies in Three Dimensions with Random Forest Classification, USA

Groundwater, EarlyView.
Random forest classification predictions of groundwater hydrochemical facies (HCFs) can be used for multiple purposes, including the mapping of salinity and other groundwater characteristics. Shown on the figure are predictions of HCFs at the water table across the conterminous United States.
Paul E. Stackelberg, Katherine J. Knierim, Kenneth Belitz, Charles A. Cravotta III, R. Blaine McCleskey, Courtney D. Killian   +5 more
wiley   +1 more source

Real-time grading method of tunnel surrounding rock based on image recognition. [PDF]

Front Artif Intell
Xiao Y, Yuan H, Shi Q, Qiu Z, Tang L, Yu Y, Li Y, Pan Y, Xiao Q.   +8 more
europepmc   +1 more source

Demonstration of a High‐Resolution Passive Profiler for Characterizing the Distribution of PFAS in Groundwater: A Comparison of Methods in the Field

Groundwater Monitoring &Remediation, EarlyView.
Abstract Characterizing the concentration and mass flux spatial distributions at sites with complex hydrogeologic conditions is critical to evaluate the fate and transport of contaminants, as well as to design effective remediation programs. PFAS (per‐ and polyfluoroalkyl substances)‐contaminated sites are even more complex due to the large number of ...
Morgan Eldridge, Jessica LaFond, Micaela Vavra, Graig Lavorgna, David Lippincott, Paul Hatzinger, Jennifer Guelfo, Todd Anderson, W. Andrew Jackson   +8 more
wiley   +1 more source

A Refined Rotation Forest-Based Ensemble Classifier for Lithological Mapping with ZY1-02D Hyperspectral Remote Sensing Imagery. [PDF]

Sensors (Basel)
Xi J, Gao H, Zang W, Zhao X, Liu M, Li B.   +5 more
europepmc   +1 more source

Computationally Efficient Ranking of Sampling Locations by Redundancy in Groundwater Monitoring Networks

Groundwater Monitoring &Remediation, EarlyView.
Abstract The long‐term and frequent sampling of groundwater monitoring networks is common practice for monitoring the concentrations of constituents of potential concern (CoPC) over space and time. Sampling can be a costly and time‐intensive process that also incurs health and safety risks. A redundancy‐based ranking of candidate sampling locations can
Peter Radvanyi, Claire Miller, Craig Alexander, Marnie Low, Luc Rock, Wayne Jones   +5 more
wiley   +1 more source

Intelligent Recognition of Muffled Blasting Sounds and Lithology Prediction in Coal Mines Based on RDGNet. [PDF]

Sensors (Basel)
Li G, Ding H, Wang K, Zhang X, Sun J.
europepmc   +1 more source