Physical Sciences and Mathematics Commons^™

Prediction Of Soil Water Content And Electrical Conductivity Using Random Forest Methods With Uav Multispectral And Ground-Coupled Geophysical Data, Yunyi Guan, Katherine R. Grote, Joel Schott, Kelsi Leverett

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and tempo-rally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agricul-ture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling …

Application Of Machine Learning In Geophysics: Ranking Teleseismic Shear Wave Splitting Measurements And Classifying Different Types Of Earthquakes, Yanwei Zhang

Doctoral Dissertations

"During the past decades, applications of Machine Learning have been explosively developed to solve various academic and industrial problems, and over-human performance has been shown in diverse areas. In geophysical research, Machine Learning, especially Convolutional Neural Network (CNN), has been applied in numerous studies and demonstrated considerable potential. In this study, we applied CNN to solve two geophysical problems, ranking teleseismic shear splitting (SWS) measurements and classifying different types of earthquakes.

For ranking teleseismic SWS measurements, we utilized a CNN-based method to automatically select reliable SWS measurements. The CNN was trained by human-verified teleseismic SWS measurements and tested using synthetic …

Groundwater Storage Loss Associated With Land Subsidence In Western United States Mapped Using Machine Learning, Ryan G. Smith, Sayantan Majumdar

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject …