Elucidating the tectonic setting of unknown rock samples has long attracted the interest of not only igneous petrologists but also a wide range of geoscientists. Recently, attempts have been made t…
High-resolution subsurface marine mapping tools, including chirp and 3D seismic, enable the reconstruction of ancient landscapes that have been buried and subsequently submerged by marine transgres…
Accurately modeling highly heterogenous aquifers is one of the big challenges in hydrogeology. There is a pressing need to develop new methods that transform high-resolution data into hydrogeologic…
Rock type classification is one of the most crucial steps of geological and geotechnical core logging. In conventional core logging, rock type classification is subjective and time-consuming. This …
Rock glaciers (RG) are landforms that occur in high latitudes or elevations and — in their active state — consist of a mixture of rock debris and ice. Despite serving as a form of groundwater s…
This presents a novel hybrid 24-h forecasting model of convective weather events based on numerical simulation and machine learning algorithms. To characterize the convective events, 13-year from 2…
Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization a…
The current utility of mud gas data is typically limited to geological and petrophysical correlation, formation evaluation, and fluid typing. A critical and comprehensive review of the literature o…
In this study we compared various machine learning techniques that used soil geochemistry to aid in geologic mapping. We tested six different sampling methods (undersample, oversample, Synthetic Mi…
Machine learning (ML) has been a technique employed to build data-driven models that can map the relationship between the input and output data provided. ML-based data-driven models offer an altern…
Domains define the boundaries of mineralisation zones, within which the grade distribution of the target minerals can be quantified via an established mineral resource estimation procedure. Availab…
A deeper understanding of the drivers of evapotranspiration and the modelling of its constituent parts (evaporation and transpiration) may be of significant importance to the monitoring and managem…
Geoscientists use observations and descriptions of the rock record to study the origins and history of our planet, which has resulted in a vast volume of scientific literature. Recent progress in n…
High precision and reliable wind speed forecasting is a challenge for meteorologists. We used multiple nonparametric tree-based machine learning techniques, for predicting the maximum wind speed at…
One important property of oil and gas reservoirs is permeability, which has proven to be difficult to predict. Empirical and regression models are the current industrial practice for predicting per…
The progressing industrialization of oceans mandates reliable, accurate and automatable subsea survey methods. Close-range photogrammetry is a promising discipline, which is frequently applied by a…
Real-time object detection and tracking is an active area of aerial remote sensing research that enables many environmental and ecological monitoring and preservation applications. Despite the deve…
Scalable and transferable methods for generating reliable reference data for automated remote sensing approaches are crucial, especially for mapping complex Earth surface processes such as gully er…
This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes…
Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote sensing are becoming standard analytical tools in the geosciences. A series of studies has presented the se…