Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these method…
Seismic inversion can be divided into time-domain inversion and frequency-domain inversion based on different transform domains. Time-domain inversion has stronger stability and noise resistance co…
The large spatial/temporal/frequency scale of geoscience and remote-sensing datasets causes memory issues when using convolutional neural networks for (sub-) surface data segmentation. Recently dev…
This research aim to evaluate hydro-meteorological data from the Yamuna River Basin, Uttarakhand, India, utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach. Th…
Emeralds – the green colored variety of beryl – occur as gem-quality specimens in over fifty deposits globally. While digital traceability methods for emerald have limitations, sample-based app…
Magnitude estimation is a critical task in seismology, and conventional methods usually require dense seismic station arrays to provide data with sufficient spatiotemporal distribution. In this con…
Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant price drops, compa…
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues. However, the complexity of porous media often limits the e…
Machine learning (ML) algorithms are frequently used in landslide susceptibility modeling. Different data handling strategies may generate variations in landslide susceptibility modeling, even when…
Alteration minerals and silicification are typically associated with a variety of ore mineralizations and could be detected using multispectral remote sensing sensors as indicators for mineral expl…
Since its arrival in late November 2022, ChatGPT-3.5 has rapidly gained popularity and significantly impacted how research is planned, conducted, and published using a generative artificial intelli…
In recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study inv…
Porosity, tortuosity, specific surface area (SSA), and permeability are four key parameters of reactive transport modeling in sandstone, which are important for understanding solute transport and g…
Seismic facies analysis plays important roles in geological research, especially in sedimentary environment identification. Traditional method is mainly based on seismic waveform or attributes of a…
Logs are valuable information for oil and gas fields as they help to determine the lithology of the formations surrounding the borehole and the location and reserves of subsurface oil and gas reser…
Earthquake early warning (EEW) is one of the important tools to reduce the hazard of earthquakes. In contemporary seismology, EEW is typically transformed into a fast classification of earthquake m…
Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization appro…
Noise suppression is an important part of microseismic monitoring technology. Signal and noise can be separated by denoising and filtering to improve the subsequent analysis. In this paper, we prop…
Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of …
Modern geodynamics is based on the study of a large set of models, with the variation of many parameters, whose analysis in the future will require Machine Learning to be analyzed. We introduce her…