In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propaga…
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…
The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles (EPBs), that in turn lead to ionospheric scintillation which can…
Land use and land cover (LULC) changes refer to alterations in land use or physical characteristics. These changes can be caused by human activities, such as urbanization, agriculture, and resource…
Compressional and shear sonic logs (DTC and DTS, respectively) are one of the effective means for determining petrophysical/geomechanical properties. However, the DTS log has limited availability m…
Data-driven prediction of time series is significant in many scientific research fields such as global climate change and weather forecast. For global monthly mean temperature series, considering t…
We propose to use a Few-Shot Learning (FSL) method for the pre-stack seismic inversion problem in obtaining a high resolution reservoir model from recorded seismic data. Recently, artificial neural…
Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random …
Reliable seismic phase identification is often challenging especially in the circumstances of low-magnitude events or poor signal-to-noise ratio. With improved seismometers and better global covera…
The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has e…