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Geology

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Prediction of Geological Parameters during Tunneling by Time Series Analysis on In Situ Data.

Computational intelligence and neuroscience
A tunnel boring machine (TBM) is a type of heavy load equipment that is widely used in underground tunnel construction. The geological conditions in the tunneling process are decisive factors that directly affect the control of construction equipment...

Optimized algorithm for multipoint geostatistical facies modeling based on a deep feedforward neural network.

PloS one
Reservoir facies modeling is an important way to express the sedimentary characteristics of the target area. Conventional deterministic modeling, target-based stochastic simulation, and two-point geostatistical stochastic modeling methods are difficu...

Physics-informed deep learning for prediction of CO storage site response.

Journal of contaminant hydrology
Accurate prediction of the CO plume migration and pressure is imperative for safe operation and economic management of carbon storage projects. Numerical reservoir simulations of CO flow could be used for this purpose allowing the operators and stake...

Study on Machine Learning Models for Building Resilience Evaluation in Mountainous Area: A Case Study of Banan District, Chongqing, China.

Sensors (Basel, Switzerland)
'Resilience' is a new concept in the research and application of urban construction. From the perspective of building adaptability in a mountainous environment and maintaining safety performance over time, this paper innovatively proposes machine lea...

Exploring planet geology through force-feedback telemanipulation from orbit.

Science robotics
Current space exploration roadmaps envision exploring the surface geology of celestial bodies with robots for both scientific research and in situ resource utilization. In such unstructured, poorly lit, complex, and remote environments, automation is...

Forecasting induced seismicity in Oklahoma using machine learning methods.

Scientific reports
Oklahoma earthquakes in the past decade have been mostly associated with wastewater injection. Here we use a machine learning technique-the Random Forest to forecast induced seismicity rate in Oklahoma based on injection-related parameters. We split ...

Shale gas geological "sweet spot" parameter prediction method and its application based on convolutional neural network.

Scientific reports
Parameters such as gas content (GAS), porosity (PHI) and total organic carbon (TOC) are key parameters that reveal the shale gas geological "sweet spot" of reservoirs. However, the lack of a three-dimensional high-precision prediction method is not c...

Dynamics of two-step reversible enzymatic reaction under fractional derivative with Mittag-Leffler Kernel.

PloS one
Chemical kinetics is a branch of chemistry that is founded on understanding chemical reaction rates. Chemical kinetics relates many aspects of cosmology, geology, and even in some cases of, psychology. There is a need for mathematical modelling of th...

Rock Crack Recognition Technology Based on Deep Learning.

Sensors (Basel, Switzerland)
The changes in cracks on the surface of rock mass reflect the development of geological disasters, so cracks on the surface of rock mass are early signs of geological disasters such as landslides, collapses, and debris flows. To research geological d...

Seismic resolution improving by a sequential convolutional neural network.

PloS one
Thin-bed soft rock is one of the main factors causing large deformations of tunnels. In addition to relying on some innovative construction techniques, detecting thin beds early during surface geological exploration and advanced geological prediction...