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Positive Reinforcements Help Algorithm Forecast Underground Natural Reserves

#artificialintelligence

Texas A&M University and University of Oklahoma researchers have designed a reinforcement-based algorithm that automates the prediction of underground oil and gas reserves. Texas A&M University (TAMU) and University of Oklahoma researchers have developed a reinforcement-based algorithm that automates forecasting of subterranean properties, enabling accurate prediction of oil and gas reserves. The algorithm focuses on the correct characterization of the underground environment based on rewards accumulated for making correct predictions of pressure and flow anticipated from boreholes. The TAMU team learned that within 10 iterations of reinforcement learning, the algorithm could correctly and rapidly predict the properties of simple subsurface scenarios. TAMU's Siddharth Misra said, "We have turned history matching into a sequential decision-making problem, which has the potential to reduce engineers' efforts, mitigate human bias, and remove the need of large sets of labeled training data."


Machine learning aids in predicting earthquakes - Express Computer

#artificialintelligence

Besides applications on problems like digital image and speech recognition, machine learning (ML) methods are also used to predict complicated patterns in earthquake activity, say researchers. It can be used to hone predictions of seismic activity, identify earthquake centres, characterise different types of seismic waves and distinguish seismic activity from other kinds of ground "noise", according to a team of seismologists. More seismologists are using the method, driven by "the increasing size of seismic data sets, improvements in computational power, new algorithms and architecture and the availability of easy-to-use open source machine learning frameworks," said the team, including Karianne Bergen from the Harvard University in the USA, in a paper published in the journal Seismological Research Letters. These methods, called deep neural networks, can explore the complex relationships between input data and their predicted output. For instance, one kind of deep neural network can be used to develop ground motion models for natural and induced earthquakes in Oklahoma, Kansas and Texas.


Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

arXiv.org Machine Learning

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.