The seismic quiescence hypothesis asserts that the number of small earthquakes decreases in and around the focal area of a great earthquake near to its time of occurrence.
Hidden Markov models (HMMs) were first introduced in the late 1960s, and later applied widely in areas including speech recognition, bioinformatics, finance and seismology.
Recurrent neural networks (RNNs) are non-autonomous dynamical systems driven by input, the behaviour of which depend on both model parameters and inputs to the system.
The Kathmandu Valley — the most developed and populated region in Nepal – is seismically active and has experienced recurring destructive earthquakes.
Many earthquakes have been induced by human activities, including wastewater disposal, geothermal production, gas injection and hydraulic fracturing (HF).
Scientists still lack a fundamental understanding of how and when complex many-body systems out of equilibrium will relax back to thermal equilibrium. Empirically, many systems do relax readily, justifying many of the core approximations of thermodynamics.
Many random processes lack memory – that is, the probability distribution for change over any time interval depends on the present state of the process but remains independent of how it behaved previously.
Many insights have recently emerged from applications of extreme value theory to the study of time series generated by dynamical systems.
It’s a common belief in areas such as psychology, economics or finance that people suffer from cognitive biases — systematic deviations from correct or rational behaviour when they make decisions.
Since the outbreak of the coronavirus, modellers from around the world have scrambled to predict the course of its spread, projecting forward from data on past infections and deaths in any region. Such estimates help public health officials and governments to judge the possible risks they face, and, on their basis, many nations have imposed […]
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