Using Deep Learning for Flexible and Scalable Earthquake Forecasting
Geophysical Research Letters, 2023 Seismology is witnessing explosive growth in the diversity and scale of earthquake catalogs. A key motivation for this community effort is that more data should translate into better earthquake forecasts. Such improvements are yet to be seen.
Kelian Dascher‐Cousineau +3 more
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Underground Space, 2018 We present and compare two flexible and effective methodologies to predict disturbance zones ahead of underground tunnels by using elastic full-waveform inversion.
Andre Lamert +3 more
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A Python framework for efficient use of pre-computed Green's functions in seismological and other physical forward and inverse source problems [PDF]
Solid Earth, 2019 The finite physical source problem is usually studied with the concept of volume and time integrals over Green's functions (GFs), representing delta-impulse solutions to the governing partial differential field equations.
S. Heimann +10 more
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Chromospheric seismology above sunspot umbrae [PDF]
, 2015 Context: The acoustic resonator is an important model for explaining the three-minute oscillations in the chromosphere above sunspot umbrae. The steep temperature gradients at the photosphere and transition region provide the cavity for the acoustic ...
G. J. J. Botha +5 more
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Geodesy and Geodynamics, 2011 A statistical correlation method is used to study the effect of instability of the calculation datum (used in traditional method of indirect adjustment) on calculated gravity results, using data recorded by Long-men Mountain regional gravity network ...
Sun Shaoan, Zhou Xin
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Estimation of seismic impacts under conditions of permafrost degradation
Вестник Камчатской региональной ассоциации "Учебно-научный центр". Серия: Науки о Земле, 2022 Permafrost is widespread in Russia in many mining and oil regions. Some of these areas are located in zones of high seismic activity. In connection with global climate change and possible degradation of permafrost, it is necessary to study the seismic ...
Trifonov B.A. +2 more
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STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI
IEEE Access, 2019 Seismology is a data rich and data-driven science. Application of machine learning for gaining new insights from seismic data is a rapidly evolving sub-field of seismology.
S. Mostafa Mousavi +3 more
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Geophysical Monograph Series, Page i-xiv., 2020 This book is Open Access. A digital copy can be downloaded for free from Wiley Online Library.
Explores the behavior of carbon in minerals, melts, and fluids under extreme conditions
Carbon trapped in diamonds and carbonate-bearing rocks in subduction zones are examples of the continuing exchange of substantial carbon ...
wiley +1 more source
ScS shear-wave splitting in the lowermost mantle: Practical challenges and new global measurements
SeismicaMany regions of the Earth's mantle are seismically anisotropic, including portions of the lowermost mantle, which may indicate deformation due to convective flow.
Jonathan Wolf, Maureen D. Long
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Fully probabilistic seismic source inversion – Part 1: Efficient parameterisation [PDF]
Solid Earth, 2014 Seismic source inversion is a non-linear problem in seismology where not just the earthquake parameters themselves but also estimates of their uncertainties are of great practical importance.
S. C. Stähler, K. Sigloch
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