Zobrazeno 1 - 10
of 454
pro vyhledávání: '"Earthquake Detection"'
Publikováno v:
Earthquake Science, Vol 37, Iss 3, Pp 210-223 (2024)
There was an evident increase in the number of earthquakes in the Xinfengjiang Reservoir from June to July 2014 after the landing of Typhoon Hagibis. To understand the spatial and temporal evolution of this microseismicity, we built a high-precision
Externí odkaz:
https://doaj.org/article/a9ef78a514034823a8b959457da3c3f2
Autor:
Krishna Bhargavi Y, Varshitha Reddy G, Sneha R, Gayatri S, Pranathi Y, Sergei Solovev, Nikolai Ivanovich Vatin, Upendra Dabral
Publikováno v:
Cogent Engineering, Vol 11, Iss 1 (2024)
The challenge of swift and reliable earthquake location prediction within earthquake early warning (EEW) systems underscore the need for innovative solutions. Existing methods provide predictions, yet there is a clear demand for enhanced accuracy and
Externí odkaz:
https://doaj.org/article/1ce11148273149fd868ab52f38ee804e
Publikováno v:
Seismica, Vol 3, Iss 1 (2024)
The aim of this study is to collect information about events in the city of Oslo, Norway, that produce a seismic signature. In particular, we focus on blasts from the ongoing construction of tunnels and under-ground water storage facilities under pop
Externí odkaz:
https://doaj.org/article/5329104a5d3442a7be797e11a0608300
Autor:
Satyam Pratap Singh, Vipul Silwal
Publikováno v:
Artificial Intelligence in Geosciences, Vol 4, Iss , Pp 150-163 (2023)
The Hindu Kush-Pamir region (HKPR) is characterized by complex ongoing deformation, unique slab geometry, and intermediate seismic activity. The availability of extensive seismological data in recent decades has prompted the use of deep learning algo
Externí odkaz:
https://doaj.org/article/09e0e3941af841f1b40b0793eb9d4ff1
Publikováno v:
Artificial Intelligence in Geosciences, Vol 4, Iss , Pp 84-94 (2023)
Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking. Despite many examples of the remarkable performance of existing (i.e., pre-trained) deep-learn
Externí odkaz:
https://doaj.org/article/3f9fc4f5b30441e4ae832d6b8ffd2896
Autor:
Cosmina-Mihaela Rosca, Adrian Stancu
Publikováno v:
Applied Sciences, Vol 14, Iss 22, p 10169 (2024)
Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic even
Externí odkaz:
https://doaj.org/article/a0a10fcf43a946769c0af08e149bf16f
Autor:
Jannes Münchmeyer, Sophie Giffard-Roisin, Marielle Malfante, William Frank, Piero Poli, David Marsan, Anne Socquet
Publikováno v:
Seismica, Vol 3, Iss 1 (2024)
Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficult to detect and characterize. The most pervasive seismic markers of s
Externí odkaz:
https://doaj.org/article/bf1ad9eb198645499fdb062f7cc1dcbe
Publikováno v:
Earthquake Science, Vol 36, Iss 2, Pp 84-94 (2023)
In recent years, artificial intelligence technology has exhibited great potential in seismic signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence i
Externí odkaz:
https://doaj.org/article/93d23501d63c44b69e7eb17995b07add
Publikováno v:
Earthquake Science, Vol 36, Iss 2, Pp 113-131 (2023)
Seismic phase pickers based on deep neural networks have been extensively used recently, demonstrating their advantages on both performance and efficiency. However, these pickers are trained with and applied to different data. A comprehensive benchma
Externí odkaz:
https://doaj.org/article/509376048bdd4966b1d7594222115805