Results 161 to 170 of about 953 (196)

Improvements from incorporating machine learning algorithms into near real-time operational post-processing. [PDF]

Sci Rep
Tepp G, Yu E, Bhaskaran A, Tam R, Zhu W, Newman Z, Jaski E, Scheckel N.   +7 more
europepmc   +1 more source

Impact of tectonic and stratigraphic evolution of Shushan Basin on hydrocarbon accumulation, north Western Desert, Egypt. [PDF]

Sci Rep
Fagelnour M, Farouk S, Sarhan MA.
europepmc   +1 more source

Enhancing Railway Earthquake Early Warning Systems with a Low Computational Cost STA/LTA-Based S-Wave Detection Method. [PDF]

Sensors (Basel)
Katakami S, Iwata N.
europepmc   +1 more source

Cellular level lipidomics of two-dimensional cultures of adherent gut epithelial cell lines confirms a metabolic switch.

Analyst
Xu Q, Penny JA, Fraser E, Orsenigo F, Barberis M, Gethings LA, Bailey MJ, Mills ENC.   +7 more
europepmc   +1 more source

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Seismic Phase Picking Using Convolutional Networks

IEEE Transactions on Geoscience and Remote Sensing, 2019
When a seismometer network records an earthquake, operators will manually review the waveforms and identify the wave phases, a task known as phase picking. Manual phase picking is a time-consuming process that can be automated using machine learning; however, automatic methods have not yet achieved human-level performance, and open-source ...
Esteban Pardo, Norberto Malpica
exaly   +2 more sources

Automatic onset phase picking for portable seismic array observation

Acta Seismologica Sinica, 2006
Automatic phase picking is a critical procedure for seismic data processing, especially for a huge amount of seismic data recorded by a large-scale portable seismic array. In this study is presented a new method used for automatic accurate onset phase picking based on the property of dense seismic array observations.
Qi-Yuan Liu, Guo Biao
exaly   +2 more sources

Automated phase attribute-based picking applied to reflection seismics

Geophysics, 2016
ABSTRACT We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user’s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques.
FORTE, Emanuele, DOSSI, MATTEO, PIPAN, MICHELE, DEL BEN, Anna   +3 more
openaire   +4 more sources

Transfer-Learning-Based SVM Method for Seismic Phase Picking With Insufficient Training Samples

IEEE Geoscience and Remote Sensing Letters, 2022
Jinghe Li, Jinghe Li
exaly   +2 more sources

SEA-net: Sequence attention network for seismic event detection and phase arrival picking

Engineering Applications of Artificial Intelligence, 2023
Xiaoming Hou, Yu Zheng, Shengli Zhang
exaly   +2 more sources

Transfer Learning for Seismic Phase Picking With Significantly Higher Precision in Faraway Seismic Stations

IEEE Transactions on Geoscience and Remote Sensing
— Earthquake data recorded in Texas are dramatically different from other places because of the various types of noise caused by oil and gas production or anthropogenic activities.
Omar M. Saad, Alexandros Savvaidis, Yangkang Chen   +2 more
openaire   +2 more sources