Results 11 to 20 of about 953 (196)

Customization of a deep neural network using local data for seismic phase picking [PDF]

Frontiers in Earth Science, 2023
Deep-learning (DL) pickers have demonstrated superior performance in seismic phase picking compared to traditional pickers. DL pickers are extremely effective in processing large amounts of seismic data.
Yoontaek Hong, Ah-Hyun Byun, Seongryong Kim, Dong-Hoon Sheen   +3 more
doaj   +4 more sources

First Arrival Picking of Zero-Phase Seismic Data by Hilbert Envelope Empirical Half Window (HEEH) Method

Sensors, 2022
First arrival travel time picking is an important step in many seismic data-processing applications. Most first arrival picking methods search for a sudden jump in seismic energy at trace onsets, which is clearly appropriate for minimum-phase data.
Amen Bargees, Abdullatif A. Al-Shuhail
doaj   +2 more sources

Automatic Horizon Picking Using Multiple Seismic Attributes and Markov Decision Process

Remote Sensing, 2023
Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing.
Chengliang Wu, Bo Feng, Xiaonan Song, Huazhong Wang, Rongwei Xu, Shen Sheng   +5 more
doaj   +2 more sources

Parametric Testing of EQTransformer’s Performance against a High-Quality, Manually Picked Catalog for Reliable and Accurate Seismic Phase Picking

The Seismic Record, 2023
This study evaluates EQTransformer, a deep learning model, for earthquake detection and phase picking using seismic data from the Southern Alps, New Zealand.
Olivia Pita-Sllim, Calum J. Chamberlain, John Townend, Emily Warren-Smith   +3 more
doaj   +3 more sources

PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning

Earth and Space Science, 2023
Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption.
T. Bornstein, D. Lange, J. Münchmeyer, J. Woollam, A. Rietbrock, G. Barcheck, I. Grevemeyer, F. Tilmann   +7 more
doaj   +6 more sources

A Lightweight Network for Seismic Phase Picking on Embedded Systems

IEEE Access
Phase picking is a critical task in seismic data processing, where deep learning methods have been applied to enhance its accuracy. While lightweight deep learning networks have been optimized for edge computing devices, there is a lack of networks ...
Yadongyang Zhu, Shuguang Zhao, Wei Wei, Fudong Zhang, Fa Zhao   +4 more
doaj   +2 more sources

Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases

Artificial Intelligence in Geosciences
The availability of a tremendous amount of seismic data demands seismological researchers to analyze seismic phases efficiently. Recently, deep learning algorithms exhibit a powerful capability of detecting and picking on P- and S-wave phases.
Congcong Yuan, Jie Zhang
doaj   +2 more sources

SeismicSense: Phase Picking of Seismic Events with Embedded Machine Learning

Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing
Analyzing seismic data is essential for understanding natural geological processes and anthropogenic activities, particularly in localizing seismic events. While recent advances in seismic analysis rely heavily on resource-intensive machine learning approaches, these methods are impractical in resource-constrained environments such as underwater ...
Tayyaba Zainab, Laura Harms, Jens Karstens, Olaf Landsiedel   +3 more
core   +5 more sources

Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking [PDF]

Nature Communications, 2020
The authors here present a deep learning model that simultaneously detects earthquake signals and measures seismic-phase arrival times. The model performs particularly well for cases with high background noise and the challenging task of picking the S ...
S. Mostafa Mousavi, William L. Ellsworth, Weiqiang Zhu, Lindsay Y. Chuang, Gregory C. Beroza   +4 more
doaj   +2 more sources

Seismic Phase Detection & Picking using EfficientNet

, 2022
The monitoring of seismic waves for the detection of earthquakes and the picking of the arrival of P and S waves has been a challenging task in the field of observational seismology. While the use of deep learning techniques has led to improved performance, the models tend to suffer from poor generalizability and poor picking performance with the S ...
Ramakrishnan Arularasan, Arunprasath
openaire   +2 more sources