Results 111 to 120 of about 15,161 (215)

Long Unrest (2007–2025) at Laguna del Maule: Linking Strain Field and Seismicity From GNSS and Seismic Data

Geophysical Research Letters, Volume 53, Issue 4, 28 February 2026.
Abstract The Laguna del Maule volcanic field in Chile has been uplifting at exceptional rates since 2007, offering a unique opportunity to examine the interplay between crustal deformation and magma dynamics. To understand this relationship, we integrate GNSS with local seismic observations from 2013 to 2024 to model the reservoir strain field ...
M. Navarrete‐Reyes, F. Delgado, S. Ruiz, J. C. Báez, B. Potin, L. Cabrera, A. Alarcón   +6 more
wiley   +1 more source

Improving Seismic First Arrival Picking in Noisy Data: A Wavelet-Based Denoising Technique

Brazilian Journal of Geophysics
Accurate seismic first arrival picking is fundamental for geophysical interpretation and subsurface imaging. This study evaluates the performance of wavelet-based denoising techniques combined with the Translation-Invariant Shrinkage (TIS) algorithm to ...
Alireza Goudarzi, Shadi Veisi, Seyed Hadi Dehghan-Manshadi, Alireza Sandroos   +3 more
doaj   +1 more source

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

Geophysical Research Letters
The application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated.
Yiyuan Zhong, Yen Joe Tan
doaj   +1 more source

Benchmarking seismic phase associators: Insights from synthetic scenarios

Seismica
Reliable seismicity catalogs are fundamental for seismological analysis. Following phase picking, phase association groups arrivals into sets with consistent origins (i.e., events), determines event counts, and identifies outlier picks.
Jorge Puente Huerta, Christian Sippl, Jannes Münchmeyer, Ian W. McBrearty   +3 more
doaj   +1 more source

Divide and conquer: separating the two probabilities in seismic phase picking

Geophysical Journal International
SUMMARY There are two fundamental probabilities in the seismic phase picking process—the probability of the existence of a seismic phase (detection probability) and the probability associated with the phase arrival time estimation (timing probability).
Yongsoo Park, Alysha D Armstrong, William L Yeck, David R Shelly, Gregory C Beroza   +4 more
openaire   +1 more source

An approach for teleseismic location by automatically matching depth phase

Frontiers in Earth Science
To deal with the low efficiency problem of accurate teleseismic hypocenter location, this paper proposes a fully automatic approach by integrating the advantages of Seismic-Scanning based on Navigated Automatic Phase-picking, which can automatically ...
Jianlong Yuan, Huilian Ma, Jiashun Yu, Zixuan Liu, Shaojie Zhang   +4 more
doaj   +1 more source

Machine learning-based aftershock seismicity of the 2015 Gorkha earthquake controlled by flat-ramp geometry and a tear faultKey points

Earthquake Science
The Main Himalayan Thrust (MHT), where the 2015 MW7.8 Gorkha earthquake occurred, features the most seismicity of any structure in Nepal. The structural complexity of the MHT makes it difficult to obtain a definitive interpretation of deep seismogenic ...
Yeyang Kuang, Jiangtao Li
doaj   +1 more source

Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

The Seismic Record
Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to ...
Chenglong Duan, Brandon Schmandt, Ross Maguire, Ruijia Wang, Qingkai Kong   +4 more
doaj   +1 more source

CREDIT-X1local: A reference dataset for machine learning seismology from ChinArray in Southwest ChinaKey points

Earthquake Science
High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology. Here, we present an earthquake dataset based on the ChinArray Phase I records (X1).
Lu Li, Weitao Wang, Ziye Yu, Yini Chen
doaj   +1 more source

Evaluating Multi-station Phase Picking Algorithm Phase Neural Operator (PhaseNO) on Local Seismic Networks

Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem
Kong, Qingkai, Chatterjee, Avigyan, Chai, Chengping, Dzubay, Alex, Kroll, Kayla A., Stachnik, Josh C., Fertig, Scott, Liefer, Jeffrey, Friberg, Paul   +8 more
openaire   +2 more sources