Results 61 to 70 of about 8,225 (218)

The Impact of School‐Based Group Adlerian Play Therapy on Internalizing Behaviors

open access: yesJournal of Counseling &Development, Volume 104, Issue 4, Page 621-632, October 2026.
ABSTRACT This study examined the impact of group Adlerian play therapy (GAdPT) compared to a waitlist control group (WG) with 94 fourth‐ and fifth‐grade students reporting clinical levels of internalizing behaviors. Forty‐eight students were assigned to GAdPT and 46 were assigned to the WG.
Kelly A. Owen   +3 more
wiley   +1 more source

Analysis of the Wenchuan aftershock data [PDF]

open access: yes, 2012
We analyse the aftershocks of theWenchuan earthquake in 2008 using a multivariate non-normal distribution fitted to the observations for the variables selected from the inter-aftershock times, depths, magnitudes, sines and cosines of the azimuths, slant
Siew, Hai-Yen, Pooi, Ah Hin *
core  

Estimating Secondary Earthquake Aftershocks from Tsunamis

open access: yesGeosciences
Nonlinear solitary waves influence the Earth’s crust because wave pressure on the ocean bottom contains non-hydrostatic components. Our physical-mathematical model allows us to calculate the surplus super-hydrostatic pressure on the Earth’s crust.
Sergey A. Arsen’yev, Lev V. Eppelbaum
doaj   +1 more source

A Spatio‐Temporal Neural Kernel Function Model for Earthquake Clustering: Bridging ETAS and Neural Point Processes

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Modeling earthquake clustering in space and time is central to understanding seismicity patterns and improving forecasting. The Epidemic‐Type Aftershock Sequence (ETAS) model, grounded in the Hawkes process, has long been the standard approach for describing aftershock triggering through fixed, empirically defined kernel functions.
Chengxiang Zhan   +3 more
wiley   +1 more source

Adapting a Pretrained LLM to Rapidly Recover Earthquake Magnitude and Location

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Determining earthquake magnitude and location through fast, automated methods is fundamental for seismic monitoring. Recent studies have shown that Large Language Models (LLMs) can operate as pattern‐recognition systems capable of handling novel domains and purely numerical tasks.
Aurora Bassani   +6 more
wiley   +1 more source

Aftershock Accelerograms Recorded on a Temporary Array [PDF]

open access: yes, 1982
We recovered 52 timed analog accelerograms from 25 aftershocks of the 1979 Imperial Valley earthquake, between 3:33p.m. P.d.t. October 16 and 5:43 a.m. October 31. The largest aftershock that we recorded (M_L =4.9) occurred at 4:16p.m.
Heaton, T. H., Anderson, J. G.
core  

An improved approach for aftershock hazard assessment

open access: yes, 2015
Estimation of aftershock hazard is one of the most critical issues in evaluation of the post-earthquake safety of damaged structures. Unfortunately, misevaluation of this risk has claimed many lives in the past.
Müderrisoğlu, Ziya, Yazgan, Ufuk
core   +1 more source

Numerical Simulations of 3D Scattering Structures Reveal Systematic Underestimation of Marsquake Magnitudes

open access: yesGeophysical Research Letters, Volume 53, Issue 17, 16 September 2026.
Abstract Determining marsquake magnitudes is essential for assessing seismic activity on Mars, yet previous estimates neglect three‐dimensional (3D) scattering effects. The exceptionally long‐lasting coda of the largest recorded marsquake, S1222a, provides a unique opportunity to quantify how crustal heterogeneities affect waveform propagation and ...
Mingwei Dai   +6 more
wiley   +1 more source

Aftershocks Preferentially Occur in Previously Active Areas

open access: yesThe Seismic Record, 2022
The clearest statistical signal in aftershock locations is that most aftershocks occur close to their mainshocks. More precisely, aftershocks are triggered at distances following a power-law decay in distance (Felzer and Brodsky, 2006).
Morgan T. Page, Nicholas J. van der Elst
doaj   +1 more source

Predicting Seismic Intensity Using Machine Learning With Ancillary Data: Evidence From Destructive Earthquakes in China

open access: yesEngineering Reports, Volume 8, Issue 9, September 2026.
A machine learning framework integrating multisource seismic, social, and geographic data accurately predicts kilometer‐scale seismic intensity, enabling rapid postearthquake damage assessment during data‐scarce “black‐box” periods. ABSTRACT Accurate and rapid assessment of seismic intensity is crucial for postearthquake emergency response.
Ma Yuan   +4 more
wiley   +1 more source

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