Articles tagged with Earthquake Forecasting
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A new seafloor study revealed that a thin, clay-rich layer hidden beneath the seafloor enabled the 2011 Japan earthquake to rupture all the way to the trench, producing massive displacement. This finding could help scientists better understand and respond to other intense earthquakes and tsunamis.
New research simulates 10,000 years of seismic activity to show how underground temperature and sediment patterns control where earthquakes start, spread, and stop. This study provides a more accurate picture of the Main Marmara Fault's behavior, essential for building codes, emergency planning, and infrastructure decisions.
Researchers confirm that a 30-meter-thick layer of soft and slippery pelagic clay at the Japan Trench enabled the earthquake to rupture all the way to the trench, producing a massive tsunami. The discovery sheds new light on why the 2011 earthquake behaved so differently from predicted models.
Researchers have developed AI-powered forecasting tools that can predict the risk of aftershocks within seconds of an initial earthquake, offering a significant improvement over current methods. The new models trained on global earthquake data demonstrate comparable accuracy to existing systems while providing near real-time results.
A team of researchers has identified a new mechanism behind the 2025 Santorini earthquakes, finding that magma intrusion waves triggered the seismic unrest. The study used advanced machine learning techniques to analyze ground vibrations recorded by seismometers and inferred the movement of pressurized magma with unprecedented detail.
Machine learning models detected subtle signals that emerge just before the onset of laboratory earthquakes. The key predictive factor is the evolution of shear stress on creeping regions of the fault.
Climate changes in Lake Turkana influenced fault activity and magma production, rewriting the story of human evolution. Researchers found that lower lake levels led to increased melting and faulting, with potential implications for future volcanic and tectonic activity in East Africa.
Citizens' smartphones can be used to create highly detailed site amplification maps, providing critical input for seismic hazard assessment and supporting earthquake emergency response. The new approach, based on the Earthquake Network initiative, aggregates thousands of measurements to yield reliable high-resolution amplification maps.
Geosciences researchers discovered that extra stress can build up on faults due to millions of years of inactivity, resulting in a single release. This acceleration causes earthquakes to occur despite textbooks suggesting otherwise. The study has important implications for the future use of subsurfaces.
Researchers will generate maps showing the intensity and extent of an earthquake, helping to identify regions with a higher probability of infrastructure damage. The simulation will recreate a hypothetical scenario of an 8.1 magnitude earthquake in Mexico's Michoacán state.
Researchers at MIT have traced the energy released by 'lab quakes' and found that 80% of a quake's energy goes into heating up the region around the epicenter, while only 10% causes physical shaking. The study's findings could help seismologists predict earthquake vulnerability in regions prone to seismic events.
A team of geoscientists used advanced satellite data to track land movements in Greece and Turkey, providing crucial information for assessing the risk of major earthquakes. The study's findings show that stress builds up at plate boundaries, leading to increased likelihood of earthquakes.
A new laboratory earthquake model connects the microscopic real contact area between fault surfaces to earthquake occurrences, offering insights into earthquake mechanics and potential prediction. Continuous monitoring of physical properties could provide new tools for short-term systems and reliable prediction.
A new study by UC Santa Cruz geophysicists explains how small tremors can yield insights into major earthquake behavior. Small earthquakes can disturb the natural rhythm of tremors, causing them to speed up or delay activity.
A new study has investigated seismic signal shear-wave splitting to provide early warnings of dangerous eruptions. The research team discovered that the amount of splitting doubles before a larger, more explosive eruption, indicating a useful relationship between the parameter and the size of the eruption.
Geologists have connected a 120-million-year-old 'super-eruption' to its source, revealing insights into Earth's complex geological history. The discovery provides a more complete history of the Pacific Ocean basin and sheds light on volcanic activity in the region.
This study used high-resolution satellite data to rapidly assess coseismic surface ruptures caused by the 2023 Türkiye earthquake doublet. The research found separate rupture zones for each earthquake and highlighted seismic hazard risks for specific fault segments, including the Malatya Fault and East Anatolian Fault Zone.
A Kobe University study found that temperature at the plate interface predicts earthquake type, while a specific plate shape causes a seismic gap. Water released from rock transformation explains slow slip events and tectonic tremors, reducing stress between plates.
Researchers found a correlation between solar heat and seismic activity, suggesting that incorporating solar activity predictions into detailed Earth temperature models can improve earthquake forecasts. This study sheds light on the role of solar heat in triggering earthquakes, potentially providing a more accurate prediction method.
A new study developed a computer model to predict household displacement duration in US communities after disasters, accounting for socioeconomic factors. The model combines physical damage estimates with socioeconomic characteristics to help inform risk mitigation strategies.
Researchers analyzed spatial interactions between active fault segments in the L'Aquila Basin, revealing a structural connection between disconnected faults. Thermoluminescence dating indicated tectonic activity began 2.7 million years ago, while stable isotope analysis revealed fault structures formed at depths of 1.5-2 kilometers.
Researchers unveil groundbreaking insights into earthquake nucleation, showing that slow, aseismic motion is necessary and triggers seismic rupture. The study's findings also emphasize the critical role of geometric transitions in controlling nucleation dynamics.
Scientists have developed a new technique to study faults, which can improve earthquake forecasts by determining the origins and directions of past rupture events. By analyzing curved scratches left on the fault plane, researchers can pinpoint where earthquakes start and spread, providing valuable insights for modeling future scenarios.
Srisharan Shreedharan leads a collaborative effort to gain knowledge of processes that could improve seismic hazard forecasting. The research aims to identify key indicators that can help predict earthquakes and reduce seismic hazards.
A machine learning model predicts soil behavior during earthquakes, identifying areas vulnerable to liquefaction and providing contour maps for safer construction sites. The study uses geological data to create detailed 3D maps of soil layers, improving prediction accuracy by 20%.
Researchers propose that branch faults serve as 'on-ramps' for rupture on more mature main faults, leading to large earthquakes. The hypothesis is based on observations of past 25 years of magnitude 7.8 or larger continental transform earthquakes, which all originated on a branch fault.
Researchers from Kyushu University have identified a link between fault strength and earthquake magnitude, suggesting that stronger faults are more likely to produce large earthquakes. The study analyzed seismic activity at over 1,000 locations and estimated the stress field and characterized faults as strong or weak.
Researchers identified abnormal seismic activity three months before two major quakes in Alaska and California. The detection method uses machine learning to analyze datasets derived from earthquake catalogs.
A rare 'dual-initiation' mechanism led to Japan's violent Noto earthquake, which killed over 280 people and damaged 83,000 homes. The quake began simultaneously at two points on the fault, encircling a barrier and breaking it, releasing intense energy.
A new study by SMU seismologists confirms that earthquakes in the Permian Basin occurring before 2017 were causally linked to underground wastewater injection. The study uses advanced earthquake location methods to reveal a spatial and temporal correlation with shallow injection activities since 2009.
Researchers developed a predictive model that maps soil-bearing layer distribution, enabling city planners to assess site suitability and optimize building design. The model improves prediction accuracy by combining geotechnical data and geographic coordinates.
The study identified two main reasons for the amplification of tsunamis: a lens effect due to shallow waters and wave refraction, as well as diffraction at capes and multiple reflections. These local conditions contributed to the high tsunamis in Iida Bay.
Researchers analyzed historical shaking data from past earthquakes, including CHIMP data, to find that hazard maps for California and other countries predicted higher shaking than observed. The discrepancy was due to conversion equation issues between map values and intensity data.
The USGS considers sharing aftershock forecasts internationally to help people affected by earthquakes, but raises concerns about scientific sovereignty and cultural barriers. Research suggests that people value the information, but there are challenges to expanding it for public consumption.
Researchers at the Universiteit van Amsterdam triggered mini-earthquakes in a lab by applying a small seismic wave to a granular material. The study shows that these events can be understood using laboratory-scale frictional experiments, and its findings are relevant for understanding remote earthquake triggering in larger faults.
Researchers at the University of Texas at Austin developed an AI algorithm that accurately predicted 14 earthquakes within about 200 miles of their location and strength, with only one false warning. The system detected statistical bumps in real-time seismic data and paired them with previous earthquakes to make predictions.
Soil liquefaction, a destructive phenomenon during earthquakes, is redefined by this groundbreaking study. Liquefaction can now be understood to occur in drained conditions with low seismic-energy density levels, triggered by seismic shaking facilitating interstitial fluid flow within the soil.
Researchers have created a new model using deep learning to forecast aftershocks, outperforming the current ETAS model on larger datasets. The Recurrent Earthquake foreCAST (RECAST) model demonstrates better performance and computational efficiency.
Researchers confirm fracking triggers tremors, which can be used to track fluid movement and monitor fault activity. This finding has implications for sustainability and climate science, as carbon sequestration through fracking may reduce atmospheric emissions.
Researchers from the University of Tokyo and Stanford University analyze slow and fast earthquakes, showing that their magnitudes vary with time. The study confirms the scaling law for slow earthquakes, which defines the relationship between magnitude and duration, and reveals physical processes governing events.
Researchers found evidence of a precursory phase of fault slip occurring two hours before large earthquakes, using global GPS time-series data from nearly 100 major quakes worldwide. The study suggests that many large earthquakes start with a precursory phase of slip or represent the tail end of a longer process.
Researchers identified global trends in foreshock sequences for large earthquakes of magnitude 7 or larger. Foreshocks occur between 15-43% of mainshocks, and are more common along plate boundaries and reverse faults.
Scientists have discovered that sinking seamounts leave behind a trail of soft sediments, which help release tectonic pressure in slow slip earthquakes. This finding can be used to adjust earthquake models and improve understanding of the mechanisms driving earthquakes.
Researchers at the University of Texas at Austin have discovered a frictional phenomenon that governs how quickly faults heal after an earthquake. This discovery could help scientists understand when and how violently faults move, providing valuable new insights into the causes and potential for large earthquakes.
Researchers investigated the relationship between slow slip events and tectonic strain in Japan's Bungo Channel, Tokai, and Boso-Oki regions. They found that not all accumulated strain is released during SSEs, but rather builds up in shallower areas before a megathrust earthquake can occur.
Researchers at Northwestern University developed a new earthquake probability model that considers the specific order and timing of previous earthquakes. This allows for more accurate forecasting and explains why earthquakes sometimes come in clusters.
The University of Texas at El Paso will establish a national Center for Collective Impact in Earthquake Science, addressing low-probability but high-impact earthquake risk and community needs. Researchers aim to develop leading-edge earthquake research projects and integrate diversity into their work.
Researchers from Brown and MIT developed a new framework that uses machine learning and sequential sampling to predict rare disasters like earthquakes and pandemics with less data. The framework, called DeepOnet, has been shown to outperform traditional modeling efforts in predicting scenarios, probabilities and timelines of rare events.
A team of researchers from Cornell University has developed a method to study delayed earthquake triggering in laboratory settings. They found that the speed and strength of 'creep fronts' are sensitive to fault stress levels from previous earthquakes, which could potentially serve as local stress meters for predicting seismic events.
The Subduction Zones in Four Dimensions (SZ4D) initiative aims to improve understanding of subduction zone hazards through a collaborative effort. The plan involves deploying new instrumentation and developing more accurate models to predict large earthquakes, volcanic eruptions, and landslides.
Researchers used AI to analyze seismic signals and predict future fault friction and next failure time with high resolution in laboratory earthquakes. The technique goes beyond previous work by predicting the future state of the fault's physical system.
A new analysis by Devin McPhillips reveals that the recurrence interval for earthquakes along some California faults may be 16% longer than previously estimated. This is due to the incorporation of event likelihood, a variable quantifying how likely it is that a past earthquake is real and unique.
Researchers found the Tonga tsunami reached 90 meters in height, outperforming previous tsunamis like 2011 Japan tsunami. The event emphasized the need for improved detection systems, as volcano-based tsunamis are currently 30 years behind earthquake-based event monitoring tools.
Researchers at the University of Illinois used single calcite crystals with varying surface roughness to simplify the physics of fault movement. The study found that friction can increase or decrease with sliding velocity depending on mineral types and environment, providing a fundamental understanding of rate-and-state equations.
A new approach based on deep learning AI detects weak gravitational signals, or PEGS, generated by large-mass motion in megaquakes. This allows for real-time tracking of earthquake growth after a magnitude 8 event.
Low-frequency tectonic tremors in Alaska are linked to high levels of dehydration in the Yakutat terrane, a subducting oceanic plateau. The study suggests that this dehydration reaction is caused by temperature and pressure conditions during plate subduction.
Aftershocks form spatial clusters around mainshocks, with rates decreasing further from mapped faults and increasing with strain rate. Hardebeck's study improves models but falls short of capturing clustering density. Future research may incorporate crustal properties for better forecasts.
Geophysicists and computer scientists collaborate to better understand the dynamics of earthquakes and tsunamis. The team has identified three major characteristics that play a significant role in determining an earthquake's potential to stoke a tsunami, including stress along the fault line, rock rigidity, and sediment layer strength.
Scientists discovered that earthquakes influence tectonic plate movement, altering frequency and patterns of quakes. This finding suggests improved earthquake risk models can be developed by incorporating feedback mechanisms after an earthquake.
A new study finds that cell phone GNSS networks can accurately track crustal deformation, offering a more comprehensive view of seismic activity. By combining private and public sector networks, researchers aim to improve fault models and enhance disaster prevention.