Predicting major earthquakes months ago

Girona et al. Abnormal low-magnitude seismicity preceding large-magnitude earthquakes. Nature Communications, August 2024.

Recent study suggests that significant earthquakes could be predicted days or months in advance by detecting low-level tectonic activity using machine learning. The researchers closely investigated two major earthquakes: the 2018 Anchorage earthquake and the 2019 Ridgecrest earthquake sequence in California. Their method involved analyzing seismic data for abnormal low-magnitude activity, particularly in the months leading up to these events. They discovered that in both earthquakes, there were about 3 months of unusual seismicity, predominantly of magnitudes below 1.5, over 15% to 25% of the affected regions. Specifically, their machine learning algorithm revealed an 80% probability of a major earthquake occurring within 30 days, increasing to 85% just days before the Anchorage quake. Similar patterns were also observed before the Ridgecrest sequence.

The researchers propose that this precursor activity is caused by increased pore fluid pressure within faults, altering their mechanical properties and leading to uneven stress distribution. This study highlights the transformative role of machine learning in analyzing large seismic datasets, allowing researchers to identify patterns that might signal impending earthquakes. However, while their method shows promise, it requires further testing in real-time scenarios and should be adapted to the specific seismic history of different regions. The ethical challenges of earthquake forecasting are significant, as false alarms could lead to widespread panic and economic disruption, while missed predictions could result in catastrophic outcomes. Nonetheless, accurate forecasting holds the potential to save lives and reduce economic losses by enabling timely warnings and preparations.

Environmental and social factors impact brain aging

Moguilner et al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nature Medicine, August 2024.

A new study reveals that brain aging varies significantly depending on social and environmental factors, particularly in older adults and those with dementia. Countries with higher inequalities, whether economic, environmental, or related to disease, tend to have populations with older brain ages. Researchers used advanced brain clocks based on deep learning to measure brain aging across a diverse sample of 5,306 participants from 15 countries. The study utilized functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to quantify the brain age gaps, which is the difference between the estimated biological brain age and the chronological age. The study found that individuals with dementia, especially Alzheimer’s disease, exhibited the most significant brain age gaps. Women in Latin American and Caribbean (LAC) countries showed larger brain age gaps, particularly those with Alzheimer’s, due to a combination of biological sex and gender disparities in health and social conditions. These findings emphasize the role of environmental and social factors in brain health disparities. The research underscores the importance of considering the interaction between large-scale environmental factors (exposome) and brain aging mechanisms. This new framework for brain health research could be critical for personalized medicine, helping to identify individuals at risk for neurodegenerative diseases and develop targeted interventions. Additionally, the study highlights the need for public health policies to address socioeconomic inequalities and environmental pollution to promote healthier brain aging across populations.

Meat consumption linked to higher type 2 diabetes risk

Chunxiao Li et al. Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1.97 million adults with 100,000 incident cases from 31 cohorts in 20 countries. The Lancet Diabetes & Endocrinology, September 2024.

Global meat consumption has surged in recent decades, often exceeding dietary guidelines, leading to concerns about its impact on health, particularly the risk of type 2 diabetes. Previous research has suggested a link between higher intakes of processed and unprocessed red meat and an increased risk of type 2 diabetes, but results have been inconsistent. To clarify this relationship, researchers utilized data from the global InterConnect project, analyzing 31 study cohorts across 20 countries. They considered various factors, including age, gender, lifestyle behaviors, energy intake, and body mass index. The study found that consuming 50 grams of processed meat daily, equivalent to two slices of ham, is associated with a 15% higher risk of developing type 2 diabetes within the next decade. Similarly, consuming 100 grams of unprocessed red meat, such as a small steak, was linked to a 10% increased risk. While habitual consumption of 100 grams of poultry daily was associated with an 8% higher risk, this association weakened under further analysis, whereas the links with processed and unprocessed red meat remained strong. This is the most comprehensive evidence to date supporting the recommendation to limit processed and unprocessed red meat consumption to reduce type 2 diabetes risk. Although the study also examined poultry consumption, the link to type 2 diabetes remains uncertain and requires further investigation. The study’s innovative use of harmonized data across diverse populations enabled a more accurate assessment of the relationship between meat consumption and diabetes, reducing potential biases. This research highlights the need for further investment in studies across under-represented regions.

The theory of plate tectonics explains what happens at plate boundaries. According to this theory, there are three primary plate boundary conditions; divergent, convergent, and transform boundaries. Divergent plate boundaries are characterized by ocean ridges and sea floor spreading; volcanoes are the most obvious setting. Here, a new crust is generated because the plates pull away from each other. Convergent plate boundaries are characterized by trenches and island arcs. In this setting, the crust is consumed in the Earth’s interior as one of the plates dives under another. In the case of transform plate boundaries, the crust is neither produced nor destroyed, as plates horizontally slide past each other. Significant earthquakes can occur under all of these boundary conditions.

We typically associate Japan with earthquakes because we know that it is a very earthquake prone island. The state of Alaska in the United States is also earthquake prone. The total number of earthquakes in Alaska per year is greater than the total number of earthquakes in the rest of the United States. The examples of Japan and Alaska reveal that more earthquakes occur at locations close to the plate boundaries. On a global scale, Japan, the Philippines, Indonesia, Chile, and western United States, are located along the so-called “Pacific Ring of Fire,” where about 90% of the world’s earthquakes and 80% of the world’s largest earthquakes occur.

The magnitude of an earthquake is a representation of the total amount of energy released by the event. Typically, it is measured using the recorded ground oscillations from a seismogram. However, the interpretation of the magnitude is not straightforward because the magnitude scale is logarithmic. For instance, a magnitude 7.0 earthquake produces approximately 10 times more ground motion and releases about 32 times more energy compared to a magnitude 6.0 earthquake (2).

According to the statistics published by the US Geological Survey, every year on average 134 earthquakes with magnitudes 6.0 to 6.9 occur worldwide, 17 earthquakes with magnitudes 7.0 to 7.9, and at least one large earthquake with a magnitude greater than 8 (Figure 3). Further, the number of earthquakes of magnitude 7.0 or greater has remained fairly constant but the number of moderate earthquakes (i.e., 6.0 or less) appears to be increasing. According to experts at the US Geological Survey, a partial explanation may lie in the fact that there is a tremendous increase in the number of seismograph stations in the world over the last twenty year. Thus, the actual number of earthquakes has not increased, but our ability to detect them. In scientific terms, this is referred to as reporting bias (3). When it comes to myths about earthquake activity related to weather and time, scientists rejects any connection. Earthquakes occur whether it is warm or windy, early in the morning or late at night.

In the United States, earthquakes are one of the most significant natural hazard for around 75 million Americans living in 39 states, including the state of California where the majority of the state’s population lives within 32 km of active faults. Historically, the region has been very active (Figure 4). To help predict earthquakes in California, a multidisciplinary group of scientists and engineers from various disciplines established a team entitled Working Group on California Earthquake Probabilities (WGCEP). The team had a very ambitious objective to develop a comprehensive earthquake rupture forecast model for the state of California using the best available science. The details of the sophisticated model are beyond the scope of this essay, but the recently released report (USGS Open File Report 2007-1437) is available for public access (5). In her essay entitled, “The big one is evitable. Catastrophe is not,” Cathleen Decker, an editor of the Los Angeles Times, refers to the future predictions presented in the report as a “Chilling look into the future” (7). Based on historical evidence and scientific data, it is almost certain (with a 99% chance) that there will be at least one earthquake with magnitude 6.7 or greater in the state of California within the next thirty years. The likelihood of a more significant earthquake (magnitude 7.5 or greater) within the next thirty years in California is 46%. In the Greater Bay Area specifically (area includes large cities such as San Francisco, San Jose, Oakland), the probability of at least one earthquake with magnitude 6.7 or greater within the next thirty years is about 67% (6).

The current state of science considerably reduces the risk of death and damage by making resources available to individuals, teachers, policy makers, and engineers, but unfortunately, science at this time can neither prevent nor predict the exact time when an earthquake will occur. Casualties, financial losses, and mental trauma are sometimes inevitable for earthquake victims. Social and emotional suffering are often not limited to actual victims, but to everyone who has access to the news. Unlike financial and material losses, the psychological consequences of an earthquake exposure are long lasting. To address these consequences, earthquake preparedness should include mental and social aspects of the disaster as well.

Reference

• This dynamic Earth The Story of Plate Tectonics by W. Jasquelyne Kious and Robert I. Tilling (Online edition) U.S. Department of the Interior, U.S. Geological Survey http://pubs.usgs.gov/publications/text/Vigil.html
• USGS Earthquake Hazard Program – Earthquake Facts and Statistics http://earthquake.usgs.gov/earthquakes/eqarchives/year/eqstats.php
• USGS Earthquake Hazard Program – Are Earthquakes Really on the Increase? http://earthquake.usgs.gov/learn/topics/increase_in_earthquakes.php
• California Geological Survey – Probabilistic Seismic Hazards Assessment – Historic Earthquakes http://www.conservation.ca.gov/cgs/rghm/psha/Pages/historic.aspx
• The Uniform California Earthquake Rupture Forecast, Version 2 (UCERF 2) By 2007 Working Group on California Earthquake Probabilities, 2008 http://pubs.usgs.gov/of/2007/1437/
• USGS Earthquake Hazard Program – 2008 Bay Area Earthquake Probabilities http://earthquake.usgs.gov/regional/nca/ucerf/
• Los Angeles Times (January 17, 2010) http://www.latimes.com/news/local/la-me-week17-2010jan17,0,4714976.story

Smart structures can sense changes in their environment and respond accordingly. These adaptive structures can autonomously modify their shapes to perform the desired task regardless of the particular environmental disturbance.

“It’s difficult to bet on technology-you cannot always pick the winners, but it looks like smart materials would be the next step in engineering design,” says Craig A. Rogers, director of Virginia Tech’s Center for Intelligent Systems and Structures.

By using smart materials instead of adding mass, engineers can endow structures with built-in responses to a myriad of contingencies. In their various forms, these materials can perform as actuators, which can adapt to their environments by changing such characteristics as shape and stiffness, or as sensors, which provide actuators with information about structural and environmental changes.

Smart structures have numerous applications, among them the following:

SPACE STRUCTURES

Large space structures are subject to a variety of dynamic perturbations produced by the crew, the docking of other spacecraft, transient thermal states during the orbit, micrometeorities, and so on. The vibration amplitude of the perturbations has to be dampened in time to avoid further nonstability in the space structure.

In addition to that, the dampening of the flexible models is a necessary ingredient in achieving robust attitude control of the spacecraft.

AIRPLANES

Smart wings. Airplanes that have smart wings will control surfaces that can reshape themselves on the fly. Airplane wings will flex themselves like fish tails. With the help of smart structures, airfoil will be shaped and the aircraft’s lift will be improved. This improved lift will help to get a single-engine fighter off the deck of an aircraft carrier without a catapult.

Replacing current (and heavy) hydraulic control systems with light-weight, high-performance smart materials could increase aircraft payloads by as much as 30 percent and flight range by 50 percent.

Adaptive surfaces would replace stiff structures designed as a compromise among ideal wing shapes for various maneuvers. Eventually vertical tails, ailerons, and stiff structures could be eliminated.

HELICOPTERS

Active helicopter blades that adjust shape continuously to respond to vibration-engendering pressure changes in the air. These fluctuations knock the machinery out of alignment and cause a lot of down time. A helicopter’s maintenance schedule is approximately 15 percent of its time. In the helicopter project, piezoelectric patches on blade surfaces function both as sensors and as actuators, or as generators of counter-force.

Flutter suppression is a particularly important problem. Recent experiments in NASA wing tunnels with unoptimized smart structure designs have shown a 70 percent decrease in displacement and a 20 percent increase in blade speed by utilizing active vibration control concepts.

Active noise suspension for helicopter cabins promise greatly decreased acoustic noise/vibration intensities. This reduces stress upon crew members involved in increasingly longer duration missions.

SUBMARINES

Stealth submarines using smart skins. Smart materials technology may result in stealth submarines. Their acoustically hypersensitive smart skins would detect the pressure of an incoming sonar wave, and then automatically generate an equal but opposite counter-pressure to cancel out the ping. With nothing reflected back to enemy boat, the submarine would be invisible.

CARS

The automotive industry also is eager to incorporate intelligent materials technology. Some of the areas where smart material will be used are:

Smart car seats. Researchers are working on an industry-sponsored project to develop smart car seats that can identify primary occupants and adapt to their preferences for height, leg room, back support, and so forth.

Maintenance information. The technology exists to enable cars to tell owners how much air pressure tires have, when oil changes are needed, and other maintenance information.

Suspension and transmission. Smart materials that can change their viscosity (inherent thickness or resistance to flow) when exposed to electric or magnetic fields. This kind of smart material will lead to new kinds of auto suspensions and transmissions.

SKIS

A revolutionary piezo control module, developed by Active Control eXperts, Inc. (ACX), serves as “the brain inside the ski.”

The ACX “brain” is a small, thin, rectangular card containing piezoelectric smart materials and control circuits, which are embedded while the skis are being made. These materials detect unwanted vibrations in the skis and convert them into useful electrical energy. The control circuitry then uses the energy to smooth out the vibrations, putting the skis back on the snow. The result is a smoother ride, more responsive turning, and “solid stability.”

SOUND

Ultra-high-fidelity stereo speakers. Using piezoelectric actuators, such speakers can expand and contract in thousandths of a second in response to applied voltage. Speaker speakers in their homes and cars to achieve maximum musical effects. Their cars and houses will offer built-in surround-sound.

BUILDINGS

New bridge systems using fiber-optic lines and other sensors as strain indicators. Embedded in building materials, these devices would generate telltale optical or electrical signals when the system is stressed. Eventually, earthquake-resistant structures could be made using materials that would alter their stiffness in response to the ground’s motion, much as horseback riders flex their legs while riding.

Earthquake resistant structures. Smart structures will shake the building to cancel the effect of the earthquake.

Early warning. Smart structures will help determine possible structural damages due to the onset of structural degradation.