The research team, led by Carlos Rodrigo Garibay Rubio of Universidad Iberoamericana and the Mexico City Secretariat of Integrated Risk Management and Civil Protection, together with Genta Nakano and Katsuya Yamori of Kyoto University, set out to answer a question that has long frustrated emergency planners: when exactly does psychological need spike, and what kinds of events trigger it? Traditional disaster mental health research has typically worked at coarse time scales, comparing survey responses months or years apart. Such approaches can identify lasting conditions like post-traumatic stress disorder, but they miss the acute, short-lived surges in demand that can overwhelm a support system within hours. Garibay Rubio and colleagues reasoned that if high-resolution time-scale data from information and communication technology-based systems and telehealth services could be analyzed systematically, researchers could identify the events associated with short-term outbursts in support demands, estimate when those demands would peak, and characterize the calls for service that arrive during them.

To do this, the team assembled an unusually rich dataset drawn from two national public institutions’ databases: the Screening Tool for Mental Health Problems, a validated instrument administered through telehealth channels, and the Lifeline Emergency Phone Call Service, a crisis hotline operated by the Mexico City government. Together, these sources comprised 138,471 interactions recorded during the first year of the COVID-19 pandemic in Mexico City, with additional earthquake-related data extending through 2023. The interactions captured responses associated with acute stress, depression, and anxiety, along with the operational characteristics of each call for service. Because the data were collected continuously and timestamped, the researchers could track demand on a daily basis, something that survey-based studies simply cannot achieve.

The analytical approach combined qualitative and quantitative methods. The team applied principal component analysis to reduce the multidimensional response data to interpretable components, computed correlations between demand patterns and event types, and used one-way analysis of variance to test whether demand differed significantly across event categories. The events they examined fell into four broad classes: public announcements of quarantine measures, mental health press conferences held by government officials, festivities during 2020, and earthquakes with public early warnings reported between 2020 and 2023. Each class represented a different kind of psychological trigger, from anticipatory anxiety about confinement to the acute alarm generated by an earthquake alert sounding on millions of phones.

The results confirmed that different event types produce distinct psychological signatures. Quarantine announcements were associated with patterns of acute stress and anxiety, as populations confronted the reality of confinement and its implications for work, family, and social life. Mental health press conferences, intended to inform and reassure, also shaped demand, suggesting that official communication itself functions as a psychological event with measurable consequences. Festivities during 2020, celebrated under pandemic restrictions, generated their own patterns, reflecting the tension between cultural traditions and public health measures. Earthquakes with public early warnings produced the most acute responses, with demand surging in the immediate aftermath of alerts, consistent with the well-documented psychological impact of seismic alarm systems on populations living in high-risk zones.

The most striking finding, however, concerned compound scenarios. When multiple threats converged, the effect on support demand was not merely additive but explosive. The documented case with a 2,651 percent increase on a single day illustrates the challenge: a support system calibrated for baseline demand can be overwhelmed almost instantaneously when a pandemic, a quarantine announcement, and an earthquake alert coincide. The researchers found that these complex scenarios with multiple threats were associated with significant short-term outbursts, posing a formidable challenge to support systems in terms of resources and timely response. The finding has direct implications for emergency planning, because it suggests that resource allocation strategies based on average demand or single-event scenarios will systematically underestimate the peaks that matter most.

Beyond documenting these surges, the study makes a novel methodological contribution: it proposes evaluation criteria for detecting potential system overloads in telehealth services before they occur. By monitoring demand patterns in real time and comparing them against thresholds derived from the historical data, operators could identify when a system is approaching overload and deploy early countermeasures, such as additional staff, automated screening tools, or triage protocols that prioritize the most urgent cases. The researchers argue that this event-wise approach, combined with the high-resolution time-scope analysis, provides valuable insights for emergency planning and resource allocation that could be applied to other cities and other types of disasters.

The study’s implications extend beyond Mexico City. Early warning systems for earthquakes, tsunamis, and other hazards are proliferating worldwide, and each alert that reaches a population generates a psychological response with measurable consequences for support services. The Mexican case is particularly instructive because the country operates one of the most extensive seismic early warning systems in the world, and its population has lived with the reality of major earthquakes for decades. The researchers suggest that the patterns they identified, including the timing of maximum demand associated with different event types, could inform the design of support systems in other high-risk regions, where the same dynamics of anticipation, alarm, and recovery are likely to play out.

The research also highlights the value of ICT-based and telehealth systems as scientific instruments in their own right. Because these platforms record interactions continuously and anonymously, they generate data that can reveal psychological dynamics invisible to traditional survey methods. The Screening Tool for Mental Health Problems, administered through telehealth channels with informed consent and ethical approval from the UNAM and INPRFM ethics committees, captured validated measures of mental health problems at population scale. The Lifeline dataset, comprising operational aggregates with no personally identifiable information, documented the real-time behavior of a crisis hotline under stress. Together, they demonstrate that the digital infrastructure built for service delivery can double as a monitoring system for population mental health, providing the evidence base for preparedness planning.

As climate change intensifies extreme weather, urbanization concentrates populations in hazard zones, and pandemics continue to emerge, the demand on psychological support systems will only grow. The Mexican experience during 2020 offers a warning and a roadmap: support systems must be designed not for average conditions but for the compound scenarios that produce the most extreme surges, and the digital tools that deliver care can also provide the early warning that keeps those systems from collapsing when they are needed most. The 2,651 percent surge documented by the research team is a reminder that in disaster preparedness, the psychological dimension is not a secondary concern but a central variable, one that can be measured, anticipated, and planned for with the right data and the right analytical approach.

Subject of Research: Psychological responses to compound disasters and the resulting short-term surges in mental health support-system demand

Article Title: Disaster preparedness and mental health: understanding psychological responses and support-system demands

Article References: Garibay Rubio, C. R., Nakano, G., Yamori, K., Peralta Gutiérrez, A. R., Morales Chainé, S., Robles García, R., Landa-Ramírez, E., Bojorge Estrada, A., & Tejadilla Orozco, D. I. (2026). Disaster preparedness and mental health: understanding psychological responses and support-system demands. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09201-9

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09201-9

Keywords: disaster preparedness, mental health, telehealth, COVID-19, earthquake early warning, acute stress, anxiety, depression, crisis hotline, principal component analysis, emergency planning, Mexico City

The physics behind the approach rests on how earthquakes nucleate. Before a fault ruptures in the plastic, seismic stage, rock undergoes a slow, elastic stage of stress accumulation. Laboratory experiments on glass samples, used as analogues for rock, have shown that this pre-plastic creep stage manifests in the development of small cracks that coalesce into larger splits, accompanied by measurable electromagnetic radiation. In a confined aquifer, this slow deformation changes the pressure field: in zones of preliminary extension, the groundwater system responds to a lower hydrostatic load before the event and a higher load afterward, while in zones of compression the trend is reversed, with a water-level rise expected before the shock. Because the duration of the elastic stage lengthens as the coming earthquake grows in magnitude, these subtle pressure changes should cross a sensor’s detection threshold earlier for larger events, which is precisely what makes wells a plausible forecasting tool rather than merely a post-event recorder.

History has long hinted at this connection. Abnormal water-level changes were documented in many Japanese wells before both the 1978 magnitude 6.8 Izu-Oshima earthquake and the devastating 1995 Kobe earthquake, alongside shifts in groundwater temperature, radon emission, chloride and sulfate concentrations, and dissolved gas ratios in mineral springs. Those recurring hydrologic precursors prompted the Japanese government to maintain water-level monitoring at more than 30 wells in seismically active areas. In Israel, earlier work along the Dead Sea Transform found indications of two moderate earthquakes in 2004 about one hour before the first seismographic record, in wells located roughly 100 kilometers apart. What has been missing, the researchers argue, is a constant, reliable, high-temporal-resolution dataset capable of turning such sporadic observations into a systematic alert scheme.

To build one, the team selected non-operational wells owned by Mekorot, the Israeli national water company, that satisfied a strict set of hydrogeological criteria. Each borehole had to tap a confined aquifer, because the higher water head in confinement amplifies the tectonic stress-imposed pressure reaction, and had to sit near an active tectonic system that acts as a vector of the regional stress field. The wells also had to be dedicated to observation only, disconnected from pumping systems, and located far enough from neighboring production wells to avoid artificial interference. The final network included the Carmel 13, Ta’anach 4, Nurit 1 and, later, Hindaj 2 wells, all positioned along major faults of the Dead Sea Transform, which links the Red Sea and Suez rifting to the great fault systems of the Eastern Mediterranean, including the Hellenistic arc and the North Anatolian system.

The instrumentation evolved through three research phases. Early observations relied on sporadic water-level readings caught by Mekorot’s maintenance system, then on a SCADA telemetry platform with a 15-minute sampling resolution that proved too coarse. The final configuration paired MSI Scantech potentiometric pressure sensors with GLI International combined temperature and electrical conductivity probes, connected to a 3Gtrack GS282-H2 GPRS data logger that samples every minute and transmits to a central Hydrogeological and Seismological Information Center every 15 minutes. Minute-level resolution matters because the precursory signals are small: groundwater level responses during the study ranged over just a few centimeters, and conductivity shifts of a few hundred micro-Siemens had to be separated from natural daily oscillations of several hundred micro-Siemens driven largely by tidal effects.

The analytical core of the system is short-term forward modeling. Water level, which descends gradually in summer and rises in winter, is modeled with linear regression whose slope and intercept are continually updated from a moving average over the previous 48 hours. Groundwater conductivity, which oscillates with a daily rhythm attributed to lunar tidal effects even in inland confined aquifers, is modeled with a nonlinear cosine-based regression whose amplitude, frequency and datum coefficients are fitted over roughly a day of training data. The model’s forward trajectory is then extrapolated four training-period lengths into the future and compared against incoming measurements. When the correlation between modeled and measured data falls below 0.8, the divergence is flagged as distinct. Anomaly detection combines Seasonal and Trend decomposition using Loess and Differential Seasonal Rolling Z statistics for the linear water-level series with a short-term memory autoencoder and Z-score methods for the nonlinear conductivity series, all applied to raw sorted data because preprocessing steps such as band-pass filtration were found to introduce bias.

The decisive test came on 27 June 2015, when the magnitude 5.5 earthquake struck offshore Saudi Arabia. Both the Hindaj 2 and Carmel 13 wells diverged from their predicted water-level trajectories several hours before the event: Hindaj 2 showed a rise of about one centimeter against its descending trend, while Carmel 13 showed a relaxation of its own decline. More strikingly, groundwater conductivity at both sites increased by approximately 200 micro-Siemens per square centimeter about six hours before the shock, while the correlation with the baseline model dropped by about 20 percent. When the automated detection system was later run on the archived data, it registered four independent indicators across the two wells, generating an alert more than an hour before the earthquake. By contrast, most smaller events, including quakes as weak as magnitude 2.3 offshore Cyprus and twin magnitude 3.1 events near Sinai and Beirut in October 2014, produced only post-event reactions or sporadic single indications that could not be distinguished from natural fluctuation.

The statistical architecture is designed to suppress false positives, the historic Achilles heel of earthquake prediction research. A single anomaly carries no significance, since anomalies attributed to earthquakes have repeatedly appeared only after events or in unsystematic patterns days, weeks or months beforehand. The system therefore requires three independent indications within a single time frame. With a three-day scanning window at five-minute sampling resolution, yielding 432 points per series, the joint probability of three coincidental independent anomalies is calculated as roughly 1.24 times 10 to the minus 8, giving a confidence level above 0.99999 that the pattern is not random. Independence is established both spatially, through multiple monitoring stations, and physically, through different measured parameters drawn from separate sensors. Validation on held-out datasets from 2016 to 2019 showed the calibrated platform reliably identifying events above magnitude 3, including advance identification of a magnitude 3.4 event on 6 March 2019, while smaller magnitude 2.6 events escaped detection.

The study’s conclusion formally rejects the null hypothesis that groundwater pressure and conductivity anomalies are unrelated to tectonic stress. The authors propose that pre-seismic slow motion, part of the pressure accumulation that precedes fault rupture, is reflected in aquifer properties before the seismic event, and that seismic stress is routed along fault systems to hydrologically reactive pressure joints, a mechanism also reported for distant earthquakes in Iran. The practical sensitivity threshold aligns with operational needs: since early warning for events below magnitude 6 is largely redundant, the system’s ability to statistically validate events of that scale and above, using single indicators for shocks above magnitude 3.5 and triple-indicator alerts above magnitude 5.5, matches the hazard that matters. Future development outlined by the team includes an automated warning platform for magnitude 6 and greater events, expanded coverage of both the Dead Sea Transform and the Carmel-Gilboa fault system, a minimum of three monitoring wells per fault system, and a triangulation algorithm that could use indications from different locations to estimate the epicenter of a coming quake.

If the approach matures, the implications extend well beyond the eastern Mediterranean. Confined, fractured rock aquifers near active faults exist along most of the world’s major plate boundaries, and the required sensors, pressure transducers and conductivity probes, are inexpensive compared with seismological networks. The method would not replace P-wave-based early warning systems such as Japan’s, which provide seconds of notice at the epicenter, but it targets a different and potentially more valuable window: hours of lead time, enough for task forces to mobilize, infrastructure to be shut down safely and populations to be alerted. For a field that has chased reliable short-term prediction since the 1970s through foreshocks, geodetic changes, radon emission and even unusual animal behavior, the sight of four independent groundwater sensors agreeing on an impending rupture, an hour in advance, marks a rare and tantalizing step from anomaly hunting toward operational forecasting.

Subject of Research: Groundwater monitoring as an early indicator of tectonic ground movements and earthquakes along the Dead Sea Transform

Article Title: Potential early indication of tectonic ground movements by water-well monitoring datasets, with short-term forward modeling

Article References: Anker, Y., Keller, A., Guttman, J., Yellin-Dror, A., Flexer, A., & Ben Moshe, B. (2026). Potential early indication of tectonic ground movements by water-well monitoring datasets, with short-term forward modeling. Environmental Earth Sciences, 85(16), Article 423. https://doi.org/10.1007/s12665-026-13154-2

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13154-2

Keywords: earthquake prediction, groundwater monitoring, Dead Sea Transform, confined aquifer, early warning systems, hydrogeology, seismology, water level anomalies, electrical conductivity, forward modeling, anomaly detection, Jordan Rift Valley

The first era, spanning 1990 to 1999, was a period of modest output and constrained ambition. Annual publication counts remained low, reflecting the limitations of sparse seismic networks, limited computational resources, and predominantly analog or early digital instrumentation. Monitoring during this decade was largely bound by data availability and processing capacity, so research clustered around waveform interpretation, source characterization, and station-based physical analysis within the classical seismological framework. It was, in essence, an instrument-centered discipline in which a handful of carefully placed stations and expert analysts defined the frontier of what could be observed about the Earth’s shaking.

The second era, from 2000 to 2018, brought sustained growth driven by an observational expansion that fundamentally changed what seismologists could see. Broadband seismic networks proliferated, dramatically improving the fidelity and coverage of ground-motion recordings. At the same time, space-based geodesy matured: continuous Global Navigation Satellite System (GNSS) observations enabled near-real-time tracking of crustal deformation, while Interferometric Synthetic Aperture Radar (InSAR) offered systematic mapping of how the ground shifted across vast regions after earthquakes. These techniques pulled geodesy and seismology into closer collaboration, broadening the research agenda from single-event analysis toward regional-scale monitoring, deformation tracking, and multi-parameter interpretation of seismic processes.

The third and most recent era, beginning in 2019, is defined by an unmistakable acceleration. Publication output rose sharply, and keyword and co-citation patterns became dominated by three emerging fronts: deep-learning-based earthquake detection and phase picking, Distributed Acoustic Sensing (DAS), and the joint use of GNSS and InSAR data. Benchmark studies such as PhaseNet and Earthquake Transformer, which apply neural networks to the automated identification of seismic phases in continuous waveform streams, have become among the most heavily co-cited works in the field. DAS, meanwhile, converts ordinary fiber-optic cables into dense seismic arrays capable of sampling ground motion every few meters over tens of kilometers, on land and across the seafloor. The authors are careful, however, to frame this surge as evidence of rising research attention rather than proof of mature, field-wide operational adoption, since the phase covers only six years and recent publications have had little time to accumulate citations.

Beyond technology, the analysis exposes a deeply uneven global landscape. Although researchers from 105 countries and regions contributed to the corpus, the United States and China together account for roughly half of all publications. The American lead reflects decades of investment in seismic and geodetic infrastructure, early adoption of open-data policies, and sustained methodological innovation. China’s rapid ascent, by contrast, corresponds to large-scale modernization of national monitoring networks, integrated GNSS-InSAR observation systems, and heavy investment in AI-enabled monitoring and earthquake early warning. A second tier of productivity is occupied by Italy, France, and Germany, and several European countries, notably France, Germany, and England, achieve higher citation rates per publication, suggesting an emphasis on methodologically integrative and internationally collaborative work.

The collaboration network analysis places the United States at the center of the field’s international architecture, with high betweenness centrality indicating a bridging role that connects otherwise separate regional research communities. At the institutional level, 2,372 organizations contributed to the corpus, led by national and transnational bodies that operate large observational systems, including the United States Department of Energy, the French National Centre for Scientific Research (CNRS), the China Earthquake Administration, and the Russian Academy of Sciences. Organizations such as INGV in Italy, GFZ in Germany, and the Helmholtz Association exemplify the fusion of operational monitoring mandates with strong research missions, occupying the interface between fundamental seismology and applied hazard mitigation. Notably, institutional productivity and average citation impact do not follow the same rankings, hinting at a structural distinction between data-producing operational agencies and conceptually influential research institutions.

Perhaps the most intellectually interesting finding concerns continuity. Despite three decades of technological upheaval, the co-cited author network remains anchored by foundational contributors to seismic source theory, wave propagation, event location, and earthquake mechanics. Modern monitoring workflows, in other words, still rest on classical seismological principles; the new tools have expanded the scale and speed at which physically grounded analyses can be performed rather than displacing the underlying physics. At the same time, a growing cluster of machine-learning researchers has become prominent in the co-citation structure, indicating a rebalancing of methodological priorities toward automated detection, phase picking, denoising, and large-scale catalog construction. The authors also caution that the growth of AI-related literature reflects not only genuine algorithmic innovation but also the diffusion of established machine-learning methods across new regional datasets and case studies.

The co-cited reference analysis identifies three benchmark directions that now structure the field’s intellectual agenda. The first is automated detection and phase-picking, exemplified by deep-learning models capable of processing vast continuous waveform archives. The second is dense and distributed sensing, where DAS benchmark studies demonstrate that existing fiber-optic infrastructure, including submarine telecommunications cables, can be repurposed as large-scale seismic observation arrays. The third is integrated and shareable data resources: curated benchmark datasets such as STanford EArthquake Dataset (STEAD) have enabled systematic model development, cross-dataset comparison, and reproducible evaluation, while GNSS-InSAR integration increasingly links seismic signals with deformation observations. Together, these clusters suggest that earthquake monitoring is converging on automation, dense observational systems, and multi-sensor data infrastructure as interconnected pillars.

The study’s authors are candid about the limitations of bibliometric evidence. Relying exclusively on the Web of Science may underrepresent regional and non-English journals, and the deliberately broad search query captured some methodologically adjacent industrial and reservoir monitoring work; a conservative sensitivity analysis flagged 376 such records, or 13.17 percent of the corpus, and confirmed that the main growth trajectory and core themes remained stable in the trimmed subset. Citation bias, time-lag effects, and database-driven visibility all complicate interpretation, and the authors stress that co-occurrence and co-citation indicators measure visibility and intellectual influence, not operational performance. No bibliometric signal can establish whether AI genuinely improves detection across all networks, whether DAS outperforms conventional arrays in practice, or whether GNSS-InSAR fusion improves warning performance.

What the analysis does offer is a roadmap. The convergence of seismic, geodetic, and fiber-optic themes points toward testable opportunities in heterogeneous monitoring: combining waveform detections with GNSS displacement for rapid magnitude characterization, using InSAR to map coseismic deformation when cloud-free acquisitions permit, and deploying DAS to densify observations along terrestrial and submarine fiber routes. The authors call for rigorous validation of AI models on geographically and temporally external networks, transparent comparison against classical baselines, error stratification by magnitude and noise regime, and reproducible benchmarks. They also highlight a representation gap: bibliometric visibility concentrates in well-funded systems, while many high-risk developing regions remain underrepresented, underscoring the need for interoperable regional catalogs, shared archives, and benchmark datasets suited to sparse networks and complex terrain. As earthquake monitoring moves toward hybrid paradigms that fuse computational intelligence with physical understanding, this study provides the clearest quantitative picture yet of where the field has been, and where its attention is now heading.

Subject of Research: Bibliometric analysis of technological evolution and research trends in earthquake monitoring from 1990 to 2024

Article Title: Technological evolution and emerging research hotspots in earthquake monitoring from 1990 to 2024 revealed by bibliometric evidence

Article References: Liu, S., Wu, L., & Liao, C. (2026). Technological evolution and emerging research hotspots in earthquake monitoring from 1990 to 2024 revealed by bibliometric evidence. Discover Geoscience, 4(1), Article 387. https://doi.org/10.1007/s44288-026-00744-7

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00744-7

Keywords: earthquake monitoring, bibliometrics, artificial intelligence, Distributed Acoustic Sensing, GNSS, InSAR, deep learning, seismic networks, earthquake early warning, crustal deformation, Web of Science, research trends