Kyoto, Japan -- Japan is well known for its large earthquakes, but not all regions show the same patterns of earthquake activity. One way to understand which places tend to experience large or small earthquakes is the b-value, a key statistical measure long used by researchers for understanding seismicity and assessing earthquake occurrence patterns.

The b-value describes the relative numbers of small and large earthquakes in a given area. A high b-value means that small earthquakes are comparatively more frequent, whereas a low b-value indicates a relative increase in the proportion of larger earthquakes.

Many previous studies have suggested that b-values may decrease before -- or increase after -- large earthquakes, possibly reflecting changes in stress before and after rupture. Other studies have proposed that large earthquakes tend to occur in areas where b-values are low. Until now, however, it has remained unclear whether time-dependent changes or spatial differences are more important when comparing many large earthquake sequences.

A bibliometric analysis of Chinese literature traces how seismic risk management research in China has evolved from historical disaster relief studies to modern themes such as emergency management, earthquake vulnerability, disaster risk management, collaborative governance, and seismic resilience. The study highlights changing research priorities and points to future directions for strengthening resilience-oriented earthquake disaster management.

POSTECH Professor Jonghun Kam’s research team, in collaboration with KRIHS, NDMI, Stanford University, and the National University of Singapore (NUS), presents AI-based, region-specific water conservation scenarios using drought news data.

A new study published in Big Earth Data  introduces the University of Bonn Soil Moisture Data Catalog (UB-SMDC), a standards-compliant metadata portal that centralizes information on publicly available soil moisture content (SMC) datasets and provides a unified platform for efficient data discovery, comparison, and access. Built on the GeoNetwork platform and supported by a controlled metadata taxonomy, the catalog analyzes 373 SMC datasets, facilitating transparent research, cross-dataset integration, and a comprehensive understanding of the global soil moisture data landscape.The data and materials supporting the findings of this study are available from the corresponding author upon reasonable request. All metadata records are cataloged and publicly accessible in the UB-SMDC GeoNetwork data portal (https://ubsmdc.sfb1502.de/).

Detecting microcystin-leucine-arginine (MC-LR) in freshwater is increasingly important as harmful algal blooms intensify, but biosensor readings are affected by changing water conditions. Researchers at Hanbat National University and the University of Central Florida developed a machine learning-based calibration framework for biosensors using water quality data. The model accurately predicted MC-LR levels while reducing the need for repeated recalibration, enabling more reliable on-site monitoring.

A new study published in Biochar shows that combining green manure with biochar can help farmers reduce nitrogen fertilizer use while improving soil health, water retention, carbon storage, nutrient supply, and microbial activity.

New study in Geology proposes a new model for understanding mud volcano dynamics.