Can we tell when trees lack water by listening to the ground? Over three growing seasons in a German forest, we recorded vibrations generated by trees swaying in the wind and compared them with changes in trunk thickness, weather and soil moisture. These signals helped predict how much water trees lacked, providing information beyond weather and soil measurements alone. Our findings suggest that ground sensors could offer a new way to monitor water conditions in forests.

Seismic phase picking and magnitude estimation are crucial components of real-time earthquake monitoring and early warning. Here, we test the potential of deep learning in real-time earthquake monitoring. We introduce DynaPicker, which leverages dynamic convolutional neural networks for event detection and arrival-time picking, and use the deep-learning model CREIME for magnitude estimation. This workflow is tested on the continuous recording of the Turkey earthquake aftershock sequences.

Earthquake magnitude is a crucial parameter in defining its damage potential, and hence its speedy determination is essential to issue an early warning in regions close to the epicentre. This study summarises our findings in an attempt to apply deep-learning-based classifiers to earthquake waveforms, particularly with respect to finding an optimum length of input data. We conclude that the input length has no significant effect on the model accuracy, which varies between 90 %–94 %.

The seismicity of Fogo and Brava, Cape Verde, is dominated by volcano-tectonic earthquakes in the area of Brava and volcanic seismic signals, such as hybrid events, on Fogo. We locate these events using a multi-array analysis, which allows the localization of seismic events occurring outside the network and of volcanic signals lacking clear phases. We observe exceptionally high apparent velocities for the hybrid events located on Fogo. These velocities are likely caused by a complex ray path.

We use numerical simulations and laboratory experiments on rock samples to investigate how stress conditions influence the geometry and roughness of fracture surfaces. The roughness of the surfaces was analyzed in terms of absolute roughness and scaling properties. The results show that the surfaces are self-affine but with different scaling properties between the numerical models and the real rock samples. Results suggest that stress conditions have little influence on the surface roughness.