Tarje Nissen-Meyer

The impacts of farming practices on soil hydrodynamics are central to understanding agricultural landscapes covering almost half of the world’s habitable land. Combining observations from distributed acoustic sensing with physics-based hydromechanical modeling, we tracked minute-resolution and meter-scale seismic and hydrological changes across agricultural fields with controlled histories of tillage and compaction. We show that dynamic capillary effects in soil govern transient stiffness and moisture redistribution in disturbed soils, producing sharp post-rain velocity drops from near-surface saturation and large hysteretic velocity rebounds driven by evapotranspiration. Our seismically inverted estimates of saturation reveal how disturbance alters flux partitioning and storage, establishing agroseismology and distributed acoustic sensing as scalable, noninvasive probes of soil hydromechanics with the potential to improve Earth system models, land management, and hazard resilience.

Soil is a critical resource for global food security. However, traditional physical analyses of soil samples and geophysical imaging techniques are often labour intensive and time-consuming. This study investigates the potential of ultra high-frequency (> 500 Hz) hammer-source seismology to characterise the physical properties of soil at the decimetre scale. We conducted experiments within a long-term field experiment near Harper Adams University (UK) aimed at comparing Conservation and Conventional agriculture. We surveyed two meter-and-a-half sections of each agricultural treatment with 16 geophones and collected soil samples with the same horizontal resolution. Our estimates of the P-wave velocity ( v _p ) and bulk density in the upper 40 cm of the soil reveal a strong and statistically significant correlation. Consistent correlation of bulk density and v _p throughout the depth profile were observed between the seismic images and interpolated bulk density data derived from physical soil samples. Our work demonstrates that ultra-high frequency seismic analysis is a promising, cost-effective tool for estimating soil bulk density, in support of agronomic and land-management decision making, and improving the accuracy of soil carbon stock quantification.

Qualitative and quantitative analysis of seismic waveforms sensitive to the core–mantle boundary (CMB) region reveal the presence of ultralow-velocity zones (ULVZs) that have a strong decrease in compressional (P) and shear (S) wave velocity, and an increase in density within thin structures. However, understanding their physical origin and relation to the other large-scale structures in the lowermost mantle are limited due to an incomplete mapping of ULVZs at the CMB. The SKS and SPdKS seismic waveforms is routinely used to infer ULVZ presence, but has thus far only been used in a limited epicentral distance range. As the SKS/SPdKS wavefield interacts with a ULVZ it generates additional seismic arrivals, thus increasing the complexity of the recorded wavefield. Here, we explore utilization of the multi-scale sample entropy method to search for ULVZ structures. We investigate the feasibility of this approach through analysis of synthetic seismograms computed for PREM, 1-, 2.5-, and 3-D ULVZs as well as heterogeneous structures with a strong increase in velocity in the lowermost mantle in 1- and 2.5-D. We find that the sample entropy technique may be useful across a wide range of epicentral distances from 100° to 130°. Such an analysis, when applied to real waveforms, could provide coverage of roughly 85% by surface area of the CMB.

We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in detecting elephants up to 100 m away, and over 90% balanced accuracy in recognising running and rumbling behaviours from the seismic data. We release the dataset used in this study: SeisSavanna represents a unique collection of seismic signals with the associated wildlife species and behaviour. Our results suggest that seismic data offer substantial benefits for monitoring wildlife, and we propose to further develop our methods using dense arrays that could result in a seismic shift for wildlife monitoring.

African elephants (Loxodonta africana) are sentient and intelligent animals that use a variety of vocalizations to greet, warn or communicate with each other. Their low-frequency rumbles propagate through the air as well as through the ground and the physical properties of both media cause differences in frequency filtering and propagation distances of the respective wave. However, it is not well understood how each mode contributes to the animals’ abilities to detect these rumbles and extract behavioural or spatial information. In this study, we recorded seismic and co-generated acoustic rumbles in Kenya and compared their potential use to localize the vocalizing animal using the same multi-lateration algorithms. For our experimental set-up, seismic localization has higher accuracy than acoustic, and bimodal localization does not improve results. We conclude that seismic rumbles can be used to remotely monitor and even decipher elephant social interactions, presenting us with a tool for far-reaching, non-intrusive and surprisingly informative wildlife monitoring.

Ultralow-velocity zones (ULVZs) at the core–mantle boundary (CMB) represent some of the most preternatural features in Earth’s mantle. These zones most likely contain partial melt, extremely high iron content ferropericlase, or combinations of both. We analyzed a new collection of 58,155 carefully processed and quality-controlled broadband recordings of the seismic phase SPdKS in the epicentral distance range from 106° to 115°. These data sample 56.9% of the CMB by surface area. From these recordings we searched for the most anomalous seismic waveforms that are indicative of ULVZ presence. We used a Bayesian approach to identify the regions of the CMB that have the highest probability of containing ULVZs, thereby identifying sixteen regions of interest. Of these regions, we corroborate well-known ULVZ existence beneath the South China Sea, southwest Pacific, the Samoa hotspot, the southwestern US/northern Mexico, and Iceland. We find good evidence for new ULVZs beneath North Africa, East Asia, and north of Papua New Guinea. We provide further evidence for ULVZs in regions where some evidence has been hinted at before beneath the Philippine Sea, the Pacific Northwest, and the Amazon Basin. Additional evidence is shown for potential ULVZs at the base of the Caroline, San Felix and Galapagos hotspots.

Seismology was developed on Earth and shaped our model of the Earth's interior over the twentieth century. With the exception of the Philae lander, all in situ extraterrestrial seismological effort to date was limited to other terrestrial planets. All have in common a rigid crust above a solid mantle. The coming years may see the installation of seismometers on Europa, Titan, and Enceladus, so it is necessary to adapt seismological concepts to the setting of worlds with global oceans covered in ice. Here we use waveform analyses to identify and classify wave types, developing a lexicon for icy ocean world seismology intended to be useful to both seismologists and planetary scientists. We use results from spectral‐element simulations of broadband seismic wavefields to adapt seismological concepts to icy ocean worlds. We present a concise naming scheme for seismic waves and an overview of the features of the seismic wavefield on Europa, Titan, Ganymede, and Enceladus. In close connection with geophysical interior models, we analyze simulated seismic measurements of Europa and Titan that might be used to constrain geochemical parameters governing the habitability of a sub‐ice ocean.