Physics

Similarity search is a popular technique for seismic signal processing, with template matching, matched filters and subspace detectors being utilized for a wide variety of tasks, including both signal detection and source discrimination. Traditionally, these techniques rely on the cross-correlation function as the basis for measuring similarity. Unfortunately, seismogram correlation is dominated by path effects, essentially requiring a distinct waveform template along each path of interest. To address this limitation, we propose a novel measure of seismogram similarity that is explicitly invariant to path. Using Earthscope's USArray experiment, a path-rich dataset of 207,291 regional seismograms across 8,452 unique events is constructed, and then employed via the batch-hard triplet loss function, to train a deep convolutional neural network which maps raw seismograms to a low dimensional embedding space, where nearness on the space corresponds to nearness of source function, regardless of path or recording instrumentation. This path-agnostic embedding space forms a new representation for seismograms, characterized by robust, source-specific features, which we show to be useful for performing both pairwise event association as well as template-based source discrimination with a single template.

Real time, accurate passive seismic event detection is a critical safety measure across a range of monitoring applications from reservoir stability to carbon storage to volcanic tremor detection. The most common detection procedure remains the Short-Term-Average to Long-Term-Average (STA/LTA) trigger despite its common pitfalls of requiring a signal-to-noise ratio greater than one and being highly sensitive to the trigger parameters. Whilst numerous alternatives have been proposed, they often are tailored to a specific monitoring setting and therefore cannot be globally applied, or they are too computationally expensive therefore cannot be run real time. This work introduces a deep learning approach to event detection that is an alternative to the STA/LTA trigger. A bi-directional, long-short-term memory, neural network is trained solely on synthetic traces. Evaluated on synthetic and field data, the neural network approach significantly outperforms the STA/LTA trigger both on the number of correctly detected arrivals as well as on reducing the number of falsely detected events. Its real time applicability is proven with 600 traces processed in real time on a single processing unit.

Among different types of hazards associated with underground mining in Polish copper mines, one of the most dangerous is rock burst hazard. Having in mind that the depth of exploitation is getting deeper, this problem will likely get worse in the near future. This kind of hazard is connected inherently with seismic events. Controlled group blasting within a potentially unstable roof stratum is considered as an active method of rock burst prevention. A number of recorded seismic events can be clearly and directly explained by the blasting works effects. With electronic detonators, it is possible to achieve a precise delay time between the detonation of explosives in the individual blastholes and mining faces. Within the framework of this paper, the analysis of seismograms recorded during selected blasting works differing in applied initiation systems, i.e. non-electric and electronic was carried out. It was assumed that this approach may be treated as a new blasting method of rock burst control in deep mines conditions or can be the basis for the modification of the currently used method.

Seismic noise with frequencies above 1 Hz is often called cultural noise and is generally correlated quite well with human activities. Recently, cities in mainland China and Italy imposed lockdown restrictions in response to COVID-19, which gave us an unprecedented opportunity to study the relationship between seismic noise above 1 Hz and human activities. Using seismic records from stations in China and Italy, we show that seismic noise above 1 Hz was primarily generated by the local transportation systems. The lockdown of the cities and the imposition of travel restrictions led to a ~4-12 dB energy decrease in seismic noise in mainland China. Data also show that different Chinese cities experienced distinct periods of diminished cultural noise, related to differences in local response to the epidemic. In contrast, there was only ~1-6 dB energy decrease of seismic noise in Italy, after the country was put under a lockdown. The noise data indicate that traffic flow did not decrease as much in Italy, but show how different cities reacted distinctly to the lockdown conditions.

We study predictions of reversals of Earth's axial magnetic dipole field that are based solely on the dipole's intensity. The prediction strategy is, roughly, that once the dipole intensity drops below a threshold, then the field will continue to decrease and a reversal (or a major excursion) will occur. We first present a rigorous definition of an intensity threshold-based prediction strategy and then describe a mathematical and numerical framework to investigate its validity and robustness in view of the data being limited. We apply threshold-based predictions to a hierarchy of numerical models, ranging from simple scalar models to 3D geodynamos. We find that the skill of threshold-based predictions varies across the model hierarchy. The differences in skill can be explained by differences in how reversals occur: if the field decreases towards a reversal slowly (in a sense made precise in this paper), the skill is high, and if the field decreases quickly, the skill is low. Such a property could be used as an additional criterion to identify which models qualify as Earth-like. Applying threshold-based predictions to Virtual Axial Dipole Moment (VADM) paleomagnetic reconstructions (PADM2M and Sint-2000) covering the last two million years, reveals a moderate skill of threshold-based predictions for Earth's dynamo. Besides all of their limitations, threshold-based predictions suggest that no reversal is to be expected within the next 10 kyr. Most importantly, however, we show that considering an intensity threshold for identifying upcoming reversals is intrinsically limited by the dynamic behavior of Earth's magnetic field.

Chemo-mechanical effects are known to be significant in a number of applications in modern geomechanics, ranging from slope stability assessment to soil improvement and CO2 sequestration. This work focuses on coupled chemo-mechanical modeling of bonded geomaterials undergoing either mechanical strengthening, due to increased cementation, or weakening, due to cement dissolution. A constitutive model is developed that accounts for the multi-scale nature of the chemo-mechanical problem, introducing some cross-scale functions establishing a relationship between the evolution of microscopic variables and the macroscopic material behavior, realistically following the evolution of the reactive surface area, cross-sectional area and the number of bonds along with dissolution/deposition. The model presented here builds up on a previously introduced framework. However, at variance with existing works, it is specialized on materials with only reactive bonds, such as carbonate cemented sandstone or microbially cemented silica sand. Model validation is provided upon reproducing different types of chemo-mechanical experimental datasets, on different naturally and artificially cemented materials, to establish the reliability of the proposed framework.

We formulate the classic Christoffel equation in the polarization variables and solve it for the slowness vectors of plane waves corresponding to a given unit polarization vector. Our analysis shows that, unless the equation degenerates and yields an infinite number of different slowness vectors, the finite nonzero number of its legitimate solutions varies from 1 to 4. Also we find a subset of triclinic solids in which the polarization field can have holes; there exist finite-size solid angles of polarization directions unattainable to any plane wave.

Throughout the recent COVID-19 pandemic when government officials around the world ordered citizens to quarantine inside their homes, real-time measurements about the use of roads, hospitals, grocery stores, and other public infrastructure became vital to accurately forecast viral infection rates and inform future government decisions. Although mobile phone locations provide some information about community-level activity, dense distributed geophysical sensing of ground motions across a city are more complete and also natively anonymous. In this paper, we demonstrate how fiber-optic Distributed Acoustic Sensing (DAS) connected to a telecommunication cable beneath Palo Alto, CA captured seismic and geodetic signals produced by vehicles during the COVID-19 pandemic outbreak and subsequent quarantine. We utilize DAS strain measurements of roadbed deformation caused by local cars and trucks in an automatic template matching detection algorithm to count the number of vehicles traveling per day over a two-month period around the timing of the San Francisco Bay Area shelter-in-place order. Using a segment of the optical fiber near a major grocery store on Sand Hill Road we find a 50% decrease in vehicle count immediately following the order, but data from near Stanford Hospital showed a far more subtle change due to on-going hospital activities. We compare the information derived from DAS measurements to other quarantine response metrics and find a strong correlation with the relative changes reported by Google and Apple using mobile phone data.

The most pronounced climate anomaly of the Holocene was the 8.2 ka cooling event. We present new 230Th/U-ages as well as high-resolution stable isotope and trace element data from three stalagmitesfrom two different cave systems in Germany, which provide important information about the structure and climate variability of the 8.2 ka event in central Europe. In all three speleothems, the 8.2 ka event is clearly recorded as a pronounced negative excursion of the {\delta}18O values and can be divided into a 'whole event' and a 'central event'. All stalagmites show a similar structure of the event with a short negative excursion prior to the 'central event', which marks the beginning of the 'whole event'. The timing and duration of the 8.2.ka event are different for the individual records, which may, however, be related to dating uncertainties. Whereas stalagmite Bu4 from Bunker Cave also shows a negative anomaly in the {\delta}13C values and Mg content during the event, the two speleothems from the Herbstlabyrinth cave system do not show distinct peaks in the other proxies. This may suggest that the speleothem {\delta}18O values recorded in the three stalagmites do not primarily reflect climate change at the cave site, but rather large-scale changes in the North Atlantic. This is supported by comparison with climate modelling data, which suggest that the negative peak in the speleothem {\delta}18O values is mainly due to lower {\delta}18O values of precipitation above the cave and that temperature only played a minor role. Alternatively, the other proxies may not be as sensitive as {\delta}18O values to record this centennial-scale cooling event. This may particularly be the case for speleothem {\delta}13C values as suggested by comparison with a climate modelling study simulating vegetation changes in Europe during the 8.2 ka event. ...

For passive satellite imagers, current retrievals of cloud optical thickness and effective particle size fail for convective clouds with 3D morphology. Indeed, being based on 1D radiative transfer (RT) theory, they work well only for horizontally homogeneous clouds. A promising approach for treating clouds as fully 3D objects is cloud tomography, and this has been demonstrated for airborne observations. For cloud tomography from space, however, more efficient forward 3D RT solvers are required. Here, we present a path forward, acknowledging that optically thick clouds have "veiled cores." Photons scattered into and out of this deep region do not contribute significant information to the observed imagery about the inner structure of the cloud. We investigate the location of the veiled core for the MISR and MODIS imagers. While MISR provides multi-angle imagery in the visible and near-IR, MODIS includes channels in the short-wave IR, albeit at a single view angle. This combination will enable future 3D retrievals to disentangle the cloud's effective particle size and optical thickness. We find that, in practice, the veiled core is located at an optical distance of ≈ 5 starting from the cloud boundary along the line-of-sight. For MODIS' absorbing wavelengths the veiled core covers a larger volume, starting at smaller optical distances. This result makes it possible to reduce the number of unknowns for the cloud tomographic reconstruction, and opens up new ways to increase the efficiency of the 3D RT solver at the heart of the reconstruction algorithm.