A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.

Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterization, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fiber-optic cables into seismic arrays with a spatial sampling density of the order of meters and a time sampling rate up to one thousand Hertz. The versatility of DAS enables dense instrumentation of traditionally inaccessible domains, such as urban, glaciated, and submarine environments. This in turn opens up novel applications such as traffic density monitoring and maritime vessel tracking. However, these new environments also introduce new challenges in handling various types of recorded noise, impeding the application of traditional data analysis workflows. In order to tackle the challenges posed by noise, new denoising techniques need to be explored that are tailored to DAS. In this work, we propose a Deep Learning approach that leverages the spatial density of DAS measurements to remove spatially incoherent noise with unknown characteristics. This approach is entirely self-supervised, so no noise-free ground truth is required, and it makes no assumptions regarding the noise characteristics other than that it is spatio-temporally incoherent. We apply our approach to both synthetic and real-world DAS data to demonstrate its excellent performance, even when the signals of interest are well below the noise level. Our proposed methods can be readily incorporated into conventional data processing workflows to facilitate subsequent seismological analyses.

Experimental and Theoretical Constraints on Amino Acid Formation from PAHs in Asteroidal Settings.

Amino acids and polycyclic aromatic hydrocarbons (PAHs) belong to the range of organic compounds detected in meteorites. In this study, we tested empirically and theoretically if PAHs are precursors for amino acids in carbonaceous chondrites, as previously suggested. We conducted experiments to synthesize amino acids from fluoranthene (PAH), with ammonium bicarbonate as a source for ammonia and carbon dioxide under mimicked asteroidal conditions. In our thermodynamic calculations, we extended our analysis to additional PAH-amino acid combinations. We explored 36 reactions involving the PAHs naphthalene, anthracene, fluoranthene, pyrene, triphenylene, and coronene and the amino acids glycine, alanine, valine, leucine, phenylalanine, and tyrosine. Our experiments do not show the formation of amino acids, whereas our theoretical results hint that PAHs could be precursors of amino acids in carbonaceous chondrites at low temperatures.

Microphysical Model Predictions of Fault Restrengthening Under Room-Humidity and Hydrothermal Conditions: From Logarithmic to Power-Law Healing.

The maximum fault strength and rate of interseismic fault strengthening ("healing") are of great interest to earthquake hazard assessment studies, as they directly relate to event magnitude and recurrence time. Previous laboratory studies have revealed two distinct frictional healing behaviors, referred to as Dieterich-type and non-Dieterich-type healing. These are characterized by, respectively, log-linear and power-law increase in the strength change with time. To date, there is no physical explanation for the frictional behavior of fault gouges that unifies these seemingly inconsistent observations. Using a microphysical friction model previously developed for granular fault gouges, we investigate fault strengthening analytically and numerically under boundary conditions corresponding to laboratory slide-hold-slide tests. We find that both types of healing can be explained by considering the difference in grain contact creep rheology at short and long time scales. Under hydrothermal conditions favorable for pressure solution creep, healing exhibits a power-law evolution with hold time, with an exponent of ~1/3, and an "apparent" cutoff time (α) of hundreds of seconds. Under room-humidity conditions, where grain contact deformation exhibits only a weak strain-rate dependence, the predicted healing also exhibits a power-law dependence on hold time, but it can be approximated by a log-linear relation with α of a few seconds. We derive analytical expressions for frictional healing parameters (i.e., healing rate, cutoff time, and maximum healing), of which the predictions are consistent with numerical implementation of the model. Finally, we apply the microphysical model to small fault patches on a natural carbonate fault and interpret the restrengthening during seismic cycles.

In Situ Nanoscale Investigation of Step Retreat on Fluoranthene Crystal Surfaces.

Fluoranthene, a polycyclic aromatic hydrocarbon, has been detected on Earth as well as in asteroids and meteorites and may have played a role in the formation of life. Increasing the ionic strength of aqueous solutions has been observed to lower the fluoranthene solubility, but it is unclear how solution composition controls the release rate of fluoranthene to an aqueous solution. To elucidate this, we performed in situ atomic force microscopy experiments in which we characterized the sublimation and dissolution behavior of fluoranthene crystal surfaces. From this, we quantify the step retreat rate upon exposure to air, deionized water, and a 0.4 M NaCl or 0.1 M MgSO4 solution. Surface roughness is the main factor that determines the dissolution or sublimation rate. The results imply that during fluoranthene remediation or breakdown in meteorites and asteroids, ionic strength will be more important than chemical composition for controlling fluoranthene release into solution.