Deep learning for denoising High-Rate Global Navigation Satellite System data

High-rate global navigation satellite system (HR-GNSS) data records ground displacements and can be used to identify earthquakes and slow slip events.  One limitation of such data is the high amplitude, cm-level noise which make it difficult to identify processes that produce surface displacements smaller than these values.  Deep learning has proven adept at performing many useful tasks in seismology and geophysics.  Here we explore using deep learning to denoise HR-GNSS data.  We develop three different convolutional neural networks with similar architectures but different targets.  Training data are synthetic HR-GNSS records and actual noise recordings that are superimposed to generate noisy signals.  We train each of the three models to output masks that can be used to reconstruct the true signal.  We use a set of performance metrics that quantify the models’ ability to denoise the testing data and find that denoising significantly improves the signal-to-noise ratio and the ability to identify first arrivals.  Finally, we test the models on HR-GNSS records from the Ridgecrest earthquakes recorded at stations that have nearly colocated strong-motion sites used ground-truth the denoising results.  We find that the models greatly improve the signal-to-noise ratios in these records and make the P-wave onset clearly identifiable

Worth a Closer Look: Raman Spectra of Lead-Pipe Scale

The identification and characterization of lead-bearing and associated minerals in scales on lead pipes are essential to understanding and predicting the mobilization of lead into drinking water. Despite its long-recognized usefulness in the unambiguous identification of crystalline and amorphous solids, distinguishing between polymorphic phases, and rapid and non-destructive analysis on the micrometer spatial scale, the Raman spectroscopy (RS) technique has been applied only occasionally in the analysis of scales in lead service lines (LSLs). This article illustrates multiple applications of RS not just for the identification of phases, but also compositional and structural characterization of scale materials in harvested lead pipes and experimental pipe-loop/recirculation systems. RS is shown to be a sensitive monitor of these characteristics through analyses on cross-sections of lead pipes, raw interior pipe walls, particulates captured in filters, and scrapings from pipes. RS proves to be especially sensitive to the state of crystallinity of scale phases (important to their solubility) and to the specific chemistry of phases precipitated upon the introduction of orthophosphate to the water system. It can be used effectively alone as well as in conjunction with more standard analytical techniques. By means of fiber-optic probes, RS has potential for in situ, real-time analysis within water-filled pipes.</jats:p

ROSES: Remote Online Sessions for Emerging Seismologists

Abstract In response to a pandemic causing the cancellation of numerous professional development programs for emerging seismologists, we successfully planned, promoted, and executed an 11 week online school for advanced graduate students worldwide during the summer of 2020. Remote Online Sessions for Emerging Seismologists included 11 distinct lessons focused on different topics in seismology. We highlight the course content, structure, technical requirements, and participation statistics. We additionally provide a series of “lessons learned” for those in the community wishing to establish similar programs.</jats:p