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The recent proliferation of broadband ocean bottom seismometer (BBOBS) deployments has generated datasets from diverse marine environments, improving our understanding of tectonics and earthquake processes. In turn, the community of scientists using this data has expanded. This growth in BBOBS data collection is likely to persist with the arrival of new seismic seafloor technologies, and continued scientific interest in marine and amphibious targets. However, the noise inherent in OBS data poses a challenge that is markedly different from that of terrestrial data. As a step towards improved understanding of the sources of variability in this noise, we present a new compilation and analysis of BBOBS noise properties from 15 years of US-led seismic deployments. We find evidence for similarity of noise properties when grouped across a variety of parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). We discuss implications for the viability of commonly used seismic analysis techniques, and future directions for improvements in the efficiency of analysis of BBOBS data., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

We present a new compilation and analysis of broadband ocean bottom seismometer noise properties from 15 years of seismic deployments. We compile a comprehensive dataset of representative four-component (seismometer and pressure gauge) noise spectra and cross-spectral properties (coherence, phase, and admittance) for 551 unique stations spanning 18 US-led experiments. This is matched with a comprehensive compilation of metadata parameters related to instrumentation and environmental properties for each station. We systematically investigate the similarity of noise spectra by grouping them according to these metadata parameters to determine which factors are the most important in determining noise characteristics. We find evidence for improvements in similarity of noise properties when grouped across parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We assess the presence of traditional sources of tilt, compliance, and microseismic noise to characterize their relative role across a variety of commonly used seismic frequency bands. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). These observations have important implications for the viability of commonly used seismic analysis techniques. Finally, we compare spectra and coherences before and after vertical channel tilt and compliance noise removal to evaluate the efficacy and limitations of these now standard processing techniques. These findings may assist in future experiment planning and instrument development, and our newly compiled noise dataset serves as a building block for more targeted future investigations by the marine seismology community.

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

El presente libro recoge los desarrollos mas relevantes a nivel mundial, las experiencias de Corpoica (hoy agrosavia) y el trabajo de decadas de los coautores nacionales e internacionales que hacen parte del mismo. La documentacion sobre los avances y las perspectivas en la materia tiene la intencion de acelerar los nuevos desarrollos en aspectos aun no estudiados del control biologico y estimular el progreso en su implementacion. Esta parte comprende informacion relacionada con la Implementacion del control biologico, ademas de reflexiones y recomendaciones de los autores sobre el futuro del control biologico.