Ambient noise Rayleigh wave tomography in western Sichuan and eastern Tibet

The enclosed document transmits the U.S. Fish and Wildlife Service’s (Service) June 6, 2007 Final Biological Opinion based on our review of the proposed construction of a harbor at the Little South America-South site in Unalaska, as modified, and its effects on the Steller’s eider (Polysticta stelleri) in accordance with section 7 of the Endangered Species Act (Act) of 1973, as amended (16 U.S.C. 1531 et seq.). On March 30, 2007, we received notification that harbor location had been moved approximately 15 meters further off shore to reduce the volume of dredged material and to avoid blasting activities in the nearshore environment. While we determined that the proposed modifications to the project fall within the scope of analysis of the existing biological opinion, we have updated population estimates for the Steller’s eider and listed the southwestern distinct population segment of the northern sea otter (Enhydra lutris kenyonii) as threatened. Consequently, we revised the life history and analysis of effects for both of these species. Our reanalysis did not result in any changes to our take estimate or terms and conditions. This letter provides only a summary of the findings included in the Biological Opinion, where a complete discussion of the effects analysis can be found. Also considered in this Biological Opinion are the potential effects of the proposed action on the short-tailed albatross (Phoebastria albatrus).

United States Department of the Interior Fish and Wildlife Service

SUMMARY

We present the surface wave dispersion results of the application of the ambient noise method to broad-band data recorded at 114 stations from the Istituto Nazionale di Geofisica e Vulcanologia (INGV) national broad-band network, some stations of the Mediterranean Very Broadband Seismographic Network (MedNet) and of the Austrian Central Institute for Meteorology and Geodynamics (ZAMG). Vertical-component ambient noise data from 2005 October to 2007 March have been cross-correlated for station-pairs to estimate fundamental mode Rayleigh wave Green's functions. Cross-correlations are calculated in 1-hr segments, stacked over periods varying between 3 months and 1.5 yr. Rayleigh wave group dispersion curves at periods from 8 to 44 s were determined using the multiple-filter analysis technique. The study region was divided into a 0.2°× 0.2° grid to invert for group velocity distributions. Checkerboard tests were first carried out, and the lateral resolution was estimated to be about 0.6°. The resulting group velocity maps from 8 to 36 s show the significant difference of the crustal structure and good correlations with known geological and tectonic features in the study region. The Po Plain and the Southern Alps evidence lower group velocities due to soft alluvial deposits, and thick terrigenous sediments. Our results also clearly showed that the Tyrrhenian Sea is characterized with much higher velocities below 8 km than the Italian peninsula and the Adriatic Sea which indicates a thin oceanic crust beneath the Tyrrhenian Sea.

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Surface wave dispersion measurements from ambient seismic noise analysis in Italy

Ambient noise Love wave tomography in the eastern margin of the Tibetan plateau

Seafloor hydrothermal fluids play a critical role in regulating a wide range of geochemical and biological processes. Chemical and physical changes to venting fluids undoubtedly occur in response to geological events, at depth or near the seafloor, with corresponding effects on vent fluid chemistry, mineralization, and biological activity. However, the uncertain timing of such events makes it impractical to anticipate their occurrence. Thus, the temporal evolution of seafloor vent fluids and coexisting mineral deposits is most often inferred from observations made using conventional deep submergence assets that may be non-continuous for a specific vent field. The recent development of submarine volcanic observatories operated by Ocean Networks Canada (ONC) and the Ocean Observatory Initiative (OOI) in the U.S. that deliver power to instruments on the seafloor and communications via high-speed fiber optic cable permits real-time communication and monitoring of seafloor vents. Here, we describe the development and application of a remotely operated hydrothermal fluid sampler that enables repeat on-demand sampling of high-temperature vent fluids triggered through the internet. The sampler was deployed from September 2019 to June 2020 at the S&M vent area, in the southern portion of Main Endeavour Field (MEF), Juan de Fuca Ridge, on ONC's NEPTUNE ocean observatory. Nine vent fluid samples were acquired over a period of nine months. Analyses of these samples confirm the moderately high-temperature origin of the source fluid, as indicated, for example, by dissolved chloride depletion relative to seawater (vapor-rich) and moderately high silica concentrations. These and other dissolved species, such as methane, a known hallmark of MEF vent fluids, are in excellent agreement with the reported composition of S&M vent fluids over the past 15 years or more, suggesting overall chemical stability and rock-dominated alteration processes. On the other hand, short-term variability of dissolved Mg, sulfate and barium indicate entrainment of conductively heated and partially reacted seawater. Both before and after the incursion of the “secondary” seawater derived fluid, however, samples reveal the lowest Mg and sulfate concentrations yet reported for seafloor hydrothermal vent fluid samples. Accordingly, these data provide new insight on the solubility of these and other elements coexisting with minerals close to the Axial Magma Chamber (AMC), from which the primary source fluid is ultimately derived. A separate noteworthy event characterized by ∼20 °C decrease in temperature and associated 24–77% decrease in dissolved Fe concentration was observed toward the end of the deployment, with implications for hydrothermal transition metal fluxes and linked biogeochemical processes. On-demand remote acquisition of a continuous series of high-temperature vent fluid samples from a single vent permits the temporal evolution of heat and mass transfer processes to be studied with a heretofore unavailable perspective. Furthermore, deployment of this sampler within the NEPTUNE ocean observatory opens the door to future studies based on comparative analysis of contemporaneous datasets. 

http://manuscript.elsevier.com/S0967063722001224/pdf/S0967063722001224.pdf

Time series of hydrothermal vent fluid chemistry at Main Endeavour Field, juan de Fuca Ridge: Remote sampling using the NEPTUNE cabled observatory

The advent of large-scale cabled ocean observatories brought about the need to handle large amounts of ocean-based data, continuously recorded at a high sampling rate over many years and made accessible in near-real time to the ocean science community and the public. Ocean Networks Canada (ONC) commenced installing and operating two regional cabled observatories on Canada’s Pacific Coast, VENUS inshore and NEPTUNE offshore in the 2000s, and later expanded to include observatories in the Atlantic and Arctic in the 2010s. The first data streams from the cabled instrument nodes started flowing in February 2006. This paper describes Oceans 2.0 and Oceans 3.0, the comprehensive Data Management and Archival System that ONC developed to capture all data and associated metadata into an ever-expanding dynamic database. Oceans 2.0 was the name for this software system from 2006–2021; in 2022, ONC revised this name to Oceans 3.0, reflecting the system’s many new and planned capabilities aligning with Web 3.0 concepts. Oceans 3.0 comprises both tools to manage the data acquisition and archival of all instrumental assets managed by ONC as well as end-user tools to discover, process, visualize and download the data. Oceans 3.0 rests upon ten foundational pillars: (1) A robust and stable system architecture to serve as the backbone within a context of constant technological progress and evolving needs of the operators and end users; (2) a data acquisition and archival framework for infrastructure management and data recording, including instrument drivers and parsers to capture all data and observatory actions, alongside task management options and support for data versioning; (3) a metadata system tracking all the details necessary to archive Findable, Accessible, Interoperable and Reproducible (FAIR) data from all scientific and non-scientific sensors; (4) a data Quality Assurance and Quality Control lifecycle with a consistent workflow and automated testing to detect instrument, data and network issues; (5) a data product pipeline ensuring the data are served in a wide variety of standard formats; (6) data discovery and access tools, both generalized and use-specific, allowing users to find and access data of interest; (7) an Application Programming Interface that enables scripted data discovery and access; (8) capabilities for customized and interactive data handling such as annotating videos or ingesting individual campaign-based data sets; (9) a system for generating persistent data identifiers and data citations, which supports interoperability with external data repositories; (10) capabilities to automatically detect and react to emergent events such as earthquakes. With a growing database and advancing technological capabilities, Oceans 3.0 is evolving toward a future in which the old paradigm of downloading packaged data files transitions to the new paradigm of cloud-based environments for data discovery, processing, analysis, and exchange.

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The Oceans 2.0/3.0 Data Management and Archival System

A method has been developed to estimate an aircraft’s flight path based on noise measurements at a number of positions within a study area. Two‐second time histories of A‐weighted sound levels were recorded on 38 digital sound level meters (SLMs) distributed by Geo‐Marine Inc. through a mountainous region of approximately 400 square miles. Six of these SLMs recorded one‐third octave spectra. Aircraft pass through this area at low altitude along straight tracks (no turns). Determining the aircrafts position from SLM data is difficult because of the complexity of sound propagation over precipitous terrain and the size of the data matrices. The computational core of Wyle Laboratories’ noise simulation model NMSIM is used to perform the propagation calculations. A least mean square method is used to curve‐fit the flight track to the SLM data. The accuracy of this approach depends on the time intervals of the SLM data, the speed of the aircraft, and the accuracy of the NMSIM propagation algorithms. The NMSIM p...

Prediction of aircraft flight tracks from noise measurements

During acoustic surveys for Mexican spotted owls, the effective detection range is presumed to be 0.25 mi (433 m). However, variations within and between surveys are observed, leading to a variance in owl density estimates. While owl behavior may explain some variation, topography and ambient noise are also likely to be important. To determine the influence of these factors, data from acoustic surveys for Mexican spotted owls in the Gila National Forest (April–July, 2000–2001) were examined. Measurements of owl and human call levels were made with a Sony TCD‐10 Pro II DAT equipped with an ACO 7013 microphone. Ambient noise was collected using 40 Larson‐Davis 820 and 824 sound level meters in owl activity centers. Wyle Laboratories NMSIM software was used to model propagation of owl and human calls. Owls produced calls with estimated maximum source levels of 92–98‐dB SPL. Human callers produced maximum source levels of 88–95‐dB SPL. Detection was possible out to more than 2 km under ideal conditions, but topography and ambient noise had a large effect. Corrections for these factors would greatly improve estimates of area surveyed, and thus owl density estimates. [Work supported by the U.S. Air Force, Air Combat Command.]

Timothy Lavallee

Papers

The Oceans 2.0/3.0 Data Management and Archival System

Prediction of aircraft flight tracks from noise measurements

Acoustic surveys for Mexican spotted owls (Strix occidentalis lucida): An analysis