Identification of unknown micro- and nano-sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging datasets, particularly scanning electron microscope - energy dispersive X-ray spectroscopy (SEM-EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non-ideal sample preparation, and the presence of mixed chemical signals generated within the electron-beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine-learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel-wise elemental datapoints modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non-negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM-EDS datasets: a synthetic mixture sample and a real-world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background-subtracted single-phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe-bearing particles and their background-subtracted chemical signals. We demonstrate a robust approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user-friendly Python code with a graphical user interface for ease of use by general users.

We attempt to construct a timeline of The Hunga Tonga – Hunga Ha’apai eruption on 15 January 2022 through analyses of seismic, barometric, infrasonic, lightning, and satellite data. Satellite imagery at 04:00 UTC showed no ash in the air, but by 04:10 UTC, a plume had risen to 18 km. Over the next 20 minutes, the plume rose to 58 km. USGS determined that Mw5.8 volcanic earthquake of unknown mechanism had occurred at 04:14:45. Gravity waves were observed in satellite imagery, and barometric and infrasound stations around the world recorded ultra-low frequency pressure variations of more than 100 Pa, inducing ground-coupled airwaves around the globe, and meteo-tsunamis in the Caribbean Sea and Mediterranean Sea. Tsunami waves were recorded in coastal areas around the Pacific Ocean. From record sections, we determined speeds of 3.9 km/s and 299 m/s for the initial seismic and infrasound signals respectively, converging to an eruption onset time of ~0402 UTC ± 1 minute. The global pressure pulse has a speed of ~314 ± 3 m/s, consistent with theoretical models for Lamb waves (Bretherton, 1969), suggesting an origin time of ~0415 ± 2 minutes (consistent with the Mw5.8 volcanic earthquake, and sharp increases in lightning flash rates), and peaking around ~0429 ± 2 minutes. We suggest that Surtseyan volcanic activity commenced at ~04:02, building to a sub-Plinian eruption ~7 minutes later, before a phreato-Plinian eruption commenced at ~04:14. The peak Lamb wave amplitude at the closest station (757 km from HTHH) was 780 Pa. Assuming geometrical spreading like 1/√r (where r is the source-receiver distance), we estimate a lower bound of ~23 kPa for reduced pressure by extrapolation back to 1 km. Adding a near field term that decays like 1/r, we estimate an upper bound of 170 kPa for reduced pressure. Comparison of these values with those from other eruptions (McNutt et al. in this session) suggests the 15 January HTHH eruption was in the VEI 5-6 range.

Seismic activity during the eruption of Soufriere Hills volcano comprised various transient signals, which were classified visually by the Montserrat Volcano Observatory (MVO), considering waveforms recorded at several stations. For 217,290 transients detected on the MVO digital seismic network between 1996/10/21 and 2008/10/16, five main classes have been identified: rockfall (ROC: 58%), hybrid (HYB: 19%), long-period (LPE: 11%), lp-rockfall (LP-ROC: 5.8%), and volcano-tectonic (VT: 3.1%). Temporal trends in the rate and energy release of these different transients (in addition to swarms and tremor) were key to short-term forecasting of eruptive activity. However, visual classification is highly subjective and non-repeatable, and the inconsistency of the catalog is a barrier to research. In a pilot study, we automatically removed waveforms with dropouts, and manually verified transient classifications until we had approximately 100 transients of each class (total 522). We found ~21% of these transients were incorrectly classified at MVO. Our re-labelled dataset was then used as a starting point for supervised learning, using code from http://github.com/malfante/AAA. This code was used by Malfante et al. (2008) to classify 109,609 transients at Ubinas volcano with a 93.5% accuracy. They transformed each waveform into a set of 102 features: 34 features for each of three domains (time, spectral, cepstral). We added 6 frequency features of our own, including band ratios, peak frequency, median frequency, bandwidth, and frequency change. The resulting 108-point vectors of features were then used for modeling. The dataset is randomly divided 50 times into training and testing datasets, to produce a robust model. One model is produced per channel. We use the Random Forest Classifier algorithm from the scikit-learn library. For each waveform, a probability is computed for each class. Initial results are promising. Separate models for 3 channels yield accuracies of 76-80%. If the LP-ROC class is omitted (following Langer et al, 2006), accuracy rises to 82-85%. If only VT and LP classes are considered, accuracy is 96-99%. We intend to expand our labelled dataset to 1000 events, add new features, build models for each channel, and reclassify the catalog of 217,290 transients by a weighted average of probabilities.

Vegetation green leaf phenology directly impacts gross primary productivity (GPP) of terrestrial ecosystems. Satellite observations of land surface phenology (LSP) provide an important means to monitor the key timing of vegetation green leaf development. However, differences between satellite-derived LSP proxies and in-situ measurements of GPP make it difficult to quantify the impact of climate-induced changes in green leaf phenology on annual GPP. Here we used 1,110 site-years of GPP measurements from eddy-covariance towers in association with time series of satellite LSP observations from 2000-2014 to show that while satellite LSP explains a large proportion of variation in annual GPP, changes in green-leaf-based growing season length (GSL) had less impact on annual GPP by ~30% than GSL changes in GPP-based photosynthetic duration. Further, maximum leaf greenness explained substantially more variance in annual GPP than green leaf GSL, highlighting the role of future vegetation greening trends on large-scale carbon budgets. We conclude that satellite LSP-based inferences regarding large-scale dynamics in GPP need to consider changes in both green leaf GSL and maximum greenness.

As the leading astrobiology university student society, run by students, for students, at the University of Manchester Astrobiology Society we are embarked on a mission to spread the word about astrobiology, enrich the university community by delivering high-quality events and resources, and create a global network of students and young professionals that will become the astrobiologists of tomorrow. Ever since our foundation, we have gone beyond the classic student society activities by inviting world-renowned researchers, creating a dedicated careers program, or even organizing the first student-led online congress at the university. In addition, our innovation team always ensures that we use the latest tools and technologies to keep thriving in our ever-evolving world. In this sense, we have developed an all-in-one website where people interested in astrobiology can discover, learn and connect. Furthermore, we have also built a global community using social media, with almost 3000 followers spread over 17 countries. By leading by example, we want to spark change among student societies and for them to redefine their boundaries and to achieve bigger. At AbSciCon 2022 we aim to further pursue this goal, grow our network, share expertise, and learn from others.

The Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) mission is a 6U CubeSat that has been making ionospheric measurements at 420 km altitude since February 19, 2020. The SORTIE sensor suite includes an Ion Velocity Meter (IVM), which is used in the present study to detect and characterize Traveling Ionospheric Disturbances (TIDs). On July 11, 2020 the SORTIE orbit passed over near-concentric TIDs that were seen in the Total Electron Content (TEC) data from ground-based Global Positioning System receivers distributed across the COntiguous United States (CONUS). The TID wave characteristics estimated from the IVM data agree well with those determined from the ground-based TEC data. The wave periods derived from the SORTIE data are shorter than the TID periods in the TEC data but are anticipated and explained in terms of the classical Doppler effect. A numerical simulation was performed using the Weather Research and Forecasting (WRF) model that shows excitation of atmospheric gravity waves (AGWs) from a deep convective storm over Texas preceding TID observations by SORTIE. We show that these AGWs were observed at stratospheric heights in close proximity to the convective storm by the Atmospheric Infrared Sounder onboard the NASA Aqua satellite, and in the lowermost mesosphere by the Cloud Imaging and Particle Size instrument onboard the NASA Aeronomy of Ice in the Mesosphere satellite. These storm-generated AGWs, or the associated higher-order AGWs, are the likely sources of the TIDs observed in the ground-based TEC and SORTIE IVM data.

Hurricane Irma affected the Florida peninsula in September 2017. The east coast of the peninsula was hit particularly hard: the city of Jacksonville flooded around the St. Johns River estuary with non-tidal water levels that exceeded 1.5 m and precipitation that surpassed 20 cm in 24 hours. This study used observations such as water and wind velocities, river discharge, and conductivity data to determine whether compounding forcings influenced flood levels. Results show that flooding was initiated by a pulse from the ocean and then exacerbated by high river discharge. The 1-2 punch from the ocean and then the river caused record flooding, with impacts that lasted through the rest of September. Peak water levels occurred while hurricane winds were receding, and river discharge was increasing. Compound flood models should consider the phase lag between driving processes, as the individual peaks may not occur simultaneously, yet exacerbate flooding.

The knowledge of the strain/stress field evolution in time is important to seismic hazard assessment and risk mitigation, and is fundamental to the understanding of the earth dynamic system. Based on the principle that past tectonic stress should have left traces in the rocks, geologists have been trying to determine the paleostress history from evidence found in rocks for decades. Recent development of techniques for automatic extraction of fracture surfaces from digital outcrop models and estimation of historical shear deformation on rock fractures provide an efficient way of quantitatively acquiring large amount of high quality fracture/fault slip data (direction and sense of slip occurs on the fault plane) from outcrops. So unlike traditional paleostress inversion methods whose data is manually collected in the field, this high quality fracture/fault slip data provide an opportunity to develop fully automatic and quantitative methods for deciphering paleostrain. In this study, for slip on each fracture, the corresponding local strain tensor is calculated, then the local strain tensors are grouped into populations corresponding to far-field strain events and local strain events using a clustering analysis technique. The applications on outcrops in the eastern Tian Shan area give a clear picture of the paleostrain variation over space and time, and also throw light on the relationship between paleostrain, fracture development and the distribution of shear displacements in a thrusting environment.

Modeling the shortwave radiation balance over the Southern Ocean region remains a challenge for Earth system models. To investigate whether this is related to the representation of aerosol-cloud interactions, we compared measurements of the total number concentration of sea spray generated particles within the Southern Ocean region to model predictions thereof. Measurements were conducted from a container laboratory aboard the R/V Tangaroa throughout an austral summer voyage to the Ross Sea. We used source-receptor modeling to calculate the sensitivity of our measurements to upwind surface fluxes. From this approach, we could constrain empirical parameterizations of sea spray surface flux based on surface wind speed and sea surface temperature. A newly tuned parameterization for the flux of sea spray particles based on the near-surface wind speed is presented. Comparisons to existing model parameterizations revealed that present model parameterizations led to over-estimations of sea spray concentrations. In contrast to previous studies, we found that including sea surface temperature as an explanatory variable did not substantially improve model-measurement agreement. To test whether or not the parameterization may be applicable globally, we conducted a similar regression analysis using a database of in situ whitecap measurements. We found that the key fitting parameter within this regression agreed well the parameterization of sea spray flux. Finally, we compared calculations from the best model of surface flux to boundary layer measurements collected onboard an aircraft throughout the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES), finding good agreement overall.

In organic soils the availability of electron acceptors determines the ratio of CO2 to CH4 formation under anoxic conditions. While typically only inorganic electron acceptors are considered, the importance of electron accepting capacities of organic matter is increasingly acknowledged. Redox properties of organic matter are yet only investigated for a limited set of peat and reference materials. Therefore, we incubated 60 peat samples of 15 sites located in five major peatland regions covering a variety of both bog and fen samples and characterized their capacities to serve as electron acceptor for anaerobic CO2 production. We quantified CO2 and CH4 formation, and changes in available EAC in anoxic incubations of 56 days. On the time scale of our experiment, on average 36.5 % of CO2 could be attributed to CH4 formation, assuming an CO2/CH4 ratio for methanogenesis of 1:1. Regarding the remaining CO2 formed, for which a corresponding electron acceptor would be needed, we could on average explain 70.8 % by corresponding consumption of EAC from both organic and inorganic electron acceptors, the latter contributing typically less than 0.1 %. When the initial EAC was high, CO2 formation from apparent consumption of EAC was high and outweighed CO2 formation from methanogenesis. A rapid depletion of available EAC resulted in a higher share of CO2 from CH4 formation. Our study demonstrates that EAC provides the most important redox buffer for competitive suppression of CH4 formation in peat soils. Moreover, electron budgets including EAC of organic matter could largely explain anaerobic CO2 production.

The scientific community is becoming more demographically diverse, and team science is becoming more common. Here, we compare metrics of success in STEM, such as acceptance rates and citations, between differing team compositions regarding nationality, gender, career stage, and race/ethnicity. We collected the final decisions and citations as of 2019 of 91,427 manuscripts submitted from 2012-2018 to journals published by the American Geophysical Union. We matched the authors by email on each manuscript to self-provided demographic information within the American Geophysical Union’s membership database. This resulted in 20,940 manuscripts matched to nation, gender, and career stage, and 6,015 manuscripts matched to race/ethnicity for manuscripts whose entire authorship team was affiliated with the U.S. Among similar sized authorship teams (teams of 2-4), acceptance rates were 2.7, 4.5, and 0.9% higher (pnation < 0.01, pgender < 0.05, pcareer stage = 0.51) with more than one nation, gender, and career stage, respectively, than non-diverse authorship teams. Diverse papers had 1.2 more citations for international teams than single-nation teams (pnation < 0.01). There were 0.4 and 1.0 fewer citations for authorship teams with more than one gender or career stage than manuscripts with one gender or one career stage (pgender = 0.21, pcareer stage = 0.36). However, racially/ethnically diverse teams were associated with 5.5% lower acceptance rates (p < 0.01) and 0.8 fewer citations (p = 0.15) than racially/ethnically homogenous teams. These results show that diversity can have tangible benefits to science, but equitable practices and inclusive cultures must also be fostered.

The Henkel plot (logarithm of magnetic susceptibility versus density of rock samples) reveals that most rocks fall on either a “magnetite trend” or a “paramagnetic trend”. Interpretation of gravity and magnetic surveys is improved when the mineralogical and lithological basis of these trends is understood. We present a quantitative mineralogical mixing model, involving the components QFC (quartz-feldspar-calcite), FM (ferromagnesian silicates), and M (magnetite), and discuss the geological processes which produce or modify these mixtures. Igneous rocks mostly plot on the magnetite trend, where the FM/M ratio is about 10. The density-susceptibility mineralogical mixing model is compatible with the CIPW mineral calculation for igneous classification from chemical analyses. Sedimentary and metamorphic processes usually involve oxidation, reduction, and/or iron loss, all which are magnetite-destructive and lead to petrophysical measurements along the paramagnetic trend where FM/M >1000. Mineralization, with the introduction of sulfides and oxides leads to dense rocks which do not plot along the magnetite nor paramagnetic trends. This quantitative analysis provides a method to integrate geological processes in the interpretation of geophysical surveys.

Rivers are an important source of freshwater that support societal needs and natural ecosystems, functioning as both collectors for watersheds and distributors along river corridors. Human-made infrastructure (dams, roads, canals) of various kinds have been built on and along rivers to access drinking water, generate energy, mitigate floods, and support industrial and agricultural production. However, due to the long and inconsistent history of constructing and recording these structures, we lack a globally consistent knowledge about where different types of infrastructure are. Here, we used a simple yet consistent method to visually locate and classify different infrastructures that could act as obstructions on rivers that are wider than 30 meters (total length ~2.1 million km globally). Our approach is based on Google Maps’ high resolution satellite images, which for many places have meter-scale resolution. We recently completed global-scale mapping and classifying different obstructions, and are conducting quality checks. In total, we identified ≥ 40,000 unique obstructions, including large dams and smaller weirs, control structures, partial barriers, as well as low-head dams that are often not included in other databases. This Global River Obstruction Dataset, or GROD, once fully validated, will be freely available to the public. We anticipate that it will be of wide interest to hydrological modeling, aquatic ecosystem, geomorphology, and water resource management communities.

River-influenced areas are very important coastal ecosystems, due to their influence on nutrients and the structure of planktonic and benthic communities. Many studies have focused on the interaction between river runoff, wind and river plume characteristics, by means of observations and modeling. This study aimed to improve our understanding about the circulation and seasonal pattern of river plumes in Central Chile, using the Regional Ocean Modeling System (ROMS). Results were in reasonable agreement with observations. The plumes exhibit a minimal area during summer-fall, with a northwest orientation caused by southwesterly winds and lower river discharges. A larger plume area is developed during June-September, as result of higher winter precipitations. The orientation of the plumes during winter is westward, driven by higher river discharge and wind from the northwest. An interesting feature reproduced by the model was the trend to coalesce as observed in satellite imagery.

In Greek mythology, Cassandra was cursed with a prophetic knowledge of the future that no one would believe. The premise of this mythological prophet is also an apt description of the challenge facing scientists in communicating climate change. Central to addressing this challenge is the ability to examine, interpret and understand the individual scaffolding that underlies people's perception of climate change. Human-centered design incorporates a deep assessment of human needs in the design of systems and services, providing an alternative lens to engage in action towards climate change. Over the course of three separate design classes, attracting a diverse array of students from undergraduate to PhD to professional degrees, from engineers to humanists, we have explored the intersection of design education and climate change communication. In Design for a Habitable Planet, students conducted broad unstructured interviews to populate a matrix of beliefs and experiences around climate change, allowing them to bridge a perceived empathy gap. Their subjects included local fisherman, who are experiencing climate change but are ideologically opposed to the concept, and Alaska Native students who are experiencing climate change as part of a larger portfolio of challenges facing their communities. Leveraging theories from behavioral psychology, students then developed and prototyped an array of climate action campaigns, using an array of mediums from Snapchat filters to crowd-sourced climate haiku on beer labels. A unique outcome of the design process was a set of strategies intended to engage voices from all corners of the climate change debate and in turn inspire action. In Design of Data, students were challenged to design a data journalism piece highlighting positive action to address climate change. Students struggled with their pre-conceived negative bias around climate change, but after an examination of their own communities, what emerged were eight compelling stories daylighting positive and collective action to address global change. Ultimately, through our classroom experiments integrating design, behavioral psychology and climate change communication, students have not only learned to engage across differences, but we have developed a first glimpse of the power of design to inspire novel strategies to incite positive action to address climate change.

My laboratory is developing an accelerometer. However, if you do not install the dedicated software, you can not browse the data. In addition, since it is necessary to go to the site on a semi-annual basis and collect data due to capacity problems, the current operation is burdened. In order to solve these problems, we devised WebGL that enables high-speed drawing when processing a large amount of data such as Big Data, and a Web application that uses server that can transmit, save, and download acquired data. In this application, an accelerometer and a raspberry pie are connected, and the raspberry pie receives data obtained from the accelerometer and sends the data to a server installed in a laboratory by SCP file transfer at regular intervals. These data specify the location and date from the client side through the Internet browser, download the data using the download function of the browser, read this data on the application and draw the waveform. The data obtained from the accelerometer in the development to date is converted into a CSV file and transferred to the server. These data can be downloaded and displayed on the web application. For the google map displayed on the right side of the screen, it is to make the application more useful by registering the location where the accelerometer was set in the future.

The Infrared Atmospheric Sounder Interferometer (IASI) is well suited for monitoring of dust aerosols because of its capability to determine both AOD and altitude of the dust layer, and because of the good match between the IASI times of observation (9.30 am and pm, local time) and the time of occurrence of the main Saharan dust uplift mechanisms. Here, starting from IASI-derived dust characteristics for an 11-year period, we assess the capability of IASI to bring realistic information on the dust diurnal cycle. We first show the morning and nighttime climatology of IASI-derived dust AOD for two major dust source regions of the Sahara: The Bodele Depression and the Adrar region. Compared with simulations from a high resolution model, permitting deep convection to be explicitly resolved, IASI performs well. In a second step, a Dust Emission Index specific to IASI is constructed, combining simultaneous information on dust AOD and mean altitude, with the aim of observing the main dust emission areas, daytime and nighttime. Comparisons are then made with other equivalent existing results derived from ground based or other satellite observations. Results demonstrate the capability of IASI to improve the documentation of dust distribution over Sahara over a long period of time. Associating observations of dust aerosols in the visible, on which a majority of aerosol studies are so far based, and in the infrared thus appears as a way to complement the results from other satellite instruments in view of improving our knowledge of their impact on climate.

It was investigated that the reproducibility of surface wind and tracer transport simulations over complex terrain in wintertime using high-resolution (5-km, 3-km, and 1-km grid) weather and transport models, in which radioactive cesium (Cs-137) emitted from the Fukushima nuclear power plant was used as a tracer. Fukushima has complex terrain, such as mountains and valleys. The model results were validated by observations collected from the national networks of the automated meteorological data acquisition system and the hourly air pollution sampling system. The reproducibility depended on the model resolution, topographic complexity, and synoptic weather conditions. Higher model resolution led to higher reproducibility of surface winds, especially in mountainous areas when the Siberian winter monsoon was disturbed. In contrast, the model improvement was negligible or nonexistent over plain/coastal areas when the synoptic field was steady. The statistical scores of the tracer transport simulations often deteriorated due to small errors in the plume locations. However, the higher-resolution models advantageously performed better transport simulations in the mountainous areas because of the lower numerical diffusion and higher reproducibility of the mass flux. The reproducibility of the tracer distribution in the valley of the Fukushima mountainous region was dramatically improved with increasing model resolution. In conclusion, a higher-resolution model is definitely recommended for tracer transport simulations over mountainous terrain at least in the range of mesoscale model resolutions (commonly 1~10 km grids).