Geophysical Signals Research Articles

The oleaginous voice: Auto-Tune, linear predictive coding, and the security-petroleum complex

ABSTRACT In the twentieth century, exploration geophysics and digital vocal processing each underwent key technical transformations based on formally similar signal processing techniques derived from prior work in statistics and information theory. Engineers at Texas Instruments (TI) and the Massachusetts Institute of Technology (MIT) developed deconvolution filtering as a way to model geological strata from seismograph recordings in order to facilitate oil exploration. Meanwhile, engineers at Bell Laboratories and Nippon Telegraph and Telephone (NTT) used similar techniques to digitally encode and transmit speech signals more efficiently – a method known as linear predictive coding (LPC). These cognate approaches to modeling geophysical and vocal signals provided a practical basis for subsequent forays by oil industry figures into the world of digital audio. Tracing the histories of four technologies – LPC secure voice transmission terminals, the TI Speak & Spell educational toy, automatic speech recognition, and the vocal pitch correction software Auto-Tune – I analyze the role of security interests in facilitating spillovers between technologies of oil and the voice. I argue that, by approaching both geophysical and vocal signals as matters of national security, the United States’ defense and intelligence services furnished the bulk of socio-economic connective tissue between petroleum geophysics and vocal processing research.

The ROC Curve Examination on Traveling Ionospheric Disturbances in FORMOSAT‐7/COSMIC‐2 IVM Ion Density Triggered by the 15 January 2022 Tonga Volcanic Eruption

AbstractThe receiver operating characteristic (ROC) and the area under the curve (AUC), initially developed for signal processing and psychology, are a test for assessing the performance of a binary classification problem at varying threshold values. The ion density (Ni) observed by FORMOSAT‐7/COSMIC‐2 is used to study traveling ionospheric disturbances (TIDs) triggered by the 15 January 2022 Tonga volcanic eruption. We examine parameters of Ni, differential Ni, and standard deviation Ni (STD_Ni) in January 2022, simulate TID wavefronts traveling with various speeds from 10 to 1,000 m/s, and apply the ROC curve to globally identify the significance of TIDs in STD_Ni triggered by the volcanic eruption. ROC and AUC results show that in addition to TIDs related to tsunami/tropospheric Lamb waves and a series of fast‐moving waves with propagation speeds of 180–350 and 450–600 m/s, respectively, those long‐lasting low‐speeds less than 70 m/s and high‐speeds about 690, 860, and 990 m/s meet 95% statistical significance, which confirms TIDs being detected. These show that the FORMOSAT‐7/COSMIC‐2 ion density can be used to globally detect various TIDs triggered by the Tonga volcanic eruption. The ROC test results also show a potential use case for detecting other geophysical signals in future applications.

GCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

High-precision global navigation satellite system (GNSS) time series data contain rich tectonic and non-tectonic motion information. However, such data also have common-mode errors (CME) related to spatiotemporal characteristics. CME will produce bias in accurate and reliable GNSS station positions and velocities estimations, ultimately deciphering erroneous geophysical signals. Currently, CME extraction methods can be divided into three categories: regional stack filtering, reference frame filtering, and statistical signal decomposition. The third method is widely used in geodetic data processing, including GNSS time series analysis, global time-varying gravity signal extraction, interferometric synthetic aperture radar (InSAR) time series analysis, etc. The demand for GNSS time series analysis software with interactive, visualization, and multifunctional features has also increased with the increase in GNSS stations. Therefore, we developed gCMEbox based on MATLAB. The gCMEbox’s primary functions include GNSS time series preprocessing, multiple statistical signal decomposition algorithms for extracting CME, GNSS time series analysis, visualization, velocity field construction, and generation of multiple time series post-processing products. Based on GNSS time series data, this study utilized the gCMEbox for data preprocessing, including format conversion, outlier removal, missing data recovery, etc. By applying statistical signal decomposition methods, gCMEbox successfully extracted CME. The primary objective of this study is to share this toolbox with the scientific community, providing a comprehensive tool for GNSS time series analysis and applications.

Archaeological investigation of burials preluded by ground penetrating radar and geospatial technologies

Ground penetrating radar (GPR) is a non-invasive and efficient scientific tool in burial analysis that can 'see the unseen,' answering both simple questions such as the existence and boundaries of burials, as well as more difficult questions like whether burials are intact or decayed. This paper reviews common reflection signatures associated with burials and applies the three fundamental GPR principles (dielectric contrast, scattering and polarity of reflections) to two distinct caseworks involving historical graves and civilian burials, which serve as benchmarks with known ground truth. It encompasses a third test case involving family tree research in a civilian cemetery, drawing upon the benchmarked results obtained from the first two caseworks. In adherence to geophysical signal reflection principles, our study discerns distinct hyperbolic traits associated with three burial types including intact shroud-wrapped, coffin burials, and decayed or mass-grave burials. A GPR-geospatial integration workflow incorporating GPR, aerial photogrammetry and global navigation satellite system - real time kinetics (GNSS-RTK), is derived to enhance the identification and investigation of burials using GPR. Our workflow encompasses a range of indicators for survey methods and burial classification, presenting a general framework for the systematic contextualization of tailored workflows to individual contexts. This work exemplifies the efficacy of GPR in the detection of burials that have been undisturbed for over a century in the soils of Hong Kong and how geophysics and geospatial science can address the limitations inherent in conventional desktop-based archaeological investigation. Its implications extend to professionals in diverse fields including historians, archaeologists, cemetery management officials, and even family members searching for their lost loved ones.

Significance testing for cross correlation: A critical examination of correlations between ENSO and GRACE-derived terrestrial water storage variabilities

The cross correlation has a wide range of applications in geophysical fields for measuring linear connections or relationships among physical quantities. Nonetheless, there remains a dearth of comprehensive discourse regarding its statistical significance testing, which is crucial for differentiating meaningful outcomes from those merely stemming from fortuity or pure randomness. Conventional theoretical methods for significance testing, commonly used in statistical analysis tools such as SPSS and MATLAB, are only applicable when dealing with idealized circumstances such as white noise. In discretionary application of these methods to analyze geophysical signals with, say, red noise may result in potentially unjustified conclusions. This study aims to develop a comprehensive approach based on the t-distribution within a rigorous statistical context, aiming to facilitate significance tests of cross correlation for general signals with specified time shifts or ranges. Extensive Monte Carlo experiments substantiate its robustness, thereby paving the way for accurate and expeditious identification of statistically (hence potentially physically) meaningful correlations in general. As an example, we examine critically the previously purported significant correlations between ENSO (El Niño Southern Oscillation) and global terrestrial water storage variations derived from the GRACE (Gravity Recovery and Climate Experiment) satellite mission, demonstrating that they are subject to questioning in the absence of complete significance testing.

Thermal-damage effects on fracturing evolution of granite under compression-shear loading

Shear-dominant instability disasters in high-temperature conditions pose a significant challenge for deep underground rock projects, necessitating a thorough understanding of rock fracture evolution and the impacts of thermal damage. To investigate how thermal damage influences shear fracturing behavior and geophysical responses, experiments were conducted on thermally-treated granite under compression-shear loading conditions. The detailed failure process was meticulously monitored using integrated acoustic emission (AE), electric potential (EP), and digital image correlation (DIC) techniques. The effects of thermal treatment on the patterns of AEs, EPs, and DIC strain fields during the loading process were analyzed. Micromechanics related to the influence of thermal treatment on mechanical properties and geophysical responses were explored using X-ray diffraction (XRD) and scanning electron microscopy (SEM). By defining damage variables through multiparameter analysis using AE, EP, and DIC, the study delved into predictive insights on the failure process of thermal-damage granite. The results indicate that the physical properties of granite undergo significant changes as the treatment temperature increases, with the peak stress gradually decreasing and the plastic characteristics increasing. These changes can be attributed to thermal damage, which causes internal water escape, expansion of mineral particles, and crack formation. Additionally, increasing the thermal treatment temperature leads to a decrease in both AE and EP intensity, as well as a reduction in the occurrence of high-magnitude AE events, while the cumulative AE count rapidly increases during the plastic stage. Notably, thermal treatment induces significant alterations in mineral composition, intergranular fractures, and the formation of a gradual zigzag crack path, resulting in the intensified development of shear microcracks. This observation is supported by the varying of AE characteristic parameter AF/RA and the analysis of DIC displacement decomposition. Moreover, the three defined damage variables exhibit accelerated growth prior to catastrophic failure, with the order of precursor occurrence observed as AE, DIC, and EP. However, the occurrence of the precursor time for these three geophysical signals becomes earlier with increasing treatment temperature. These research findings shed light on the mechanism of compression-shear failure of thermal-damage rock and provide valuable references for predicting precursors of the shear-dominant instability disasters such as induced earthquakes.

Detection and Characterization of Seismic and Acoustic Signals at Pavlof Volcano, Alaska, Using Deep Learning

AbstractVolcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible.

Intrusive mechanism of the 2018 Sierra Negra Galápagos eruption, constrained by 4D tomographic images

Understanding the mechanisms of magma intrusion underpins our ability to interpret geophysical monitoring signals at volcanoes and thus issue reliable forecasts of future activity. The basaltic caldera volcanoes of the western Galápagos Islands, Ecuador, exhibit exceptionally high rates of deformation driven by shallow magma accumulation and migration. However, the nature and evolution of magma storage at these volcanoes is poorly constrained by earthquake hypocentre locations or geodetic inversions. Here we show that transient variations in seismic velocity before, during, and after the 2018 eruption of the Sierra Negra volcano track the accumulation of magma in a shallow sill complex and the emplacement of a lateral flank intrusion. A four-dimensional tomographic technique applied to the P-wave arrivals of local earthquakes provides high spatiotemporal resolution of changes in the physical properties of the shallow volcanic system. In the month before the eruption, the expansion of a low-velocity zone above the sub-caldera sill complex coincides with caldera uplift and near-surface fracturing, driven by persistent shallow magma accumulation. A new low-velocity anomaly appeared progressively in the western flank of the volcano in the days after the onset of the eruption, coinciding with the opening of a curved sill that supplied magma to secondary eruptive fissures. The anomaly disappeared as the curved sill deflated after the initial opening, despite it remaining the conduit for magma from the caldera complex to the flank fissures. Low velocities across the shallow caldera after the end of the eruption likely result from rapid inflation due to recharge with fresh magma from depth. These results indicate a previously unknown complexity to the magmatic plumbing system at Sierra Negra and suggest velocity changes resulting from an interplay of thermal and stress perturbations.

Determination of the geophysical signature of soft-clay and hard lateritic soils and the implications on geotechnical works using electrical resistivity imaging

Understanding the geophysical signature of subsurface is quite essential in mapping site conditions for geotechnical engineering and simulating geophysical surveys, especially in coastal areas. This article presents a comparative geophysical study of topsoil (often refer as engineering layer) of soft clay deposit and hard lateritic soil to evaluate the implications on geotechnical work using electrical resistivity imaging (ERI). The results of the 2D ERI show that the hard lateritic soil produces three distinctive geological layers with the topsoil having high apparent electrical resistivity (AER) values varying between 300 Ωm–1000 Ωm and a thickness between 2.0 m – 10.0 m. The layer between the unsaturated and saturated regions has a moderate layer AER and thickness values ranging from 100 Ωm–300 Ωm and 2.5 m–9.5 m, respectively. In contrast, the saturated (weathered) layer has a low AER value (< 100Ω m), which represents the regional groundwater unit, located beneath the subsurface of a depth > 6.0 m. However, the soft clay soil shows no distinctive subsurface layers, as it is characterized by a nearly homogeneity subsurface of poor AER variability. The top-soft clay deposit has very AER values ranging from 11.0 Ωm–80 Ωm, which extends beyond 6.5 m. Higher and lower AER values noted within the overburdened topsoil of lateritic and clay extractions, respectively, were due to the accumulations of compacted clay and lateritic soil, which are very essential for geotechnical works. In addition, the geophysical signal was conspicuously attenuated in soft clay soil, which shows that soft clay soil is not a favourite target of geophysical investigation. However, the geophysical signals in hard lateritic generate a fine geo-electrical layer pattern. It was also noted that the existence of soft clay and lateritic soil decreases with depth. This kind of information is very essential in geotechnical works.

Bayesian joint inversion of seismic and electromagnetic data for reservoir lithofluid facies, including geophysical and petrophysical rock properties

A Bayesian approach is developed to estimate lithofluid facies and other rock properties conditioned on seismic and electromagnetic data for reservoir characterization. Prior distributions are assumed to be facies-related Gaussian modes of geophysical rock properties directly acquired or converted from petrophysical properties by calibrated rock-physics modeling. An original generalization includes two distributions in the same marginalization integral, analytically solved under a linearized Gaussian assumption to provide a facies model likelihood conditioned on geophysical data. Because computing this probability for all possible facies configurations may be impractical, a Markov chain Monte Carlo algorithm efficiently samples models to provide a full posterior distribution. The linearized Gaussian approach allows the computation of the conditional distributions of geophysical and petrophysical rock properties by applying local deterministic inversions over the many sampled facies models. The inversion uses simulated geophysical data from a 1D synthetic model based on the geologic scenario and a well from a selected marine oil field. Two other wells from the same reservoir are used to gather prior distributions. Data from the well, calibration of the rock-physics modeling, and facies matching between the priors and the synthetic model are presented and discussed. Numerical tests validate nonlinear forward-modeling adaptations based on the assumed linearized Gaussian approach. The simulated stand-alone and joint geophysical data sets are then inverted for lithofluid facies models under different prior inputs. Two challenging geoelectric scenarios also are tested, one with lower resistivity contrasts and another with a misguided background model. All results demonstrate a gain in precision and accuracy when associating these geophysical signals to estimate the oil column. Facies-conditioned inversions for the rock properties also indicate potential for quantitative reservoir interpretations.

Banding in the Margins of Basaltic Dykes Indicates Pulsatory Propagation During Emplacement

AbstractBasaltic fissure eruptions, which are the most common type of eruption on Earth, are fed by dykes which mediate magma transport through the crust. Dyke propagation processes are important because they determine the geometry of the transport pathway and the nature of any geophysical signals associated with magma ascent. Here, we investigate small‐scale (mm–cm wide) banding features at the margins of dykes in the Teno Massif (Tenerife, Spain) and the Columbia River Basalt Province (CRBP) (USA). Similar marginal bands have been reported for dykes in numerous localities around the world. Dyke margins record valuable information about propagation because they are the first material to solidify against the host rock at the propagating dyke tip. We find that the marginal bands are defined by cyclic variations in phenocryst concentration and vesicularity, and we infer that these cyclic variations in texture are a product of cyclic variations in magma flow rates and pressures within the dyke tip. This indicates that dyke emplacement occurs in pulses, with propagation repeatedly hindered by the rapid cooling and solidification of magma in the narrow dyke tip. Using a 1D conduction model, we estimate the time taken for each band to cool and solidify, which provides a timescale of several minutes to tens of minutes for the pulses. The occurrence of similar bands in various volcanic settings suggests that pulsatory propagation is a common, if not ubiquitous, process associated with dyke emplacement.

Tracing Magma Migration at Mt. Etna Volcano during 2006–2020, Coupling Remote Sensing of Crater Gas Emissions and Ground Measurement of Soil Gases

The geochemical monitoring of volcanic activity today relies largely on remote sensing, but the combination of this approach together with soil gas monitoring, using the appropriate parameters, is still not widely used. The main purpose of this study was to correlate data from crater gas emissions with flank emissions of soil gases at Mt. Etna volcano from June 2006 to December 2020. Crater SO2 fluxes were measured from fixed stations around the volcano using the DOAS technique and applying a modeled clear-sky spectrum. The SO2/HCl ratio in the crater plume was measured with the OP-FTIR technique from a transportable instrument, using the sun as an IR source. Soil CO2 efflux coupled with the 220Rn/222Rn activity ratio in soil gases (named SGDI) were measured at a fixed monitoring site on the east flank of Etna. All signals acquired were subject both to spectral analysis and to filtering of the periodic signals discovered. All filtered signals revealed changes that were nicely correlated both with other geophysical signals and with volcanic eruptions during the study period. Time lags between parameters were explained in terms of different modes of magma migration and storage inside the volcano before eruptions. A comprehensive dynamic degassing model is presented that allows for a better understanding of magma dynamics in an open-conduit volcano.

The effects of local variations in conditions on carbon storage and release in the continental mantle.

Recent advances indicate that the amount of carbon released by gradual degassing from the mantle needs to be revised upwards, whereas the carbon supplied by plumes may have been overestimated in the past. Variations in rock types and oxidation state may be very local and exert strong influences on carbon storage and release mechanisms. Deep subduction may be prevented by diapirism in thick sedimentary packages, whereas carbonates in thinner sequences may be subducted. Carbonates stored in the mantle transition zone will melt when they heat up, recognized by coupled stable isotope systems (e.g. Mg, Zn, Ca). There is no single 'mantle oxygen fugacity', particularly in the thermal boundary layer (TBL) and lowermost lithosphere, where very local mixtures of rock types coexist. Carbonate-rich melts from either subduction or melting of the uppermost asthenosphere trap carbon by redox freezing or as carbonate-rich dykes in this zone. Deeply derived, reduced melts may form further diamond reservoirs, recognized as polycrystalline diamonds associated with websteritic silicate minerals. Carbon is released by either edge-driven convection, which tears sections of the TBL and lower lithosphere down so that they melt by a mixture of heating and oxidation, or by lateral advection of solids beneath rifts. Both mechanisms operate at steps in lithosphere thickness and result in carbonate-rich melts, explaining the spatial association of craton edges and carbonate-rich magmatism. High-pressure experiments on individual rock types, and increasingly on reactions between rocks and melts, are fine-tuning our understanding of processes and turning up unexpected results that are not seen in studies of single rocks. Future research should concentrate on elucidating local variations and integrating these with the interpretation of geophysical signals. Global concepts such as average sediment compositions and a uniform mantle oxidation state are not appropriate for small-scale processes; an increased focus on local variations will help to refine carbon budget models.

3D geophysical image translated into photorealistic virtual outcrop geology using generative adversarial networks

Outcrop analogues play a pivotal role in resolving meter-scale depositional facies heterogeneity of carbonate strata. Two-dimensional outcrops are insufficient to decipher the 3D heterogeneity of carbonate facies. Near-surface geophysical methods, notably ground-penetrating radar (GPR), can be employed to step into 3D and extend the dimensionality of the outcrops to behind the outcrop. However, interpreting geophysical images requires specific geophysical expertise, often unfamiliar to field geologists who are more familiar with the actual rock than the geophysical data. A novel generative adversarial network (GAN) application is presented that constructs a photorealistic 3D virtual outcrop behind-the-outcrop model. The method combines GPR forward modeling with a conditional generative adversarial network (CGAN) and exploits the apparent similarities between outcrop expressions of lithofacies with their radargram counterparts. We exemplified the methodology and applied it to the open-source GPR data acquired from the Late Oxfordian-Early Kimmeridgian Arabian carbonate outcrop. We interpret a 4 km long outcrop photomosaic from a digital outcrop model (DOM) for its lithofacies, populate the DOM with GPR properties, and forward model the synthetic GPR response of these lithofacies. We pair the synthetic GPR with DOM lithofacies and train them using CGAN. Similarly, we pair the DOM lithofacies with outcrop photos and train them using CGAN. We chain the two trained networks and apply them to construct an approximately 2 km long 2D and an approximately 60 m2 3D volume of photorealistic artificial outcrop model. This model operates in a visual medium familiar to outcrop geologists, providing a complementary instrument to visualize and interpret rock formation instead of geophysical signals. This virtual outcrop replicates the visual character of outcrop-scale lithofacies features, such as the intricate bedding contacts and the outline of reef geobodies.

Ozone Trend Analysis in Natal (5.4°S, 35.4°W, Brazil) Using Multi-Linear Regression and Empirical Decomposition Methods over 22 Years of Observations

Ozone plays an important role in the Earth’s atmosphere. It is mainly formed in the tropical stratosphere and is transported by the Brewer–Dobson Circulation to higher latitudes. In the stratosphere, ozone can filter the incoming solar ultraviolet radiation, thus protecting life at the surface. Although tropospheric ozone accounts for only ~10%, it is a powerful GHG and pollutant, harmful to the health of the environment and living beings. Several studies have highlighted biomass burning as a major contributor to the tropospheric ozone budget. Our study focuses on the Natal site (5.40°S, 35.40°W, Brazil), one of the oldest ozone-observing stations in Brazil, which is expected to be influenced by fire plumes in Africa and Brazil. Many studies that examined ozone trends used the total atmospheric columns of ozone, but it is important to assess ozone separately in the troposphere and the stratosphere. In this study, we have used radiosonde ozone profiles and daily TCO measurements to evaluate the variability and changes of both tropospheric and stratospheric ozone separately. The dataset in this study comprises daily total columns of colocalized ozone and weekly ozone profiles collected between 1998 and 2019. The tropospheric columns were estimated by integrating ozone profiles measured by ozone sondes up to the tropopause height. The amount of ozone in the stratosphere was then deduced by subtracting the tropospheric ozone amount from the total amount of ozone measured by the Dobson spectrometer. It was assumed that the amount of ozone in the mesosphere is negligible. This produced three distinct time series of ozone: tropospheric and stratospheric columns as well as total columns. The present study aims to apply a new decomposition method named Empirical Adaptive Wavelet Decomposition (EAWD) that is used to identify the different modes of variability present in the analyzed signal. This is achieved by summing up the most significant Intrinsic Mode Functions (IMF). The Fourier spectrum of the original signal is broken down into spectral bands that frame each IMF obtained by the Empirical Modal Decomposition (EMD). Then, the Empirical Wavelet Transform (EWT) is applied to each interval. Unlike other methods like EMD and multi-linear regression (MLR), the EAWD technique has an advantage in providing better frequency resolution and thus overcoming the phenomenon of mode-mixing, as well as detecting possible breakpoints in the trend mode. The obtained ozone datasets were analyzed using three methods: MLR, EMD, and EAWD. The EAWD algorithm exhibited the advantage of retrieving ~90% to 95% of ozone variability and detecting possible breakpoints in its trend component. Overall, the MRL and EAWD methods showed almost similar trends, a decrease in the stratosphere ozone (−1.3 ± 0.8%) and an increase in the tropospheric ozone (+4.9 ± 1.3%). This study shows the relevance of combining data to separately analyze tropospheric and stratospheric ozone variability and trends. It highlights the advantage of the EAWD algorithm in detecting modes of variability in a geophysical signal without prior knowledge of the underlying forcings.

Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog

Data-driven approaches to identify geophysical signals have proven beneficial in high dimensional environments where model-driven methods fall short. GNSS offers a source of unsaturated ground motion observations that are the data currency of ground motion forecasting and rapid seismic hazard assessment and alerting. However, these GNSS-sourced signals are superposed onto hardware-, location- and time-dependent noise signatures influenced by the Earth’s atmosphere, low-cost or spaceborne oscillators, and complex radio frequency environments. Eschewing heuristic or physics based models for a data-driven approach in this context is a step forward in autonomous signal discrimination. However, the performance of a data-driven approach depends upon substantial representative samples with accurate classifications, and more complex algorithm architectures for deeper scientific insights compound this need. The existing catalogs of high-rate (≥1Hz) GNSS ground motions are relatively limited. In this work, we model and evaluate the probabilistic noise of GNSS velocity measurements over a hemispheric network. We generate stochastic noise time series to augment transferred low-noise strong motion signals from within 70 kilometers of strong events (≥ MW 5.0) from an existing inertial catalog. We leverage known signal and noise information to assess feature extraction strategies and quantify augmentation benefits. We find a classifier model trained on this expanded pseudo-synthetic catalog improves generalization compared to a model trained solely on a real-GNSS velocity catalog, and offers a framework for future enhanced data driven approaches.

The Effect of Conduit Walls Roughness on Volcanic Jets and Their Seismo‐Acoustic Radiation: An Experimental Investigation

AbstractTo explore the effect of conduit roughness on volcanic jet dynamics and on the related seismo‐acoustic radiation we performed a series of shock‐tube experiments using pipes with variable inner surface fractal dimension D. Variable starting pressure produced subsonic to supersonic jets visualized using high‐speed shadowgraph and recorded with an array of accelerometers and microphones. At all starting pressures, increasing D increases the energy transfer from the gas to the conduit walls, decreasing the jet exit velocity (Mach number) and, for supersonic cases, the related shock‐cell spacing, and increasing the seismic to acoustic radiation amplitude ratio. The roughness‐induced changes in jet velocity and turbulence affect the dominant sources of the jet noise and modulates the spectral properties of the acoustic signals. From our study we show that conduit wall roughness is an important and yet largely neglected factor in the dynamics of explosive volcanic eruptions and their monitored geophysical signals.

The ascent of subduction zone mélanges: Experimental constraints on mélange rock densities and solidus temperatures

Mélanges are mixtures of subducted materials and serpentinized mantle rocks that form along the slab-mantle interface in subduction zones. It has been suggested that mélange rocks may be able to ascend from the slab-top into the overlying mantle, as solid or partially molten buoyant diapirs, and transfer their compositional signatures to the source regions of arc magmas. However, their ability to buoyantly rise is in part tied to their phase equilibria during melting and residual densities after melt extraction, all of which are poorly constrained. Here, we report a series of piston-cylinder experiments performed at 1.5–2.5 GPa and 500–1050°C on three natural mélange rocks that span a range of mélange compositions. Using phase equilibria, solidus temperatures, and densities for all experiments, we show that melting of mélanges is unlikely to occur along the slab-top at pressures ≤ 2.5 GPa, so that diapirism into the hotter mantle wedge would be required for melting to initiate. For the two metaluminous mélange compositions, diapir formation is favored up to pressures of at least 2.5 GPa. For the peraluminous mélange composition investigated, diapir buoyancy is possible at 1.5 GPa but limited at 2.5 GPa due to the formation of high-density garnet, primarily at the expense of chlorite. We also evaluate whether thermodynamic modeling (Perple_X) can accurately reproduce the phase equilibria, solidus temperatures, and density evolution of mélange compositions. Our analysis shows good agreement between models and experiments in mélange compositions with low initial water contents and low-pressure (≤ 1.5 GPa) conditions. However, discrepancies between the thermodynamic models and experiments become larger at higher pressures and high-water contents, highlighting the need for an improved thermodynamic database that can model novel bulk compositions beyond the canonical subducting lithologies. This study provides experimental constraints on mélange buoyancy that can inform numerical models of mélange diapirism and influence the interpretations of both geophysical signals and geochemical characteristics of magmas in subduction zones.

Characterizing lithological, weathering, and hydrothermal alteration influences on volcanic rock properties via spectroscopy and laboratory testing: a case study of Mount Ruapehu volcano, New Zealand

The geomechanical characterization of volcanic material has important implications for geothermal and mineral exploration, engineering design, geophysical signals of volcano unrest, and models of instability and mass flows. Chemical weathering and hydrothermal systems can alter the host rock, leading to changes in mechanical behavior and failure mode. Here, we compare the physical and mechanical properties of lava, autoclastic breccia, and pyroclastic (scoria) rocks from Mount Ruapehu volcano (Ruapehu) in New Zealand to mineralogical composition determined via infrared spectroscopy and scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS). We use correlation matrices, principal component analysis, and parametric analysis to determine which parameters can be used to predict physical and mechanical properties and form the basis for transfer functions. Laboratory-based spectroscopy shows that the samples contain absorption features indicative of Al- and Mg-rich hydrous phyllosilicates (e.g., kaolinite, halloysite, montmorillonite), Fe- oxides (e.g., goethite), and sulfates attributed to surface weathering, supergene, and steam-heated alteration. We find that porosity and primary lithology are the predominant control on physical and mechanical properties, followed by the pervasiveness of weathering/alteration, and then mineralogical composition. Several properties, such as porosity, uniaxial compressive strength, P-wave seismic velocity, density, and Young’s modulus, show strong correlations with other properties, indicating the potential for transfer functions between these properties. Hydrothermally altered rocks near the vent complex (up to ~ 400 m depth beneath the crater lake) with high-intensity hydrothermal alteration do not follow typical physical and mechanical property trends due to high clay content, low permeability, and low strength. The presence of these rocks within the edifice at Ruapehu implies local barriers to fluid flow and subsequent pore pressure variations. Additionally, they may have less than half the strength than would be dictated by typical porosity-strength trends for surface rocks, increasing the likelihood of structural failure. Trends in the pervasiveness of weathering with physical and mechanical properties, along with shifts in the position of spectral absorption peaks as hydrothermal/weathering alteration increases, suggest that it may be possible to extrapolate properties from imaging spectroscopy.

Assessing the capability of three different altimetry satellite missions to observe the Northern Current by using a high-resolution model

Abstract. Over the last 3 decades, satellite altimetry has observed sea surface height variations, providing a regular monitoring of the surface ocean circulation. Altimetry measurements have an intrinsic signal-to-noise ratio that limits the spatial scales of the currents that can be captured. However, the recent progress made on both altimetry sensors and data processing allows us to observe smaller geophysical signals, offering new perspectives in coastal areas where these structures are important. In this methodological study, we assess the ability of three altimeter missions with three different technologies to capture the Northern Current (northwestern Mediterranean Sea) and its variability, namely Jason-2 (Ku-band low-resolution-mode altimeter, launched in 2008), SARAL/AltiKa (Ka-band low-resolution-mode altimeter, launched in 2013) and Sentinel-3A (synthetic aperture radar altimeter, launched in 2016). Therefore, we use a high-resolution regional model as a reference. We focus along the French coast of Provence, where we first show that the model is very close to the observations of high-frequency radars and gliders in terms of surface current estimates. In the model, the Northern Current is observed 15–20 km from the coast on average, with a mean core velocity of 0.39 m s−1. Its signature in terms of sea level consists of a drop whose mean value at 6.14∘ E is 6.9 cm, extending over 20 km. These variations show a clear seasonal pattern, but high-frequency signals are also present most of the time. In comparison, in 1 Hz altimetry data, the mean sea level drop associated with the Northern Current is overestimated by 3.0 cm for Jason-2, but this overestimation is significantly less with SARAL/AltiKa and Sentinel-3A (0.3 and 1.4 cm respectively). In terms of corresponding sea level variability, Jason-2 and SARAL altimetry estimates are larger than the model reference (+1.3 and +1 cm respectively), whereas Sentinel-3A shows closer values (−0.4 cm). When we derive geostrophic surface currents from the satellite sea level variations without any data filtering, in comparison to the model, the standard deviations of the velocity values are also very different from one mission to the other (3.7 times too large for Jason-2 but 2.4 and 2.9 times too large for SARAL and Sentinel-3A respectively). When low-pass filtering altimetry sea level data with different cutoff wavelengths, the best agreement between the model and the altimetry distributions of velocity values are obtained with a 60, 30 and 40–50 km cutoff wavelength for Jason-2, SARAL and Sentinel-3A data respectively. This study shows that using a high-resolution model as a reference for altimetry data allows us not only to illustrate how the advances in the performances of altimeters and in the data processing improve the observation of coastal currents but also to quantify the corresponding gain.