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A 6-7 year cycle in the Earth System.
http://publi2-as.oma.be/record/5982
Cazenave, A.Thu, 29 Dec 2022 14:14:17 GMThttp://publi2-as.oma.be/record/59822022A 6-7 year cycle in the climate system.
http://publi2-as.oma.be/record/5978
Pfeffer, J.Thu, 29 Dec 2022 14:02:37 GMThttp://publi2-as.oma.be/record/59782022The Global Patterns of Interannual and Intraseasonal Mass Variations in the Oceans from GRACE and GRACE Follow-on records.
http://publi2-as.oma.be/record/5946
Delforge, D.Thu, 29 Dec 2022 10:50:26 GMThttp://publi2-as.oma.be/record/59462022Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series
http://publi2-as.oma.be/record/5869
Understanding and modelling the properties of the stochastic variations in geodetic time series is crucial to obtain realistic uncertainties for deterministic parameters, e.g., long-term velocities, and helpful in characterizing non-modelled processes. With the increasing span of geodetic time series, it is expected that additional observations would help better understand the low-frequency properties of these stochastic variations. In the meantime, recent studies evidenced that the choice of the functional model for the time series biases the assessment of these low-frequency stochastic properties. In particular, frequent offsets in position time series can hinder the evaluation of the noise level at low frequencies and prevent the detection of possible random-walk-type variability. This study investigates the ability of the Maximum Likelihood Estimation (MLE) method to correctly retrieve low-frequency stochastic properties of geodetic time series in the presence of frequent offsets. We show that part of the influence of offsets reported by previous studies results from the MLE method estimation biases. These biases occur even when all offset epochs are correctly identified and accounted for in the trajectory model. They can cause a dramatic underestimation of deterministic parameter uncertainties. We show that one can avoid biases using the Restricted Maximum Likelihood Estimation (RMLE) method. Yet, even when using the RMLE method or equivalent, adding offsets to the trajectory model inevitably blurs the estimated low-frequency properties of geodetic time series by increasing low-frequency stochastic parameter uncertainties more than other stochastic parameters.Gobron, K.Thu, 27 Oct 2022 14:44:51 GMThttp://publi2-as.oma.be/record/58692022A naive Bayesian method to chase mantle plumes in global tomography models
http://publi2-as.oma.be/record/5855
This study provides a quantitative approach to search for mantle plumes in global seismic tomography models without any preconceived notions about the associated mantle velocity anomalies, other than the assumption that the plumes are not significantly deflected horizontally by more than 6°, anywhere in the mantle. We design identification tests with a reasonable detection threshold while keeping false alarms at a level lower than 5%. This is based on a naive Bayesian clustering analysis, which is possible thanks to the varimax principal component analysis that provides components of the tomography models that are much more independent than the original number of depth slices in the models. We find that using such independent components greatly reduces detection errors compared to using an arbitrary number of depth slices due to correlations between the different slices. We detect a wide range of behaviour of the seismic velocity profiles underneath the hotspots investigated in this study. Moreover, we retrieve locations away from hotspots that have similar seismic velocity profile signatures to those underneath some hotspots. Hence, it is not possible to obtain a unique definition of seismic velocity anomalies that are associated with hotspots and thus care needs to be taken when searching for mantle plumes beneath hotspots using prior assumptions about the velocity anomalies that might be associated with them. On the other hand, we establish a probability distribution of the seismic velocity profiles that is specific to a sub-list of hotspots. Overall, the mantle plume zones identified in our analysis do not appear to surround the Africa and Pacific large low shear velocity provinces (LLSVPs), but are rather within them. This rules out the idea that LLSVPs correspond to compact, dense piles with mantle plumes rising from their edges. Instead, our analysis suggests two possible options that either the LLSVPs: (1) correspond to bundles of thermochemical mantle plumes; or (2) are made up of compact piles topped by a bundle of plumes.Van Camp, M.Wed, 19 Oct 2022 13:55:30 GMThttp://publi2-as.oma.be/record/58552022Impact of Offsets on Assessing the Low-Frequency Stochastic Properties of Geodetic Time Series
http://publi2-as.oma.be/record/5763
Understanding and modelling the properties of the stochastic variability -- often referred to as noise -- in geodetic time series is crucial to obtain realistic uncertainties for deterministic parameters, e.g., long-term velocities, and helpful in characterizing non-modelled processes. With the ever-increasing span of geodetic time series, it is expected that additional observations would help better understanding the low-frequency properties of the stochastic variability. In the meantime, recent studies evidenced that the choice of the functional model for the time series may bias the assessment of these low-frequency stochastic properties. In particular, the presence of frequent offsets, or step discontinuities, in position time series tends to systematically flatten the periodogram of position residuals at low frequencies and prevents the detection of possible random-walk-type variability. In this study, we investigate the ability of frequently-used statistical tools, namely the Lomb-Scargle periodogram and Maximum Likelihood Estimation (MLE) method, to correctly retrieve low-frequency stochastic properties of geodetic time series in the presence of frequent offsets. By evaluating the biases of each method for several functional models, we demonstrate that neither of these tools is reliable for low-frequency investigation. By assessing alternative approaches, we show that using Least-Squares Harmonic Estimation and Restricted Maximum Likelihood Estimation (RMLE) solves part of the problems reported by previous works. However, we evidence that, even when using those optimal methods, the presence of frequent offsets inevitably blurs the estimated low-frequency properties of geodetic time series by increasing low-frequency stochastic parameter uncertainties more than that of other stochastic parameters.Gobron, K.Wed, 06 Apr 2022 15:17:31 GMThttp://publi2-as.oma.be/record/57632022Extreme hydrometeorological events, a challenge for geodesy and seismology networks
http://publi2-as.oma.be/record/5760
The use of seismometer and gravimeter captures complementary data and brings a new understanding of the July 2021 catastrophic floods in Belgium. A sudden increase in seismic noise coincides with the testimony reporting on a “tsunami” downstream of the Membach geophysical station, along the Vesdre valley. Concurrently, the gravimeter evidenced a rising saturation of the weathered zone, thus showing less and less water accumulation. When rain re-intensified after a 3-hour break, the saturated state of the subsoil induced an accelerated increase of the runoff, as revealed by the Vesdre River flow, in a much stronger way than during the rainy episodes just before. We show that a gravimeter can detect in real-time the saturation of the catchment subsoil and soil. This saturation resulted, when the rain re-intensified, in a sudden, devastating and deadly flood. This opens perspectives to use real-time gravity for early warnings of such eventsVan Camp, M.Wed, 06 Apr 2022 15:10:07 GMThttp://publi2-as.oma.be/record/57602022A naive Bayesian method to chase mantle plumes in global tomography models
http://publi2-as.oma.be/record/5759
We propose a quantitative approach to search for mantle plumes in global seismic tomography models without prior assumptions on the associated mantle velocity anomalies. We design detection tests with a reasonable detection threshold while keeping false detections at a level lower than 5%. This is based on naive Bayesian clustering analysis, which is possible thanks to the varimax principal component analysis that provides components that are much more independent than the original number of depths slices in the models. We find that using such independent components greatly reduces detection errors compared to using an arbitrary number of depth slices due to correlations between the different slices. We detect a wide range of behaviour of the seismic velocity profiles underneath the hotspots investigated in this study. Moreover, we retrieve locations away from hotspots that have a similar seismic velocity profile signature to that underneath some hotspots. Hence, it is not possible to obtain a unique definition of seismic velocity anomalies that are associated with mantle plumes and thus care needs to be taken when searching for mantle plumes using prior assumptions about the velocity anomalies that might be associated with them. On the other hand, we identify a criterion that allows establishing a probability distribution of the seismic velocity profiles that is specific to a sub-list of hotspots and we show that this distribution does not occur significantly elsewhere. Overall, the mantle plume zones identified in our analysis do not appear to surround the Africa and Pacific large low shear velocity provinces (LLSVPs) but are rather within them. This supports the idea that LLSVPs may correspond to bundles of thermochemical mantle plumes rather than to compact, dense piles.de Viron, O.Wed, 06 Apr 2022 15:08:17 GMThttp://publi2-as.oma.be/record/57592022Extreme hydrometeorological events, a challenge for gravimetric and seismology networks
http://publi2-as.oma.be/record/5755
xtreme events will become more common due to global change, requiring enhanced monitoring and pushing conventional observation networks to their limits. This encourages us to combine all the possible sources of information to obtain a complete picture of extreme events and their evolution. This commentary builds on an example of the July 2021 catastrophic floods that hit northwest Europe, for which the use of seismometer and gravimeter captures complementary data and brings a new understanding of the event and its dynamics. A sudden increase in seismic noise coincides with the testimony reporting on a “tsunami” downstream of the geophysical station. Concurrently, the gravimeter showed increasing saturation of the weathered zone, showing less and less water accumulation and increasing runoff. When rain re-intensified after a 3-hour break, the subsoil’s saturation state induced an accelerated runoff increase, as revealed by the river flow, in a much stronger way than during the rainy episodes just before. We show that the gravimeter detected the saturation of the catchment subsoil and soil in real-time. When the rain re-intensified, this saturation resulted in a sudden, devastating and deadly flood. Our study opens up the possibility of integrating real-time gravity in early warning systems for such events.Van Camp, M.Tue, 05 Apr 2022 13:25:56 GMThttp://publi2-as.oma.be/record/57552022Influence of aperiodic non-tidal atmospheric and oceanic loading deformations on the stochastic properties of global GNSS vertical land motion time series
http://publi2-as.oma.be/record/5549
Monitoring vertical land motions (VLMs) at the level of 0.1 mm/yr remains one of the most challenging scientific applications of global navigation satellite systems (GNSS). Such small rates of change can result from climatic and tectonic phenomena, and their detection is important to many solid Earth-related studies, including the prediction of coastal sea-level change and the understanding of intraplate deformation. Reaching a level of precision allowing to detect such small signals requires a thorough understanding of the stochastic variability in GNSS VLM time series. This paper investigates how the aperiodic part of non-tidal atmospheric and oceanic loading (NTAOL) deformations influences the stochastic properties of VLM time series. Using the time series of over 10,000 stations, we describe the impact of correcting for NTAOL deformation on 5 complementary metrics, namely: the repeatability of position residuals, the power-spectrum of position residuals, the estimated time-correlation properties, the corresponding velocity uncertainties, and the spatial correlation of the residuals. We show that NTAOL deformations cause a latitude-dependent bias in white noise plus power-law model parameter estimates. This bias is significantly mitigated when correcting for NTAOL deformation, which reduces velocity uncertainties at high latitudes by 70%. Therefore, removing NTAOL deformation before the statistical analysis of VLM time series might help to detect subtle VLM signals in these areas. Our spatial correlation analysis also reveals a seasonality in the spatial correlation of the residuals, which is reduced after removing NTAOL deformation, confirming that NTAOL is a clear source of common-mode errors in GNSS VLM time seriesGobron, K.Thu, 06 Jan 2022 14:46:48 GMThttp://publi2-as.oma.be/record/55492021Comparing global seismic tomography models using varimax principal component analysis
http://publi2-as.oma.be/record/5409
Global seismic tomography has greatly progressed in the past decades, with many global Earth models being produced by different research groups. Objective, statistical methods are crucial for the quantitative interpretation of the large amount of information encapsulated by the models and for unbiased model comparisons. Here we propose using a rotated version of principal component analysis (PCA) to compress the information in order to ease the geological interpretation and model comparison. The method generates between 7 and 15 principal components (PCs) for each of the seven tested global tomography models, capturing more than 97% of the total variance of the model. Each PC consists of a vertical profile, with which a horizontal pattern is associated by projection. The depth profiles and the horizontal patterns enable examining the key characteristics of the main components of the models. Most of the information in the models is associated with a few features: large low-shear velocity provinces (LLSVPs) in the lowermost mantle, subduction signals and low-velocity anomalies likely associated with mantle plumes in the upper and lower mantle, and ridges and cratons in the uppermost mantle. Importantly,all models highlight several independent components in the lower mantle that make between 36% and 69% of the total variance, depending on the model, which suggests that the lower mantle is more complex than traditionally assumed. Overall, we find that varimax PCA is a useful additional tool for the quantitative comparison and interpretation of tomography models.de Viron, O.Thu, 22 Jul 2021 14:30:43 GMThttp://publi2-as.oma.be/record/54092021Influence of non-tidal atmospheric and oceanic loading deformation on the stochastic properties of over 10,000 GNSS vertical land motion time series
http://publi2-as.oma.be/record/5325
Over the past two decades, numerous studies demonstrated that the stochastic variability in GNSS position time series – often referred to as noise – is both temporally and spatially correlated. The time correlation of this stochastic variability can be well approximated by a linear combination of white noise and power-law stochastic processes with different amplitudes. Although acknowledged in many geodetic studies, the presence of such power-law processes in GNSS position time series remains largely unexplained. Considering that these power-law processes are the primary source of uncertainty for velocity estimates, it is crucial to identify their origin(s) and to try to reduce their influence on position time series. Using the Least-Squares Variance Component Estimation method, we analysed the influence of removing surface mass loading deformation on the stochastic properties of vertical land motion time series (VLMs). We used the position time series of over 10,000 globally distributed GNSS stations processed by the Nevada Geodetic Laboratory at the University of Nevada, Reno, and loading deformation time series computed by the Earth System Modelling (ESM) team at GFZ-Potsdam. Our results show that the values of stochastic parameters, namely, white noise amplitude, spectral index, and power-law noise amplitude, but also the spatial correlation, are systematically influenced by non-tidal atmospheric and oceanic loading deformation. The observed change in stochastic parameters often translates into a reduction of trend uncertainties, reaching up to -75% when non-tidal atmospheric and oceanic loading deformation is highest.Gobron, KevinThu, 04 Mar 2021 09:12:10 GMThttp://publi2-as.oma.be/record/53252021Investigating groundwater content by using terrestrial gravity measurements
http://publi2-as.oma.be/record/5096
Presenting the use of gravity measurements to monitor groundwater changes.Van Camp, M.Thu, 07 Jan 2021 10:13:27 GMThttp://publi2-as.oma.be/record/50962020Investigating ground water content by using terrestrial gravity measurements
http://publi2-as.oma.be/record/4971
Presenting the way gravimetry can contribute to the research in hydrogeology, focussing here on the water scrarcity in FlandersVan Camp, M.Wed, 21 Oct 2020 09:03:26 GMThttp://publi2-as.oma.be/record/49712020Vertical land movements in northwest Europe
http://publi2-as.oma.be/record/4941
Vertical land movements in northwest Europe, and in particular, Belgium. We report on more than 20 years of repeated absolute gravity measurements, GNSS and PSInSAR data. We discuss the observations in terms of geological, seismological, climatological and anthropogenic effects.Van Camp, MFri, 10 Jul 2020 11:15:28 GMThttp://publi2-as.oma.be/record/49412020Measuring gravity changes for decades
http://publi2-as.oma.be/record/4852
Being sensitive to any phenomena associated with mass transfer, terrestrial gravimetry allows the monitoring of many phenomena at the 10E-10 g level (1 nm/s²) such as Earth tides, groundwater content, tectonic deformation, or volcanic activity. This sensitivity is richness, but also a source of problems because data interpretation requires separating the signatures from the different sources, including possible measurement artefacts associated with high precision. Separating the signal from a given source requires a thorough knowledge of both the instrument and the phenomena. At the Membach geophysical laboratory, Belgium, the same superconducting gravimeter has monitored gravity continuously for more than 24 years. Together with 300 repeated absolute gravity measurements and environmental monitoring, this has allowed us to reach an unprecedented metrological knowledge of the instrument and of its sensitivity to hydrological and geophysical signals. Separation is possible whenever the phenomena exhibit distinct time/frequency signatures, such as (pseudo)periodic phenomena or long-term processes, so that the signatures from other sources average out by stacking. For example, when performing repeated gravity measurements to evidence slow tectonic deformation, the easiest way to mitigate hydrological effects is to accumulate measurements for many years, at the same epoch of the year: the impact of seasonal variations is then minimized, and the interannual variations cancel out. Using 10 repeated absolute gravity campaigns at the same epoch of the year, we showed that the gravity rate of change uncertainty reaches on average 3–4 nm/s²/yr. Concurrently, using superconducting gravimeter time series longer than 10 years, we also investigated the time variations of tidal parameters. It is also possible to separate phenomena, by observing them by both gravity and some other techniques, with a different transfer function. By using 11 year-long times series from the gravimeter and soil moisture probes, and by stacking the observations, we measured directly the groundwater mass loss by evapotranspiration in the forest above the laboratory of Membach. Always with a precision better than 1 nm/s² (~2.5 mm of water), we also monitored ground partial saturation dynamics and combining the gravity data with a weather radar allowed measuring convective precipitation at a scale of up to 1 km². Extracting and interpreting those elusive signals could only by achieved throughout multi-instrumentation, multi-disciplinary collaborative studies, and 25 years of hard work.Van Camp, M.Tue, 10 Mar 2020 06:53:18 GMThttp://publi2-as.oma.be/record/48522020Assessment of Sea Level Measurement Technologies by the Combination of Co-located Time Series
http://publi2-as.oma.be/record/4293
Since the use of the first tide poles for maritime navigation and tide prediction, sea level engineers have never stopped devising new technologies to provide more convenient and accurate ways to measure sea level. However, the operational use of newly developed technologies depends on their ability to provide accurate measurements in real conditions. To evaluate this ability, the sea level records of the new technologies are usually compared to that of a reference sensor, often using the so-called Van de Casteele test. If this type of buddy-checking method can provide an assessment of systematic biases, it can however not rigorously assess the precision (e.g., standard deviation of the sea level measurements from the new technology) because the random errors of the reference sensor also impact the differences. To address this metrological issue, we propose a new method for the cross-calibration of tide gauges. Based on the combination of at least three co-located sea level time series, it takes advantage of the Least-Squares Variance Component Estimation method to assess both sea-level biases and uncertainties in real conditions. The method was applied to a multi-instrument experiment carried out on Aix island, France, in 2016. Six water level gauges, including two GNSS buoys, were deployed to carry out simultaneous sea level recordings for 11 hours. We show that the proposed method allows assessing both the biases and the precision - i.e., the full accuracy - for each instrument. The results obtained with the proposed combination method have also been compared to that of a buddy-checking method. It showed that the combination of all time series also provides more precise bias estimates.Wöppelmann, G.Wed, 13 Nov 2019 07:30:50 GMThttp://publi2-as.oma.be/record/42932019Spatial Behavior of Time-Correlated Noise in the Position Time Series of 10,000 GPS Stations
http://publi2-as.oma.be/record/4292
Obtaining reliable estimates about geophysical processes from GPS products requires considering time-correlated noise in position time series. For about two decades, the time dependence of noise has been actively investigated. The most common noise model consists of the combination of power-law processes with various spectral indexes, including white noise. However, the origin of such power-law time correlations in position time series remains unclear. Analyzing the spatial dependence of noise provides a way to investigate the causes of power-law processes but requires a dense GPS network. Here, we analyze the data products of 10 000 GPS stations processed by the Nevada Geodetic Laboratory (NGL). We first iteratively detected outliers and offsets using a modified multivariate Detection Identification and Adaptation (DIA) method. Then, we used the Non-Negative Least Squares Variance Component Estimation method (NNLS-VCE) to assess the white noise and correlated noise amplitudes for each component of each station, i.e., a total of 30 000 time series. Our analysis evidences a multi-scale spatial variability of noise for the three North, East, and Up components. In particular, short spatial variations (a few hundred kilometers) of power-law amplitudes across the USA and Europe might point to either the presence of non-modeled regional geophysical signals or the influence of regional networks in the observations.Gobron, K.Wed, 13 Nov 2019 07:28:18 GMThttp://publi2-as.oma.be/record/42922019Comparing global seismic tomography models using the varimax Principal Component Analysis
http://publi2-as.oma.be/record/4291
Classical analysis of new tomography models consists in a comparison with others by correlation, spectral profiles, or localisation of patterns. To interpret the models in a quantitative, objective way and ease comparisons, we analyse the model information using principal component (PC) analysis. The varimax criterion applied to the PCs separates modes associated with different depth ranges. This enables determining the importance of different parts of the tomography models when reconstructing them. We apply this method to the isotropic part of 6 global shear-wave speed models: SAVANI (Auer et al., 2014), S20RTS (Ritsema et al., 1999), S40RTS (Ritsema et al., 2011), SEMUCB-WM1 (French & Romanowicz, 2014), SGLOBE-rani (Chang et al., 2015), and S362WMANI+M (Moulik & Ekstrom, 2014). According to the models, the method generates 7 to 15 independent varimax PCs, capturing more than 97% of the total information. A comparison of the PCs with the information extracted from the full models shows that no interpretable information is lost. Each mode is composed of a vertical anomaly profile, to which we associate a horizontal pattern by orthogonal projection. The maximum of the depth profile and the geographical distribution of the horizontal pattern enable examining the key characteristics of the main components of the models. For a fair comparison, we also compute a varimax PCA on a concatenation of the 6 models together. This imposes a projection of the average vertical profile, which allows for a mode-by-mode comparison between the model set. Similar main regions are identified when applying the analysis either to the individual tomography models or to the concatenation of the models: (i) Large Low Shear Velocity Provinces (LLSVPs); (ii) Mid-lower mantle (~1,200 km depth) showing some deep subduction signals and low-velocity anomalies beneath the Pacific and Africa, possibly associated with mantle plumes; (iii) Uppermost lower mantle (~800 km depth), also with deep subduction and low-velocity in the southeastern Pacific ocean; (iv) Transition zone (~400 km depth), showing subduction and low velocity anomalies beneath the Pacific and Indian oceans; and, (v) ridges and cratons in the uppermost upper mantle (~200 km depth). We discuss the significance and potential implications of these main regions that are identified.Van Camp, M.Wed, 13 Nov 2019 07:25:52 GMThttp://publi2-as.oma.be/record/42912019Hydrogeological effects on terrestrial gravity measurements
http://publi2-as.oma.be/record/4286
For the 20 last years, terrestrial and satellite gravity measurements have reached such a precision that they allow for identification of the signatures from water storage fluctuations. In particular, hydrogeological effects induce significant time-correlated signature in the gravity time series. Gravity response to rainfall is a complex function of the local geologic and climatic conditions, e.g., rock porosity, vegetation, evaporation, and runoff rates. The gravity signal combines contributions from many geophysical processes, source separation being a major challenge. At the local scale and short-term, the associated gravimetric signatures often exceed the tectonic and GIA effects, and monitoring gravity changes is a source of information on local groundwater mass balance, and contributes to model calibrations. Main characteristics of the aquifer can then be inferred by combining continuous gravity, geophysical and hydrogeological measurements. In Membach, Belgium, a superconducting gravimeter has monitored gravity continuously for more than 24 years. This long time series, together with 300 repeated absolute gravity measurements and environmental monitoring, has provided valuable information on the instrumental, metrological, hydrogeological and geophysical points of view. This has allowed separating the signal sources and monitoring partial saturation dynamics, convective precipitation and evapotranspiration at a scale of up to 1 km², for signals smaller than 1 nm/s², equivalent to 2.5 mm of water. Based on this experience, another superconducting gravimeter was installed in 2014 in the karst zone of Rochefort, Belgium. In a karst area, where the vadose zone is usually thicker than in other contexts; combining gravity measurements at the surface and inside accessible caves is a way to separate the contribution from the unsaturated zone lying between the two instruments, from the saturated zone underneath the cave, and the common mode effects from the atmosphere or other regional processes. Those experiments contribute to the assessment of the terrestrial hydrological cycle, which is a major challenge of the geosciences associated with key societal issues: availability of freshwater, mitigation of flood hazards, or measurement of evapotranspiration.Van Camp, M.Thu, 03 Oct 2019 06:06:07 GMThttp://publi2-as.oma.be/record/42862019Comparing global seismic tomography models using the varimax Principal Component Analysis
http://publi2-as.oma.be/record/4285
Classical analysis of new tomography models consists in a comparison with others by correlation, spectral profiles, or localisation of patterns. To interpret the models in a quantitative, objective way and ease comparisons, we analyse the model information using principal component (PC) analysis. The varimax criterion applied to the PCs separates modes associated with different depth ranges. This enables determining the importance of different parts of the tomography models when reconstructing them. We apply this method to the isotropic part of 6 global shear-wave speed models: SAVANI (Auer et al., 2014), S20RTS (Ritsema et al., 1999), S40RTS (Ritsema et al., 2011), SEMUCB-WM1 (French & Romanowicz, 2014), SGLOBE-rani (Chang et al., 2015), and S362WMANI+M (Moulik & Ekstrom, 2014). According to the models, the method generates 7 to 15 independent varimax PCs, capturing more than 97% of the total information. A comparison of the PCs with the information extracted from the full models shows that no interpretable information is lost. Each mode is composed of a vertical anomaly profile, to which we associate a horizontal pattern by orthogonal projection. The maximum of the depth profile and the geographical distribution of the horizontal pattern enable examining the key characteristics of the main components of the models. For a fair comparison, we also compute a varimax PCA on a concatenation of the 6 models together. This imposes a projection of the average vertical profile, which allows for a mode-by-mode comparison between the model set. Similar main regions are identified when applying the analysis either to the individual tomography models or to the concatenation of the models: (i) Large Low Shear Velocity Provinces (LLSVPs); (ii) Mid-lower mantle (~1,200 km depth) showing some deep subduction signals and low-velocity anomalies beneath the Pacific and Africa, possibly associated with mantle plumes; (iii) Uppermost lower mantle (~800 km depth), also with deep subduction and low-velocity in the southeastern Pacific ocean; (iv) Transition zone (~400 km depth), showing subduction and low velocity anomalies beneath the Pacific and Indian oceans; and, (v) ridges and cratons in the uppermost upper mantle (~200 km depth). We discuss the significance and potential implications of these main regions that are identified.Van Camp, M.Thu, 03 Oct 2019 06:01:47 GMThttp://publi2-as.oma.be/record/42852019Assessment of tide gauges biases and precisions by the combination of multiple co-located time series
http://publi2-as.oma.be/record/4268
This study proposes a method for the cross-calibration of tide gauges. Based on the combination of at least three co-located sea level time series, it takes advantage of the Least-Squares Variance Component Estimation method to assess both sea-level biases and uncertainties in real conditions. The method was applied to a multi-instrument experiment carried out on Aix island, France, in 2016. Six tide gauges were deployed to carry out simultaneous sea level recordings for 11 hours. The best results were obtained with an electrical contact probe, which reaches a 3-millimeter uncertainty. The method allows assessing both the biases and the precision – i.e., the full accuracy – for each instrument. The results obtained with the proposed combination method have been compared to that of a buddy-checking method. It showed that the combination of all time series also provides more precise bias estimates.Gobron, K.Mon, 09 Sep 2019 08:15:17 GMThttp://publi2-as.oma.be/record/42682019Amélioration de la caractérisation des performances des marégraphes lors de campagnes d'inter-comparaison
http://publi2-as.oma.be/record/4189
Gobron, K.Thu, 02 May 2019 09:44:47 GMThttp://publi2-as.oma.be/record/41892019Calibration de marégraphes par combinaison d'instruments
http://publi2-as.oma.be/record/4188
Gobron, K.Thu, 02 May 2019 09:43:14 GMThttp://publi2-as.oma.be/record/41882018Universal Units Reflect Their Earthly Origins
http://publi2-as.oma.be/record/3656
On November 16, 2018, the kilogram joined its fellow metric units with a definition based on fundamental physical constants, but these units maintain links to their roots in the geosciences.Van Camp, M.Mon, 19 Nov 2018 06:49:57 GMThttp://publi2-as.oma.be/record/36562018