Geophysical Signals Research Articles

System approach to identification of geopulses

A new system approach to the identification and systematization of geophysical pulses is described. It includes stages of detection, analysis, object and structural description, pulse classification. In order to identify the pulses in geoacoustic emission, we apply a method based on the calculation of adaptive threshold, the values of which are estimated by the results of signal root-mean-square deviation in a moving window of a defined size. The detected pulses are described and identified by an adaptive matching pursuit algorithm which allows us to decompose the pulse into linear combination of basic functions from a combined Gauss-Berlage dictionary with minimum spatial and time costs and with the required accuracy of constructed approximations. Within the framework of object approach, pulses are described by a combination of features such as the number of functions in a decomposition, function type, parameters etc. We present a method of reduction of innumerable diversity of identified pulses to a denumerable set of patterns. It is based on structural transformation of geoacoustic emission pulses with simultaneous automatic procedure of classification. The results obtained during the application of the described system approach to the analysis of geoacoustic signals are summarized in a Geophysical Signal Catalogue.

THE PRESENCE OF GEOPHYSICAL LOADINGS IN GPS OBSERVATIONS USING GENERAL LEAST SQUARES APPROACHES

Abstract. The earth’s crust undergoes natural deformation due to the geophysical loadings that consist of the earth body tide, ocean tide loading, atmospheric pressure loading and pole tide. This periodic displacement is generated by the changes of the gravitational attraction between the moon and the sun acting upon the earth’s rotation, along with the temporal atmospheric changes and the variability of the ocean tide. The study of the geophysical loadings is important in the geodesy field as the magnitude of the signals is significant and can contribute to errors in space geodetic measurements such as Global Positioning System (GPS), Very-Long Baseline Interferometry (VLBI) and Altimeter. This study is conducted to evaluate the percentage of geophysical loadings in GPS observations by adopting general least square approaches. The presence of the geophysical loadings indicates that as many as 76% to 93% of the geophysical loadings signal are contained in the GPS time series. The findings reveal that earth body tide signals are more significant if compared to ocean tide loading signals because the magnitude of the earth body tide is greater than that of the ocean tide loading and it affects the coordinate system particularly at up component. Results illustrated the potential of GPS to provide the local parameters of the geophysical loadings that are beneficial for earth tidal modelling and that can be used to improve the quality of space geodetic measurements.

Unsupervised machine learning for acoustic tomography and event detection

We present acoustic tomography and event detection approaches based on unsupervised machine learning (ML). ML describes a set of techniques for automatically detecting and utilizing patterns in data. These techniques can roughly be categorized as either supervised or unsupervised learning. In supervised learning, an ML system (e.g., neural network (NN)) is trained to produce a desired output based on labeled training examples. In unsupervised learning, no labels are given and the task is to discover interesting or useful structure within the data. In acoustic and geophysical signal processing, often large training datasets are available, but have few labeled examples. We discuss how unsupervised ML can leverage training data without explicit labels to improve acoustic models. We first give an overview of the relevant ML theory. Next, we describe an approach to array tomography, called locally-sparse travel time tomography (LST). LST regularizes the inversion of travel time measurements for geophysical structure using dictionary learning (unsupervised). Dictionary learning is here used to obtain a dictionary of small-scale geophysical features that best represent the measurements. Finally, we discuss an approach to automated event detection in acoustic time series based on autoencoder NNs, which learn salient features from NN inputs with unsupervised learning.

The net primary productivity of early Permian peatland and their control factors: evidenced by the no. 6 coal seam of Jungar coalfield in North China

Purpose The purpose of this paper is to take the early Permian no.6 coal seam in Jungar coalfield of North China as an example, this paper studied the net primary productivity (NPP) level of the early Permian peatland and its relationship with the atmospheric environment at that time, analyzed the influence of the atmospheric environment, and discussed its control factors. Design/methodology/approach First, geophysical logging signals were used for a spectrum analysis to obtain the Milankovitch cycle parameters in the no. 6 coal seam, including the eccentricity (95 ka); obliquity (35.6 ka); and precession (21.2 ka). These were then used to calculate the accumulation rate of the residual carbon in the no. 6 coal seam, which was determined to be between 49.44 and 50.57 gC/(m2 · a). The carbon loss could be calculated according to the density and residual carbon content of the no. 6 coal seam. Then, the total carbon accumulation rate of the peatland was further derived as being between 64.91 and 66.40 gC/(m2 · a). Also, the NPP of the peatland was determined to be between 129.82 and 132.8 gC/(m2 · a). Finding The result showed that the NPP of the early Permian peatland area was lower than that of the Holocene at the same latitude, and also lower than that of the later Permian of South China. Originality/value This study’s comprehensive analysis indicated that the temperature and humidity conditions, along with the oxygen and carbon dioxide levels in the atmosphere, were the main control factors of the NPP of the early Permian peatland. Also, wildfires were found to play a role.

Data-adaptive spatio-temporal filtering of GRACE data

SUMMARYMeasurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter.In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability.We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements.Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.

Study of the hydraulic disturbance suppression of a vibrator under high-frequency vibration based on experiments and numerical simulations

Hydraulic disturbance of a vibrator is the main cause of distortion of the vibroseis geophysical exploration signal that is caused by high-frequency fluid vibration during servo valve cavity commutation. This disturbance is closely related to the structure, size, and arrangement of the hydraulic flow path of the vibrator reaction mass. In this work, a vibrator disturbance–suppression mechanism was examined, an experimental study was carried out on an improved design of the hydraulic flow channel, and the source of the hydraulic disturbance of the horizontal channel vibrator was determined. The vibrator hydraulic flow channel was changed to a vertical flow channel in the piston rod. In addition, the structure of the track was designed, and the angle of the oil inlet of the piston rod was tested at 0°, 45°, and 90°. Then, the fluid flow state and fluid–solid coupling analysis method were used to improve the vibration-suppression effect of the vibrator. The results showed that the optimal oil inlet angle is 90°, for which the turbulent intensity of the oil inlet is the smallest, the pressure drop loss is reduced, and the influence of the oil flow on the piston rod deformation is minimal. The vibration reaction mass test of the vibrator showed that the disturbance inhibition rate of the piston rod with a 90° inlet structure design reaches 64.087%; the hydraulic disturbance had the best suppression effect and effectively reduced the distortion of the seismic prospecting signals with this oil inlet angle, improving the quality and efficiency of oil and gas exploration.

Influence of Geophysical Signals on Coordinate Variations GNSS Permanent Stations in Central Europe

Abstract This article presents an analysis of the extent of the impact of deformations of the earth’s crust resulting from geophysical models on changes in the coordinates of Global Navigation Satellite System (GNSS) stations. The author presents the results of analyses of the spatial correlation coefficient of deformation components for the non-tidal atmospheric loading (NTAL), non-tidal ocean loading (NTOL) and hydrological loading (HYDRO) models of geophysical deformation. In addition, the author calculated the correlation coefficients between station’s coordinate series to determine whether the deformations of the earth’s crust have a more global, large-area (regional scale) or local-range (local scale) impact, limited to the nearest of stations. In addition to correlation coefficients, the author analysed the similarity in periodic components between station coordinates by calculating the coherence between them. The results of the analysis showed that for the height components (Up), we observe the global range of deformation models, and the NTAL deformation has the greatest influence on the change in them. The lack of correlation between coordinate signals for horizontal components may result from specific local conditions in the place of the station, low-resolution of geophysical models and small amplitudes of these signals in relation to noise. An analysis of the coherence coefficients showed that each station coordinates shows completely different periodic components in the North, East and Up directions.

Common Mode Component and Its Potential Effect on GPS-Inferred Three-Dimensional Crustal Deformations in the Eastern Tibetan Plateau

Surface and deep potential geophysical signals respond to the spatial redistribution of global mass variations, which may be monitored by geodetic observations. In this study, we analyze dense Global Positioning System (GPS) time series in the Eastern Tibetan Plateau using principal component analysis (PCA) and wavelet time-frequency spectra. The oscillations of interannual and residual signals are clearly identified in the common mode component (CMC) decomposed from the dense GPS time series from 2000 to 2018. The newly developed spherical harmonic coefficients of the Gravity Recovery and Climate Experiment Release-06 (GRACE RL06) are adopted to estimate the seasonal and interannual patterns in this region, revealing hydrologic and atmospheric/nontidal ocean loads. We stack the averaged elastic GRACE-derived loading displacements to identify the potential physical significance of the CMC in the GPS time series. Interannual nonlinear signals with a period of ~3 to ~4 years in the CMC (the scaled principal components from PC1 to PC3) are found to be predominantly related to hydrologic loading displacements, which respond to signals (El Niño/La Niña) of global climate change. We find an obvious signal with a period of ~6 yr on the vertical component that could be caused by mantle-inner core gravity coupling. Moreover, we evaluate the CMC’s effect on the GPS-derived velocities and confirm that removing the CMC can improve the recognition of nontectonic crustal deformation, especially on the vertical component. Furthermore, the effects of the CMC on the three-dimensional velocity and uncertainty are presented to reveal the significant crustal deformation and dynamic processes of the Eastern Tibetan Plateau.

Processing harmonic EM noise with multiple or unstable frequency content in surface NMR surveys

SUMMARY The harmonic electromagnetic noise produced by anthropic electrical structures is a critical component of the global noise affecting geophysical signals and increasing data uncertainty. It is composed of a series of harmonic signals whose frequencies are multiple integers of the fundamental frequency specific to the electrical noise source. To date, most model-based noise removal strategies assume that the fundamental frequency constraining the harmonic noise is single and constant over the duration of the geophysical record. In this paper, we demonstrate that classical harmonic processing methods lose efficacy when these assumptions are not valid. We present several surface nuclear magnetic resonance field data sets, which testify the increasing probability of recording the harmonic noise with such multiple or unstable frequency content. For each case (multiple frequencies or unstable frequency) we propose new processing strategies, namely, the 2-D grid-search and the segmentation approach, respectively, which efficiently manage to remove the harmonic noise in these difficult conditions. In the process, we also apply a fast frequency estimator called the Nyman, Gaiser and Saucier estimation method, which shows equivalent performance as classical estimators while allowing a reduction of the computing time by a factor of 2.5.

Analysis of ring laser gyroscopes including laser dynamics

Inertial sensors are important at application level and also in fundamental physics. Ring laser gyroscopes, which measure angular rotation rates, are among the most sensitive ones. Large area ring laser reach sensitivities at the level of fractions of prad/s, allowing measurements of relevant geophysical signals. Improvements of a factor 10–100 would make these instruments able to measure general relativity effects; this is the goal e.g. of the GINGER project, an Earth based experiment aiming to test the Lense–Thirring effect with an accuracy of 1%. However, the laser induces non-linearities, effects larger in small scale instruments. We discuss a novel technique to analyse data, able to reduce non-linear laser effects. We apply this technique to data from two ring laser prototypes, and compare the precision of the measurement of the angular rotation rate obtained with the new and the standard methods. We show that the back-scatter problem of the ring laser gyroscopes is negligible with a proper analysis of the data. These results not only allow to improve the performance of large scale ring laser gyroscopes but also pave the way to the development of small scale instruments with nrad/s sensitivity, which are precious for environmental studies and as inertial platforms.

TREND-RUN model application of surface temperature and its implications for South African forestry and reforestation using local weather services data

Temperature can directly and indirectly impact the livelihood of inhabitants of a country and the natural environment as a whole. The surface temperature trend approximations for South Africa (SA) were calculated using a linear-regression fitting model. The model was adapted at The University of Reunion Island and was referred to as the Trend-Run model. The geophysical signal of the model was split into a sum of oscillations, which was used to clarify most of its variability. The trend values were calculated from the residual terms as a linear function. The model used atmospheric oscillations, which included Annual (AO), Semi-Annual (SAO), Quasi-Biennial Oscillations (QBO), El Niño-Southern Oscillation (ENSO), the 11-years solar cycle-Sun Spot Number (SSN) and Indian Ocean Dipole (IOD). The South African Weather Service (SAWS) data were used for the study. Data sets over a 31-year period, from March 1980 to December 2011, were used to measure the validity of the Trend-Run model, to determine the contribution and effect of this particular oscillation, and the validity of the model. The Trend-Run model showed very high applicability to the surface temperatures in all provinces across the SA region under investigation. High coefficient of determination values between (0.70-0.91) were recorded for surface temperatures across all provinces in the country with minor variations. The AO, ENSO and SAO were the highest contributing forcings in the model, thereby showing their high relevance to the success of this model in the study area. The temperature increases are expected to negatively impact on the biomes of SA, including the forest biome. Selected tree species of Acacia, Eucalyptus and Pinus could be impacted negatively with rising temperatures, which would negatively impact on the forestry industry in SA. As expected, the model did obtain a high success rate that ranged from 70% to 91% in the areas under study, however, there was still room for improvement by the possible inclusion of additional atmospheric forcings to the model that maybe be applicable to the weather and forestry distribution in SA.

Retrieving geophysical signals from GPS in the La Plata River region

Over the last few years, efforts to model short-term deformations of the Earth’s crust have multiplied. Sudden water level rise can cause sporadic, but significant, motions in the solid Earth’s surface. In this work, we address the problem of retrieving reliable estimates of the vertical displacement of a Global Positioning System (GPS) station located very close to the eastern shore of the La Plata River, during a strong storm surge event. Capturing sub-daily GPS displacements demands an elaborate processing strategy because several highly correlated parameters must be estimated simultaneously. We present a successful strategy that reduces the number of unknowns that have to be estimated simultaneously, by using an empirical model that describes the elastic response of the Earth’s crust to the hydrological load variations. We incorporate this model into the observation equations so that, instead of estimating the station position, we estimate every epoch a single parameter of the empirical model, i.e., the empirical elastic parameter EEP, that is assumed to be a constant of the Earth’s crust in the region of the GPS station. We verify that the estimated parameter agrees well with the value calculated from the CRUST 1.0-A model of the Earth’s crust. The GPS receiver was tied to an external cesium clock, which allowed us to process the data according to two different strategies: (a) estimating the receiver clock error (Δt) as an epoch-wise free parameter, which is equivalent to ignoring the presence of the external clock, and (b) conditioning the variability of that estimate with a small a priori variance compatible with the external clock’s variability. We find that, without having an external atomic clock, the estimation of all the parameters, i.e., the zenith tropospheric delay, Δt, and the EEP, worsens when the GPS station is affected by sub-daily vertical displacements.

GOCE-Derived Coseismic Gravity Gradient Changes Caused by the 2011 Tohoku-Oki Earthquake

In contrast to most of the coseismic gravity change studies, which are generally based on data from the Gravity field Recovery and Climate Experiment (GRACE) satellite mission, we use observations from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) Satellite Gravity Gradient (SGG) mission to estimate the coseismic gravity and gravity gradient changes caused by the 2011 Tohoku-Oki Mw 9.0 earthquake. We first construct two global gravity field models up to degree and order 220, before and after the earthquake, based on the least-squares method, with a bandpass Auto Regression Moving Average (ARMA) filter applied to the SGG data along the orbit. In addition, to reduce the influences of colored noise in the SGG data and the polar gap problem on the recovered model, we propose a tailored spherical harmonic (TSH) approach, which only uses the spherical harmonic (SH) coefficients with the degree range 30–95 to compute the coseismic gravity changes in the spatial domain. Then, both the results from the GOCE observations and the GRACE temporal gravity field models (with the same TSH degrees and orders) are simultaneously compared with the forward-modeled signals that are estimated based on the fault slip model of the earthquake event. Although there are considerable misfits between GOCE-derived and modeled gravity gradient changes (ΔVxx, ΔVyy, ΔVzz, and ΔVxz), we find analogous spatial patterns and a significant change (greater than 3σ) in gravity gradients before and after the earthquake. Moreover, we estimate the radial gravity gradient changes from the GOCE-derived monthly time-variable gravity field models before and after the earthquake, whose amplitudes are at a level over three times that of their corresponding uncertainties, and are thus significant. Additionally, the results show that the recovered coseismic gravity signals in the west-to-east direction from GOCE are closer to the modeled signals than those from GRACE in the TSH degree range 30–95. This indicates that the GOCE-derived gravity models might be used as additional observations to infer/explain some time-variable geophysical signals of interest.

Functional Implications of RFRP-3 in the Central Control of Daily and Seasonal Rhythms in Reproduction.

Adaptation of reproductive activity to environmental changes is essential for breeding success and offspring survival. In mammals, the reproductive system displays regular cycles of activation and inactivation which are synchronized with seasonal and/or daily rhythms in environmental factors, notably light intensity and duration. Thus, most species adapt their breeding activity along the year to ensure that birth and weaning of the offspring occur at a time when resources are optimal. Additionally, female reproductive activity is highest at the beginning of the active phase during the period of full oocyte maturation, in order to improve breeding success. In reproductive physiology, it is therefore fundamental to delineate how geophysical signals are integrated in the hypothalamo-pituitary-gonadal axis, notably by the neurons expressing gonadotropin releasing hormone (GnRH). Several neurochemicals have been reported to regulate GnRH neuronal activity, but recently two hypothalamic neuropeptides belonging to the superfamily of (Arg)(Phe)-amide peptides, RFRP-3 and kisspeptin, have emerged as critical for the integration of environmental cues within the reproductive axis. The goal of this review is to survey the current understanding of the role played by RFRP-3 in the temporal regulation of reproduction, and consider how its effect might combine with that of kisspeptin to improve the synchronization of reproduction to environmental challenges.

Separation and Recovery of Geophysical Signals Based on the Kalman Filter with GRACE Gravity Data

Monthly gravitational field solutions as spherical harmonic coefficients produced by the GRACE satellite mission require post-processing to reduce the effects of shortwave-length noises and north–south stripe errors. However, the spatial smoothing and de-striping filter commonly used in the post-processing step will either reduce spatial resolution or remove short-wavelength features of geophysical signals, mainly at high latitudes. Here, by using prior covariance information that reflects the spatial and temporal features of the geophysical signals and the correlated errors derived from the synthetic model, together with the covariance matrix of the formal errors for the monthly gravity spherical harmonic coefficients, we apply the Kalman filter to separate the geophysical signal from GRACE Level-2 data and simultaneously to estimate the correlated errors. By increasing the number of observations, the iterative process is applied to update the state vector and covariance in the Kalman filter because the prior information is not accurate. Due to the inevitable truncation error, multiple gridded-gain factors method considering different temporal frequencies has been developed to recover the geophysical signal. The results show that the Kalman filter can reduce the high-frequency noises and correlated errors remarkably. When compared with the commonly used filter, no spatial filter (such as Gaussian filter) is used in the Kalman filter. Therefore, the estimated signal preserves its natural resolution, and more detailed information is retained. It shows good consistency when compared with mascon solutions in both secular trend and annual amplitude.

Resolving Geophysical Signals by Terrestrial Gravimetry: A Time Domain Assessment of the Correction‐Induced Uncertainty

AbstractTerrestrial gravimetry is increasingly used to monitor mass transport processes in geophysics boosted by the ongoing technological development of instruments. Resolving a particular phenomenon of interest, however, requires a set of gravity corrections of which the uncertainties have not been addressed up to now. In this study, we quantify the time domain uncertainty of tide, global atmospheric, large‐scale hydrological, and nontidal ocean loading corrections. The uncertainty is assessed by comparing the majority of available global models for a suite of sites worldwide. The average uncertainty expressed as root‐mean‐square error equals 5.1 nm/s2, discounting local hydrology or air pressure. The correction‐induced uncertainty of gravity changes over various time periods of interest ranges from 0.6 nm/s2 for hours up to a maximum of 6.7 nm/s2 for 6 months. The corrections are shown to be significant and should be applied for most geophysical applications of terrestrial gravimetry. From a statistical point of view, however, resolving subtle gravity effects in the order of few nanometers per square second is challenged by the uncertainty of the corrections.

Toward Creating a Subsurface Camera.

In this article, the framework and architecture of a Subsurface Camera (SAMERA) are envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals and computes a 3D subsurface image in situ in real time. The basic mechanism is geophysical waves propagating/reflected/refracted through subsurface enter a network of geophysical sensors, where a 2D or 3D image is computed and recorded; control software may be connected to this network to allow view of the 2D/3D image and adjustment of settings such as resolution, filter, regularization, and other algorithm parameters. System prototypes based on seismic imaging have been designed. SAMERA technology is envisioned as a game changer to transform many subsurface survey and monitoring applications, including oil/gas exploration and production, subsurface infrastructures and homeland security, wastewater and CO2 sequestration, and earthquake and volcano hazard monitoring. System prototypes for seismic imaging have been built. Creating SAMERA requires interdisciplinary collaboration and the transformation of sensor networks, signal processing, distributed computing, and geophysical imaging.

A Review of Geophysical Methods for Soil Structure Characterization

AbstractThe growing interest in the maintenance of favorable soil structure is largely motivated by its central role in plant growth, soil ecological functioning, and impacts on surface water and energy fluxes. Soil structure pertains to the spatial arrangement of voids and solid constituents, their aggregation, and mechanical state. As a fragile product of soil biological activity that includes invisible ingredients (mechanical and ecological states), soil structure is difficult to define rigorously, and measurements of relevant metrics often rely on core samples or on episodic point measurements. The presence of soil structure has not yet been explicitly incorporated in climate and Earth systems models, partially due to incomplete methodological means to characterize it at relevant scales and to parameterize it in spatially extensive models. We seek to review the potential of harnessing geophysical methods to fill the scale gap in characterization of soil structure directly (via impact of soil pores, transport, and mechanical properties on geophysical signals) or indirectly by measurement of surrogate variables (wetness and rates of drainage). We review basic aspects of soil structure and challenges of characterization across spatial and temporal scales and how geophysical methods could be used for the task. Additionally, we propose the use of geophysical models, inversion techniques, and combination of geophysical methods for extracting soil structure information at previously unexplored spatial and temporal scales.

Separating Geophysical Signals Using GRACE and High‐Resolution Data: A Case Study in Antarctica

AbstractTo fully exploit data from the Gravity Recovery and Climate Experiment (GRACE), we separate geophysical signals observed by GRACE in Antarctica by deriving high‐spatial resolution maps for present‐day glacial isostatic adjustment (GIA) and ice‐mass changes with the least possible noise level. For this, we simultaneously (i) improve the postprocessing of gravity data and (ii) consistently combine them with high‐resolution data from Ice Cloud and land Elevation Satellite altimeter (ICESat) and Regional Atmospheric Climate Model 2.3 (RACMO). We use GPS observations to discriminate between various candidate spatial patterns of vertical motions caused by GIA. The ICESat‐RACMO combination determines the spatial resolution of estimated ice‐mass changes. The results suggest the capability of the developed approach to retrieve the complex spatial pattern of present‐day GIA, such as a pronounced subsidence in the proximity of the Kamb Ice Stream and pronounced uplift in the Amundsen Sea Sector.

Limits of Detection of Gravimetric Signals on Earth

Gravimetry is a well-established tool to probe the deep Earth’s processes. Geophysical signals coming from the deep Earth, like the inner core free oscillations, have however never been detected. Challenging quests raise the question of the limits of detection of elusive signals at the Earth’s surface. Knowledge of the instrumental limits and of the environmental noise level at a site is fundamental to judge the true sensitivity of an instrument. We perform a noise level comparison of various gravimeters and a long-period seismometer at the J9 gravimetric observatory of Strasbourg (France) to provide a reference of instrumental performances. We then apply a three-channel correlation analysis of time-varying surface gravity from superconducting gravimeter records to isolate the instrumental self-noise from the environmental noise. The self-noise coherence analysis shows that the instrumental noise level remains flat towards lower frequencies till 10−4 Hz. At seismic frequencies, the self-noise is well explained by a Brownian thermal noise model. At daily and sub-daily time-scales, self-noise is increasing with the period but to a much lesser extent than observed noise level. Observed Earth’s ambient noise level at sub-seismic frequencies is hence mostly due to unmodeled geophysical processes. At hourly time-scales, our ability to detect elusive signals coming from the deep Earth’s interior is not limited by the instrument capability but is mostly due to the environmental effects.