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Cross-correlations of seismic ambient noise are frequently used to image Earth structure. Usually, tomographic studies assume that noise sources are uniformly distributed and interpret noise correlations as empirical Green's functions. However, previous research suggests that this assumption can introduce errors in the estimated models, especially when noise correlation waveforms are inverted. In this paper, we investigate changes in subsurface models inferred from noise correlation waveforms depending on whether the noise source distribution is considered to be uniform. To this end, we set up numerical experiments that mimic a tomographic study in Southern California exploiting ambient noise generated in the Pacific Ocean. Our results show that if the distribution of noise sources is deemed uniform instead of being numerically represented in the wave simulations, the misfit of the estimated models increases. In our experiments, the model misfit increase ranges between 5 and 21 per cent, depending on the heterogeneity of the noise source distribution. This indicates that assuming uniform noise sources introduces source-dependent model errors. Since the location of noise sources may change over time, these errors are also time-dependent. In order to mitigate these errors, it is necessary to account for the noise source distribution. The spatial extent to which noise sources must be considered depends on the propagation distance of the ambient noise wavefield. If only sources near the study area are considered, model errors may arise. [ABSTRACT FROM AUTHOR]

In recent years, self‐supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labelled data in the training stage, an unobtainable requirement for seismic data. However, current self‐supervised methods usually suppress simple noise types, such as random and trace‐wise noise, instead of the complicated, aliased ground roll. Here, we propose an adaptation of a self‐supervised procedure, namely, blind‐fan networks, to remove aliased ground roll within seismic shot gathers without any requirement for clean data. The self‐supervised denoising procedure is implemented by designing a noise mask with a predefined direction to avoid the coherency of the ground roll being learned by the network while predicting one pixel's value. Numerical experiments on synthetic and field seismic data demonstrate that our method can effectively attenuate aliased ground roll. [ABSTRACT FROM AUTHOR]

Ambient noise source localization is of great significance for estimating seismic noise source distribution, understanding source mechanisms and imaging subsurface structures. The commonly used methods for source localization, such as the matched field processing and the full-waveform inversion, are time-consuming and not applicable for time-lapse monitoring of the noise source distribution. We propose an efficient alternative of using deep learning for noise source localization. In the neural network, the input data are noise cross-correlation functions and the output are matrices containing the information of noise source distribution. It is assumed that the subsurface structure is a horizontally layered earth model and the model parameters are known. A wavefield superposition method is used to efficiently simulate ambient noise data with quantities of local noise sources labelled as training data sets. We use a weighted binary cross-entropy loss function to address the prediction inaccuracy caused by a sparse label matrix during training. The proposed deep learning framework is validated by synthetic tests and two field data examples. The successful applications to locate an anthropogenic noise source and a carbon dioxide degassing area demonstrate the accuracy and efficiency of the proposed deep learning method for noise source localization, which has great potential for monitoring the changes of the noise source distribution in a survey area. [ABSTRACT FROM AUTHOR]

The basic assumption of low‐rank methods is that noise‐free seismic data can be represented as a low‐rank matrix. Effective noise reduction can be achieved through the low‐rank approximation of Hankel matrices composed of the data. However, selecting the appropriate rank parameter and avoiding expensive singular value decomposition are two challenges that have limited the practical application of this method. In this paper, we first propose symplectic geometric decomposition that avoids singular value decomposition. The symplectic similarity transformation preserves the essence of the original time sequence as well as the signal's basic characteristics and maintains the approximation of the Hankel matrix. To select an appropriate rank, we construct the symplectic geometric entropy according to the distribution of eigenvalues and search for high‐contributing eigenvalues to determine the needed rank parameter. Therefore, we provide an adaptive approach to selecting the rank parameter by the symplectic geometric entropy method. The synthetic examples and field data results show that our method significantly improves the computational efficiency while adaptively retaining more effective signals in complex structures. Therefore, this method has practical application value. [ABSTRACT FROM AUTHOR]

(Multichannel) Singular spectrum analysis is considered as one of the most effective methods for seismic incoherent noise suppression. It utilizes the low-rank feature of seismic signal and regards the noise suppression as a low-rank reconstruction problem. However, in some cases the seismic geophones receive some erratic disturbances and the amplitudes are dramatically larger than other receivers. The presence of this kind of noise, called erratic noise, makes singular spectrum analysis (SSA) reconstruction unstable and has undesirable effects on the final results. We robustify the low-rank reconstruction of seismic data by a reweighted damped SSA (RD-SSA) method. It incorporates the damped SSA, an improved version of SSA, into a reweighted framework. The damping operator is used to weaken the artificial disturbance introduced by the low-rank projection of both erratic and random noise. The central idea of the RD-SSA method is to iteratively approximate the observed data with the quadratic norm for the first iteration and the Tukeys bisquare norm for the rest iterations. The RD-SSA method can suppress seismic incoherent noise and keep the reconstruction process robust to the erratic disturbance. The feasibility of RD-SSA is validated via both synthetic and field data examples. [ABSTRACT FROM AUTHOR]

Reservoir monitoring is essential to guarantee safe operations for all activities involving the production and injection of fluids into the subsurface, such as hydrocarbon production, gas storage and the exploitation of geothermal reservoirs. For this purpose, microseismic monitoring networks are operated in real time in order to identify and locate any possible seismic events in the vicinity of the reservoir. The goal of this study is to investigate whether the large amount of ambient seismic noise recorded by seismic reservoir monitoring networks can be used to infer a one‐dimensional shear‐wave velocity profile representative of the area covered by the network. Shear‐wave velocities are generally difficult to characterize and constrain, yet they are key to precisely locate seismic events. The adopted workflow consists of three steps: first, the cross‐correlation functions between all station pairs are retrieved, using 1 year of continuous data; second, the average group‐ and phase velocity dispersion curves are extracted; third, a joint group and phase velocity inversion is done. For validation, the obtained average shear‐wave velocity profile is compared with a regional model of the area as well as with local shear‐wave velocity measurements from a sonic log. [ABSTRACT FROM AUTHOR]

The presence of coherent noise in seismic data leads to errors and uncertainties, and as such it is paramount to suppress noise as early and efficiently as possible. Self‐supervised denoising circumvents the common requirement of deep learning procedures of having noisy‐clean training pairs. However, self‐supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to 'see inside the black box' that is the denoising network and use the gained knowledge to replace the need for any prior knowledge of the noise itself. This is achieved in practice by leveraging bias‐free networks and the direct linear link between input and output provided by the associated Jacobian matrix; we show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels provides an indication of the most effective mask to suppress noise present in the data. The proposed method, therefore, becomes a fully automated denoising procedure requiring no clean training labels or prior knowledge. Realistic synthetic examples with noise signals of varying complexities, ranging from simple time‐correlated noise to complex pseudo‐rig noise propagating at the velocity of the ocean, are used to validate the proposed approach. Its automated nature is highlighted further by an application to two field data sets. Without any substantial pre‐processing or any knowledge of the acquisition environment, the automatically identified blind masks are shown to perform well in suppressing both trace‐wise noise in common shot gathers from the Volve marine data set and coloured noise in post‐stack seismic images from a land seismic survey. [ABSTRACT FROM AUTHOR]

The diffusion field theory has been widely used to interpret ambient noise wave fields. Based on this theory, 1-D subsurface velocity structure inversion method is developed. However, few studies have referred to the noise horizontal to vertical (NHV) spectral ratio inversion of 3-D subsurface velocity structures, and almost no effective 3-D NHV inversion tools have been developed. To develop a useful tool for obtaining 3-D soil layer velocity structures, we combined the NHV forward calculation formula derived from diffusion field theory with the guided Monte Carlo algorithm and then extended the single-point NHV inversion to multipoint joint inversion through a joint objective function. Subsequently, a new 3-D soil layer velocity structure inversion method was proposed. Subsequently, a synthetic 2-D case was used to verify the proposed method. Finally, the proposed method was applied to the Xiangtang Array in Tangshan, China, to identify the 3-D velocity structures of the site based on noise observations. The results show that the proposed multipoint joint 3-D inversion method is effective for identifying 3-D underground velocity structures. [ABSTRACT FROM AUTHOR]

Effective attenuation of noise in seismic data is important for high‐quality seismic imaging. Noise suppression in Ocean‐Bottom Cable data is particularly challenging. The challenge for the geophysicist is to process the individual hydrophone and vertical geophone data up to a level where they can conveniently be combined for effective multiple suppressions. In this study, we propose a deep learning‐based solution for noise attenuation and signal recovery of the P‐component of Ocean‐Bottom Cable data. To effectively attenuate complex noise, a denoising model based on dense convolutional network is proposed for Ocean‐Bottom Cable data processing. The backbone of the denoising network uses dense blocks to extract the potential features. Dense connections are applied to fuse the features at each stage to further enhance the effective information and thus improve the reconstruction of the signal. A high‐quality training set was built for the training network to ensure that the trained model was suitable for noise suppression. Synthesis and field experiments show that the proposed method can completely eliminate complex noise and recover weak signals from the P‐component data of the Ocean‐Bottom Cable data. [ABSTRACT FROM AUTHOR]

Seafloor ambient noise in the Arctic Ocean is related to sea ice. The characteristics of low‐frequency seafloor ambient noise (<10 Hz) in the northern Chukchi Sea have rarely been reported. Here, we investigated the seafloor ambient noise recorded using ocean‐bottom seismometers in the northern Chukchi Sea under four different sea ice‐concentration periods from 3 to 23 August 2020, in the 11th Chinese National Arctic Research Expedition. The causes and mechanisms of the changes in seafloor ambient noise that correspond to the variation in sea ice concentration were discussed. The energy of infragravity waves and primary microseisms is weak compared with other oceans. Combined with the analysis of land station, we argue that the variations in sea ice extent have little effect on the energy of the primary microseisms and infragravity waves. The power of secondary microseisms is growth with the loss of sea ice and is influenced by storms. We find that a high concentration of sea ice can impede the process of storm‐sea surface interaction, which in turn affects the power of microseisms inspired by storm. [ABSTRACT FROM AUTHOR]

Ambient noise technology can efficiently extract surface wave signals from seismic background noise and has been extensively utilized in imaging lithospheric structures. However, retrieving crustal body wave signals, such as PmP or SmS phases, still poses a challenge. Only a limited number of reports have successfully extracted these regional-scale body wave signals from ambient noise in only a few limited study areas. It remains unclear why these signals are difficult to retrieve from ambient noise data. To investigate the mechanism of recovering body wave signals in noise cross-correlations, we calculate cross-correlation functions at four regions and observe the similarity of the recovered body waves. Through a series of synthetic simulations, we demonstrate that the appearance of body wave signals in noise cross-correlations is closely related to the distribution of noise sources. Among these signals, the post-critical SmS wave proves to be the most readily recoverable from ambient noise data, primarily stemming from distant sources. In contrast, the recovery of P -wave requires the array to be in proximity to the sources. Our experiments also reveal that the main origin of PL waves is the multiple reflections of S -waves propagating in the crust. [ABSTRACT FROM AUTHOR]

Passive acoustic monitors analyze sound signals emitted by seafloor gas bubbles to measure leakage rates. In scenarios with low-flux gas leaks, individual bubble sounds are typically non-overlapping. Measurement methods for these bubble streams aim to estimate the frequency peak of each bubble sound, which correlates with the bubble's size. However, the presence of ocean ambient noise poses challenges to accurately estimating these frequency peaks, thereby affecting the measurement of gas leakage rates in shallow sea environments using passive acoustic monitors. To address this issue, we propose a robust measurement method that includes a noise-robust sparse time-frequency representation algorithm and an adaptive thresholding approach for detecting bubble frequencies. We demonstrate the effectiveness of our proposed method using experimental data augmented with ocean ambient noise and ship-transit noise recorded from a bay area. [ABSTRACT FROM AUTHOR]

Mimoun et al. (Space Sci Rev 211(1–4):383–428, 2017) developed a pre-landing noise model of the Martian seismometer package SEIS onboard InSight that analysed all the external and internal noise sources. We updated the environmental and instrumental parameters of the model as well as the ground properties with InSight mission data. We compared the output of the in situ noise model to the actual noise measured during the full mission for each individual noise source as well as for the full noise model. We evaluate in detail the efficiency of the model to fit the measured data and discuss the transient noise and other sources that were not included in the model. The main noise sources in the seismic bandwidth are the pressure noise and the lander noise, which is increased from the pre-landing model and overestimated when compared to the data; the magnetic field noise was overestimated in the pre-landing model and is now found to be negligible. The conclusions and models from this study could benefit future space missions. [ABSTRACT FROM AUTHOR]

The ambient noise analysis was performed at ten installed broadband seismic stations of the Eastern Dharwar Craton in Telangana state, India. We calculated the power spectral densities and their corresponding probability density functions, using different types of noise to assess the stations' performance. One year of continuous data from January 2021 to December 2021 was used for the analysis. The results suggest that the observed vertical noise levels are within the defined global noise limits for all stations. In the short-period band, the stations are dominated by cultural noise due to human activities, industrial machinery, automobile traffic, etc., which also causes a strong variation between daytime and night-time. At the long-period band, the vertical component lies above the new low-noise model (NLNM) and the horizontal components marginally overshoot the new high-noise model (NHNM) due to the tilt effect. An assessment of the noise variation was also performed during the partial lockdown due to COVID-19 in 2021 imposed by the Telangana state government in India, which revealed no significant variations in short-period noise levels. It may show the effect of remote installation away from cultural noise. Overall, our estimation suggests stability and validates the performance of all the installed stations of the Eastern Dharwar Craton in Telangana state. Research Highlights: In this study, we performed ambient noise analysis for ten broadband seismic stations of the Eastern Dharwar Craton, in Telangana state. We observed a significant diurnal variation as well as seasonal variation at all stations. We compared the noise variation during the period of partial lockdown in 2021 imposed by the Telangana state government in India and found no significant variations in short-period noise levels before and after partial lockdown. [ABSTRACT FROM AUTHOR]

Seismic random noise is one of the main factors that degrade the quality of seismic data. Therefore, seismic random noise attenuation should be performed appropriately through several stages during seismic data processing, and this requires sufficient experience and knowledge because the proper hyperparameters need to be determined based on the features of the noise in the target seismic data. Recently, machine learning–based seismic noise attenuation has been widely studied because it provides suitable results by learning noise features from noisy data, unlike conventional physics‐based approaches. There are many important factors in machine learning, and, here, we focus on the loss functions of machine learning in terms of seismic random noise attenuation. The most widely used loss function is l2, but we train a model with various kinds of single and multiple loss functions and attenuate seismic random noise. We analyse the efficiency of loss functions by comparing the noise‐attenuated results of synthetic and field seismic data qualitatively and quantitatively. Our analysis indicates that the multiple loss function with the l1 norm can be a proper choice for random noise suppression of seismic data. [ABSTRACT FROM AUTHOR]

The cross-correlation function (CCF) of seismic noise recorded at two stations often exhibits a prominent burst of energy near zero time. However, the excitation mechanism of such unexpected signals remains unclear. In this study, we demonstrate that this phenomenon arises from constructive interference of body waves radiating from sources near the mid-vertical plane between two receivers. This interference is driven by the occurrence of an inflection point effect in the traveltime difference between waves reaching the two receivers. When noise source distribution is asymmetric, the cumulative effect of such sources results in the generation of zero-time waves. It is noteworthy that a noise source distribution that is either uniform or follows a cos ϕ pattern, with ϕ denoting the azimuth from the centre of the receiver pair, can counteract the formation of these waves. Additionally, our investigation reveals that this constructive interference does not occur in the correlation of 2-D surface waves. We used numerical simulation to support our findings. In conclusion, this study offers a theoretical explanation for the emergence of zero-time waves on the CCF and provides efficient means of investigating the extraction of inter-receiver waves with uneven noise source distribution. [ABSTRACT FROM AUTHOR]

Deep learning has shown a considerable potential to significantly improve processing efficiency but has not yet been widely deployed to production projects of seismic signal separation such as seismic interference attenuation. The main reasons are: First, the industry has high standards for signal fidelity, which are critical for the success of subsequent seismic imaging, and deep neural network methods have not yet matched the required level; second, the network's interpretability issue has affected many geophysicists and sponsors' trust in the deep learning technique. To develop deep neural network methods towards the end of benefiting real‐world production, we first attempt to better understand their performance, especially in how they make use of local and global features of the data. A novel quantitative research of the overall network model behaviour on synthetic data is conducted. We simulate three types of coherent seismic data components in the shot domain, blend them together and then train a network to separate them. In this process, random noise, a component having only learnable local features, is selectively injected into the network's training pairs. Three network models sharing the same architecture are trained individually, and they show distinctive behaviours when applied to the same test data. Step‐by‐step analysis of each of them reveals that training the network with additional random noise injected into both the input and the output channel of the desired signal can lead to a decent prediction of the coherent noise based on good learning of the global features and, in the meantime, preserve almost all the data information from being lost. We propose this key lesson we learnt as a new method to improve the network's signal fidelity for shot‐domain seismic interference attenuation, which is essentially a signal separation task. Its effectiveness is demonstrated on field data from Africa with a comparison to a conventional physics‐based seismic interference attenuation method used in production. [ABSTRACT FROM AUTHOR]

The rapid development of industrial activity leads steadily to an increase in the influence of various factors that have a negative impact on the environment. One of them is a high level of technogenic low-frequency seismoacoustic pollution of settlements. This paper presents the results of in situ measurements of microseismic and acoustic noise in various frequency ranges in conditions of dense urban development using the example of the settlement of Mosrentgen. As a result of processing the primary data, we have obtained averaged values normalized to the reference station, compared the values of microseismic and acoustic noise, and localized areas of increased noise pollution. The features of the spatiotemporal distribution of microseismic and acoustic noise in the area of Mosrentgen are given. It is shown that the Moscow Ring Road and the metro station under construction are sources of low-frequency noise pollution. At the same time, it is recorded that the maximum permissible values of vibration and acoustic background up to 28 dB are exceeded. [ABSTRACT FROM AUTHOR]

One of the significant challenges in processing and analyzing seismic data is the contamination of the seismic signal with noise from various sources. Coherent or incoherent noise, multiples, and other types of noise can all be eliminated using a number of techniques, but it is still complicated to attenuate random noise to the desired level. The non-homogeneous curvelet transform is first applied in this study, and the regularized calculation is then carried out using the inverted operator. Following the application of the linear calculation method, the threshold value is modified to remove the coefficient noise during the iteration process, allowing the homogeneous coefficients and noise-free seismic data to be obtained. We choose some real data as an example and apply the suggested curvelet theory to obtain homogeneous data in order to verify the denoising effect. Tests on real data show that, when the suggested approach interpolates sampled data to be homogeneous, random noise can be effectively reduced. The important benefit of this approach is that features like multi-resolution, and locality introduce minimal overlapping between coefficients representing signal and noise in curvelet domain. [ABSTRACT FROM AUTHOR]

Applications of acoustic noise interferometry to passive remote sensing of the ocean rely on retrieval of empirical Green's functions (EGFs) from cross-correlations of ambient sound at spatially separated points. At ranges of tens of ocean depths, obtaining stable and accurate EGF estimates usually requires noise averaging periods of hours or days. Using data acquired in the Shallow Water 2006 experiment on the continental shelf off New Jersey, it is found that at ranges of 40–70 ocean depths, the EGFs can be retrieved with noise averaging times as short as 64 s. The phenomenon is observed for various receiver pairs but does not occur simultaneously in all azimuthal directions. The rapidly emerging EGFs have a wider frequency band and a richer normal mode content than the EGFs obtained in previous studies using long averaging times and are better suited for monitoring physical processes in the water column. Available acoustic and environmental data is examined to understand the conditions leading to rapid EGF emergence from diffuse noise. Strong intermittency is observed in the horizontal directionality of ambient sound. Rapid emergence of EGF in shallow-water waveguide is found to occur when the directionality of diffuse ambient noise is favorable. [ABSTRACT FROM AUTHOR]

The waveform distortion exists in the noise-reduced microseismic signal, in order to achieve better noise reduction effect, a novel denoising method based on U-Net is proposed in this paper. The dilated convolution with expansion coefficient variation is introduced to established the U-Net convolutional neural networks noise attenuation model. The envelope entropy is introduced as a new loss function so as to realize unsupervised processing of field microseismic signals. The results of U-Net convolutional neural networks for noise attenuation(U-NetNA method) is compared with wavelet thresholding, time-frequency peaking, and convolutional neural network. The results show that: when the U-NetNA method is applied to noise attenuation of synthetic and real microseismic data, it is feasible and effective. Compared with other noise reduction methods, the U-NetNA can obtain richer effective signal features, more effectively suppress the noise, and improve the signal-to-noise ratio of microseismic signals. The result is informative for microseismic event identification, inversion localization and fracture interpretation. [ABSTRACT FROM AUTHOR]

We present a method for estimating seismic ambient noise sources by acoustic full waveform inversion (FWI) of interstation cross-correlations. The method is valid at local scales for laterally heterogeneous media, and ambient noise sources confined to the Earth's surface. Synthetic tests performed using an actual field array geometry, are used to illustrate three unique aspects of our work. First: the method is able to recover noise sources of arbitrary spatial distribution, both within and outside the receiver array, with high fidelity. This holds true for complex velocity models and does not require a good initial guess for inversion, thereby addressing an outstanding issue in the existing research literature. Second: we analyse the extent of biases in source inversion that arise due to inaccurate velocity models. Our findings indicate that source inversion using simplified (e.g. homogeneous) velocity models may work reliably when lateral variations in velocity structure are limited to 5 or 10 per cent in magnitude, but is vitiated by strong variations of 20 per cent or higher, wherein the effect of strong scattering and/or phase distortions become significant. Finally, our technique is implemented without the adjoint method, which is usually inextricably linked to FWI. Inversions are performed using source kernels computed for each receiver pair, and this approach is computationally tractable for real-world problems with small aperture seismic arrays. [ABSTRACT FROM AUTHOR]

Following ambient noise seismic interferometry principles, we retrieved virtual zero‐offset vertical seismic profile shot gather using continuous seismic records obtained simultaneously during an active‐source data acquisition study from Soma coal basin, Western Turkey. We treated the data to include pure ambient noise by eliminating earthquakes, surface sweeps and quarry explosions due to mining activities. We have implemented a series of data processing steps, including spectral normalization and filtering to increase the signal‐to‐noise ratio. We produced a zero‐offset virtual shot gather by cross‐correlation and cross‐coherence methods in a coal basin for the first time and we observed the P‐wave first arrivals and reflections from the deepest coal layer (km2). Our results demonstrate that the ambient noise seismic interferometry method can serve as an alternative approach for seismic imaging and monitoring in areas where the use of active seismic sources is limited due to field conditions, provided that there are sufficient and appropriate noise sources at the wellhead. [ABSTRACT FROM AUTHOR]

Noises in marine seismic data are one of the biggest obstacles in seismic imaging. The most significant step in seismic data processing is the removal of seismic noise, which can be classified as instrument and background noise. Noise attenuation usually results in improved seismic interpretation by increasing the signal-to-noise ratio. In this study, we will focus on attenuating these seismic noises with several data processing techniques. A number of denoising examples describing swell, strumming/tugging, and cavitation, which are background-type noises, and streamer-mounted device noise (Nautilus), which is an instrument-type noise, were illustrated by analysing a marine 3D seismic dataset recorded by Oruç Reis Research Vessel in Black Sea project of Mineral Research and Exploration (MTA). This study was achieved by implementing an f-x prediction filter (SPARC, DENOISE3D) and f-k filter (DWATT) in the t-x domain, and radon filter (RADATT) in Tau-P domain by the use of Geovation 2.0 software. [ABSTRACT FROM AUTHOR]

Ambient noise sources in urban environments are generally not perfectly uniformly distributed or travel exactly along the axial direction of the receiver array. The horizontally deployed distributed acoustic sensing (DAS) fiber‐optic cable is able to record both Rayleigh and Love waves. Therefore, resolving the source propagation direction and the content of Rayleigh and Love waves from the DAS ambient noise data is essential to resolve a reliable near‐surface shear‐wave velocity model. However, a standard geophone‐based beamforming procedure may not apply to DAS ambient noise data due to the complex measurements of DAS. To address this issue, we investigate the validity of beamforming results by analyzing the simulated seismic ambient noise recorded by a DAS array close to an L‐shape under different source conditions. Synthetic examples demonstrate the importance of correcting amplitude imbalances and polarity reversals in the DAS ambient noise data to ensure a reliable beamforming analysis. In addition, we estimate the Rayleigh‐to‐Love wave (R/L) ratio from the DAS ambient noise data with the resolved source direction, the known array geometry, and the measured amplitude information. Finally, we apply the method to two 2 min DAS ambient noise data recorded by the Stanford DAS Array‐1. The results suggest that the analyzed data were mainly generated from the local traffic and dominated by Love waves. Plain Language Summary: Human activities are the primary source that generates seismic ambient noise at higher frequencies (above 1 Hz). Resolving the main source propagation direction is essential to extract a reliable near‐surface shear‐wave velocity model from the seismic ambient noise recorded by the horizontal components of seismic sensors. Beamforming is a commonly used method to resolve the ambient noise source propagation directions. However, it is designed for seismic ambient noise data recorded by the vertical component of traditional seismic sensors. Due to the complex measurements of distributed acoustic sensing (DAS), beamforming may not apply to DAS ambient noise data. We investigate the validity of beamforming results resolved from DAS ambient noise data with several synthetic examples. We also improve the resolution of beamforming results with careful processing. Besides, the horizontally deployed DAS records Rayleigh and Love waves, both of which would be used to invert the shear‐wave velocity model since they cannot be separated without enough information about the sources. We propose to estimate the Rayleigh‐to‐Love wave (R/L) ratio based on the resolved source direction, the known array geometry, and the measured amplitude information. Finally, we apply the method to the field DAS ambient noise data recorded by the Stanford DAS Array‐1. Key Points: Geophone and distributed acoustic sensing (DAS) ambient noise data are simulated and their differences and implications on beamforming results are analyzedBeamforming results from DAS ambient noise data are confirmed reliable with careful processing when noise sources are unidirectionalRayleigh and Love waves are identified from the DAS field data and the Rayleigh‐to‐Love wave ratio is resolved [ABSTRACT FROM AUTHOR]

SUMMARY: The local ground motion amplification related to the geology at a specific site (i.e. the so-called site effects) may be classically quantified through the SSR (standard spectral ratio) technique applied on earthquake recordings. However, such a quantification might be challenging to carry out in low-to-moderate seismicity regions. Methods based on background ambient noise, such as noise-based standard spectral ratio (SSRn), might be of great interest in these areas. But noise-derived amplification is particularly sensitive to local anthropogenic sources, which may introduce biases in the evaluation of site effects by dominating the geological effects, especially for frequencies higher than 1 Hz. A hybrid approach (SSRh), developed to reduce biases in noise-based spectral ratios by combining classical earthquake-based spectral ratio (SSR) and SSRn, was recently introduced and relies on a site reference. We here investigate the applicability of SSRn and SSRh in a heavily industrialized environment in the Tricastin Valley (south-east France), where critical facilities are located. We continuously recorded ambient noise from 2020 February to March on a 400-sensor seismic array covering an area of about 10 km by 10 km. We demonstrate that SSRn and SSRh computed below 1 Hz are able to reproduce amplification factors provided by SSR. By contrast, at frequencies higher than 1 Hz, SSRn strongly deviates from SSR. SSRh shows closer results to SSR but presents a dependence to the choice of the local site reference, thereby questioning the possibility to use SSRh blindly to estimate the local amplification in our context. These discrepancies reflect the impact of local anthropogenic sources. We therefore introduced a two-step workflow to mitigate the influence of local sources. The first step is to define a characteristic time window to optimally isolate significant transient signals. The second step consists in selecting the time segments that do not contain these transients with a clustering-based approach. By applying this workflow, we were able to remove some strong anthropogenic transient signals likely to be generated by local sources at some sites and therefore to locally improve the amplification assessment through noise-based spectral ratios. However, stationary sources, whose impact cannot be removed through our procedure, remain a major issue. This study provides some insights into the application of SSRn and SSRh in noisy industrialized areas, especially regarding the impact of local noise sources. It illustrates the difficulty of having a procedure for mitigating the impact of these sources that is efficient everywhere inside such a complex anthropized environment, where different types of sources (including stationary sources) cohabit. [ABSTRACT FROM AUTHOR]

Ambient seismic recordings taken at broad locations across Ross Ice Shelf and a dense array near West Antarctic Ice Sheet (WAIS) Divide, Antarctica, show pervasive temporally variable resonance peaks associated with trapped seismic waves in near-surface firn layers. These resonance peaks feature splitting on the horizontal components, here interpreted as frequency-dependent anisotropy in the firn and underlying ice due to several overlapping mechanisms driven by ice flow. Frequency peak splitting magnitudes and fast/slow axes were systematically estimated at single stations using a novel algorithm and compared with good agreement with active source anisotropy measurements at WAIS Divide determined via active sources recorded on a 1 km circular array. The approach was further applied to the broad Ross Ice Shelf (RIS) array, where anisotropy axes were directly compared with visible surface features and ice shelf flow lines. The near-surface firn, depicted by anisotropy above 30 Hz, was shown to exhibit a novel plastic stretching mechanism of anisotropy, whereby the fast direction in snow aligns with accelerating ice shelf flow. [ABSTRACT FROM AUTHOR]

Every ambient noise study employs the source-path-receiver structure to explore the overall behaviour of sound. Noise levels are affected by changes in distance, intervening barriers, and atmospheric conditions along the transmission path between the source and the receiver. The objective of this study is to quantify the influence of transmission path characteristics for a realistic time-varying moving line source. In this context, railway noise was considered to explore the variance of noise in an urban setting over a variety of measuring distances, including 25, 50, 100, and 200 m, with variables such as air temperature, humidity, and wind condition. The data corresponding to 106 trains was collected for analysis, and it was observed that the effect of the wind was more significant for larger distances between the source and the receiver. When the sound levels were measured in two opposite wind directions, a considerable noise level difference was observed. For every 1 m/s increase in wind speed, within a distance of 50 m, the average sound attenuation induced by the upwind phenomena was 0.2 dBA. The impact of air temperature changes on received sound level from a moving source was insignificant within the range of temperatures considered in the study. The effect of humidity is less at shorter distances but at larger distances, increasingly attenuates noise levels. Analysis of variance was performed on the selected variables to determine whether the means of each group were significantly different from each other and found that train speed had a more significant impact on railway noise compared to other parameters. [ABSTRACT FROM AUTHOR]

In the Gobi desert region, the predominant frequency of the horizontal-to-vertical spectral ratio (HVSR) curve of seismic noise ( f H / V ) is typically above 3 Hz. However, most of the existing formulas for estimating cover thickness (H) based on f H / V are only applicable for f H / V values below 5 Hz. Therefore, new formulas that can accurately calculate H in this region are crucially needed. To this end, a case study was conducted where noise measurements were carried out in the Gobi region in China, as well as in the east and northeast regions of China. The HVSR curves and f H / V were calculated, and a formula that correlates f H / V with H was derived. In addition, formulas that correlate with Vs20 (average shear-wave velocities to depth 20 m) and Vs30 (average shear-wave velocities to depth 30 m) f H / V were fitted. The relationship between the site fundamental period ( T s ) and T H / V (1 / f H / V ) was also investigated. Finally, by using all of the noise measurements and applying the derived relationships, a method for rapidly measuring the site type was proposed. The results showed that the equation for calculating H in this paper is highly reliable, with relative errors less than 30% compared to other researchers. The correlation between f H / V and Vs30 was higher than that between f H / V and Vs20. The accuracy rate of the proposed method for fast identification of ground types was greater than 80%. [ABSTRACT FROM AUTHOR]

The ocean‐bottom currents are believed to be largely responsible for the high noise level in ocean bottom seismograph (OBS) data, in particular on the horizontal components. Due to the lack of in‐situ experiments and measurements, the generation mechanism and characteristics of the current‐induced noise are still poorly understood. In this paper, we designed an experiment to explore the features of current‐induced noise. A sensor module from a typical passive‐source OBS was installed in a water flume that can produce controllable steady water flows. We measured and analyzed the recorded noise with changing current velocities, with and without shielding. With other noise sources, such as infragravity waves precluded, this experiment demonstrates that the currents can generate low‐frequency noise, particularly <0.1 Hz. The noise level depends on the current velocity as well as the frequency. While the current‐induced noise affects the horizontal components significantly, its impacts on the vertical component appear negligible. The experiment shows that current‐induced noise has a dominant direction approximately perpendicular to the currents, a pattern consistent with an actual OBS on the flank of Mariana Trench, where the current direction is supposed to be along the trench. Shielding the sensor module with a plastic casing can substantially suppress the noise, indicating that shielding is a practical, low‐cost scheme to reduce the current‐induced noise. Key Points: Current‐induced noise exists in a wide frequency band; its level depends on the current velocity and the frequencyCurrent‐induced noise shows a dominant direction roughly perpendicular to currents, consistent with that at an actual ocean bottom seismograph in Mariana TrenchShielding the sensor can suppress the current‐induced noise substantially [ABSTRACT FROM AUTHOR]

This study includes the properties and installation stages of the microseismic network planned to be installed in a geothermal field in Turkey, which is currently producing electricity, in order to monitor the induced seismicity that will occur due to production and re-injection wells. The results of how the optimum microseismic monitoring network should be installed, the installation studies and the noise analysis of the station locations are given for this and similar geothermal fields. 7 borehole type seismometers were placed in wells drilled 65 meters deep and microseismic records of the field were obtained. The leading field equipment is SARA-SS10BHV three-component (3C) borehole type short-period velocity seismometers, and the analog data were digitized with 24-bit SL06 brand recorders. In data analysis, high frequency, low energy and small amplitude microseismic events were dealt within passive seismic records. Therefore, noise analysis is the most important step of the network installation. In the noise analysis studies, the background noise at the locations where the microseismic network stations will be deployed was determined by measurements from both on the surface and through the well, and then the results were compared with each other. According to the results, it has been determined that the noise level in the well is very low compared to the surface and this is at a suitable level for microseismic monitoring networks. [ABSTRACT FROM AUTHOR]

The SEIS (seismic experiment for the interior structure of Mars) experiment on the NASA InSight mission has catalogued hundreds of marsquakes so far. However, the detectability of these events is controlled by the weather which generates noise on the seismometer. This affects the catalogue on both diurnal and seasonal scales. We propose to use machine learning methods to fit the wind, pressure and temperature data to the seismic energy recorded in the 0.4–1 and 2.2–2.6 Hz bandwidths to examine low- (LF) and high-frequency (HF) seismic event categories respectively. We implement Gaussian process regression and neural network models for this task. This approach provides the relationship between the atmospheric state and seismic energy. The obtained seismic energy estimate is used to calculate signal-to-noise ratios (SNR) of marsquakes for multiple bandwidths. We can then demonstrate the presence of LF energy above the noise level during several events predominantly categorized as HF, suggesting a continuum in event spectra distribution across the marsquake types. We introduce an algorithm to detect marsquakes based on the subtraction of the predicted noise from the observed data. This algorithm finds 39 previously undetected marsquakes, with another 40 possible candidates. Furthermore, an analysis of the detection algorithm's variable threshold provides an empirical estimate of marsquake detectivity. This suggests that events producing the largest signal on the seismometer would be seen almost all the time, the median size signal event 45–50 per cent of the time and smallest signal events 5−20 per cent of the time. [ABSTRACT FROM AUTHOR]

The incorporation of effective denoising techniques is a crucial requirement for seismic data processing during the acquisition phase due to the inherent susceptibility of the seismic data acquisition process to various forms of interference, such as random and coherent noise. For random noise, the Residual Neural Network (Resnet), with its notable ability to effectively suppress noise in seismic data, has garnered widespread utilization in removing unwanted disturbances or interference due to its elegant simplicity and outstanding performance. Despite the considerable advancements achieved by conventional Resnet in the field of suppressing noise, it is irrefutable that there is still room for amelioration in their ability to filter out unwanted disturbances. As a result, this paper puts forth a novel attention-based methodology for Resnet, intended to overcome the present constraints and attain an optimal seismic signal enhancement. Specifically, we add the convolutional block attention module (CBAM) after the convolutional layer of the residual module and add channel attention on the shortcut connections to filter out the disturbance. We replace the commonly used ReLU activation function in the network with ELU, which is better suited for suppressing seismic noise. Empirical assessments conducted on both synthetic and authentic datasets have demonstrated the efficacy of the proposed methodology in amplifying the denoising prowess of Resnet. Our proposed method remains stable even when dealing with seismic data that has complex waveforms. The findings of this investigation evince that the recommended approach furnishes a substantial augmentation in the signal-to-noise ratio (SNR), thereby facilitating the efficient and robust extraction of the underlying signal from the noisy observations. [ABSTRACT FROM AUTHOR]

Proxy system models (PSMs) are an essential component of paleoclimate data assimilation and for testing climate field reconstruction methods. Generally, current statistical PSMs consider the noise in the output (proxy) variable only, and ignore the noise in the input (environmental) variables. This problem is exacerbated when there are several input variables. Here we develop a new PSM, the Measurement Error Proxy System Model (MEPSM), which includes noise in all variables, including noise auto- and cross-correlation. The MEPSM is calibrated using a quasi-Bayesian solution, which leverages Gaussian conjugacy to produce a fast solution. Another advantage of MEPSM is that the prior can be used to stabilize the solution between an informative prior (e.g. with a non-zero mean) and the maximum likelihood solution. MEPSM is illustrated by calibrating a proxy model for δ 18Ocoral with multiple inputs (marine temperature and salinity), including noise in all variables. MEPSM is applicable to many different climate proxies, and will improve our understanding of the effects of predictor noise on PSMs, data assimilation, and climate reconstruction. [ABSTRACT FROM AUTHOR]

We present a new compilation and analysis of broad-band ocean bottom seismometer noise properties from 15 yr of seismic deployments. We compile a comprehensive data set of representative four-component (seismometer and pressure gauge) noise spectra and cross-spectral properties (coherence, phase and admittance) for 551 unique stations spanning 18 U.S.-led experiments. This is matched with a comprehensive compilation of metadata parameters related to instrumentation and environmental properties for each station. We systematically investigate the similarity of noise spectra by grouping them according to these metadata parameters to determine which factors are the most important in determining noise characteristics. We find evidence for improvements in similarity of noise properties when grouped across parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We assess the presence of traditional sources of tilt, compliance, and microseismic noise to characterize their relative role across a variety of commonly used seismic frequency bands. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). These observations have important implications for the viability of commonly used seismic analysis techniques. Finally, we compare spectra and coherences before and after vertical channel tilt and compliance noise removal to evaluate the efficacy and limitations of these now standard processing techniques. These findings may assist in future experiment planning and instrument development, and our newly compiled noise data set serves as a building block for more targeted future investigations by the marine seismology community. [ABSTRACT FROM AUTHOR]

Accounting for an accurate noise model is essential when dealing with real data, which are noisy due to the effect of environmental noise, failures and limitations in data acquisition and processing. Quantifying the noise model is a challenge for practitioners in formulating an inverse problem, and usually, a simple Gaussian noise model is assumed as a white noise model. Here we propose a pragmatic approach to use an estimated seismic wavelet to capture the correlated noise model (coloured noise) for the processed reflection seismic data. We assess the proposed method through a direct inversion of post‐stack seismic data associated with a carbonate reservoir of an oil field in southwest Iran to porosity, using a probabilistic sampling‐based inversion algorithm. In the probabilistic formulation of the inverse problem, we assume eight different noise models with varying bandwidth and magnitude and investigate the corresponding posterior statistics. The results indicate that if the correlated nature of the noise samples is ignored in the noise covariance matrix, some unrealistic features are generated in porosity realizations. In addition, if the noise magnitude is underestimated, the inversion algorithm overfits the data and generates a biased model with low uncertainty. Furthermore, by considering an imperfect bandwidth for the noise model, the error is propagated to the posterior realizations. Assuming the correlated noise in a probabilistic inversion resolves these issues significantly. Therefore, for inverting real seismic data where the estimation of the magnitude and correlations of the noise is not straightforward, the wavelet, which is estimated from the real seismic data, provides a good proxy for describing the correlation of the noise samples or equivalently the bandwidth of the noise model. In addition, it might be better to overestimate the noise magnitude than to underestimate it. This is true especially for an uncorrelated noise model and to a lesser degree also for the correlated noise model. [ABSTRACT FROM AUTHOR]

Periodic noise is a well-known problem in seismic exploration, caused by power lines, pump jacks, engine operation, or other interferences. It contaminates seismic data and affects subsequent processing and interpretation. The conventional methods to attenuate periodic noise are notch filtering and some model-based methods. However, these methods either simultaneously attenuate noise and seismic events around the same frequencies, or need expensive computation time. In this work, a new method is proposed to attenuate periodic noise based on sparse representation. We use a noise dictionary to sparsely represent periodic noise. The noise dictionary is constructed based on ambient noise. An advantage of our method is that it can automatically suppress monochromatic periodic noise, multitoned periodic noise and even periodic noise with complex waveforms without pre-known noise frequencies. In addition, the method does not result in any notches in the spectrum. Synthetic and field examples demonstrate that our method can effectively subtract periodic noise from raw seismic data without damaging the useful seismic signal. [ABSTRACT FROM AUTHOR]

Seismic impedance inversion is an important method to identify the spatial characteristics of underground rock physical properties. Seismic inversion results and uncertainty evaluation are the important scientific basis for risk decision-making in oil and gas development. Under the assumption that the impedance and the error of the observed seismic data meet the Gaussian distribution or log–Gaussian distribution, the Bayesian linear seismic inversion can analytically obtain the posterior probability distribution of impedance. However, errors from observation, calculation, model and other factors can lead to an inaccurate and incomplete uncertainty evaluation. In this paper, the noise variance is used to represent the noise level of seismic data and the uncertainties from seismic wavelet extraction and noise level estimation are considered in inversion. Assuming that the probability distribution of the noise variance meets the inverse gamma distribution and the seismic wavelet meets the Gaussian distribution, we could obtain the conditional distribution for one variable given another analytically using well-log data and seismic data. In order to integrate the uncertainty from noise level estimation and wavelet extraction into the seismic impedance inversion, the Gibbs sampler algorithm was applied to draw a set of realizations of noise variance and wavelet. For each realization, the corresponding posterior probability model of impedance was achieved by Bayesian linear inversion and the final posterior probability of the impedance model was obtained by integrating all the single posterior probabilities for each pair of wavelet and noise variance. Synthetic and real data experiments showed that the uncertainties of seismic wavelet extraction and noise level estimation have an important influence on inversion results and their uncertainties. The proposed method could effectively integrate the uncertainty of wavelet and noise estimation to obtain a more accurate and comprehensive uncertainty evaluation. Under the assumption that the model meets the linear relationship and the parameters meet some specified distribution, the proposed method has high calculation efficiency. However, it also loses some accuracy when the assumptions are not completely consistent with the actual situation. [ABSTRACT FROM AUTHOR]

Ambient noise tomography is a powerful tool that has found increasing application in reservoir analysis and imaging. The Cachar fold belt in lower Assam, northeast India encompasses several wells under active hydrocarbon production, along with several dry wells. To overcome the lack of active seismic data over the entire fold belt, a passive seismic study was carried out to image the concealed three‐dimensional sub‐surface structures. The data were recorded from February to November 2011 by a network of 65 wideband seismometers spanning an area of about 40 × 60 km2. The data are crosscorrelated in the 2–5 s band, followed by phase‐weighted stacking to estimate noise correlation functions with surface wave signatures. The traveltimes picked from the frequency‐time analysis are utilized in a tomographic inversion for Rayleigh wave group velocities. The group velocity anomalies have a lateral resolution of ~ 3.5 × 5.5 km2 and variations of up to ±20%$\pm 20\%$ for each period. The group velocities are in turn inverted for S‐wave velocity distribution as a function of depth. The three‐dimensional S‐wave velocity tomograms reveal the tight anticlines and broad synclines, with high‐ and low‐velocity zones corresponding to structural highs and lows, respectively. The structural interpretation is supported for the part of the region with producing wells and covered by active seismic data, wherein the post‐stack time migrated seismic section shows anticlinal and synclinal features similar to those obtained from ambient noise tomography. The structures revealed by ambient noise tomography can help identify zones of interest to be targeted by active seismic surveys in the Cachar fold belt. [ABSTRACT FROM AUTHOR]

We present a method to characterize the noise in ground-based gravitational-wave observatories such as the Laser Gravitational-Wave Observatory (LIGO). This method uses linear regression algorithms such as the least absolute shrinkage and selection operator to identify noise sources and analyzes the detector output vs noise witness sensors to quantify the coupling of such noise. Our method can be implemented with currently available resources at LIGO, which avoids extra coding or direct experimentation at the LIGO sites. We present two examples to validate and estimate the coupling of elevated ground motion at frequencies below 10 Hz with noise in the detector output. [ABSTRACT FROM AUTHOR]

Seismograms contain multiple sources of seismic waves, from distinct transient signals such as earthquakes to continuous ambient seismic vibrations such as microseism. Ambient vibrations contaminate the earthquake signals, while the earthquake signals pollute the ambient noise's statistical properties necessary for ambient-noise seismology analysis. Separating ambient noise from earthquake signals would thus benefit multiple seismological analyses. This work develops a multitask encoder–decoder network named WaveDecompNet to separate transient signals from ambient signals directly in the time domain for 3-component seismograms. We choose the active-volcanic Big Island in Hawai'i as a natural laboratory given its richness in transients (tectonic and volcanic earthquakes) and diffuse ambient noise (strong microseism). The approach takes a noisy 3-component seismogram as input and independently predicts the 3-component earthquake and noise waveforms. The model is trained on earthquake and noise waveforms from the STandford EArthquake Dataset (STEAD) and on the local noise of seismic station IU.POHA. We estimate the network's performance by using the explained variance metric on both earthquake and noise waveforms. We explore different neural network designs for WaveDecompNet and find that the model with long-short-term memory (LSTM) performs best over other structures. Overall, we find that WaveDecompNet provides satisfactory performance down to a signal-to-noise ratio (SNR) of 0.1. The potential of the method is (1) to improve broad-band SNR of transient (earthquake) waveforms and (2) to improve local ambient noise to monitor the Earth's structure using ambient noise signals. To test this, we apply a short-time average to a long-time average filter and improve the number of detected events. We also measure single-station cross-correlation functions of the recovered ambient noise and establish their improved coherence through time and over different frequency bands. We conclude that WaveDecompNet is a promising tool for a broad range of seismological research. [ABSTRACT FROM AUTHOR]

Absorption compensation is a process involving the exponential amplification of reflection amplitudes. This process amplifies the seismic signal and noise, thereby substantially reducing the signal-to-noise ratio of seismic data. Therefore, this paper proposes a multichannel inversion absorption compensation method based on structure tensor regularization. First, the structure tensor is utilized to extract the spatial inclination of seismic signals, and the spatial prediction filter is designed along the inclination direction. The spatial prediction filter is then introduced into the regularization condition of multichannel inversion absorption compensation, and the absorption compensation is realized under the framework of multichannel inversion theory. The spatial predictability of seismic signals is also introduced into the objective function of absorption compensation inversion. Thus, the inversion system can effectively suppress the noise amplification effect during absorption compensation and improve the recovery accuracy of high-frequency signals. Synthetic and field data tests are conducted to demonstrate the accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Site characterization of metropolitan areas, especially after an earthquake, is of paramount importance for interpretation of spatial damage distribution and taking measures that assure realistic design actions to strengthen existing constructions and create new ones. Such a case is the city of Durres, Albania, that was hit by the disastrous earthquake of 26 November 2019 (M6.4). Significant differences in structural damage were observed throughout the city, despite its uniform epicentral distance (approximately 15 km); this could be either due to varying vulnerability of the affected constructions and/or to spatial variation of strong ground motion in the city, resulting from local site effects; the latter factor was investigated in this study. This was achieved by taking single station ambient noise measurements throughout the city, at approximately 80 sites. Ambient noise measurements are favorable, as acquiring ambient noise data is an easy and effective noninvasive approach within urban environments. Measurements were processed using the widely applied Horizontal-to-Vertical Spectral Ratio (HVSR) method, following the SESAME project (2004) guidelines. Their fundamental and dominant frequencies, fo and fd, respectively, were calculated and related to the iso-depth contours of the investigated area, as well as their corresponding amplitudes, Ao, and Ad. These experimental parameters and the HVSR curves were used to group all examined sites into classes with similar properties. This clustering provided a zonation map with four categories consisting of similar shapes and amplitudes, applicable to the city of Durres. This map can be utilized as a first level zonation of local site effects for the city. In addition, dynamic properties of soil profiles in selected sites were investigated and tested using 1D synthetic ambient noise data, based on the Hisada (1994, 1995) simulation method, and compared to experimental HVSRs in proximity to the selected sites. A comparison of the proposed four categories zonation map to the observed damage of the 26 November 2019, mainshock is attempted and evaluated. The four categories zonation map with similar expected local site effects proposed in this study can be used as a first level seismic microzonation of Durres. Undoubtedly, corrections for 2D/3D effects on ground shaking must be applied to sites lying in the edges of the Durres basin. [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing (DAS) is an emerging technology increasingly employed to monitor changes of formation properties, production noise and micro-seismic activity, and as an array of sensors in active seismic surveys. The data recorded with the DAS systems are very rich; some features observed in DAS records are often not well understood, and thus are underutilised. A systematic analysis of the data recorded passively with a DAS system in a 900-m deep well over a period of 12 weeks in the Perth metropolitan area, Western Australia, reveals the presence of several types of ambient energy in the subsurface, such as earthquakes, ocean swell and urban noise. In particular, over 85 days of the experiment, the analysis detected sixteen earthquakes, with epicentres ranging from 126 km to 900 km (for the local events) and from 2300 km to 6400 km (for the remote events). Signals with frequencies below 0.9 Hz are dominated by the oceanic swell. The recorded urban noise includes mine blasting, machinery and traffic. The experiment shows the ability of DAS to detect these events and as such is potentially useful for subsurface characterisation and monitoring. [ABSTRACT FROM AUTHOR]

SUMMARY: Cross-correlation functions of ambient seismic noise sometimes show multimode characteristics of surface waves, especially in observations in sedimentary areas and ocean areas. Multimode dispersion curves are useful for improving the depth resolution of subsurface imaging; nevertheless, measuring the multimode dispersion curves is not easy. Multimode interference of surface waves makes the cross-correlation functions complicated even without lateral heterogeneity of the subsurface structure, and the complex waveforms may result in unphysical dispersion measurement. We developed a method to determine multimode phase velocity dispersion curves based on the fitting of the synthetic cross-spectra to observed ones. The phase velocity in the synthetic cross-spectra is modelled as the function of a 1-D velocity structure, which achieves the measurement of physically realizable dispersion curves. The 1-D structures do not necessarily represent the Earth structure directly but act as model parameters of the dispersion curves within station pairs. The cross-spectral fitting has two steps, that is, array-based and single-pair fittings. The first step estimates the amplitude of each surface wave mode and the reference 1-D structure from the cross-spectral data within an array. The second step estimates the pair-dependent dispersion curves from the cross-spectra of a single station pair using the modal amplitudes and the reference structure estimated by the first step. The dispersion measurement based on the cross-spectral fitting can work even at short distances where the multimode inference is significant in the time-domain cross-correlation functions. We applied this method to synthetic and field data in seafloor observations. The synthetic and field applications show that the simultaneous use of multicomponent cross-correlation functions is effective to determine multimode dispersion curves. The multimode phase velocity dispersion curves in the ocean area are estimated stably even though the signal-to-noise ratio of cross-correlation functions is not high. The pair-dependent multimode dispersion curves estimated by the present method can serve as robust input data for high-resolution surface wave tomography. [ABSTRACT FROM AUTHOR]

With the outbreak of the COVID-19 pandemic, many seismologists all over the world have noted a sharp (up to 30–50%) decrease in the daily background seismic noise during the period from March to May, 2020 (Lecocq et al., Science 369(6509):1338–1343, 2020). The authors studied the influence of the self-isolation regime introduced in the Russian Federation from March 30, 2020 and, as a consequence, the restriction of the work of public institutions and the mobility of the population, on the quality of seismological observations at seismic stations in large cities of the Russian Far East for the period from March 23, 2020 to April 12, 2020. The work analyses the records of seismic noise by the seismic stations of Khabarovsk and Vladivostok located in busy parts of the cities and, accordingly, strongly influenced by anthropogenic impact, as well as it analyses the records of the Yuzhno-Sakhalinsk seismic station located in the relatively «calm» part of the city. Power spectra and temporal variations of microseismic noise levels for the listed above seismic stations were constructed based on the data of broadband seismometers records in the range of 1–20 Hz. The analysis of noise level variations with the data on the population mobility was carried out on the basis of self-isolation index by Yandex, which shows the level of town activity over a selected period. The main sources of the increased microseismic noise at seismic stations were identified. [ABSTRACT FROM AUTHOR]

In recent years, supervised-deep-learning methods have shown some advantages over conventional methods in seismic data denoising, such as higher signal-to-noise ratio after denoising, complete separation of signals and noise in shared frequency bands and intelligent denoising without artificial parameter tuning. However, the lack of real noise data matched with raw seismic data has greatly limited its further application. In this paper, we take the surface seismic shot gather as an example to explore the corresponding solutions and propose a novel supervised-deep-learning method with weak dependence on real noise data based on the data augmentation of a generative adversarial network. We utilize the generative adversarial network to augment the pre-arrival noise data acquired from the shot gather itself, thereby obtaining a large amount of synthetic noise data whose probability distribution is extremely similar to that of the real noise in shot gather; the augmented synthetic noise data and sufficient synthetic signal data obtained by forward modeling together form the augmented training dataset. Meanwhile, the dilated convolution and gradual denoising strategy are adopted to construct the basic architecture of denoising convolution neural network. Finally, the above augmented dataset is used to train the network, so as to establish a nonlinear and complex mapping relationship between raw seismic data and desired signals. Both synthetic and real experiments demonstrate that our method can realize the intelligent denoising of different common-shot-point records in shot gather with the help of limited pre-arrival noise data. Article Highlights: We introduce the data augmentation strategy into the field of deep-learning-based seismic denoising, thereby alleviating the dependence of supervised-deep-learning methods on real noise data We propose a novel denoising network architecture with strong recovery ability for weak desired signals by using the gradual denoising strategy and dilated convolution The augmented synthetic noise data can meet the requirement of supervised-deep-learning methods on the quantity and authenticity of training data, so this data augmentation strategy by using the Generative Adversarial Net (GAN) is a solution to the lack of real noise data [ABSTRACT FROM AUTHOR]

Secondary microseisms are the most energetic noise in continuous seismometer recordings. They are generated by interactions between ocean waves, including between gravity waves incident on and reflected from the coast. Coastal reflections of ocean waves leading to coastal microseismic sources are hard to estimate in various global numerical wave models, and independent quantification of these coastal sources through direct measurements can greatly improve these models. Here, we exploit a 41-km-long submarine optical fibre (OF) cable located offshore Toulon, France, using distributed acoustic sensing (DAS). We record both the amplitude and frequency of seafloor strains induced by ocean surface gravity waves, as well as secondary microseisms caused by the interaction of gravity waves incident and reflected from the coast. By leveraging the spatially distributed nature of DAS measurements, additional fundamental information is recovered such as the velocity and azimuth of the waves. We find that on average 30 per cent of the gravity waves are reflected at the coast generating local sources of secondary microseisms that manifest as Scholte waves. These local sources represent the most energetic contribution to the seismic noise recorded along the OF and by an onshore broad-band station located near the DAS interrogator. Furthermore, we estimate a coastal reflection coefficient of ocean surface gravity waves R 2 of about 0.07, which provides improved constraints for seismic noise generation models. In addition, we show that new local sources of microseisms can be generated when gravity waves characteristics (azimuth and frequency content) change and lead to some delays between the OF cable and buoy recordings. These analyses pave the way for a wide use of DAS data to monitor ocean–solid earth interactions as they provide a wealth of information on the reflection of gravity waves, coastal microseismic sources, and new constraints for numerical models of microseismic noise. [ABSTRACT FROM AUTHOR]

Source-generated linear noise is one of the most common types of noise in seismic data. One or more groups of slanted linear events with strong energy often appear in seismic records and severely degrade the quality of subsurface reflections. Therefore, how to effectively remove this noise is the key to improve the quality of reflections. Here, we use a method that combines a feed-forward denoising convolutional neural network (DnCNN) with asymmetric convolution blocks (ACB) to attenuate linear noise in seismic data. Compared with traditional filter methods, this method involves less assumptions concerning the signals and noise; we merely train the neural network to recognise the features of reflections in seismic data. The DnCNN is a supervised deep learning method. It needs sufficient training data to optimise network parameters. Therefore, we generate numerous pairs thereof – including synthetic and real seismic data – to feed to the network. This input of data enables the network to identify reflections in seismic data directly and thus obtain the denoised data. Based on the characteristics of linear noise in seismic data, we build an asymmetric network architecture by combining the DnCNN with ACB. This enables the network to develop an ability to automatically identify reflected signals in seismic data. To validate the performance of the proposed method, we apply it to synthetic and real seismic data. The results demonstrate the method can effectively identify signals from noisy data and obtain better results in attenuation of linear noise and preservation of signals compared with the four other methods. [ABSTRACT FROM AUTHOR]

Limited polar geographical range, narrowly defined migratory routes, and deep‐diving behaviours make narwhals exceptionally vulnerable to anthropogenic disturbances including oceanic noise. Although behavioural studies indicate marked responses of cetaceans to disturbance, the link between fear reactions and possible injury from noise exposure is limited for most species.To address this, we deployed custom‐made heart rate‐accelerometer‐depth recorders on 13 adult narwhals in Scoresby Sound, East Greenland across a five‐year period (2014–2018). Physiological responses of the cetaceans were monitored in the absence (n = 13 animals) or presence (n = 2 animals across 3 acoustic events) of experimentally directed, seismic airgun pulses and associated vessels (full volume source level = 241 dB re 1 μPa‐m).We found that anthropogenic noise resulted in marked cardiovascular, respiratory and locomotor reactions by two narwhals exposed to seismic pulses across three acoustic events. The general behavioural response to seismic and vessel noise included an 80% reduction in the duration of gliding during dive descents by seismic‐exposed narwhals compared to controls, and the prolongation of high intensity activity (ODBA > 0.20 g) with elevated stroke frequencies exceeding 40 strokes per minute. Noise exposure also resulted in intense (<10 bpm) bradycardia that was decoupled from stroking frequency. This decoupling instigated increased variability in heart rate, with the heart switching rapidly between bradycardia and exercise tachycardia during noise exposure. The maximum respiratory frequency following seismic exposure, 12 breaths/min, was 1.5 times control levels.Overall, the effect of seismic/ship noise exposure on Arctic narwhals was a 2.0–2.2‐fold increase in the energetic cost of diving, which paradoxically occurred during suppression of the cardiac exercise response. This unusual relationship between diving heart rate and exercise intensity represents a new metric for characterizing the level of fear reactions of wild marine mammals exposed to different environmental stressors. Together, the multi‐level reactions to anthropogenic noise by this deep‐diving cetacean demonstrated how a cascade of effects along the entire oxygen pathway could challenge physiological homeostasis especially if disturbance is prolonged. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]