Seismic Signals Research Articles

Multichannel Analysis of Surface Waves based on Common Virtual Source Gathers of Seismic Ambient Noise Cross-Correlations: A Case Study at an Earth Dam in Brazil

The S-wave velocity (Vs) is a valuable parameter for assessing the mechanical properties of subsurface materials for geotechnical purposes. Seismic surface wave methods have become prominent for estimating near-surface Vs models. Researchers have proposed methods based on passive seismic signals as efficient alternatives to enhance depth of investigation, lateral resolution and reduce field effort. This study presents the Multichannel Analysis of Surface Waves (MASW) utilizing Common Virtual Source Gathers (CVSGs) derived from seismic ambient noise cross-correlations, based on Ambient Noise Seismic Interferometry concepts. The method is applied to passive data acquired with an array of receivers at the Paranoá earth dam in Brasília, Brazil, to construct a pseudo-2D Vs image of the massif for interpretation. Our findings showcase the adopted processing flow and combination of methods as an effective approach for near-surface Vs estimation, demonstrating its usability also for large earth dam embankments.

Design and Application of Real‐Time and High‐Precision Seismic Acquisition Station in Antarctic Region

ABSTRACTIn this study, a polar seismic acquisition station based on the FPGA + ARM architecture was successfully developed. This system can achieve real‐time multifunctional acquisition and transmission of seismic signals and environmental information in the extreme low‐temperature environment of Antarctica at −40°C. The system adopts a modular design, with high‐precision data acquisition encoded by the main controller and uploaded to the host computer in real‐time. Integrated display controls enable real‐time data display and acquisition station location mapping for user interaction. We propose improving data transmission in traditional seismic acquisition stations by adopting a distributed computing model with data segment return. Integrating the first arrival picking algorithm as an IP core will reduce computational demands on the data processing center. This acquisition system features high precision (24‐bit), a wide dynamic range (> 130dB), low noise (0.5 V rms@40dB), and high‐precision timing control (200 ns). It supports real‐time wireless data transmission over 120 channels up to 1 km. The system also implements a self‐storage plus wired and wireless networking solution, allowing flexible networking based on actual needs. Test results indicate that the ice sheet thickness at the acquisition site is approximately 526.49 m, validating the experimental design's effectiveness and providing technical support for subsequent drilling and seismic exploration.

An adaptive 6-dimensional floating-search multi-station seismic-event detector (A6-DFMSD) and its application to low-frequency earthquakes in the East Eifel Volcanic Field, Germany

We introduce a seismic event detector that applies signal analysis in the time and frequency domains. Signals are searched for with matching coincidences at neighbouring recording stations in space and time. No a priori waveform information is needed for the Adaptive 6-Dimensional Floating-search Multi-station Seismic-event Detector (A6-DFMSD). It combines a short / long time average algorithm (STA/LTA), frequency range selection, energy envelope matching, and backprojection techniques to find a robust detection model. As a challenging test example, the new detector is tuned and applied to a dataset with five months of microearthquake (ML < 2) recordings in the East Eifel Volcanic Field (EEVF), Germany. There, both magmatic and tectonic earthquakes occur in a depth range between 3 km and 43 km. A6-DFMSD detected 4.3 times as many events as were already known and it discovered a previously unknown event cluster. After manual localization and classification of the events, we show that A6-DFMSD finds events of different origins: tectonic, magmatic, atmospheric, and anthropogenic. In particular, low-frequency (LF) earthquakes of magmatic origin with a complicated waveform coda are very well identified. We suggest that seismological networks monitoring local seismicity in similar target zones would benefit from the use of A6-DFMSD to allow the detection of a wide range of different seismic signals.

Dynamics and Detection of Pulsed Tremor at Whakaari (White Island), Aotearoa New Zealand

AbstractVolcanic tremor is a crucial indicator for assessing the state and hazard potential of volcanic systems. At Whakaari (White Island volcano, Aotearoa New Zealand), a pulsed tremor signal emerged after a hydrothermal explosion in August 2012. The tremor accompanied the extrusion of a lava dome, before gradually disappearing prior to the onset of renewed hydrothermal activity in January 2013. We interpret this seismic signal to represent discrete gas transfers from a magmatic intrusion toward a permeable cap—possibly a hydrothermal seal—in the upper layers of Whakaari's hydrothermal system. Such tremor may thus be associated with heightened potential for hazardous explosive activity but is difficult to detect using conventional seismic monitoring parameters. To highlight the emergence of subtle periodic signals, we experiment with Lomb‐Scargle periodograms (LS). LS detect the tremor 5 days before it becomes visible in seismograms, thus facilitating the recognition of such elusive seismic patterns.

An adaptive parameter-free seismic data denoising approach by combining general cross-validation thresholding and pixel connectivity in synchrosqueezed domain

High signal-to-noise ratio (SNR) seismic waveform data are conductive to various studies in seismology. Seismic denoising aims to enhance SNR by eliminating additive noise through signal processing while preserving important features of the seismic signal. Conventional parametric seismic denoising methods often require selecting appropriate parameters to achieve optimal results, which may be limiting when dealing with various types and scales of seismic data. Here, we develop an adaptive parameter-free denoising method by combining general cross-validation (GCV) thresholding and pixel connectivity in synchrosqueezed (SS) domain. In this denoising framework, the synchrosqueezed continuous wavelet transform (SS-CWT) is first applied to obtain a high-resolution time–frequency representation. Then, the GCV approach, which allows for choosing the (nearly) optimal threshold without relying on any prior knowledge about the noise level, is employed to attenuate most of the low-energy noise. After that, the relatively isolated high-energy residual noise remaining in the SS-CWT spectrum is removed using pixel connectivity thresholding. Finally, the inverse SS-CWT is applied to the thresholded spectrum to obtain the denoised seismic record. As the thresholds for GCV and pixel connectivity are derived from the spectrum characteristics of the data being analyzed, the proposed denoising approach is highly adaptive and parameter-free. We demonstrate the effectiveness and versatility of the proposed denoising framework using synthetic data and real seismic data from diverse monitoring scenarios, including land, ocean, and emerging distributed acoustic sensing (DAS). The results indicate that the method is a stable and efficient tool for seismic data denoising.Graphical

A real scale application of a novel set of spatial and similarity features for detection and classification of natural seismic sources from Distributed Acoustic Sensing data

Summary Distributed Acoustic Sensing (DAS) turns a fiber optic into a very dense network of equally-distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyze DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatio-temporal and similarity features for the event classification, and Markov Random Field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalog. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along à 91 km fiber optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fiber optic path, the proposed method succeeded in detecting earthquakes of magnitude &amp;gt;0.4 and quarry blasts of magnitude &amp;gt;1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fiber optic systems.

Explosive eruption processes inferred from high-frequency seismic waveforms of eruption tremor and explosion events

Summary We investigated the relation between high-frequency seismic signals and eruption size and duration using seismic data of eruption tremor and explosion events generated during sub-Plinian and Vulcanian eruptions, respectively, at various volcanoes. We estimated source amplitude functions from seismic envelope seismograms in the 5−10 Hz band, in which S-waves are assumed to radiate isotropically. Because seismic data associated with explosive eruptions can be contaminated by infrasound signals, we confirmed that contamination did not significantly affect the source amplitude functions quantified from our analysed waveforms. We approximated the source amplitude functions of eruption tremor and explosion events by quadrilateral and triangular shapes. For eruption tremor, the durations of the source amplitude functions increased with decreasing slope of the initial phase, i.e. between onset and maximum amplitude. For explosion events, both the maximum and cumulative amplitudes of the source amplitude functions increased with increasing slope of the initial phase, but the overall durations clustered around a typical value. Moreover, the initial phase durations of eruption tremor were longer than those of explosion events. Based on eruption models proposed by previous studies, Vulcanian and sub-Plinian eruptions have been thought to be triggered by accumulation of magma at a shallow part in a conduit and mixing of cool mushy magma with hot fresh magma in a reservoir, respectively. The above differences between the source amplitude functions of eruption tremor and explosion events can be explained by the distinct eruption triggering processes of sub-Plinian and Vulcanian eruptions. Our results suggest that source amplitude functions are useful for investigating eruption processes and estimating eruption sizes and durations for seismic eruption monitoring.

How a non harmonic-like tremor at a volcanic lake could be caused by sustained wind: A case of study of Taupō volcano

Taupō volcano lies underneath the largest lake in New Zealand. It undergoes unrest roughly every ten years, has minor eruptions every few hundred years, and has supereruptions. Understanding volcanic seismic signals provides insights into the volcano dynamics and helps to anticipate eruptions. Harmonic and non-harmonic volcanic tremor are common signals that indicate fluid mobilisation in volcanic systems. We observed a signal that resembled non-harmonic tremor during 2019 at Taupō volcano. Careful time-frequency analysis of seismic data and weather data from around the lake revealed that the signals were not magmatic in origin, but were most likely from lake microseisms caused by special conditions of the wind, which generates wind-driven waves in the lake. The frequency range of lake microseisms at Taupō starts and ends with a dominant frequency of 0.7–1.0 Hz, and during the maximum energy peak, the dominant frequency is of 0.50–0.56 Hz. Such signals may be common in caldera lakes, which need to be distinguished from tremor caused by volcanic activity, so as not to exaggerate the probability of eruptions.

Grid-search method for short-term over long-term average parameter tuning: an application to Stromboli explosion quakes

The collection of a significant catalogue of seismo-volcanic data involves the selection of relevant parts of raw signals, which can be automatised by using the short-term over long-term average (STA/LTA) method. The STA/LTA method employs the “Characteristic Function” to describe a section of a seismic record in terms of trace amplitude and first-time difference. This function is calculated in a short-term and long-term window; the ratio between the two windows defines a quantity that is controlled through threshold values, i.e., trigger on and trigger off. These threshold values indicate whether there is an increase in the energy in the seismic signal compared to the background noise. The common approach to the selection of the STA/LTA values is the adoption of literature-suggested ones. This could be a limitation as there may be cases in which a choice adapted to a specific raw signal may significantly help in the extraction of the relevant parts. To overcome the possible drawbacks of a non-adaptive choice imposed by such standard literature values, in this study, we propose a methodology for the automatic selection of STA/LTA values that can optimise the extraction of explosion quakes (EQs) from a seismo-volcanic raw signal. The values are obtained through a grid search over an index named quality–numerosity index (QNI) that measures the accordance in the automatic cuts and the consequent number of triggered seismo-volcanic events with the ones suggested by a human expert. The method was applied in the volcano domain for the specific application of the explosion quake signal extraction at Stromboli volcano. The experiments were conducted by selecting a subset of the dataset as training where to search for the best values, which were subsequently adopted in a test set. The results prove that the values suggested by our approach significantly improve the quality of the relevant part compared to the one extracted by adopting the values indicated in the literature. The methodology presented in this study can be applied to a wider typology of signals of volcanic, seismic, and other origin, potentially becoming a widely used approach in parameter optimisation processes.

Quantification of nonlinear effect in seismic signal using Holo-Hilbert spectral analysis

Affected by excitation conditions, absorption, and attenuation of stratum and receiving instruments, the seismic signal is non-stationary and nonlinear. Time-frequency spectral analysis methods are effective and widely used in describing the time-varying features of seismic signals. However, these methods are all based on additive expansions, and cannot represent the nonlinearity of signal, which is achieved through a multiplicative process. Therefore, the information provided by the time-frequency spectrum is incomplete. To show the nonlinearity of seismic signal, that is, the modulation effect of low-frequency components on high-frequency components, the Holo-Hilbert spectral analysis method is introduced in this paper. Holo-Hilbert spectral analysis (HHSA) contains nested empirical mode decomposition and Hilbert transform to achieve a full informational spectrum represented by two frequency dimensions. In the Holo-Hilbert spectrum, one dimension represents frequency-modulated (FM) frequency, which characterizes the non-stationarity of the signal, and the other dimension represents the amplitude-modulated (AM) frequency, which indicates the nonlinear effect in the signal. Synthetic and field data examples verify the feasibility of HHSA in characterizing the cross-frequency interaction of non-stationary seismic signals as a potential tool to detect reservoirs.

Experimental Investigation of Seismic Signal Characteristics Arising from Basal Stress Fluctuations in Granular Flow

Experimental Investigation of Seismic Signal Characteristics Arising from Basal Stress Fluctuations in Granular Flow

The role of underground archaeological remains on the seismic response of a historic tower

The Italian territory features plenty of historic masonry buildings, many of which have undergone reconstruction, often atop ancient ruins. The presence of archaeological remains in the shallow layers of the ground is always disregarded in the seismic assessment of masonry buildings. To address this gap, the paper delves into the influence of underground masonry ruins on the seismic response of a bell tower built above a historic soil deposit. A comprehensive 3D finite-element model encompassing the tower, foundation, soil, and underground remains was developed. A parametric analysis was conducted, involving modifications to the geometry, mechanical properties, and location of the buried ruins. From the parametric study it emerged that the buried walls could alter the seismic signal at the base of the structure. The most critical scenario occurs when the buried archaeological ruins have a grid spacing comparable to the characteristic size of the foundation, especially in soft soils. Analysis of displacements, accelerations, and spectral accelerations at different elevations along the tower height revealed that the presence of archaeological ruins could modify the seismic response of the structure built above them.

Precise Relocation and Source Characterization of Two Earthquake Sequences in Yellow Sea Using Regional Lg-Wave Observations

ABSTRACT Two significant earthquakes of magnitude ML 4.6 and 4.5 occurred on 18 January and 3 December 2021 in the central region of the Yellow Sea, respectively. The earthquakes occurred beneath the Gunsan sedimentary basin at about 10 km depth with a strike-slip faulting mechanism on nodal planes striking northwest–southeast (NW–SE) and north-northeast–south-southwest (NNE–SSW). Despite a lack of close-by seismographic stations, we successfully utilized regional Lg-wave observations on both coasts of the sea—the Korean peninsula on the east and eastern China on the west. For nine earthquakes in two event sequences, the Lg-wave differential travel times of the nearby event pairs at the common station are carefully measured using the waveform cross-correlation technique. The double-difference earthquake relocation method is employed to obtain precise relative epicentral locations using the Lg correlation measurements. Relocated epicenters align along the NW–SE direction, indicating that the nodal plane striking the same direction is the likely fault plane on which both sequences occurred. This is the first case reported in the literature in which the causative fault plane has been identified for earthquakes in the central Yellow Sea region. It has an important implication for current regional tectonics; it favors neither old tectonic features trending NE–SW (Qianliyan uplift) nor the north–south alignment of significant earthquakes in the region along the Amur plate boundary. The Lg waves from the earthquake sequences are dominant seismic signals on all three-component records at stations in 160–550 km and allowed us to analyze source properties of the two largest earthquakes using the empirical Green’s function approach. Azimuthal variations of the source corner frequencies suggest that earthquake rupture likely propagated toward southeast (125°) along the fault plane, supporting the aftershock relocation results.

Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

Observation and analysis of infrasound signals from the 1 January 2024 Japan earthquake

The 1 January 2024 Japan Earthquake occurred off the coast of Noto Peninsula, Japan. Three infrasound arrays s located more than 1400 km away from the epicenter received both local seismic and infrasound signals atmospherically propagating from the epicenter. The back azimuth, elevation angle and other parameters are calculated for the two types of infrasound signals received by the subarray through procedures of Progressive Multi-Channel Correlation (PMCC) and Frequency-Wavenumber (F-K) analysis. Source localization and other processing are also conducted. A recognition feature based on a low-frequency multi-scale entropy algorithm is proposed, and the received seismic signals of this event are correctly identified by using Support Vector Machine (SVM). This paper explores the calculation of earthquake parameters based on local infrasound wave and atmospherically propagated infrasound signals excited by distant earthquake events, and proposes a new method for infrasound signals event identification.

Enhancing Seismic Data Accuracy: An Advanced Health Diagnosis Method for Seismometers Performance Evaluation

Abstract Seismometers play a crucial role in monitoring seismic activities, yet their performance can degrade over time due to environmental conditions, aging components, and external disturbances. This study introduces an innovative seismometer health diagnosis method based on seismic signal analysis tailored for the Indonesian earthquake station network. The proposed method involves trim signal, signal resampling, filtering, and deconvolution of instrument response to process seismic data from the earthquake station network in Indonesia, specifically from Honshu, Japan 2024 of seismic events in the regions of Java, Indonesia, using Python software. By understanding the condition of the seismometer, the accuracy of data can be optimized or improved, damage can be minimized, and maintenance costs can be reduced. Seismometer health diagnoses are conducted using cross-correlation analysis and amplitude ratios of seismic signals from 3 neighbour seismic stations using 88 stations to diagnosis, we found that 90% of the seismometers in the Java, Indonesia region are still health condition and 10% of the seismometers are unhealth condition. The health parameters could be used to diagnose seismometer performance in the Indonesian earthquake station network.

Geomagnetic and ionospheric effects two consecutive strong earthquakes in Morocco on september 08, 2023

The geophysical effects of a strong seismic event in the form of two earthquakes of magnitude 6.8 and 4.9 that occurred on September 08, 2023 in Morocco at close times 22:11 and 22:30 UTC with an epicentral distance between the foci of ~ 4 km are considered. We used data from a number of observatories of the INTERMAGNET network and the results of magnetic registration at the Mikhnevo Geophysical Observatory of IDG RAS. It was shown that in the absence of significant global disturbances of the Earth’s magnetic field, earthquakes were accompanied by a series of three positive bay-shaped geomagnetic variations with maximum amplitudes from ~1 to ~10 nT, following each other after ~60 min. The maxima of the induced magnetic field variations were observed almost synchronously at distances from ~800 to ~10000 km. The delay time of the magnetic effect relative to the main shock of the first earthquake was ~70 min. Taking into account the almost planetary nature and high synchronicity of the magnetic field disturbances caused over a significant range of distances, as well as time delays corresponding in order of magnitude to the travel time of the seismic signal of a distance multiple of the size of the Earth, it is suggested that the magnetic effect of the seismic event in question was caused by a global source, which can serve as an excited geodynamo. The ionospheric effect of the seismic event under consideration is presented in the form of variations of the critical frequency f0F2 calculated from the data of the ground-based sounding station of the del Ebre Observatory.

Characterization of Seismic Signal Patterns and Dynamic Pore Pressure Fluctuations Due to Wave-Induced Erosion on Non-Cohesive Slopes

Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert–Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action.

Cross‐equalization for time‐lapse sparker seismic data

AbstractTime‐lapse seismic data processing is an important technique for observing subsurface changes over time. The conventional time‐lapse seismic exploration has been conducted using a large‐scale exploration system. However, for efficient monitoring of shallow subsurface, time‐lapse monitoring based on the small‐scale exploration system is required. Small‐scale exploration system using a sparker source offers high vertical resolution and cost efficiency, but it faces challenges, such as inconsistent waveforms of sparker sources, inaccurate positioning information and a low signal‐to‐noise ratio. Therefore, this study proposes a data processing workflow to preserve the signal and enhance the repeatability of small‐scale time‐lapse seismic data acquired using a sparker source. The proposed workflow has three stages: pre‐stack, post‐stack and machine learning–based data processing. Conventional seismic data processing methods were applied to enhance the quality of the sparker seismic data during the pre‐stack data processing stage. In the post‐stack processing stage, the positions and energy correction were performed, and the machine learning–based data processing stage attenuated random noise and applied a matched filter. The data processing was performed using only the seismic signals recorded near the seafloor, and the results confirmed the improvement in the repeatability of the entire seismic profile, including that of the target area. According to the repeatability quantification results, the predictability increased and the normalized root mean square decreased during data processing, indicating improved repeatability. In particular, the repeatability of the data was greatly improved through vertical correction, energy correction and matched filtering approaches. The processing results demonstrate that the data processing method proposed in this study can effectively enhance the repeatability of high‐resolution time‐lapse seismic data. Consequently, this approach could contribute to a more accurate understanding of temporal changes in subsurface structure and material properties.

Applied experimental research on in-situ online monitoring instrument for soil radon in fault zone

As an emerging method for seismic precursory signals in underground fluid, the reliability and stability of in-situ online monitoring devices for soil radon are crucial performance indicators that are directly related to the successful implementation of continuous monitoring of soil radon in seismic areas. This study conducted laboratory testing and field applications of a new in-situ online monitoring instrument for seismic soil radon, utilizing the experimental testing conditions provided by the radon monitoring instrument detection platform of the China Earthquake Administration and the natural experimental site at Xianshuihe Fault Zone in the China Seismic Experimental Site. The results indicate that laboratory measurement repeatability of the instrument is 5.53%, the relative intrinsic error of radon volume response activity is 1.53%, and the response capability of hourly sampling at high radon volume response activity (greater than 1.0 × 105 Bq/m3) lags behind the standard instrument by 2 h, essentially meeting the requirements for seismic observation. Under field conditions, the instruments exhibit good synchronization in response at different depths at the same location. However, further improvements are needed to enhance the consistency of long-term operation in the field, as well as the adaptability to outdoor self-powering, data transmission, and environmental conditions.