Beast Quake (Taylor’s Version): Analysis of Seismic Signals Recorded during Two Taylor Swift Concerts in Seattle, July 2023

Seismic signals are ground vibrations used to detect seismic events. However, seismic signal captured from sensors is distorted signal contains noise and makes actual event detection difficult. In most cases, external noise such as manmade or any heavy vehicle vibration always overlaps with the seismic reflections over time. The presence of noise in the seismic signal makes it difficult to determine the magnitude at which the seismic events have occurred. The aim of our study is to process the signals received from seismic sensor and identify it as seismic events signal and non-seismic events signal based on the magnitude. The authors propose a robust noise suppression method using bandpass filter, IIR Wiener filter and event detection using recursive Short-Term Average (STA)/Long Term Average (LTA) and Carl Short Term Average (STA)/Long Term Average (LTA). The proposed study determines reference magnitude to distinguish seismic and non-seismic activity. The projected study is based on the analysis of seismic signal received from single sensor and sensor networks (SN) and determines the magnitude to distinguish seismic and nonseismic events and time of an actual earthquake event. The experimental dataset is a broadband seismic signal from BSVK and CUKG station sensors located at Basavakalyan, Karnataka, and the Central University of Karnataka respectively. The proposed approach helps to extract the information about preseismic event, actual seismic event, post-seismic event activities and identify the abnormal pattern that supports to detect heearth’s activities before the actual seismic event.

The analysis of signals containing multiple chirps occurs in many situations in acoustics, for example, in the bioacoustics field in analyzing the sounds of bats and dolphins. In sonar and seismic, chirp signals are used because of their pulse compression properties. Recently, the fractional Fourier transform has shown that linear chirp signals may be recognized and this has greatly facilitated the use of these signals. This paper shows a method similar to that of the fractional transform and shows how other types of chirp signals, polynomial, and other functional forms may also be analyzed. It also looks at the resolution that can be achieved when separating chirps both in rate and in time. This is of particular relevance when dealing with dispersion in the propagation of signals through different media. Examples will be presented, for the analysis of some bat signals and for some seismic signals. The analysis of signals in the time frequency domain using piecewise linear chirps will also be demonstrated, and some further examples from synthetic seismic signals will be demonstrated.

Development of the 10–11 July 2015 two-stage sequence of multiple emplacements of pyroclastic density currents at Volcán de Colima, México: Insight from associated seismic signals

A few methods of time–frequency distributions (TFD) have been employed to analyze and detect the occurrence of the predominant frequencies in the seismic signal. Comprehensive joint time–frequency analysis of seismic signal using the well-known short-time Fourier transform (STFT), Gabor transform (GT), Wigner–Ville distribution (WVD), and Choi–Williams distribution (CWD) have been carried out to overcome the problem encountered upon the analysis of seismic signal with fast Fourier transform (FFT), which fails to provide temporal information of the seismic signal. Furthermore, the seismic detection method is proposed using a new time–frequency method, which is known as Gabor–Wigner transform (GWT), to achieve better time–frequency resolution by removing the cross-terms. The performance of the time–frequency distributions including Gabor–Wigner transform on the seismic signal has been quantified by Renyi entropy. The present analysis proves the efficacy of the GWT on seismic signal detection. The detection of predominant frequency facilitates in determining the earthquake magnitude. The suitability of the proposed GWT method has been investigated for earthquake early warning systems.

The high accuracy time-frequency representation of non-stationary signals is one of the key researches in seismic signal analysis. Low-frequency part of the seismic data often has a higher frequency resolution, on the contrary it tends to have lower frequency resolution in the high frequency part. It's difficult to fine characterize the time-frequency variation of non-stationary seismic signals by conventional time-frequency analysis methods due to the limitation of the window function. Therefore based on the Ricker wavelet, we put forward the matching pursuit seismic trace decomposition method. It decomposes the seismic records into a series of single component atoms with different frequency and energy, by making use of the Wigner-Ville distribution, has the time-frequency resolution of seismic signal reach the limiting resolution of the uncertainty principle and skillfully avoid the impact of interference terms in conventional Wigner-Ville distribution.

This paper is concerned with anisotropic effects on seismic data and signal analysis for transversely isotropic rock media with vertical anisotropy. It is understood that these effects are significant in many practical applications, e.g. earthquake forecasting, materials exploration inside the Earth’s crust, as well as various practical works in oil industry. Under the framework of the most accepted anisotropic media model (i.e. VTI media, transverse isotropy with a vertical axis symmetry), with applications of a set of available anisotropic rock parameters for sandstone and shale, we have performed numerical calculations of the anisotropic effects. We show that for rocks with strong anisotropy, the induced relative depth error can be significantly large. Nevertheless, with an improved understanding of the seismic-signal propagation and proper data processing, the error can be reduced, which in turn may enhance the probability of forecasting accurately the various wave propagations inside the Earth’s crust, e.g. correctly forecasting the incoming earthquakes from the center of the Earth.

Mars atmospheric pressure variations induce ground displacements through elastic deformations. The various sensors of the InSight mission were designed in order to be able to understand and correct for these ground deformations induced by atmospheric effects. Particular efforts were made, on one hand, to avoid direct pressure and wind effects on the seismometer and, on the other hand, to have a high performance pressure sensor operating in the same frequency range as the seismometer. As a consequence of these technical achievements and the low background seismic noise of Mars, the InSight mission is opening a new science domain in which the ground displacements can be used to perform atmospheric science. This study presents an analysis of pressure and seismic signals and the relations between them. After a short description of the pressure and seismic sensors, we present an analysis of these signals as a function of local time at the InSight location. Then the coherent signals recorded by both pressure and seismic sensors are described and interpreted in terms of atmospheric signals and ground deformation processes. Two different methods to remove the pressure effects recorded by SEIS sensors are presented, and their efficiency is estimated and compared. These decorrelation methods allow the pressure generated noise to be reduced by a factor of 2 during the active day time period. Finally, an analysis of SEIS signals induced by gravity waves demonstrates the interest of ground displacement measurements to characterize their arrival azimuth.

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better “focalizing” function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algorithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase.

The purpose of this paper is to share the experience gained in, and the efforts made toward, introducing and implementing a new course in the challenging and important area of geophysical signal processing at the Electrical Engineering (EE) Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia. The new course, titled “Geo-Signal Processing,” was offered both at the graduate level and as a special topics course to undergraduates. The course was developed in collaboration with the Earth Sciences Department at KFUPM. This paper contributes new information because it stresses the multidisciplinary aspects of digital signal processing (DSP) technologies when applied to estimating the Earth's layered structure on the basis of seismic data. Unlike many Earth sciences seismic data processing courses, this Geo-Signal Processing course also emphasizes that the perspective taken by those working in DSP is different from that taken by geophysicists. The course presents DSP with particular emphasis on seismic data signals and the artifacts accompanying them while covering the principles and algorithms needed for processing seismic signals in both deterministic and statistical fashion. Topics include, but are not limited to, basic seismic theory, acquisition of seismic data, analysis of seismic signals and noise, deterministic filtering of seismic data, and statistical processing of seismic data.

Application of the local maximum synchrosqueezing transform for seismic data

One of the key problems of seismic monitoring is the identification of earthquakes and signals from technogenic sources detected by a network of seismic stations. In peacetime, technogenic events are mainly associated with industrial mining developments, however, with the beginning of russia's full-scale aggression against sovereign Ukraine, thousands of seismic signals from explosions as a result of missile, aircraft, artillery strikes were registered by the seismological network of the Main Center of Special Monitoring of the State Space Agency of Ukraine. This significantly complicates the process of assessing seismicity and makes the question of determining the nature of registered events extremely relevant. Based on the analysis of seismic signals, the relationships between energy classes (K), magnitudes (mb), maximum amplitudes of longitudinal volumetric phases , and yields (Y) of explosions in TNT equivalent in Kyiv, Zhytomyr, Vinnytsia, Khmelnytsky, Chernihiv regions. Energy characteristics can be used to identify the nature of seismic events, and the results of the analysis of the ratios , , make it possible to yield estimate of explosions in TNT equivalent and determine the probable types of ammunition based on the received data. The energy from the signal source in the case of an explosive event can be determined additionally by infrasound data, the presence of an acoustic wave serves as an additional criterion for identifying the event. At the same time, energy characteristics make it possible to identify natural sources, an example of which is the tectonic earthquake of May 26, 2023 in the Poltava region.One of the key problems of seismic monitoring is the identification of earthquakes and signals from technogenic sources detected by a network of seismic stations. In peacetime, technogenic events are mainly associated with industrial mining developments, however, with the beginning of russia's full-scale aggression against sovereign Ukraine, thousands of seismic signals from explosions as a result of missile, aircraft, artillery strikes were registered by the seismological network of the Main Center of Special Monitoring of the State Space Agency of Ukraine. This significantly complicates the process of assessing seismicity and makes the question of determining the nature of registered events extremely relevant. Based on the analysis of seismic signals, the relationships between energy classes (K), magnitudes (mb), maximum amplitudes of longitudinal volumetric phases , and yields (Y) of explosions in TNT equivalent in Kyiv, Zhytomyr, Vinnytsia, Khmelnytsky, Chernihiv regions. Energy characteristics can be used to identify the nature of seismic events, and the results of the analysis of the ratios , , make it possible to yield estimate of explosions in TNT equivalent and determine the probable types of ammunition based on the received data. The energy from the signal source in the case of an explosive event can be determined additionally by infrasound data, the presence of an acoustic wave serves as an additional criterion for identifying the event. At the same time, energy characteristics make it possible to identify natural sources, an example of which is the tectonic earthquake of May 26, 2023 in the Poltava region.

Seismic signals discrimination is a multidimensional problem since recorded events may vary in terms of type, location, energy, etc. Recently, two discrimination features based on instantaneous frequency (IF) were proposed by the Authors. The first of these features is determined by distribution of the signals’ first Intrinsic Mode Function’s (IMF) IF. The second one is a particular simplification of the previous one as it gives information about the most frequently occurring instantaneous frequency in the considered first IMF. In order to exhibit features’ potential in distinguishing of seismic vibration signals, one has to use clustering algorithms. The features were already subjected to k-means algorithm. In this paper we show results of agglomerative hierarchical clustering (AHCA) and compare it with outcomes of k-means. In order to test optimal number of clusters, method based on average silhouette was accomplished. The results are illustrated by analysis of real seismic vibration signals from an underground copper ore mine.

Abstract. The prompt detection of explosive volcanic activity is crucial since this kind of activity can release copious amounts of volcanic ash and gases into the atmosphere, causing severe dangers to aviation. In this work, we show how the joint analysis of seismic and infrasonic data by wavelet transform coherence (WTC) can be useful to detect explosive activity, significantly enhancing its recognition that is normally done by video cameras and thermal sensors. Indeed, the efficiency of these sensors can be reduced (or inhibited) in the case of poor visibility due to clouds or gas plumes. In particular, we calculated the root mean square (RMS) of seismic and infrasonic signals recorded at Mt. Etna during 2011. This interval was characterised by several episodes of lava fountains, accompanied by lava effusion, and minor strombolian activities. WTC analysis showed significantly high values of coherence between seismic and infrasonic RMS during explosive activity, with infrasonic and seismic series in phase with each other, hence proving to be sensitive to both weak and strong explosive activity. The WTC capability of automatically detecting explosive activity was compared with the potential of detection methods based on fixed thresholds of seismic and infrasonic RMS. Finally, we also calculated the cross correlation function between seismic and infrasonic signals, which showed that the wave types causing such seismo-acoustic relationship are mainly incident seismic and infrasonic waves, likely with a common source.

In physical terms, periodic movements of a human body resulting from walking produce a pulse sequence with repetition time T(1) (instant cadence frequency, 1/T(1)) and duration time T(2). Footstep forces generate periodic T(1) broadband seismic and sound signals due to the dynamic forces between the foot and the ground/floor with duration time T(2), which is equal to the time interval for a single footstep from heel strike to toe slap and weight transfer. In a human gait study (for normal speeds of walking), T(1) was detected as 0.5-0.69 s and double limb support takes up about 12% of the gait cycle (2T(1)), so T(2) is greater than 0.12-0.17 s. Short range (of about 50 m) signatures for 30 humans were recorded simultaneously by four orthogonal sensor types at two locations. The sensor types were active Doppler sonar/radar and passive seismic/acoustics. Analysis of signals from these four sensors collected for walking humans showed temporal synchronization and stability of the cadence frequencies, and the cadence frequency from each sensor was equivalent. The time delay between signals from these sensors due to the differences in speeds of propagation for seismic, sound, and electromagnetic waves allows calculation of the distance from a walker to the sensor suite.

Abstract. One purpose of landslide research is to establish early warning thresholds for rainfall-induced landslides. Insufficient observations of past events have inhibited the analysis of critical rainfall conditions triggering landslides. This difficulty may be resolved by extracting the timing of landslide occurrences through analysis of seismic signals. In this study, seismic records of the Broadband Array in Taiwan for Seismology were examined to identify ground motion triggered by large landslides that occurred in the years 2005 to 2014. A total of 62 landslide-induced seismic signals were identified. The seismic signals were analyzed to determine the timing of landslide occurrences, and the rainfall conditions at those times – including rainfall intensity (I), duration (D), and effective rainfall (Rt) – were assessed. Three common rainfall threshold models (I–D, I–Rt, and Rt–D) were compared, and the crucial factors of a forecast warning model were found to be duration and effective rainfall. In addition, rainfall information related to the 62 landslides was analyzed to establish a critical height of water model, (I-1.5)⋅D=430.2. The critical height of water model was applied to data from Typhoon Soudelor of 2015, and the model issued a large landslide warning for southern Taiwan.