scholarly journals HVSR Analysis of Rockslide Seismic Signals to Assess the Subsoil Conditions and the Site Seismic Response

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Alessia Lotti ◽  
Veronica Pazzi ◽  
Gilberto Saccorotti ◽  
Andrea Fiaschi ◽  
Luca Matassoni ◽  
...  
Keyword(s):  
Seismic Data ◽  
Seismic Noise ◽  
Spectral Ratio ◽  
Effect Of Temperature ◽  
Precipitation Trend ◽  
Seismic Signals ◽  
Rock Falls ◽  
Time Frequency ◽  
Passive Seismic ◽  
On Line

Many Italian rock slopes are characterized by unstable rock masses that cause constant rock falls and rockslides. To effectively mitigate their catastrophic consequence thorough studies are required. Four velocimeters have been placed in the Torgiovannetto quarry area for an extensive seismic noise investigation. The study area (with an approximate surface of 200×100 m) is located near the town of Assisi (Italy) and is threatened by a rockslide. In this work, we present the results of the preliminary horizontal to vertical spectral ratio analysis of the acquired passive seismic data aimed at understanding the pattern of the seismic noise variation in case of stress state and/or weathering conditions (fluid content and microfracturing). The Torgiovannetto unstable slope has been monitored since 2003 by Alta Scuola of Perugia and the Department of Earth Sciences of the University of Firenze, after the observation of a first movement by the State Forestry Corps. The available data allowed an extensive comparison between seismic signals, displacement, and meteorological information. The measured displacements are well correlated with the precipitation trend, but unfortunately no resemblance with the seismic data was observed. However, a significant correlation between temperature data and the horizontal to vertical spectral ratio trend of the seismic noise could be identified. This can be related to the indirect effect of temperature on rock mass conditions and further extensive studies (also in the time frequency domain) are required to better comprehend this dependency. Finally, the continuous on-line data reveal interesting applications to provide near-real time warning systems for emerging potentially disastrous rockslides.

scholarly journals Seismic monitoring of torrential and fluvial processes

2016 ◽  
Vol 4 (2) ◽  
pp. 285-307 ◽  
Author(s):  
Arnaud Burtin ◽  
Niels Hovius ◽  
Jens M. Turowski
Keyword(s):  
Seismic Noise ◽  
Seismic Waves ◽  
Seismic Signal ◽  
Seismic Monitoring ◽  
Surface Processes ◽  
Seismic Signals ◽  
Seismic Arrays ◽  
Time Frequency ◽  
Continuous Survey ◽  
Long Time

Abstract. In seismology, the signal is usually analysed for earthquake data, but earthquakes represent less than 1 % of continuous recording. The remaining data are considered as seismic noise and were for a long time ignored. Over the past decades, the analysis of seismic noise has constantly increased in popularity, and this has led to the development of new approaches and applications in geophysics. The study of continuous seismic records is now open to other disciplines, like geomorphology. The motion of mass at the Earth's surface generates seismic waves that are recorded by nearby seismometers and can be used to monitor mass transfer throughout the landscape. Surface processes vary in nature, mechanism, magnitude, space and time, and this variability can be observed in the seismic signals. This contribution gives an overview of the development and current opportunities for the seismic monitoring of geomorphic processes. We first describe the common principles of seismic signal monitoring and introduce time–frequency analysis for the purpose of identification and differentiation of surface processes. Second, we present techniques to detect, locate and quantify geomorphic events. Third, we review the diverse layout of seismic arrays and highlight their advantages and limitations for specific processes, like slope or channel activity. Finally, we illustrate all these characteristics with the analysis of seismic data acquired in a small debris-flow catchment where geomorphic events show interactions and feedbacks. Further developments must aim to fully understand the richness of the continuous seismic signals, to better quantify the geomorphic activity and to improve the performance of warning systems. Seismic monitoring may ultimately allow the continuous survey of erosion and transfer of sediments in the landscape on the scales of external forcing.

On bias and noise in passive seismic data from finite circular array data processed using SPAC methods

Geophysics ◽  
2006 ◽  
Vol 71 (6) ◽  
pp. V153-V162 ◽  
Author(s):  
Michael W. Asten
Keyword(s):  
Seismic Data ◽  
Seismic Noise ◽  
Imaginary Component ◽  
Empirical Measure ◽  
Restricted Range ◽  
Circular Array ◽  
Spatial Averaging ◽  
Seismic Arrays ◽  
Spectral Estimates ◽  
Passive Seismic

The finite nature of typical small seismic arrays used in conjunction with spatial autocorrelation (SPAC) processing for observing the microtremor wavefield causes predictable perturbations of the SPAC spectrum when sources of seismic noise are confined to a restricted range of azimuths. Such perturbations are especially evident at higher frequencies where wavelengths are on the order of the array radius. The effects are readily modeled and show that the triangular array geometries commonly used for microtremor studies require azimuthal distributions of wave energy on the order of [Formula: see text] or greater to have a high probability of being free of such perturbations. The imaginary component of the SPAC spectrum, which is ideally zero for a sufficiently dense circular array and/or a sufficiently isotropic wavefield, is in practice often nonzero and provides three quality-control indicators: (1) an indication of insufficient spatial averaging, (2) an empirical measure of the level of statistical uncertainty in SPAC spectral estimates, and (3) an indication of departures from plane-wave stationarity of the seismic noise wavefield.

Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. V211-V227 ◽  
Author(s):  
S. Mostafa Mousavi ◽  
Charles A. Langston
Keyword(s):  
Seismic Data ◽  
Ambient Noise ◽  
Cross Validation ◽  
Single Channel ◽  
Threshold Level ◽  
Noise Removal ◽  
Seismic Signals ◽  
Hard Thresholding ◽  
Resolution Time ◽  
Time Frequency

Recorded seismic signals are often corrupted by noise. We have developed an automatic noise-attenuation method for single-channel seismic data, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time-frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross-validation approach that does not rely on any prior knowledge about the noise level. The efficiency of the thresholding has been improved by adding a preprocessing step based on kurtosis measurement and a postprocessing step based on adaptive hard thresholding. The proposed algorithm can either attenuate the noise (either white or colored) and keep the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or preprocessing in ambient noise studies. We tested the performance of the proposed method on synthetic, microseismic, and earthquake seismograms.

Integrated Analysis of Microtremor Horizontal to Vertical Spectral Ratio, Surface Waves Dispersion Curve, and Seismic Refraction Tomography to Estimate Weathered Layer Thickness and Seismic Vulnerability: Case Study Kalirejo Village, Kokap Sub-District, Kulon Progo Regency

2020 ◽  
Vol 22 (3) ◽  
pp. 23
Author(s):  
Anna Wahyu ◽  
Ade Filla Intan ◽  
Arddhiles Adhitama ◽  
Febrian Nur Fadhli ◽  
Ferda Elita Putri ◽  
...  
Keyword(s):  
Surface Waves ◽  
Dispersion Curve ◽  
Layer Thickness ◽  
Seismic Data ◽  
Seismic Vulnerability ◽  
Seismic Refraction ◽  
Spectral Ratio ◽  
Refraction Tomography ◽  
Passive Seismic ◽  
Weathered Layer

Subduction of Indo-Australia plate to Eurasia plate and locally active fault nearby Kulon Progo play as major source for earthquake events. After effect due to earthquake has different level of damage which depend on the magnitude and site characteristics. The horizontal-to-vertical spectral ratio (HVSR) passive seismic method is being used drastically to help in mapping the level of site vulnerability to earthquake event. HVSR analysis results help us acquire some physical values including weathered layer thickness where Vs 30 reference came from surface waves dispersion curve analysis of the MASW method as it is used as a parameter in calculating thickness value. Seismic refraction tomography is used to create subsurface model thus we may see the extent of underlying layer and its implication to earthquake event.Data measurements distribution are scattered in Kalirejo Village with the total of 63 passive seismic data, 33 MASW data, and 7 lines of seismic refraction acquisition. Some data show inadequate quality to be taken into further processing step, so data sorting activity should be carefully done. As a result, 21 of 63 passive seismic data are considered adequate to represent site physical values. Dominant frequency values ranging from 2 to 20 Hz, amplification factor varies between 1.5-12.5, and seismic vulnerability indices varies between 0.3-20. Surface waves dispersion curve inversion results are Vs 30 values ranging from 350 m/s to 980 m/s and seismic refraction tomography model shows Vp model with velocity values ranging from 0.2 to 3.2 km/s.

Seismic Data Denoising Simulation Research Based on Wavelet Transform

2014 ◽  
Vol 490-491 ◽  
pp. 1356-1360 ◽  
Author(s):  
Shu Cong Liu ◽  
Er Gen Gao ◽  
Chen Xun
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Packet ◽  
Real Data ◽  
Noise Removal ◽  
Wavelet Packet Transform ◽  
Seismic Signals ◽  
Transform Method ◽  
Simulation Research ◽  
Time Frequency

The wavelet packet transform is a new time-frequency analysis method, and is superior to the traditional wavelet transform and Fourier transform, which can finely do time-frequency dividion on seismic data. A series of simulation experiments on analog seismic signals wavelet packet decomposition and reconstruction at different scales were done by combining different noisy seismic signals, in order to achieve noise removal at optimal wavelet decomposition scale. Simulation results and real data experiments showed that the wavelet packet transform method can effectively remove the noise in seismic signals and retain the valid signals, wavelet packet transform denoising is very effective.

scholarly journals Application of Geologically Constrained Machine Learning Method in Characterizing Paleokarst Reservoirs of Tarim Basin, China

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1765
Author(s):  
Wei Xin ◽  
Fei Tian ◽  
Xiaocai Shan ◽  
Yongjian Zhou ◽  
Huazhong Rong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Machine Learning Algorithms ◽  
Seismic Exploration ◽  
Seismic Signals ◽  
Time Frequency ◽  
Mode Decomposition ◽  
New Perspective ◽  
Geological Constraints ◽  
Frequency Features

As deep carbonate fracture-cavity paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity paleokarst reservoirs. By empirical mode decomposition, the time–frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly.

Seismic anelastic attenuation estimation using prestack seismic gathers

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. M37-M49
Author(s):  
Naihao Liu ◽  
Bo Zhang ◽  
Jinghuai Gao ◽  
Hao Wu ◽  
Shengjun Li
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Synthetic Data ◽  
Spectral Ratio ◽  
Time Frequency ◽  
Anelastic Attenuation ◽  
Time Migration ◽  
Seismic Quality Factor ◽  
Zero Offset ◽  
Nmo Stretch

The seismic quality factor [Formula: see text] quantifies the anelastic attenuation of seismic waves in the subsurface and can be used in assisting reservoir characterization and as an indicator of hydrocarbons. Usually, the [Formula: see text]-factor is estimated by comparing the spectrum changes of vertical seismic profiles and poststack seismic data. However, seismic processing such as the normal moveout (NMO) stretch would distort the spectrum of the seismic data. Hence, we have estimated [Formula: see text] using prestack time migration gathers. To mitigate the NMO stretch effect, we compensate the NMO stretch of prestack seismic gathers in the time-frequency domain. Similar to the log spectral method, our method obtains the [Formula: see text] by measuring the log spectral ratio (LSR) of seismic events of the top and base of the reservoir at a zero-offset seismic trace. The LSR has a linear relationship with a new parameter [Formula: see text] by assuming that the source wavelet is a constant-phase wavelet. The parameters [Formula: see text] and LSR vary with the offset value (traveltime). We use the values of [Formula: see text] and LSR obtained from nonzero-offset seismic traces to simulate the values of [Formula: see text] and LSR at a zero-offset seismic trace. Finally, we obtain [Formula: see text] by applying the classic LSR method to the simulated [Formula: see text] and LSR. To demonstrate the validity and effectiveness of our method, we first apply it to noise-free and noisy synthetic data examples and then to real seismic data acquired over the Sichuan Basin, China. The synthetic and real seismic applications demonstrate the effectiveness of our method in highlighting high anelastic-attenuation zones.

Q estimation and its application in the 3D shallow weathering zone

2017 ◽  
Vol 5 (1) ◽  
pp. SC29-SC38 ◽  
Author(s):  
Ying Liu ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Zhikai Wang ◽  
Yiran Xu ◽  
...  
Keyword(s):  
Seismic Data ◽  
Empirical Formula ◽  
Synthetic Data ◽  
Spectral Ratio ◽  
Frequency Resolution ◽  
Deep Part ◽  
Time Frequency ◽  
Generalized S Transform ◽  
Velocity Information ◽  
Weathering Zone

Attenuation in the shallow weathering zone is relatively strong, causing severe energy loss during wave propagation. It is difficult to estimate accurate [Formula: see text] values in the shallow weathering zone, and the influence of shallow weathering zone is seldom considered into attenuation estimation and compensation in the deep part. We achieved [Formula: see text] value estimation where there exist microlog data in the shallow weathering zone using the generalized S transform (GST); then, we establish an empirical formula using the velocity and [Formula: see text] value estimated with microlog data; finally, the [Formula: see text] value in the 3D shallow weathering zone can be obtained using the established formula and the velocity information. During the first procedure, the GST is used to provide reasonable time-frequency resolution, and linear regression is used in the obtained logarithmic spectral ratio to get the estimated [Formula: see text] value. An empirical formula is established using the estimated [Formula: see text] value and the velocity where there exists microlog data in the second procedure. In the third step, [Formula: see text] estimation in the whole shallow weathering zone can be obtained using the established formula and the velocity information, which can overcome the inaccuracy of spatial interpolation with the estimated [Formula: see text] factors where there exist different twin-well microlog data. Attenuation compensation to seismic data obtained from the deep part is carried out to prove the effectiveness of the estimated [Formula: see text] in the shallow weathering zone. After compensation, the resolution of seismic data is effectively increased, which demonstrates the validity of the estimated [Formula: see text] values in the shallow weathering zone. Synthetic data and field data examples demonstrate the validity of our method.

scholarly journals Characterizing the complexity of seismic signals at slow-moving clay-rich debris slides: The Super-Sauze (Southeastern France) and Pechgraben (Upper Austria) case studies

2017 ◽  
Author(s):  
Naomi Vouillamoz ◽  
Sabrina Rothmund ◽  
Manfred Joswig
Keyword(s):  
Case Studies ◽  
Seismic Data ◽  
Early Warning Systems ◽  
Microseismic Monitoring ◽  
Seismic Signals ◽  
Warning Systems ◽  
Detection Systems ◽  
Upper Austria ◽  
Passive Seismic ◽  
Slow Moving

Abstract. Soil and debris slides are prone to rapid and dramatic reactivation. Deformation within the instability is accommodated by sliding, whereby weak seismic energies are released through material deformation. Thus, passive microseismic monitoring provides information that relate to the slope dynamics. In this study, passive seismic data acquired at Super-Sauze (Southeastern France) and Pechgraben (Upper Austria) slow-moving clay-rich debris slides (“clayey landslides”) are investigated. Observations are benchmarked to previous similar case studies to provide a comprehensive and homogenized typology of seismic signals at clayey landslides. A well knowledge of the various seismic signals potentially triggered by the slope deformation is crucial for the future development of automatic detection systems to be implemented in early-warning systems. Detected seismic events range from short duration (

Sign in / Sign up

Export Citation Format

Share Document