Unsupervised Clustering of Continuous Ambient Noise Data to Get Higher Signal Quality in Seismic Surveys

2021 ◽  
Author(s):  
Joseph Soloman Thangraj ◽  
Jay Pulliam ◽  
Mrinal K. Sen
Keyword(s):  
Wave Energy ◽  
Ambient Noise ◽  
Body Wave ◽  
Unsupervised Clustering ◽  
Complete Dataset ◽  
Passive Seismic ◽  
Noise Data ◽  
Spatio Temporal ◽  
Using Data ◽  
Strong Body

Abstract Seismic interferometry has been shown to extract body wave arrivals from ambient noise seismic data. However, surface waves dominate ambient noise data, so cross-correlating and stacking all available data may not succeed in extracting body wave arrivals. A better strategy is to find portions of the data in which body wave energy dominates and to process only those portions. One challenge is that passive seismic recordings comprise huge volumes of data, so identifying portions with strong body-wave energy could be difficult or time-consuming. We use spatio-temporal features, calculated with data recorded by all receivers together, to perform unsupervised clustering. Using data recorded by a dense seismic array in Sweetwater, TX we were able to identify five clusters, representing a subsets of the complete dataset that contain similar features, and extract a 7 km/s body wave arrival from one cluster. This arrival did not emerge when we performed the same cross-correlation and stacking regimen on the entire dataset.

scholarly journals Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network

2021 ◽  
Vol 13 (3) ◽  
pp. 389
Author(s):  
Miłosz Mężyk ◽  
Michał Chamarczuk ◽  
Michał Malinowski
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wave Energy ◽  
Ambient Noise ◽  
Body Wave ◽  
The Body ◽  
Detection Accuracy ◽  
Spatial And Temporal Patterns ◽  
Large N ◽  
Noise Data

Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge volume of the recorded ambient noise, only selected time periods (noise panels) are contributing constructively to the retrieval of reflections. We address the issue of automatic scanning of ambient noise data recorded by a large-N array in search of body–wave energy (body–wave events) utilizing a convolutional neural network (CNN). It consists of computing first both amplitude and frequency attribute values at each receiver station for all divided portions of the recorded signal (noise panels). The created 2-D attribute maps are then converted to images and used to extract spatial and temporal patterns associated with the body–wave energy present in the data to build binary CNN-based classifiers. The ensemble of two multi-headed CNN models trained separately on the frequency and amplitude attribute maps demonstrates better generalization ability than each of its participating networks. We also compare the prediction performance of our deep learning (DL) framework with a conventional machine learning (ML) algorithm called XGBoost. The DL-based solution applied to 240 h of ambient seismic noise data recorded by the Kylylahti array in Finland demonstrates high detection accuracy and the superiority over the ML-based one. The ensemble of CNN-based models managed to find almost three times more verified body–wave events in the full unlabelled dataset than it was provided at the training stage. Moreover, the high-level abstraction features extracted at the deeper convolution layers can be used to perform unsupervised clustering of the classified panels with respect to their visual characteristics.

Detection of the deep cavern at the Felsopeteny, Hungary site using seismic ambient noise data

2020 ◽  
Author(s):  
Miriam Kristekova ◽  
Jozef Kristek ◽  
Peter Moczo ◽  
Peter Labak
Keyword(s):  
Free Surface ◽  
Ambient Noise ◽  
Spectral Power ◽  
Hydrothermal Activity ◽  
Underground Cavity ◽  
Seismic Ambient Noise ◽  
Nuclear Explosions ◽  
Passive Seismic ◽  
Noise Data ◽  
Sandstone Formation

<p>Nuclear explosions are banned by the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Obviously, the CTBT needs robust and comprehensive verification tools to make sure that no nuclear explosion goes undetected. The detection of underground cavity due to nuclear explosions is a primary task for an on-site inspection (OSI) and resonance seismometry. Recently we have developed the finite-frequency-range spectral-power method that makes it possible to use seismic ambient noise recorded at the free surface above an underground cavity for localizing it. In this contribution we present results of application of the method to data recorded at a site of the Great Cavern near Felsopeteny, Hungary.</p><p>CTBTO performed several active and passive seismic measurements at the free surface above the Great Cavern in September 2019. Seismic ambient noise was recorded one week continuously at almost 50 stations with interstation distance around 50 m covering area 400 x 400 m.</p><p>The oval shaped cavern with a diameter of 28 m located 70 m below the surface was discovered within a clay mine in N-Hungary. The deep basement is composed of Triassic limestone, the cavern is in the overlying Oligocene sandstone formation. As a result of hydrothermal activity in the Pleistocene a cave formed in the limestone which may have collapsed over time. The opening of the deep part of the cave influenced the overlying sandstone formation but the collapse did not reach the surface.</p><p>We present the procedure of pre-processing and identification of a position of the cavern based on the recorded seismic ambient noise. We checked robustness of the obtained results. The results demonstrate potential of our methodology for the OSI purposes.</p>

Interferometric body-wave retrieval from ambient noise after polarization filtering: application to shallow reflectivity imaging

Geophysics ◽  
2021 ◽  
pp. 1-58
Author(s):  
Deepankar Dangwal ◽  
Michael Behm
Keyword(s):  
Wave Energy ◽  
Ambient Noise ◽  
Body Wave ◽  
Body Waves ◽  
P Wave ◽  
Receiver Array ◽  
Short Period ◽  
Wave Imaging ◽  
Polarization Filtering ◽  
The Impact

Interferometric retrieval of body waves from ambient noise recorded at surface stations is usually challenged by the dominance of surface-wave energy, in particular in settings dominated by anthropogenic activities (e.g., natural resource exploitation, traffic, infrastructure construction). As a consequence, ambient noise imaging of shallow structures such as sedimentary layers remains a difficult task for sparse and irregularly distributed receiver networks. We demonstrate how polarization filtering can be used to automatically extract steeply inclined P-waves from continuous three-component recordings and in turn improves passive body-wave imaging. Being a single-station approach, the technique does not rely on a dense receiver array and is therefore well suited for data collected during surveillance monitoring for tasks such as reservoir hydraulic stimulation, CO_2 sequestration, and wastewater disposal injection. We apply the method on a continuous dataset acquired in the Wellington oilfield (Kansas, US), where local and regional seismicity, and other forms of ambient noise provide an abundant source of both surface- and body-wave energy recorded at 15 short-period receivers. We use autocorrelation to derive the shallow (lt; 1 km) reflectivity structure below the receiver array and validate our workflow and results with well logs and active seismic data. Raytracing analysis and waveform modeling indicates that converted shear waves need to be taken into account for realistic ambient noise body-wave source distributions, as they can be projected on the vertical component and might lead to misinterpretation of the P-wave reflectivity structure. Overall, our study suggests that polarization filtering significantly improves passive body-wave imaging on both autocorrelation and interstation crosscorrelation. It reduces the impact of time-varying noise source distributions and is therefore also potentially useful for time-lapse ambient noise interferometry.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

Seismic wave energy inferred from long-period body wave inversion

1988 ◽  
Vol 78 (5) ◽  
pp. 1707-1724
Author(s):  
Masayuki Kikuchi ◽  
Yoshio Fukao
Keyword(s):  
Seismic Wave ◽  
Wave Energy ◽  
Body Wave ◽  
Empirical Relation ◽  
P Waves ◽  
Average Value ◽  
Long Period ◽  
Wave Inversion ◽  
Moment Ratio ◽  
Order Of Magnitude

Abstract The seismic wave energy is evaluated for 35 large earthquakes by inverting far-field long-period P waves into the multiple-shock sequence. The results show that the seismic wave energy thus obtained is systematically less than that inferred from the Gutenberg-Richter's formula with the seismic magnitude. The difference amounts to one order of magnitude. The results also show that the energy-moment ratio is well confined to a narrow range: 10−6 < ES/Mo < 10−5 with the average of ∼5 × 10−6. This average value is exactly one order of magnitude as small as the energy-moment ratio inferred from the Gutenberg-Richter's formula using the moment magnitude. Comparing the energy-moment ratio with Δσo/2μ, where Δσo and μ are the stress drop and the rigidity, we obtain an empirical relation: ES/Mo ∼ 0.1 × Δσ0/2μ. Such a relation can be interpreted in terms of a subsonic rupture where the energy loss due to cohesion is not negligible to the seismic wave energy.

Statistical Energy Storage Sizing for Point Absorber Wave Energy Converters (WECs): A Device for Operation off the U.S. East Coast

2018 ◽  
Author(s):  
Xiang Zhou ◽  
Mehdi Jafari ◽  
Ossama Abdelkhalik ◽  
Umesh A. Korde ◽  
Lucia Gauchia
Keyword(s):  
Time Series ◽  
Energy Storage ◽  
Wave Energy ◽  
Power Distribution ◽  
Body Wave ◽  
Storage System ◽  
Ground Truth ◽  
Energy Storage System ◽  
Sensing Applications ◽  
Extracted Power

This paper addresses the sizing problem of an energy storage system (ESS) while considering statistical tolerance for a two-body wave energy converter (WEC), which is designed to support ocean sensing applications with sustained power for long-term functioning. The power is extracted by assuming ideal power take-off (PTO) based upon historical ocean data record (significant wave height and period of wave swell) from Martha’s Vineyard Coastal Observatory. A gamma distribution is applied to generate the extracted power distribution of each sample in the time-series using Bayesian methodology. The means and standard deviation of the extracted power distributions compose the statistical annual power time-series. Finally, the required capacities for the ESS sizing are estimated and discussed while considering both ground truth values and statistical values.

scholarly journals The Seismic Potential in the Seismic Gap Between the Wenchuan and Lushan Earthquakes Revealed by the Joint Inversion of Receiver Functions and Ambient Noise Data

Tectonics ◽  
2018 ◽  
Vol 37 (11) ◽  
pp. 4226-4238 ◽  
Author(s):  
Zhiqiang Liu ◽  
Chuntao Liang ◽  
Qian Hua ◽  
Ying Li ◽  
Yihai Yang ◽  
...  
Keyword(s):  
Ambient Noise ◽  
Joint Inversion ◽  
Receiver Functions ◽  
Seismic Gap ◽  
Seismic Potential ◽  
Noise Data

Extracting Long-Period Surface Waves and Free Oscillations Using Ambient Noise Recorded by Global Distributed Superconducting Gravimeters

2020 ◽  
Vol 91 (4) ◽  
pp. 2234-2246
Author(s):  
Hang Li ◽  
Jianqiao Xu ◽  
Xiaodong Chen ◽  
Heping Sun ◽  
Miaomiao Zhang ◽  
...  
Keyword(s):  
Surface Waves ◽  
Group Velocity ◽  
Ambient Noise ◽  
Velocity Dispersion ◽  
Group Velocity Dispersion ◽  
Dispersion Curves ◽  
Free Oscillations ◽  
Long Period ◽  
Superconducting Gravimeters ◽  
Noise Data

Abstract Inversion of internal structure of the Earth using surface waves and free oscillations is a hot topic in seismological research nowadays. With the ambient noise data on seismically quiet days sourced from the gravity tidal observations of seven global distributed superconducting gravimeters (SGs) and the seismic observations for validation from three collocated STS-1 seismometers, long-period surface waves and background free oscillations are successfully extracted by the phase autocorrelation (PAC) method, respectively. Group-velocity dispersion curves at the frequency band of 2–7.5 mHz are extracted and compared with the theoretical values calculated with the preliminary reference Earth model. The comparison shows that the best observed values differ about ±2% from the corresponding theoretical results, and the extracted group velocities of the best SG are consistent with the result of the collocated STS-1 seismometer. The results indicate that reliable group-velocity dispersion curves can be measured with the ambient noise data from SGs. Furthermore, the fundamental frequency spherical free oscillations of 2–7 mHz are also clearly extracted using the same ambient noise data. The results in this study show that the SG, besides the seismometer, is proved to be another kind of instrument that can be used to observe long-period surface waves and free oscillations on seismically quiet days with a high degree of precision using the PAC method. It is worth mentioning that the PAC method is first and successfully introduced to analyze SG observations in our study.

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