Removing residual airborne sensor motion for measuring seismic signals from a drone

2021 ◽  
pp. 1-20
Author(s):  
Thomas Rapstine ◽  
Paul Sava
Keyword(s):  
Ground Motion ◽  
Seismic Data ◽  
Analytic Form ◽  
Seismic Signal ◽  
Signal Separation ◽  
Seismic Signals ◽  
Sensor Noise ◽  
Separation Problem ◽  
Level Sensor ◽  
Motion Measurements

Acquiring seismic data using drones requires excellent knowledge of the drone’s motion since positional measurements made from an airborne sensor represent a combination of sensor and ground motion. Recent advancements in laser Doppler vibrometry and repeat lidar surveys show that the frequency and resolution of non-contact motion measurements is increasing to the point necessary for measuring seismic signals. We explore the conditions under which separation of sensor motion from ground motion can be accomplished in practice. We assume (i) that the translation and rotation of a stabilized airborne sensor follows an analytic form in time that is either known or can be estimated from the sensor’s measurements, (ii) that the seismic signal we observe has compact support contained within the measurement window, and (iii) that the ground motion can be described by a rigid translation. We analyze the effectiveness of our signal separation problem as a function of peak signal, sensor noise level, sensor rotation angle, and sensor point sampling density by defining a boundary where SNR = 0 dB for various combinations of these parameters. We find that under the set of assumptions, lower rotation angles, lower sensor noise, and denser point samplings on the ground provide better signal separation using our method.

scholarly journals Applicataion of ormsby wavelet for generation of synthetic seismic signals

2018 ◽  
Vol 7 (2.7) ◽  
pp. 794
Author(s):  
E Sai Sumanth ◽  
V Joseph ◽  
Dr K S Ramesh ◽  
Dr S Koteswara Rao
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Transforms ◽  
Seismic Signal ◽  
Core Structure ◽  
Seismic Signals ◽  
Seismic Reflections ◽  
Input Wave

Investigation of signals reflected from earth’s surface and its crust helps in understanding its core structure. Wavelet transforms is one of the sophisticated tools for analyzing the seismic reflections. In the present work a synthetic seismic signal contaminated with noise is synthesized  and analyzed using Ormsby wavelet[1]. The wavelet transform has efficiently extracted the spectra of the synthetic seismic signal as it smoothens the noise present in the data and upgrades the flag quality of the seismic data due to termers. Ormsby wavelet gives the most redefined spectrum of the input wave so it could be used for the analysis of the seismic reflections. 

scholarly journals Application of zero phase wavelet on synthetic seismic signals with noise

2018 ◽  
Vol 7 (2.7) ◽  
pp. 906
Author(s):  
Sai Srinivas Goli ◽  
Sireesha Papanaboyina ◽  
Satya Ramesh Kanchumarthi ◽  
Koteswara Rao Sanagapallea
Keyword(s):  
Event Detection ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Seismic Signal ◽  
Accurate Information ◽  
Seismic Signals ◽  
Reflected Wave ◽  
Zero Phase

Detection of time of occurrences of different phases and frequencies is of highest importance in seismic reflected signals. Seismic reflection analysis gives us accurate information about the event detection, source acquisition of triggered seismic data and its mechanisms. In the present work an attempt is made to generate a synthetic seismic with noise generally present in the seismograph using. The synthetic seismic signal is extracted by zero phase wavelet. Crews software is used in this extraction. The zero phase wavelet could efficiently extract the seismic signal present in the reflected wave.  

SEISMIC SIGNAL ENHANCEMENT WITH THREE‐COMPONENT DETECTORS

Geophysics ◽  
1964 ◽  
Vol 29 (5) ◽  
pp. 664-671 ◽  
Author(s):  
Michael Shimshoni ◽  
Stewart W. Smith
Keyword(s):  
Ground Motion ◽  
Major Axis ◽  
Seismic Signal ◽  
Cross Product ◽  
Signal Enhancement ◽  
Radial Motion ◽  
Seismic Signals ◽  
Original Signal ◽  
P And Sv Waves

The time‐averaged cross‐product of vertical and radial components of ground motion is multiplied by the original signal producing a function of ground motion in which rectilinearly polarized motion is enhanced. This provides some separation of seismic signals from noise when both occupy the same frequency and velocity bands. The vertical and radial motion is decomposed into its Fourier components, and the parameters of an equivalent ellipse are computed at each instant of time. The eccentricity, major axis, and angle of inclination of the ellipse provide criteria for identifying P and SV waves of different velocities.

NOISE AND TARGET SIGNALS SIMULATION IN PASSIVE SEISMIC LOCATION SYSTEMS

2018 ◽  
pp. 73-78
Author(s):  
Yu. V. Morozov ◽  
M. A. Rajfeld ◽  
A. A. Spektor
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Simulation Procedure ◽  
Proposed Model ◽  
Seismic Location ◽  
Passive Seismic ◽  
System Characteristics ◽  
Primary Pulse

The paper proposes the model of a person seismic signal with noise for the investigation of passive seismic location system characteristics. The known models based on Gabor and Berlage pulses have been analyzed. These models are not able wholly to consider statistical properties of seismic signals. The proposed model is based on almost cyclic character of seismic signals, Gauss character of fluctuations inside a pulse, random amplitude change from pulse to pulse and relatively small fluctuation of separate pulses positions. The simulation procedure consists of passing the white noise through a linear generating filter with characteristics formed by real steps of a person, and the primary pulse sequence modulation by Gauss functions. The model permits to control the signal-to-noise ratio after its reduction to unity and to vary pulse shifts with respect to person steps irregularity. It has been shown that the model of a person seismic signal with noise agrees with experimental data.

Local seismic attributes

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. A29-A33 ◽  
Author(s):  
Sergey Fomel
Keyword(s):  
Image Registration ◽  
Seismic Signal ◽  
Seismic Attributes ◽  
Data Sets ◽  
Local Similarity ◽  
Seismic Signals ◽  
Practical Applications ◽  
Regularized Inversion ◽  
Signal Characteristics ◽  
Land Survey

Local seismic attributes measure seismic signal characteristics not instantaneously, at each signal point, and not globally, across a data window, but locally in the neighborhood of each point. I define local attributes with the help of regularized inversion and demonstrate their usefulness for measuring local frequencies of seismic signals and local similarity between different data sets. I use shaping regularization for controlling the locality and smoothness of local attributes. A multicomponent-image-registration example from a nine-component land survey illustrates practical applications of local attributes for measuring differences between registered images.

Bayesian feature learning for seismic compressive sensing and denoising

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. O91-O104 ◽  
Author(s):  
Georgios Pilikos ◽  
A. C. Faul
Keyword(s):  
Compressive Sensing ◽  
State Of The Art ◽  
Feature Learning ◽  
Seismic Signal ◽  
Basis Functions ◽  
Sparse Signals ◽  
Seismic Signals ◽  
Process Factor ◽  
Learned Features ◽  
Fixed Basis

Extracting the maximum possible information from the available measurements is a challenging task but is required when sensing seismic signals in inaccessible locations. Compressive sensing (CS) is a framework that allows reconstruction of sparse signals from fewer measurements than conventional sampling rates. In seismic CS, the use of sparse transforms has some success; however, defining fixed basis functions is not trivial given the plethora of possibilities. Furthermore, the assumption that every instance of a seismic signal is sparse in any acquisition domain under the same transformation is limiting. We use beta process factor analysis (BPFA) to learn sparse transforms for seismic signals in the time slice and shot record domains from available data, and we use them as dictionaries for CS and denoising. Algorithms that use predefined basis functions are compared against BPFA, with BPFA obtaining state-of-the-art reconstructions, illustrating the importance of decomposing seismic signals into learned features.

Strong Ground-Motion Measurements during the 2003 Bam, Iran, Earthquake

2005 ◽  
Vol 21 (1_suppl) ◽  
pp. 165-179 ◽  
Author(s):  
Mehdi Zaré ◽  
Hossein Hamzehloo
Keyword(s):  
Ground Motion ◽  
Local Time ◽  
Strong Ground Motion ◽  
Strike Slip ◽  
Bam Earthquake ◽  
Lateral Strike Slip ◽  
Slip Mechanism ◽  
Motion Measurements ◽  
The City ◽  
Strong Ground

The Bam earthquake of 26 December 2003 ( Mw 6.5) occurred at 01:56:56 (GMT, 05:26:56 local time) near the city of Bam in the southeast of Iran. Two strong phases of energy are seen on the accelerograms. The first comprises a starting subevent with right-lateral strike-slip mechanism located south of Bam. The mechanism of the second subevent was a reverse mechanism.

scholarly journals Dynamics of the Askja caldera July 2014 landslide, Iceland, from seismic signal analysis: precursor, motion and aftermath

2017 ◽  
Author(s):  
Anne Schöpa ◽  
Wei-An Chao ◽  
Bradley Lipovsky ◽  
Niels Hovius ◽  
Robert S. White ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal Analysis ◽  
Slope Failure ◽  
Stable State ◽  
Seismic Signal ◽  
Early Warning Systems ◽  
High Energy ◽  
Stick Slip ◽  
Short Period ◽  
Large Landslide

Abstract. Using data from a network of 58 seismic stations, we characterise a large landslide that occurred at the southeastern corner of the Askja caldera, Iceland, on 21 July 2014, including its precursory tremor and mass wasting aftermath. Our study is motivated by the need for deeper generic understanding of the processes operating not only at the time of catastrophic slope failure, but also in the preparatory phase and during the transient into the subsequent stable state. In addition, it is prompted by the high hazard potential of the steep caldera lake walls at Askja as tsunami waves created by the landslide reached famous tourist spots 60 m above the lake level. Since direct observations of the event are lacking, the seismic data give valuable details on the dynamics of this landslide episode. The excellent seismic data quality and coverage of the stations of the Askja network made it possible to jointly analyse the long- and short-period signals of the landslide to obtain information about the triggering, initiation, timing, and propagation of the slide. The seismic signal analysis and a landslide force history inversion of the long-period seismic signals showed that the Askja landslide was a single, large event starting at the SE corner of the caldera lake at 23:24:05 UTC and propagating to the NW in the following 2 min. The bulk sliding mass was 7–16 × 1010 kg, equivalent to a collapsed volume of 35–80 × 106 m3, and the centre of mass was displaced horizontally downslope by 1260 ± 250 m during landsliding. The seismic records of stations up to 30 km away from the landslide source area show a tremor signal that started 30 min before the main landslide failure. It is harmonic, with a fundamental frequency of 2.5 Hz and shows time-dependent changes of its frequency content. We attribute the complex tremor signal to accelerating and decelerating stick-slip motion on failure planes at the base and the sides of the landslide body. The accelerating motion culminated in aseismic slip of the landslide visible as a drop in the seismic amplitudes down to the background noise level 2 min before the landslide high-energy signal begins. We propose that the seismic signal of the precursory tremor may be developed as an indicator for landslide early-warning systems. The 8 hours after the main landslide failure are characterised by smaller slope failures originating from the destabilised caldera wall decaying in frequency and magnitude. We introduce the term afterslides for this subsequent, declining slope activity after a large landslide.

scholarly journals Monitoring and analysis of seismic data during the 2018 sunda strait tsunami

2021 ◽  
Vol 331 ◽  
pp. 07006
Author(s):  
Wahyu Kurniawan ◽  
Daryono ◽  
IDK Kerta ◽  
Bayu Pranata ◽  
Tri Winugroho
Keyword(s):  
Seismic Data ◽  
Warning System ◽  
Seismic Signal ◽  
The Body ◽  
Volcanic Earthquake ◽  
Time Zone ◽  
Ground Shaking ◽  
Tsunami Early Warning ◽  
Seismograph Station ◽  
Tsunami Signal

The tsunami of Sunda Strait occurred on December 22, 2018, at 21:03 West Indonesia Time (zone). An eruption of Mount Anak Krakatau caused an eruption that triggered a landslide on the slopes of Mount Anak Krakatau covering an area of 64 hectares that hit the coastal area of western Banten and southern Lampung and resulted in 437 deaths, 14.059 people were injured, and 33.721 people were displaced. Before the tsunami, signal transmissions (gaps) at the Lava seismograph station installed on the body of Mount Anak Krakatau experienced broken so that Mount Anak Krakatau Observation Post could not record volcanic earthquake signals since December 22, 2018, at 21.03 West Indonesia Time (zone). Given these facts, proper monitoring and analysis were required to monitor and analyze the source of ground vibrations originating from the eruption of Mount Anak Krakatau. Therefore, this study aims to confirm the eruptive activity of Mount Anak Krakatau based on seismic monitoring and analysis sourced from the BMKG's seismic sensor network. The method the author uses is by monitoring the seismic signal recorded by the seismometer and analyzing the seismic signal using the Seiscomp3 software. By the results of monitoring and analysis of seismic data, it was found that the location of the center of the ground shaking was on Mount Anak Krakatau with a magnitude of 3.4, and a depth of 1 km. To anticipate similar tsunami events in the future, it is very necessary to have a tsunami early warning system originating from volcanic activity and volcanic body avalanches.

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