Robust and reliable techniques for epicenter location using time and slowness observations

1990 ◽  
Vol 80 (1) ◽  
pp. 140-149 ◽  
Author(s):  
Frances Cassidy ◽  
Anders Christoffersson ◽  
Eystein S. Husebye ◽  
Bent O. Ruud
Keyword(s):  
Travel Time ◽  
Single Site ◽  
Waveform Data ◽  
Arrival Times ◽  
Distance Range ◽  
Slowness Vector ◽  
Phase Type ◽  
Event Location ◽  
Epicenter Location

Abstract The ever-increasing flow of parameterized and waveform data into various kinds of seismological centers cannot be managed properly unless preliminary epicenter locations are available. Here we demonstrate robust and flexible techniques for fast and reliable event location using the P slowness vector, which is easily derived from arrival times and/or waveform data from arrays, networks, and/or single-site three-component stations. Location schemes are tied to 1) azimuth minimization and 2) slowness vector summation on a sphere using slowness from N arbitrarily positioned stations. The advantage of using azimuth alone is that no assumption is needed of phase type, distance range, or structure (travel-time tables). The viability of our location techniques are demonstrated using a variety of P recordings from networks and three-component stations.

Travel-time study of six central Appalachian earthquakes 1962-1968

1970 ◽  
Vol 60 (2) ◽  
pp. 629-637
Author(s):  
G. A. Bollinger
Keyword(s):  
North Carolina ◽  
Standard Deviation ◽  
Travel Time ◽  
Input Data ◽  
Time Study ◽  
Inverse Slope ◽  
Average Standard Deviation ◽  
Time Curve ◽  
Arrival Times ◽  
Distance Range

abstract Travel time studies are made of the data from six central Appalachian earthquakes that occurred during the period 1962 through 1968 in the states of Virginia, West Virginia, Maryland, and North Carolina. Arrival times from forty-two station-epicenter pairs served as input data for the study. Epicentral locations were obtained for the six events with an average standard deviation for the travel time residuals of 0.5 seconds. Nineteen Pn observations in the distance range of 300 to 800 km yield a composite Pn travel-time curve given by t = ( 8.98 ± 0.14 ) sec + Δ / ( 8.22 ± 0.02 ) km / sec with a 0.19 second standard deviation in the travel-time observations. The inverse slope for the Pg phase was found as 6.24 ± 0.03 km/sec and for the Sg or Lg phases as 3.67 ± 0.02 km/sec. Magnitude determinations using meus of Evernden (1967) are made for two of the five shocks.

scholarly journals Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Vol 178 (2) ◽  
pp. 313-339
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Random Effects ◽  
Mean Squared Error ◽  
Random Effects Model ◽  
Location Error ◽  
Crust And Upper Mantle ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Event Location ◽  
Travel Time Uncertainty

AbstractThe regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

scholarly journals An automatically generated high-resolution earthquake catalogue for the 2016–2017 Central Italy seismic sequence, including P and S phase arrival times

2020 ◽  
Author(s):  
D Spallarossa ◽  
M Cattaneo ◽  
D Scafidi ◽  
M Michele ◽  
L Chiaraluce ◽  
...  
Keyword(s):  
Real Time ◽  
Central Italy ◽  
Earthquake Catalogue ◽  
Hazard Evaluation ◽  
Depth Range ◽  
Data Set ◽  
Epicentral Area ◽  
Earthquake Parameters ◽  
Waveform Data ◽  
Arrival Times

Summary The 2016–17 central Italy earthquake sequence began with the first mainshock near the town of Amatrice on August 24 (MW 6.0), and was followed by two subsequent large events near Visso on October 26 (MW 5.9) and Norcia on October 30 (MW 6.5), plus a cluster of 4 events with MW > 5.0 within few hours on January 18, 2017. The affected area had been monitored before the sequence started by the permanent Italian National Seismic Network (RSNC), and was enhanced during the sequence by temporary stations deployed by the National Institute of Geophysics and Volcanology and the British Geological Survey. By the middle of September, there was a dense network of 155 stations, with a mean separation in the epicentral area of 6–10 km, comparable to the most likely earthquake depth range in the region. This network configuration was kept stable for an entire year, producing 2.5 TB of continuous waveform recordings. Here we describe how this data was used to develop a large and comprehensive earthquake catalogue using the Complete Automatic Seismic Processor (CASP) procedure. This procedure detected more than 450,000 events in the year following the first mainshock, and determined their phase arrival times through an advanced picker engine (RSNI-Picker2), producing a set of about 7 million P- and 10 million S-wave arrival times. These were then used to locate the events using a non-linear location (NLL) algorithm, a 1D velocity model calibrated for the area, and station corrections and then to compute their local magnitudes (ML). The procedure was validated by comparison of the derived data for phase picks and earthquake parameters with a handpicked reference catalogue (hereinafter referred to as ‘RefCat’). The automated procedure takes less than 12 hours on an Intel Core-i7 workstation to analyse the primary waveform data and to detect and locate 3000 events on the most seismically active day of the sequence. This proves the concept that the CASP algorithm can provide effectively real-time data for input into daily operational earthquake forecasts, The results show that there have been significant improvements compared to RefCat obtained in the same period using manual phase picks. The number of detected and located events is higher (from 84,401 to 450,000), the magnitude of completeness is lower (from ML 1.4 to 0.6), and also the number of phase picks is greater with an average number of 72 picked arrival for a ML = 1.4 compared with 30 phases for RefCat using manual phase picking. These propagate into formal uncertainties of ± 0.9km in epicentral location and ± 1.5km in depth for the enhanced catalogue for the vast majority of the events. Together, these provide a significant improvement in the resolution of fine structures such as local planar structures and clusters, in particular the identification of shallow events occurring in parts of the crust previously thought to be inactive. The lower completeness magnitude provides a rich data set for development and testing of analysis techniques of seismic sequences evolution, including real-time, operational monitoring of b-value, time-dependent hazard evaluation and aftershock forecasting.

scholarly journals Microseismic hydraulic fracture imaging in the Marcellus Shale using head waves

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. KS1-KS10 ◽  
Author(s):  
Zhishuai Zhang ◽  
James W. Rector ◽  
Michael J. Nava
Keyword(s):  
Marcellus Shale ◽  
P Wave ◽  
Head Wave ◽  
Wave Polarization ◽  
Horizontal Section ◽  
Location Accuracy ◽  
Arrival Times ◽  
Microseismic Data ◽  
Head Waves ◽  
Event Location

We have studied microseismic data acquired from a geophone array deployed in the horizontal section of a well drilled in the Marcellus Shale near Susquehanna County, Pennsylvania. Head waves were used to improve event location accuracy as a substitution for the traditional P-wave polarization method. We identified that resonances due to poor geophone-to-borehole coupling hinder arrival-time picking and contaminate the microseismic data spectrum. The traditional method had substantially greater uncertainty in our data due to the large uncertainty in P-wave polarization direction estimation. We also identified the existence of prominent head waves in some of the data. These head waves are refractions from the interface between the Marcellus Shale and the underlying Onondaga Formation. The source location accuracy of the microseismic events can be significantly improved by using the P-, S-wave direct arrival times and the head wave arrival times. Based on the improvement, we have developed a new acquisition geometry and strategy that uses head waves to improve event location accuracy and reduce acquisition cost in situations such as the one encountered in our study.

scholarly journals P-wave first-motion polarity determination of waveform data in western Japan using deep learning

2019 ◽  
Vol 71 (1) ◽  
Author(s):  
Shota Hara ◽  
Yukitoshi Fukahata ◽  
Yoshihisa Iio
Keyword(s):  
Training Data ◽  
P Wave ◽  
Data Sets ◽  
Waveform Data ◽  
Arrival Times ◽  
Distribution Maps ◽  
Seismic Waveforms ◽  
Polarity Determination ◽  
Western Japan ◽  
First Motion

AbstractP-wave first-motion polarity is the most useful information in determining the focal mechanisms of earthquakes, particularly for smaller earthquakes. Algorithms have been developed to automatically determine P-wave first-motion polarity, but the performance level of the conventional algorithms remains lower than that of human experts. In this study, we develop a model of the convolutional neural networks (CNNs) to determine the P-wave first-motion polarity of observed seismic waveforms under the condition that P-wave arrival times determined by human experts are known in advance. In training and testing the CNN model, we use about 130 thousand 250 Hz and about 40 thousand 100 Hz waveform data observed in the San-in and the northern Kinki regions, western Japan, where three to four times larger number of waveform data were obtained in the former region than in the latter. First, we train the CNN models using 250 Hz and 100 Hz waveform data, respectively, from both regions. The accuracies of the CNN models are 97.9% for the 250 Hz data and 95.4% for the 100 Hz data. Next, to examine the regional dependence, we divide the waveform data sets according to the observation region, and then we train new CNN models with the data from one region and test them using the data from the other region. We find that the accuracy is generally high ($${ \gtrsim }$$≳ 95%) and the regional dependence is within about 2%. This suggests that there is almost no need to retrain the CNN model by regions. We also find that the accuracy is significantly lower when the number of training data is less than 10 thousand, and that the performance of the CNN models is a few percentage points higher when using 250 Hz data compared to 100 Hz data. Distribution maps, on which polarities determined by human experts and the CNN models are plotted, suggest that the performance of the CNN models is better than that of human experts.

Contaminant Transport and Spill Reference Tables for the St. Clair River

2012 ◽  
Vol 46 (5) ◽  
pp. 34-47 ◽  
Author(s):  
Eric J. Anderson ◽  
David J. Schwab
Keyword(s):  
Travel Time ◽  
Cloud Model ◽  
Vertical Mixing ◽  
Leading Edge ◽  
Decision Makers ◽  
Time Duration ◽  
Validation Data ◽  
Arrival Times ◽  
Response Planning ◽  
Potential Contaminant

AbstractThe goal of this work is to calibrate a real-time hydrodynamic model for spill tracking in the St. Clair River and to provide decision makers with information for response planning and in the event of a spill. In order to provide experimental validation data, three dye releases were carried out to simulate movement of a potential contaminant in the river. Measurements of dye concentration were used to provide estimates of lateral and vertical mixing as well as travel time of the dye cloud. Model simulations were able to recreate the dye movement and concentrations with model-estimated arrival times within 14 min of the observed plume arrival times and concentrations within 0.005 normalized concentration units of the observed concentrations (which ranged from 0.06 to 0.004).Following model calibration, a set of spill scenarios was chosen to encompass the types and locations of spills commonly experienced in the St. Clair River. These spill scenarios were then simulated with the HECWFS model to predict transport characteristics such as plume leading edge travel time, duration, concentration, and cross-channel mixing. Results from the scenarios were compiled into reference tables in which spill characteristics are listed at several downstream transects. These spill reference tables provide water intake operators with information before the event of a spill, enabling decision makers to plan for potential or common spills as well as providing a quick reference library that can be accessed immediately after a spill is detected to aid in mitigating the effects on drinking water supply.

scholarly journals Estimation of relative source locations from seismic amplitude: application to earthquakes and tremors at Meakandake volcano, eastern Hokkaido, Japan

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Masashi Ogiso ◽  
Kiyoshi Yomogida
Keyword(s):  
Amplitude Ratio ◽  
Linear Equations ◽  
Site Amplification ◽  
New Method ◽  
Seismic Events ◽  
Arrival Times ◽  
Event Location ◽  
Tectonic Earthquakes ◽  
The Difference ◽  
Seismic Amplitudes

AbstractAlthough seismic amplitudes can be used to estimate event locations for volcanic tremors and other seismic events with unclear phase arrival times, the precision of such estimates is strongly affected by site amplification factors. Therefore, reduction of the influence of site amplification will allow more precise estimation of event locations by this method. Here, we propose a new method to estimate relative event locations using seismic amplitudes. We use the amplitude ratio between two seismic events at a given station to cancel out the effect of the site amplification factor at that station. By assuming that the difference between the hypocentral distances of these events is much smaller than their hypocentral distances themselves, we derive a system of linear equations for the differences in relative event locations. This formulation is similar to that of a master event location method that uses differences in phase arrival times. We applied our new method to earthquakes and tremors at Meakandake volcano, eastern Hokkaido, Japan. Comparison of the hypocentral distributions of volcano-tectonic earthquakes obtained thereby with those obtained from phase arrival times confirmed the validity of our new method. Moreover, our method clearly identified source migration among three source regions in the tremor on 16 November 2008, consistent with previous interpretations of other geophysical observations in our study area. Our method will thus be useful for detailed analyses of seismic events whose onset times are ambiguous.

scholarly journals Unexpected Shallow Earthquake of August 1st, 2020 in the North of Indramayu, West Java, Indonesia

2021 ◽  
Vol 873 (1) ◽  
pp. 012043
Author(s):  
Jaya Murjaya ◽  
Pepen Supendi ◽  
Dwikorita Karnawati ◽  
Subagyo Pramumijoyo
Keyword(s):  
Seismic Station ◽  
Moment Tensor ◽  
Focal Mechanism Solution ◽  
West Java ◽  
Waveform Data ◽  
Arrival Times ◽  
S Waves ◽  
The North ◽  
Focus Depth ◽  
Deep Focus

Abstract During the last one hundred years, there are no shallow seismicity in the north of Java. This area is dominated by intermediate and deep focus earthquakes due to the subducted Indo-Australian slab. An earthquake with magnitude ML 4.5 struck Indramayu, north of West Java on August 1, 2020. According to the Agency for Meteorology, Climatology, and Geophysics (BMKG), the earthquake was felt III MMI scale in Indramayu and its vicinity. We used waveform data from BMKG seismic station in West Java, then we picked P-and S-waves arrival times from each station and hypocenter location was determined by Geiger method. We have detected Pn before Pg phase on four BMKG seismic stations, indicating a shallow crustal earthquake. Our inversion show that the earthquake occurred in 6.1805° S, 108.2612° E with 5 km focus depth at 16:24:38 GMT+7. Our focal mechanism solution was determined by using moment tensor inversion shows a strike-slip faulting, which corresponds to the active fault in the north of Indramayu.

scholarly journals An extended trajectory-mechanics approach for calculating the path of a pressure transient: travel-time tomography

2019 ◽  
Vol 23 (11) ◽  
pp. 4541-4560
Author(s):  
Donald W. Vasco ◽  
Joseph Doetsch ◽  
Ralf Brauchler
Keyword(s):  
Travel Time ◽  
Quantum Dynamics ◽  
Reference Model ◽  
Hydraulic Head ◽  
Propagation Path ◽  
Field Site ◽  
Arrival Times ◽  
Imaging Algorithm ◽  
Northern Switzerland ◽  
Analytic Expressions

Abstract. The application of a technique from quantum dynamics to the governing equation for hydraulic head leads to a trajectory-based solution that is valid for a general porous medium. The semi-analytic expressions for propagation path and velocity of a change in hydraulic head form the basis of a travel-time tomographic imaging algorithm. An application of the imaging algorithm to synthetic arrival times reveals that a cross-well inversion based upon the extended trajectories correctly reproduces the magnitude of a reference model, improving upon an existing asymptotic approach. An inversion of hydraulic head arrival times from cross-well slug tests at the Widen field site in northern Switzerland captures a general decrease in permeability with depth, which is in agreement with previous studies, but also indicates the presence of a high-permeability feature in the upper portion of the cross-well plane.

A probabilistic, multi-parametric real-time earthquake location method

2020 ◽  
Author(s):  
Alessandro Caruso ◽  
Aldo Zollo ◽  
Simona Colombelli ◽  
Luca Elia ◽  
Grazia De Landro
Keyword(s):  
Real Time ◽  
Central Italy ◽  
Early Warning Systems ◽  
P Wave ◽  
Earthquake Location ◽  
Arrival Times ◽  
The Real ◽  
Location Parameters ◽  
Event Location ◽  
Wave Arrival

<p>For network-based Earthquake Early Warning Systems (EEWS), the real-time earthquake location is crucial for a correct estimation of event location/magnitude and therefore, for a reliable prediction of the potential expected shaking at the target sites in terms of predicted maximum ground shaking. Different approaches have been recently proposed for the real-time location which mainly use absolute (or differential) P-wave travel times at a set of minimum available stations or measurement of the initial P-wave arrival time (Elarms, Presto, Horiuchi), polarization (Eiserman and Bock) or amplitude and time (Yamada). In this work, we propose a new method which is able to exploit the continuous, real-time information available from both time, amplitude and polarization of initial P-wave signals acquired by dense three component arrays deployed in the source zones. The methodology we propose is an evolutionary and Bayesian probabilistic technique that combines three different observed parameters: 1) the differential arrival times of P-waves (which are computed using a 1D velocity model for the estimation of the theoretical arrival times); 2) the differential P-wave amplitudes in terms of P-wave peak velocity) [reference]  (which are computed using an existing P-peak motion prediction equation) and 3) the real-time estimation of back-azimuthal direction, measured shortly after the P-wave arrival. These three parameters are measured in real-time and are used as prior and conditional information to estimate the posterior probability of the event location parameters, e.g. the hypocenter coordinates and the origin time. The method is evolutive, since it updates the location parameters as new data are acquired by more and more distant stations as the P-wavefront propagates across the network. The output is a multi-dimensional Probability Density Function (PDF), which contains the complete information about the maximum likelihood parameter estimation with their uncertainty. The method is computationally efficient and optimized for running in real-time applications, where the earthquake location has to be retrieved in a very short time window (around 1 sec) after data acquisition. We tested the proposed strategy on a sequence of 29 earthquakes of the 2016-2017 central Italy seismic sequence acquired by the RAN (Rete Accelerometrica Nazionale) network with a magnitude range of 4.2-6.5. For the testing phase, we also simulated non-optimal conditions in terms of source-to-receiver geometry. Specifically, we tested the method  by ssimulating the case of “offshore” earthquakes recorded by a coastal network and in the case of a linear “barrier-type” geometry of the network. Our approach turned out to be suitable to work in condition of a sparse network, with a limited number of nodes and poor azimuthal coverage. In most of the cases, reliable location errors, less than 10 km, are achieved within few seconds from the first recorded P wave. As compared to other classical location techniques (i.e RTLOC in PRESTo) our approach shows an improvement of the solutions, especially for the first instants (2 seconds after the first P-wave arrival at network) when a poor number of stations (less than 4) is available.</p>

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