Detection of possible seismic station phase reversals using parametric data from seismological bulletins

2021 ◽  
Author(s):  
Konstantinos Lentas
Keyword(s):  
Goodness Of Fit ◽  
Seismic Station ◽  
Waveform Data ◽  
Seismic Stations ◽  
Velocity Models ◽  
Parametric Data ◽  
Source Mechanisms ◽  
First Motion ◽  
Earthquake Mechanism ◽  
Using Data

<p>A simple and fast technique to detect systematic changes in the performance of seismic stations by using parametric data is being presented. The methodology is based on a simple principal, notably, quantifying the goodness of fit of first motion manually picked polarities from seismological bulletins versus available earthquake mechanism solutions over time. The probability of the reporting polarity data fitting (and not fitting) source mechanisms is quantified by calculating the probability distribution of several Bernoulli trials over a randomly perturbed set of hypocentres and velocity models for each earthquake mechanism - station polarity combination. The method was applied to the registered seismic stations in the bulletin of the International Seismological Centre (ISC) after grouping each polarity pick by reporting agency, using data from the past two decades. The overall agreement of first motion polarities against source mechanisms is found to be good with a few cases of seismic stations showing indications of systematic phase reversals over certain time periods. Specifically, results were obtained for 50% of the registered stations at the ISC, and from these stations 70% show reliable operation during the operational time period under investigation, with only 3% showing the opposite, and 7% showing evidence of systematic changes in the quality of the reported first motion polarities. The rest showed great variability over short periods of time, which does not allow one to draw any conclusions. Comparing waveform data with the associated reported polarities revealed a mixture of cases of either questionable picking or true station phase reversals.</p>

Single-station fault plane solutions

1982 ◽  
Vol 72 (3) ◽  
pp. 729-744
Author(s):  
Charles A. Langston
Keyword(s):  
Fault Plane ◽  
Fault Plane Solutions ◽  
Waveform Data ◽  
Motion Study ◽  
Single Station ◽  
Source Mechanisms ◽  
First Motion ◽  
Motion Studies ◽  
Using Data ◽  
Pattern Information

abstract Fault plane solutions are derived from systematic trial-and-error (“grid”) testing of three-component body waveform data from a single station. Modeling P and SH waveform data from five shallow events recorded teleseismically demonstrates that radiation pattern information contained within the interference of the direct wave and surface reflections and the overall relative amplitude between P and SH waveforms is sufficient to discriminate between fault type (e.g., strike-slip versus dip-slip) and often agrees with well-constrained first-motion studies. Events studied are the 9 April 1968 Borrego Mountain, California; 20 June 1978 Thessaloniki, Greece; 13 August 1978 Santa Barbara, California; 20 May 1979 Alaska; and 6 August 1979 Coyote Lake, California, earthquakes. It is also shown using data from the 27 July 1980 Sharpsburg, Kentucky, earthquake that inclusion of pP/P and sP/P polarity and amplitude information to an otherwise unconstrained first-motion study can significantly improve the quality of the fault plane solution. Although there are many potential problems (source multiplicity, directivity, etc.) which can prohibit finding a good model with these techniques and inclusion of data from many stations is clearly desirable, the results of this study suggest that sparse, high-quality waveform data sets may be as or more useful for obtaining source mechanisms than standard first-motion studies. At a minimum, they should be performed together as a consistency check. This procedure would be most useful in the common situation where only a few receivers are available for a particular event.

scholarly journals Earthquake location in tectonic structures of the Alpine Chain: the case of the Constance Lake (Central Europe) seismic sequence

2021 ◽  
Author(s):  
Francesca D’Ajello Caracciolo ◽  
Rodolfo Console
Keyword(s):  
Central Europe ◽  
Velocity Model ◽  
Moho Depth ◽  
Seismic Sequence ◽  
Tectonic Structures ◽  
Study Region ◽  
Waveform Data ◽  
Seismic Stations ◽  
Velocity Models ◽  
Master Event

AbstractA set of four magnitude Ml ≥ 3.0 earthquakes including the magnitude Ml = 3.7 mainshock of the seismic sequence hitting the Lake Constance, Southern Germany, area in July–August 2019 was studied by means of bulletin and waveform data collected from 86 seismic stations of the Central Europe-Alpine region. The first single-event locations obtained using a uniform 1-D velocity model, and both fixed and free depths, showed residuals of the order of up ± 2.0 s, systematically affecting stations located in different areas of the study region. Namely, German stations to the northeast of the epicenters and French stations to the west exhibit negative residuals, while Italian stations located to the southeast are characterized by similarly large positive residuals. As a consequence, the epicentral coordinates were affected by a significant bias of the order of 4–5 km to the NNE. The locations were repeated applying a method that uses different velocity models for three groups of stations situated in different geological environments, obtaining more accurate locations. Moreover, the application of two methods of relative locations and joint hypocentral determination, without improving the absolute location of the master event, has shown that the sources of the four considered events are separated by distances of the order of one km both in horizontal coordinates and in depths. A particular attention has been paid to the geographical positions of the seismic stations used in the locations and their relationship with the known crustal features, such as the Moho depth and velocity anomalies in the studied region. Significant correlations between the observed travel time residuals and the crustal structure were obtained.

scholarly journals Implications of 3D Seismic Raytracing on Focal Mechanism Determination

2019 ◽  
Vol 109 (6) ◽  
pp. 2746-2754
Author(s):  
Katharina Newrkla ◽  
Hasbi Ash Shiddiqi ◽  
Annie Elisabeth Jerkins ◽  
Henk Keers ◽  
Lars Ottemöller
Keyword(s):  
Focal Mechanism ◽  
Historical Earthquakes ◽  
Velocity Model ◽  
Waveform Data ◽  
Focal Mechanism Solutions ◽  
Velocity Models ◽  
Maximum Value ◽  
3D Velocity Model ◽  
First Motion

Abstract The purpose of this study is to investigate apparent first‐motion polarities mismatch at teleseismic distances in the determination of focal mechanism. We implement and compare four seismic raytracing algorithms to compute ray paths and travel times in 1D and 3D velocity models. We use the raytracing algorithms to calculate the takeoff angles from the hypocenter of the 24 August 2016 Mw 6.8 Chauk earthquake (depth 90 km) in central Myanmar to the stations BFO, GRFO, KONO, and ESK in Europe using a 3D velocity model of the upper mantle below Asia. The differences in the azimuthal angles calculated in the 1D and 3D velocity models are considerable and have a maximum value of 19.6°. Using the takeoff angles for the 3D velocity model, we are able to resolve an apparent polarity mismatch where these stations move from the dilatational to the compressional quadrant. The polarities of synthetic waveforms change accordingly when we take the takeoff angles corresponding to the 3D model into account. This method has the potential to improve the focal mechanism solutions, especially for historical earthquakes where limited waveform data are available.

scholarly journals Twin enigmatic microseismic sources in the Gulf of Guinea observed on intercontinental seismic stations

2013 ◽  
Vol 194 (1) ◽  
pp. 362-366 ◽  
Author(s):  
Yingjie Xia ◽  
Sidao Ni ◽  
Xiangfang Zeng
Keyword(s):  
North America ◽  
Temporal Variation ◽  
Crustal Structure ◽  
Source Region ◽  
Broad Band ◽  
Active Volcano ◽  
Gulf Of Guinea ◽  
Waveform Data ◽  
Seismic Stations ◽  
The Earth

Abstract Based on studies of continuous waveform data recorded on broad-band seismograph stations in Africa, Europe and North America, we report evidences for two temporally persistent and spatially localized monochromatic vibrating sources (around 0.036 and 0.038 Hz, respectively) in the Gulf of Guinea, instead of just one source (0.038 Hz or 26 s) found 50 yr ago. The location of the 0.036 Hz source is close to the Sao Tome Volcano, therefore it may be related to volcano processes. However, the 0.038 Hz source cannot be explained with known mechanisms, such as tectonic or oceanic processes. The most likely mechanism is volcano processes, but there is no reported active volcano in source region. Such repetitive vibration sources may provide valuable tools for detecting temporal variation of crustal structure of the Earth.

scholarly journals Order Picking and E-Commerce: Introducing Non-Parametric Efficiency Measurement for Sustainable Retail Logistics

2021 ◽  
Vol 16 (4) ◽  
pp. 846-858
Author(s):  
Matthias Klumpp ◽  
Dominic Loske
Keyword(s):  
Goodness Of Fit ◽  
New Technologies ◽  
Order Picking ◽  
Free Disposal Hull ◽  
Free Disposal ◽  
Efficiency Assessment ◽  
Parametric Data ◽  
Proposed Model ◽  
Dea Models ◽  
Non Parametric

Order picking is a crucial but labor- and cost-intensive activity in the retail logistics and e-commerce domain. Comprehensive changes are implemented in this field due to new technologies like AI and automation. Nevertheless, human worker’s activities will be required for quite some time in the future. This fosters the necessity of evaluating manual picker-to-part operations. We apply the non-parametric Data Envelopment Analysis (DEA) to evaluate the efficiency of n = 23 order pickers processing 6109 batches with 865,410 stock keeping units (SKUs). We use distance per location, picks per location, as well as volume per SKU as inputs and picks per hour as output. As the convexity axiom of standard DEA models cannot be fully satisfied when using ratio measures with different denominators, we apply the Free Disposal Hull (FDH) approach that does not assume convexity. Validating the efficiency scores with the company’s efficiency assessment, operationalized by premium payments shows a 93% goodness=of-fit for the proposed model. The formulated non-parametric approach and its empirical application are promising ways forward in implementing empirical efficiency measurements for order picking operations within e-commerce operations.

scholarly journals Minimum description length and psychological clustering models

2019 ◽  
Author(s):  
Michael David Lee ◽  
Danielle Navarro
Keyword(s):  
Data Analysis ◽  
Goodness Of Fit ◽  
Minimum Description Length ◽  
Additive Clustering ◽  
Similarity Data ◽  
Additive Trees ◽  
Using Data

Clustering is one of the most basic and useful methods of data analysis. This chapter describes a number of powerful clustering models, developed in psychology, for representing objects using data that measure the similarities between pairs of objects. These models place few restrictions on how objects are assigned to clusters,and allow for very general measures of the similarities between objects and clusters.Geometric Complexity Criteria (GCC) are derived for these models, and are used to fit the models to similarity data in a way that balances goodness-of-fit with complexity. Complexity analyses, based on the GCC, are presented for the two most widely used psychological clustering models, known as “additive clustering”and “additive trees”

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.

Assessing uncertainties in velocity models and images with a fast nonlinear uncertainty quantification method

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. R63-R75 ◽  
Author(s):  
Gregory Ely ◽  
Alison Malcolm ◽  
Oleg V. Poliannikov
Keyword(s):  
Uncertainty Quantification ◽  
Seismic Imaging ◽  
Low Frequency ◽  
Expansion Method ◽  
Velocity Model ◽  
Image Features ◽  
Visible Image ◽  
Waveform Data ◽  
Velocity Models ◽  
Seismic Reflections

Seismic imaging is conventionally performed using noisy data and a presumably inexact velocity model. Uncertainties in the input parameters propagate directly into the final image and therefore into any quantity of interest, or qualitative interpretation, obtained from the image. We considered the problem of uncertainty quantification in velocity building and seismic imaging using Bayesian inference. Using a reduced velocity model, a fast field expansion method for simulating recorded wavefields, and the adaptive Metropolis-Hastings algorithm, we efficiently quantify velocity model uncertainty by generating multiple models consistent with low-frequency full-waveform data. A second application of Bayesian inversion to any seismic reflections present in the recorded data reconstructs the corresponding structures’ position along with its associated uncertainty. Our analysis complements rather than replaces traditional imaging because it allows us to assess the reliability of visible image features and to take that into account in subsequent interpretations.

scholarly journals On age difference in joint lifetime modelling with life insurance annuity applications

2018 ◽  
Vol 12 (2) ◽  
pp. 350-371 ◽  
Author(s):  
François Dufresne ◽  
Enkelejd Hashorva ◽  
Gildas Ratovomirija ◽  
Youssouf Toukourou
Keyword(s):  
Life Insurance ◽  
Joint Distribution ◽  
Goodness Of Fit ◽  
Married Couple ◽  
Insurance Market ◽  
Age Difference ◽  
Insurance Company ◽  
The Past ◽  
Copula Approach ◽  
Using Data

AbstractInsurance and annuity products covering several lives require the modelling of the joint distribution of future lifetimes. In the interest of simplifying calculations, it is common in practice to assume that the future lifetimes among a group of people are independent. However, extensive research over the past decades suggests otherwise. In this paper, a copula approach is used to model the dependence between lifetimes within a married couple using data from a large Canadian insurance company. As a novelty, the age difference and the gender of the elder partner are introduced as an argument of the dependence parameter. Maximum likelihood techniques are thus implemented for the parameter estimation. Not only do the results make clear that the correlation decreases with age difference, but also the dependence between the lifetimes is higher when husband is older than wife. A goodness-of-fit procedure is applied in order to assess the validity of the model. Finally, considering several annuity products available on the life insurance market, the paper concludes with practical illustrations.

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