Reassessment of source parameters of ‘major’ southern African earthquakes

2020 ◽  
Vol 123 (1) ◽  
pp. 59-74
Author(s):  
V. Midzi ◽  
T. Pule ◽  
T. Mulabisana ◽  
B. Zulu ◽  
B. Manzunzu
Keyword(s):  
Source Parameters ◽  
Ground Truth ◽  
Velocity Model ◽  
Parametric Data ◽  
Reliable Source ◽  
Hazard And Risk ◽  
Location Algorithm ◽  
Phase Data ◽  
International Seismological Centre ◽  
Large Earthquakes

Abstract Moderate to large earthquakes within an earthquake catalogue contribute significantly to the seismic hazard and risk assessment results of any region. Thus it is prudent to ensure these events have reliable source parameters (epicentres and magnitude). The dataset of events compiled in this study contains a total of 117 instrumentally recorded events of magnitude M ≥5.0, whose parameters were obtained from the Council for Geoscience (CGS) and International Seismological Centre (ISC) databases. The events are mostly located in South Africa with a few in neighbouring countries. Parametric data made up of all available phase data and amplitudes associated with each of the earthquakes were compiled. The availability of these data enabled the earthquake epicentres and magnitude values to be recalculated using the velocity model and the local magnitude relation that are currently being used by the CGS in its analysis of national seismic data. The accuracy of the relocations was determined by producing and analysing three parameters, the azimuthal distribution of seismograph stations (GAP), root-mean-square of travel time residuals (RMS) and epicenter location error data. The analysis of these parameters showed that there was an improvement in the accuracy of the relocated events. Using the ISC location algorithm, iLOC, eight preselected events were further analysed. From this analysis, two earthquakes were found to satisfy the conditions for Ground Truth (GT595%) candidacy whilst four events satisfied the criteria for GT2090% candidacy.

Relocation of seismicity of the Pannonian Basin using the Bayesloc multiple event location algorithm between 1996 and 2019

2020 ◽  
Author(s):  
Barbara Czecze ◽  
István Bondár
Keyword(s):  
Pannonian Basin ◽  
Ground Truth ◽  
Velocity Model ◽  
Measure Data ◽  
Seismic Events ◽  
Seismicity Pattern ◽  
Statistical Framework ◽  
Event Location ◽  
Location Algorithm ◽  
Location Methods

<p>The objective of this work was to relocate the entire seismicity of the Pannonian Basin with the Bayesloc algorithm, a Markov-Chain Monte Carlo inversion scheme using a Bayesian statistical framework.</p><p><span>In the Hungarian National Seismological Bulletin the magnitudes and event locations are determined with the iLoc location algorithm using the 3D global RSTT velocity model, and we used these locations as initial coordinates. In our work, we have used all of the instrumentally registered seismic events between 1996 and 2019 in the Pannonian Basin.</span></p><p><span>During data preprocessing we used graph theory to measure data connectivity. Similar to all multiple-event location methods, Bayesloc performs better when events are recorded on a common network. </span></p><p><span>We used</span> <span>several hundreds</span> <span>of ground truth events (quarry blasts, mine explosions, earthquakes)</span> <span>to tie down</span> <span>the seismicity pattern to known ground truth locations by giving them tighter prior distributions.</span></p><p><span>Based on the day-time peak on the origin-hour distribution of the bulletin earthquakes we assume that there are anthropogenic events labeled as earthquakes in the catalog, therefore we created a „Suspected</span> <span>explosions (SX)” group to set prior constrains.</span></p><p><span>The results show that the events around the mines are dramatically better clustered. The prior constraints contributed remarkably to the outcome of the relocation. We show that the results present an improved view of the seismicity of the region.</span></p>

scholarly journals Structure-controlled asperities of the 1920 Haiyuan M8.5 and 1927 Gulang M8 earthquakes, NE Tibet, China, revealed by high-resolution seismic tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Quan Sun ◽  
Shunping Pei ◽  
Zhongxiong Cui ◽  
Yongshun John Chen ◽  
Yanbing Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Tomography ◽  
High Velocity ◽  
Three Dimensional ◽  
Large Earthquake ◽  
Velocity Model ◽  
Tectonic Stress ◽  
Double Difference ◽  
Source Regions ◽  
Large Earthquakes

AbstractDetailed crustal structure of large earthquake source regions is of great significance for understanding the earthquake generation mechanism. Numerous large earthquakes have occurred in the NE Tibetan Plateau, including the 1920 Haiyuan M8.5 and 1927 Gulang M8 earthquakes. In this paper, we obtained a high-resolution three-dimensional crustal velocity model around the source regions of these two large earthquakes using an improved double-difference seismic tomography method. High-velocity anomalies encompassing the seismogenic faults are observed to extend to depths of 15 km, suggesting the asperity (high-velocity area) plays an important role in the preparation process of large earthquakes. Asperities are strong in mechanical strength and could accumulate tectonic stress more easily in long frictional locking periods, large earthquakes are therefore prone to generate in these areas. If the close relationship between the aperity and high-velocity bodies is valid for most of the large earthquakes, it can be used to predict potential large earthquakes and estimate the seismogenic capability of faults in light of structure studies.

The numerical reconstruction of three past eruptions at Gede volcano (Indonesia)

2021 ◽  
Author(s):  
Eleanor Tennant ◽  
Susanna Jenkins ◽  
Annie Winson ◽  
Christina Widiwijayanti ◽  
Hendra Gunawan ◽  
...  
Keyword(s):  
Epistemic Uncertainty ◽  
Source Parameters ◽  
Frictional Resistance ◽  
Collapse Mechanism ◽  
Eruption Dynamics ◽  
Pyroclastic Density Currents ◽  
Density Currents ◽  
Basal Friction ◽  
Column Collapse ◽  
Hazard And Risk

<p>Understanding past eruption dynamics at a volcano is crucial for forecasting the range of possible future eruptions and their associated hazards and risk. In this work we reconstructed pyroclastic density currents and tephra fall from three eruptions at Gede volcano, Indonesia with the aim of gaining further insight into past eruptions and identifying suitable eruption source parameters for future hazard and risk assessment. Gede has the largest number of people living within 100 km of any volcano worldwide, and has exhibited recent unrest activity, yet little is known about its eruption history. For pyroclastic density currents, we used Titan2D to reconstruct geological deposits dated at 1200 and c. 1000 years BP. An objective and quantitative multi-criteria method was developed to evaluate the fit of over 300 pyroclastic density current (PDC) model simulations to field observations. We found that the 1200 years BP geological deposits could be reproduced with either a dome collapse or column collapse as the generation mechanism although a relatively low basal friction of 6 degrees would suggest that the PDCs were markedly mobile. Lower basal frictions may reflect the occurrence of previous PDCs that smoothed the path, reducing frictional resistance and enabling greater runout for the reconstructed unit. For the 1,000 years BP PDC, a column collapse mechanism and higher basal friction was required to fit the geological deposits. In agreement with previous studies, we found that Titan2D simulations were most sensitive to the basal friction; however, we also found that the internal friction – often fixed and considered of low influence on outputs - can have a moderate effect on the simulated average deposit thickness. We used Tephra2 to reconstruct historic observations of tephra dispersed to Jakarta and other towns during the last known magmatic eruption of Gede in 1948. In the absence of observable field deposits, or detailed information from the published literature, we stochastically sampled eruption source parameters from wide ranges informed by analogous volcanic systems. Our modelling suggests that the deposition of tephra in Jakarta during the November 1948 eruption was a very low probability event, with approximately a 0.03 % chance of occurrence. Through this work, we exemplify the reconstruction of past eruptions when faced with epistemic uncertainty, and improve our understanding of past eruption dynamics at Gede volcano, providing a crucial step towards the reduction of risk to nearby populations through volcanic hazard assessment.</p>

scholarly journals Characteristics of local earthquake seismograms of varying dislocation sources in a stratified upper crust and modeling for P and S velocity structure: comparison with observations in the Koyna-Warna region, India

2016 ◽  
Vol 58 (6) ◽  
Author(s):  
V. G. Krishna
Keyword(s):  
Velocity Structure ◽  
Source Parameters ◽  
Velocity Model ◽  
Strike Slip ◽  
Synthetic Seismograms ◽  
Structure Comparison ◽  
Earthquake Sources ◽  
Crustal Velocity ◽  
Source Mechanisms ◽  
Local Earthquake

<p>Vertical component record sections of local earthquake seismograms from a state-of-the-art Koyna-Warna digital seismograph network are assembled in the reduced time versus epicentral distance frame, similar to those obtained in seismic refraction profiling. The record sections obtained for an average source depth display the processed seismograms from nearly equal source depths with similar source mechanisms and recorded in a narrow azimuth range, illuminating the upper crustal P and S velocity structure in the region. Further, the seismogram characteristics of the local earthquake sources are found to vary significantly for different source mechanisms and the amplitude variations exceed those due to velocity model stratification. In the present study a large number of reflectivity synthetic seismograms are obtained in near offset ranges for a stratified upper crustal model having sharp discontinuities with 7%-10% velocity contrasts. The synthetics are obtained for different source regimes (e.g., strike-slip, normal, reverse) and different sets of source parameters (strike, dip, and rake) within each regime. Seismogram sections with dominantly strike-slip mechanism are found to be clearly favorable in revealing the velocity stratification for both P and S waves. In contrast the seismogram sections for earthquakes of other source mechanisms seem to display the upper crustal P phases poorly with low amplitudes even in presence of sharp discontinuities of high velocity contrasts. The observed seismogram sections illustrated here for the earthquake sources with strike-slip and normal mechanisms from the Koyna-Warna seismic region substantiate these findings. Travel times and reflectivity synthetic seismograms are used for 1-D modeling of the observed virtual source local earthquake seismogram sections and inferring the upper crustal velocity structure in the Koyna-Warna region. Significantly, the inferred upper crustal velocity model in the region reproduces the synthetic seismograms comparable to the observed sections for earthquake sources with differing mechanisms in the Koyna and Warna regions.</p>

Source Parameter Sensitivity of Earthquake Simulations assisted by Machine Learning 

2021 ◽  
Author(s):  
Marisol Monterrubio-Velasco ◽  
J. Carlos Carrasco-Jimenez ◽  
Otilio Rojas ◽  
Juan E. Rodriguez ◽  
David Modesto ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Moment Tensor ◽  
Source Parameters ◽  
Velocity Model ◽  
Source Parameter ◽  
Earthquake Simulation ◽  
Centroid Moment Tensor ◽  
Ground Shaking ◽  
Earthquake Simulations

&lt;p&gt;After large magnitude earthquakes have been recorded, a crucial task for hazard assessment is to quickly estimate Ground Shaking (GS) intensities at the affected region. Urgent physics-based earthquake simulations using High-Performance Computing (HPC) facilities may allow fast GS intensity analyses but are very sensitive to source parameter values. When using fast estimates of source parameters such as magnitude, location, fault dimensions, and/or Centroid Moment Tensor (CMT), simulations are prone to errors in their computed GS. Although the approaches to estimate earthquake location and magnitude are consolidated, depth location estimates are largely uncertain. Moreover, automatic CMT solutions are not always provided by seismological agencies, or such solutions are available at later times after waveform inversions allow the determination of moment tensor components. The uncertainty on these parameters, especially a few minutes after the earthquake has been registered, strongly affects GS maps resulting from simulations.&lt;/p&gt;&lt;p&gt;In this work, we present a workflow prototype to produce an uncertainty quantification method as a function of the source parameters. The core of this workflow is based on Machine Learning (ML) techniques. As a study case, we consider a domain of 110x80 km centered in 63.9&amp;#186;N-20.6&amp;#186;W in Southern Iceland, where the 17 best-mapped faults have hosted the historical events of the largest magnitude. We generate synthetic GS intensity maps using the AWP-ODC finite-difference code for earthquake simulation and a one-dimensional velocity model, with 40 recording surface stations. By varying a few source parameters (e.g. event magnitude, CMT, and hypocenter location), we finally model tens of thousands of hypothetical earthquakes. Our ML analog will then be able to relate GS intensity maps to source parameters, thus simplifying sensitivity studies.&lt;/p&gt;&lt;p&gt;Additionally, the results of this workflow prototype will allow us to obtain ML-based intensity maps a few seconds after an earthquake occurs exploiting the predictive power of ML techniques. We will evaluate the accuracy of these maps as standalone complements to GMPEs and simulations.&lt;/p&gt;

RECOGNITION AND INTERPRETATION OF THE SPATIAL IRREGULARITIES IONOSPHERE FOR FEBRUARY – MARCH 2010 OVER THE SEISMIC ZONES OF SOUTH AMERICA BY RADIOPHYSICAL METHODS

2021 ◽  
pp. 7-23
Author(s):  
Titova A. M. ◽  
V. I. Zakharov ◽  
S. A. Pulinets
Keyword(s):  
South America ◽  
Large Scale ◽  
Complex Analysis ◽  
Ionospheric Disturbances ◽  
Acoustic Gravity Waves ◽  
Relative Contribution ◽  
Phase Data ◽  
Perturbation Parameters ◽  
Large Earthquakes ◽  
Gps Observations

The ionospheric disturbances detected during large-scale earthquakes occurred of early 2010 in South America on the base the analysis of data GPS-observations are considered. The complex analysis of the ground-based stations data integrated into the international IGS and UNAVCO networks was carried out the selected region. Extensive spatial-time measurements statistics of more than 10 million phase data were processed. Stable estimates of distributions are obtained for ionospheric plasma perturbation parameters. The relative contribution of acoustic-gravity waves to the ionospheric disturbances formation during the large earthquakes preparation is investigated.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network

2019 ◽  
Vol 7 (3) ◽  
pp. SE113-SE122 ◽  
Author(s):  
Yunzhi Shi ◽  
Xinming Wu ◽  
Sergey Fomel
Keyword(s):  
Large Scale ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Seismic Image ◽  
Data Generator

Salt boundary interpretation is important for the understanding of salt tectonics and velocity model building for seismic migration. Conventional methods consist of computing salt attributes and extracting salt boundaries. We have formulated the problem as 3D image segmentation and evaluated an efficient approach based on deep convolutional neural networks (CNNs) with an encoder-decoder architecture. To train the model, we design a data generator that extracts randomly positioned subvolumes from large-scale 3D training data set followed by data augmentation, then feed a large number of subvolumes into the network while using salt/nonsalt binary labels generated by thresholding the velocity model as ground truth labels. We test the model on validation data sets and compare the blind test predictions with the ground truth. Our results indicate that our method is capable of automatically capturing subtle salt features from the 3D seismic image with less or no need for manual input. We further test the model on a field example to indicate the generalization of this deep CNN method across different data sets.

scholarly journals Predictive tracking with improved motion models for optical belt sorting

2020 ◽  
Vol 68 (4) ◽  
pp. 239-255
Author(s):  
Florian Pfaff ◽  
Christoph Pieper ◽  
Georg Maier ◽  
Benjamin Noack ◽  
Robin Gruna ◽  
...  
Keyword(s):  
Ground Truth ◽  
Velocity Model ◽  
Conveyor Belt ◽  
Separation Mechanism ◽  
Multitarget Tracking ◽  
Bulk Materials ◽  
Ground Truth Data ◽  
Single Observation ◽  
Line Scan ◽  
Motion Models

AbstractOptical belt sorters are a versatile means to sort bulk materials. In previous work, we presented a novel design of an optical belt sorter, which includes an area scan camera instead of a line scan camera. Line scan cameras, which are well-established in optical belt sorting, only allow for a single observation of each particle. Using multitarget tracking, the data of the area scan camera can be used to derive a part of the trajectory of each particle. The knowledge of the trajectories can be used to generate accurate predictions as to when and where each particle passes the separation mechanism. Accurate predictions are key to achieve high quality sorting results. The accuracy of the trajectories and the predictions heavily depends on the motion model used. In an evaluation based on a simulation that provides us with ground truth trajectories, we previously identified a bias in the temporal component of the prediction. In this paper, we analyze the simulation-based ground truth data of the motion of different bulk materials and derive models specifically tailored to the generation of accurate predictions for particles traveling on a conveyor belt. The derived models are evaluated using simulation data involving three different bulk materials. The evaluation shows that the constant velocity model and constant acceleration model can be outperformed by utilizing the similarities in the motion behavior of particles of the same type.

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