Geodetic Model of the 2018 Mw 7.2 Pinotepa, Mexico, Earthquake Inferred from InSAR and GPS Data

2020 ◽  
Vol 110 (3) ◽  
pp. 1115-1124 ◽  
Author(s):  
Yanchuan Li ◽  
Xinjian Shan ◽  
Chuanhua Zhu ◽  
Xin Qiao ◽  
Lei Zhao ◽  
...  
Keyword(s):  
Time Series Data ◽  
Source Parameters ◽  
Source Region ◽  
Seismogenic Zone ◽  
Series Data ◽  
Gps Data ◽  
Earthquake Hazard Assessment ◽  
Stress Changes ◽  
Mexico Earthquake ◽  
Large Earthquakes

ABSTRACT Investigating the interaction between slow-slip events (SSEs) and large earthquakes provides insights into earthquake-triggering mechanisms and is crucial for earthquake hazard assessment. In this study, we used Sentinel-1 Interferometric Synthetic Aperture Radar and Global Positioning System (GPS) data to estimate the source parameters of the 2018 Mw 7.2 Pinotepa, Mexico, earthquake. The results show that the earthquake ruptured both the seismogenic zone and a long-term SSE area, with two asperities ruptured during the event. GPS time-series data indicate that an SSE was initiated approximately during March 2017 below Oaxaca; the SSE ruptured an area below the source region of the Pinotepa earthquake and intruded into the seismogenic zone. The spatiotemporal proximity may suggest that the SSE triggered the Pinotepa earthquake. We propose that the triggering mechanism may either have been Coulomb stress loading or weakening of the source area by fluid migration. Furthermore, we calculated the seismic moment deficit and Coulomb failure stress changes and conclude that the Oaxaca area still has the potential for large earthquakes.

Rapid determination of source parameters for the 2017 Mw 8.2 Mexico earthquake based on high-rate GPS data

2019 ◽  
Vol 64 (5) ◽  
pp. 1148-1159
Author(s):  
Yunfei Xiang ◽  
Jianping Yue ◽  
Dongjian Cai ◽  
Hao Wang
Keyword(s):  
Rapid Determination ◽  
Source Parameters ◽  
High Rate ◽  
Gps Data ◽  
Mexico Earthquake

scholarly journals Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms

2021 ◽  
Vol 11 (24) ◽  
pp. 11852
Author(s):  
Astri Novianty ◽  
Irwan Meilano ◽  
Carmadi Machbub ◽  
Sri Widiyantoro ◽  
Susilo Susilo
Keyword(s):  
Time Series ◽  
Genetic Algorithms ◽  
Early Warning ◽  
Time Series Data ◽  
Source Parameters ◽  
Large Earthquake ◽  
Series Data ◽  
Inversion Method ◽  
Permanent Displacement ◽  
Gps Time Series

To minimize the impacts of large losses and optimize the emergency response when a large earthquake occurs, an accurate early warning of an earthquake or tsunami is crucial. One important parameter that can provide an accurate early warning is the earthquake’s magnitude. This study proposes a method for estimating the magnitude, and some of the source parameters, of an earthquake using genetic algorithms (GAs). In this study, GAs were used to perform an inversion of Okada’s model from earthquake displacement data. In the first stage of the experiment, the GA was used to inverse the displacement calculated from the forward calculation in Okada’s model. The best performance of the GA was obtained by tuning the hyperparameters to obtain the most functional configuration. In the second stage, the inversion method was tested on GPS time series data from the 2011 Tohoku Oki earthquake. The earthquake’s displacement was first estimated from GPS time series data using a detection and estimation formula from previous research to calculate the permanent displacement value. The proposed method can estimate an earthquake’s magnitude and four source parameters (i.e., length, width, rake, and slip) close to the real values with reasonable accuracy.

scholarly journals Detecting Thermal Anomalies of Earthquake Process Within Outgoing Longwave Radiation Using Time Series Forecasting Models

2020 ◽  
Vol 63 (5) ◽  
Author(s):  
Dulin Zhai ◽  
Xueming Zhang ◽  
Pan Xiong
Keyword(s):  
Time Series ◽  
Outgoing Longwave Radiation ◽  
Time Series Data ◽  
Arima Model ◽  
Longwave Radiation ◽  
Time Series Forecasting ◽  
Series Data ◽  
Jiuzhaigou Earthquake ◽  
Forecasting Models ◽  
Mexico Earthquake

  The catastrophic damages caused by the Jiuzhaigou earthquake in China of August 8, 2017 and the Mexico earthquake of September 20, 2017 have revealed some important weaknesses of currently operational earthquake-monitoring and forecasting systems. In this work, six time series forecasting models were applied to detect pre-earthquake anomalies within infrared outgoing longwave radiation. After comparing their prediction results using non-seismic time series data, the autoregressive integrated moving average (ARIMA) model was selected as the optimal model, and then a new prediction method based on this ARIMA model was proposed. The results show that the values observed on July 27 and August 5 before the Jiuzhaigou earthquake in China exceed the confidence interval for prediction and reaches the maximum on August 5, 2017. This indicates the infrared outgoing longwave radiation (IR-OLR) anomalies before the Jiuzhaigou earthquake in China. For the Mexico earthquake, pre-earthquake IR-OLR anomalies are detected on September 14, 18, and 19, and reaches the maximum on September 14, 2017. This demonstrates that the proposed time series forecasting model based on ARIMA could be an effective method for earthquake anomalies detection within infrared outgoing longwave radiation.

Interactions of Earthquakes in Central Italy over the Past 100 Yr through Coulomb Stress Changes, and Implications for Seismic Hazards

2020 ◽  
Vol 110 (1) ◽  
pp. 178-190 ◽  
Author(s):  
Bin Shan ◽  
Yashan Feng ◽  
Chengli Liu ◽  
Xiong Xiong
Keyword(s):  
Central Italy ◽  
Active Faults ◽  
Earthquake Hazard ◽  
Seismic Hazards ◽  
Historical Seismicity ◽  
Normal Faults ◽  
Coulomb Stress Changes ◽  
Earthquake Hazard Assessment ◽  
Stress Changes ◽  
Large Earthquakes

ABSTRACT Italy has a historical earthquake record that is complete for events with a magnitude above 5.8 since A.D. 1349, making it possible to study Coulomb failure stress changes (ΔCFS) over a long period. In this study, we investigated the interactions between moderate-to-large earthquakes through ΔCFS over 100 yr in central Italy. This region is characterized by intense seismicity with predominantly extensional components. Hence, earthquake hazard assessment is of great public concern. Besides, earthquake interactions on normal faults are relatively less studied compared to reverse and strike-slip faults. ΔCFS calculations in this study incorporated both coseismic stress transfer and postseismic viscoelastic relaxation, and found the epicenters of 13 out of 15 events located in positively stressed lobes induced by previous earthquakes, confirming a correlation between the ΔCFS pattern and locations of moderate-to-large earthquakes. Next, we estimated the current distribution of ΔCFS on active faults, and after a comprehensive analysis of ΔCFS accumulation, slip rates, historical seismicity, and locations of populated cities, we identified three regions of potential seismic hazards in this region.

scholarly journals Seismicity at the northeast edge of the Mexican Volcanic Belt (MVB) and activation of an undocumented fault: the Peñamiller earthquake sequence of 2010–2011, Querétaro, Mexico

2013 ◽  
Vol 13 (10) ◽  
pp. 2521-2531 ◽  
Author(s):  
A. Clemente-Chavez ◽  
A. Figueroa-Soto ◽  
F. R. Zúñiga ◽  
M. Arroyo ◽  
M. Montiel ◽  
...  
Keyword(s):  
Source Parameters ◽  
Volcanic Belt ◽  
Focal Mechanisms ◽  
Seismogenic Zone ◽  
Normal Faults ◽  
Central Mexico ◽  
Ground Acceleration ◽  
Mexican Volcanic Belt ◽  
Shallow Earthquakes ◽  
Large Earthquakes

Abstract. The town of Peñamiller in the state of Querétaro, Mexico, is located at the northeast border of the seismogenic zone known as the Mexican Volcanic Belt (MVB), which transects the central part of Mexico with an east–west orientation. In the vicinity of this town, a sequence of small earthquakes occurred during the end of 2010 and beginning of 2011. Seismicity in the continental regimen of central Mexico is not too frequent; however, it is known that there are precedents of large earthquakes (Mw magnitude greater than 6.0) occurring in this zone. Three large earthquakes have occurred in the past 100 yr: the 19 November 1912 (MS = 7.0), the 3 January 1920 (MS = 6.4), and the 29 June 1935 (MS = 6.9) earthquakes. Prior to the instrumental period, the earthquake of 11 February 1875, which took place near the city of Guadalajara, caused widespread damage. The purpose of this article is to contribute to the available seismic information of this region. This will help advance our understanding of the tectonic situation of the central Mexico MVB region. Twenty-four shallow earthquakes of the Peñamiller seismic sequence of 2011 were recorded by a temporary accelerograph network installed by the Universidad Autónoma de Querétaro (UAQ). The data were analyzed in order to determine the source locations and to estimate the source parameters. The study was carried out through an inversion process and by spectral analysis. The results show that the largest earthquake occurred on 8 February 2011 at 19:53:48.6 UTC, had a moment magnitude Mw = 3.5, and was located at latitude 21.039° and longitude −99.752°, at a depth of 5.6 km. This location is less than 7 km away in a south-east direction from downtown Peñamiller. The focal mechanisms are mostly normal faults with small lateral components. These focal mechanisms are consistent with the extensional regimen of the southern extension of the Basin and Range (BR) province. The source area of the largest event was estimated to have a radius of 0.5 km, which corresponds to a normal fault with azimuth of 174° and an almost pure dip slip. Peak ground acceleration (PGA) was close to 100 cm s−2 in the horizontal direction. Shallow earthquakes induced by crustal faulting present a potential seismic risk and hazard within the MVB, considering the population growth. Thus, the necessity to enrich seismic information in this zone is very important since the risk at most urban sites in the region might even be greater than that posed by subduction earthquakes.

scholarly journals Physical laws for precursory phenomena of impending large earthquakes and their applications to predictions

2018 ◽  
Author(s):  
Fumihide Takeda
Keyword(s):  
Particle Motion ◽  
Seismic Waves ◽  
Source Parameters ◽  
Rupture Time ◽  
Mathematical Tool ◽  
Virtual Particle ◽  
Crustal Stresses ◽  
Earthquake Source Parameters ◽  
Stress Changes ◽  
Large Earthquakes

Abstract. Changes in crustal stresses create an earthquake fault motion which radiates seismic waves. Their analyses quantify the properties of the earthquake with its rupture time, location, fault motion and size that are called earthquake source parameters. We may then regard the event as the emergence of a virtual particle of unit mass at a position in the property space whose coordinate axes are source parameters. At the next event, the particle takes a new position in the space. The consecutive events draw a pathway of the moving particle, which is a trace of the stress changes causing the particle motion. The pathway is zigzagged and non–derivative with respect to time. A mathematical tool named physical wavelets is applied to extracting the equations of particle motion. The extracted equations detect periodic anomalous accelerations precursory to large impending earthquakes weeks and months ahead of time. The periodic particle motions enable us to predict the fault size and motion, rupture time, and epicenter of impending large earthquakes. The mathematical tool with which to extract deterministic precursors embedded in the highly irregular time series of natural and earth systems is also concisely described for mitigating their hazards.

scholarly journals Record-breaking earthquake intervals in a global catalogue and an aftershock sequence

2010 ◽  
Vol 17 (2) ◽  
pp. 169-176 ◽  
Author(s):  
M. R. Yoder ◽  
D. L. Turcotte ◽  
J. B. Rundle
Keyword(s):  
Time Series Data ◽  
Aftershock Sequence ◽  
Series Data ◽  
Short Intervals ◽  
Starting Time ◽  
Elapsed Time ◽  
Natural Time ◽  
Noisy Time Series ◽  
Parkfield Earthquake ◽  
Large Earthquakes

Abstract. For the purposes of this study, an interval is the elapsed time between two earthquakes in a designated region; the minimum magnitude for the earthquakes is prescribed. A record-breaking interval is one that is longer (or shorter) than preceding intervals; a starting time must be specified. We consider global earthquakes with magnitudes greater than 5.5 and show that the record-breaking intervals are well estimated by a Poissonian (random) theory. We also consider the aftershocks of the 2004 Parkfield earthquake and show that the record-breaking intervals are approximated by very different statistics. In both cases, we calculate the number of record-breaking intervals (nrb) and the record-breaking interval durations Δtrb as a function of "natural time", the number of elapsed events. We also calculate the ratio of record-breaking long intervals to record-breaking short intervals as a function of time, r(t), which is suggested to be sensitive to trends in noisy time series data. Our data indicate a possible precursory signal to large earthquakes that is consistent with accelerated moment release (AMR) theory.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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