Probabilistic seismic hazard function based on spatiotemporal earthquake likelihood simulation and Akaike information criterion: The PSHF study around off the west coast of Sumatra Island before large earthquake events

The probabilistic seismic hazard function (PSHF) before large earthquake events based on the hypothesis earthquake forecast algorithm using the Akaike information criterion (AIC) is performed in this study. The motivation for using the AIC is to better understand the reliability model used to construct the PSHF. The PSHF as the function of the b-value is calculated based on a 5-year window length with a 1-year moving window (instantaneous PSHF) before a large earthquake event. The AIC is calculated based on the likelihood of success and failure using shallow earthquake catalog data around the west coast of Sumatra Island. The probability of occurrence defines the success criteria as more significant than the average probability of greater than or equal to the given magnitude; otherwise, it is defined as failure. Seismic potency has been determined based on the likelihood of an earthquake occurring in several decades or a hundred years. The seismicity rate model is developed based on the integrated data of pre-seismic shallow crustal movement data and the shallow crustal earthquake catalog data. Furthermore, the AIC is calculated based on the likelihood of success and failure as a function of b(t). The b(t) is the change in the b-value as a time function estimated based on shallow earthquake data from 1963 to 2016. In addition, the AIC before M7.9 of 2000, M8.5 of 2007, and M7.8 of 2010 is assessed. The δAIC is then introduced as a function of (AICmodel–AICreference) during the observation time. The positive δAIC implies that the likelihood of having a large earthquake is more significant; otherwise, it is smaller. By plotting the time of observation versus δAIC and the PSHF estimated as the function of b(t), we could identify a large positive gradient and increase the PSHF at each certain probability exceedance (PE) level before the great earthquake event. It consistently happened for the three events that were evaluated. It suggested that the results of this study might be very beneficial for probabilistic seismic hazard analysis (PSHA) and seismic mitigation realization

An Improvement of Velocity Variation with Offset (VVO) Method in Estimating Anisotropic Parameters

Seismic anisotropy causes deviation of traveltimereflection from hyperbolic moveout. The deviation can be seen atfar offset and its deviation depends on anisotropic parameter andoffset. This paper discuss velocity variation with offset (VVO)method as a tool for estimating anisotropic parameters; ε and δ.Anisotropic parameter is one of important aspect in seismicanisotropy analysis. While other methods use non-hyperbolicmoveout for estimating anisotropic parameter, VVO method useshyperbolic assumption for moveout correction and leave reflectorunflat at far offset because anisotropy. The method calculatesresidual traveltime and then changes it into anisotropy velocity toobtain anisotropic parameter using linear inversion method. Thispaper provides an improvement and limitation of VVO methodin estimating anisotropic parameter. Comparison between VVOmethod and other established method is discussed theoretically inthis paper. To test the method, synthetic model is built and theresult show promising outcome in predicting ε. Meanwhileaccuracy for δ estimation depends on accuracy of moveoutvelocity. Advantage of VVO method is that ε and δ can beestimated separately using P-wave gather data without wellinformation

Velocity versus Offset (VVO) Estimation Using Local Event Correlation and Its Application in Seismic Processing & Analysis

Conventional velocity analysis is usually done in a relatively spare grid, for instance every half kilometers, during the processing of seismic data. It is very laborious work and very subjective. To deliver an accurate velocity picking, processing geophysicists must have a good understanding of geological background of area being analyzed and experiences. Velocity errors often occur during picking. Proper quality control and checking are a must. A good and reliable velocity field is very important in seismic processing for achieving high-quality seismic images as well as for delivering an accurate depth conversion. The new method presented here, was developed to correct velocity errors automatically by means of residual velocity correction, and to produce an offset-dependent RMS velocity field at the same time. The method is data driven, based on the normal move out equation (NMO) and measuring the local even correlation between adjacent traces. The stacking velocity is derived simply by averaging the velocity field. The proposed method was tested on synthetic and real data examples with good result. The velocity field has certain characteristics related to hydrocarbon presence. Supriyono (2011 and 2012) developed a new DHI method using velocity gradient attributes by cross-plotting the velocity versus offset (VVO). The velocity gradient exhibits high anomalous values in the presence of gas

Velocity Versus Offset (VVO) Estimation Using Local Event Correlation and Its Application in Seismic Processing & Analysis

Conventional velocity analysis is usually done in a relatively spare grid, for instance every half kilometers, during the processing of seismic data. It is very laborious work and very subjective. To deliver an accurate velocity picking, processing geophysicists must have a good understanding of geological background of area being analyzed and experiences. Velocity errors often occur during picking. Proper quality control and checking are a must. A good and reliable velocity field is very important in seismic processing for achieving high-quality seismic images as well as for delivering an accurate depth conversion. The new method presented here, was developed to correct velocity errors automatically by means of residual velocity correction, and to produce an offset-dependent RMS velocity field at the same time. The method is data driven, based on the normal move out equation (NMO) and measuring the local even correlation between adjacent traces. The stacking velocity is derived simply by averaging the velocity field. The proposed method was tested on synthetic and real data examples with good result. The velocity field has certain characteristics related to hydrocarbon presence. Supriyono (2011 and 2012) developed a new DHI method using velocity gradient attributes by cross-plotting the velocity versus offset (VVO). The velocity gradient exhibits high anomalous values in the presence of gas

Study on earthquake and tsunami hazard: evaluating probabilistic seismic hazard function (PSHF) and potential tsunami height simulation in the coastal cities of Sumatra Island

This study uses integrated geological, geodesy, and seismology data to assess the potential tsunami and Probabilistic Seismic Hazard Function (PSHF) near Sumatra’s coastal cities. It focuses on estimating the possible level of ground shaking due to the seismic activity within the Sumatran Fault Zone (SFZ) and subduction zone. It uses the Peak Ground Acceleration (PGA) as a measure. An amplification factor that is based on the previous study is used. It is calculated through the Horizontal-Vertical Spectral Ratio (HVSR), which measures possible surface ground shaking. The Seismic Hazard Function (SHF) is calculated considering magnitudes 6.5 to 9.0 for subduction sources and 6.5 to 7.8 for SFZ sources. Also, the PGA based on the Maximum Possible Earthquake (MPE) magnitude is estimated, and tsunami heights are simulated to assess the possible hazard risk. The tsunami source model in this study is characterized by considering the possibility of the long-term perspectives on giant earthquakes and tsunamis that might occur in subduction zones around the off-coast of southern Sumatra Island. The potentiality source zone is characterized based on the utilization of the cross-correlation of correlation dimension (DC) based on the shallow earthquake catalog of 2010 to 2022 and the SHmax-rate of surface strain rate. Based on the MPE, the relatively high estimated PGA at the base rock was found around Mentawai and Pagai Utara islands at about 0.224 g and 0.328 g, with the largest estimated PGA based on the MPE at the surface with values of about 0.5 g and 0.6 g. The possible maximum tsunami height (Hmax) estimated based on source scenarios position around the west coast of Sumatera Island, such as for Kota Padang and Kota Bungus, reaches up to 12.0 m and 22.0 m, respectively. The findings provide valuable insight into seismic and tsunami hazards, benefiting future mitigation strategies

Applying the Akaike Information Criterion (AIC) in earthquake spatial forecasting: a case study on probabilistic seismic hazard function (PSHF) estimation in the Sumatra subduction zone

AbstractThis study uses complete earthquake catalog data and spatio-temporal analysis to construct a reliable model to forecast the potential seismogenic earthquake or earthquake fault zones. It integrates models developed based on different researchers’ methods and earthquake catalogs from different periods. It constructs and compares models - Model-1, Model-2, and Model-3 - from the complete shallow earthquake catalog between 1963-1999 and 1963-2006. The δAIC is used to evaluate the reliability of the models, with Model-3 emerging as the most reliable in all tests in this study. The model is constructed based on the product of the normalized model of the combined smooth seismicity model of a relatively small to moderate complete earthquake catalog data with a relatively uniform background model and weighted by the normalized seismic moment rate derived from the surface strain rate. It is suggested that a more extended observation period and using a complete, albeit relatively small-to-moderate, earthquake catalog leads to a more reliable and accurate model. Implementation of the Probabilistic Seismic Hazard Function (PSHF) window using the b-value of a 5-year window length with a 1-year sliding window prior to a significant seismic event proved successful, and the methodology demonstrates the importance of the temporal "b-value" in conjunction with the reliable seismicity rate and spatial probabilistic earthquake forecasting models in earthquake forecasting. The results showed large changes in the PSHF prior to giant and large earthquakes and the finding of a correlation between decreased b-value time window length and earthquake magnitude. The results have implications for the implementation of seismic mitigation measures

Karakterisasi sumber gempa Yogyakarta 2006 berdasarkan data GPS

http://dx.doi.org/10.17014/ijog.vol3no1.20085The southern part of Central Jawa is one of earthquake hazard prone areas in Indonesia an earthquake occurred on May 27, 2006 and had the moment magnitude 6.3. Base on the GPS observation, the characterization of the epicenter and source of this Yogyakarta earthquake can be estimated using the displacement estimation and strain at the measurement point by using a simple kriging and sequential gaussian simulation method. The direction of the displacement and maximum shear strain anomaly in this research was shown by the fault of SW – NE direction and the displacement pattern shows that this fault is left lateral strike slip movement. The positive anomaly of the maximum shear strainis located about 10 km east of Bantul, which suggests as the position of Yogyakarta 2006 earthquake source, with the moment seismic and moment magnitude values are 8.1385 x 1025 dyne cm, and 6.5 respectively.    </p

Spatial correlation of the maximum shear strain loading rate and the correlation dimension along the Sumatra subduction margin for potential earthquake and tsunami hazard study and analysis

ABSTRACTThe potential earthquake and tsunami hazard along the Sumatra subduction margin, especially around the coast of West Sumatra-Bengkulu, was investigated based on the availability of pre-seismic surface displacement data and shallow crustal earthquake catalogue data from 1907 to 2016. The pre-seismic surface displacement data is based on the displacement data prior to and corrected displacement data after major earthquakes. Using the results of our previous study on the local covariance function and the relationship of Correlation Dimension (DC) with the b-value of Gutenberg-Richter (GR) Law, we estimated the maximum horizontal crustal strain rate (SHmax) and DC around the study area. Least squares prediction based on horizontal displacement data using the local covariance function is used to estimate the displacement model in the entire gridding study area with a 10 km × 10 km size. Furthermore, DC is calculated based on the b-value using the maximum likelihood method based on the input of a constant number of earthquake samples, assuming the regional b-value of GR Law equals 1. Furthermore, the spatial correlation of SHmax and DC can define the area of possible earthquake hazard potential. The identification results are then linked with previous stress reconstruction results for seismic hazard study and analysis. Based on the finding, we then estimate the Seismic Hazard Function (SHF) and Tsunami Height simulation to estimate the possible hazard risk at several observation points. We suggest that the result of this study could be beneficial to understand better the potential seismic and tsunami hazard in the future, mainly to support mitigation purposes

Modeling the Impact of the Viscoelastic Layer Thickness and the Frictional Strength to the Lithosphere Deformation in a Strike-Slip Fault: Insight to the Seismicity Pattern along the Great Sumatran Fault

As an earthquake is capable of causing significant losses, a strain buildup and release model following an earthquake is of importance for mitigation purposes. In this study, we aim to model strain buildup and release on a strike-slip fault which consists of elastic–brittle (upper crust) and elastic–viscous (lower crust and upper mantle) layers using a finite element model. The fault strength during strain buildup is controlled by the friction coefficient and cohesion, in addition to the viscoelastic parameter, as shown in the deformation model using Maxwell’s material. In the strain buildup model, we found that the differential stress on the elastic layer is larger than that on the viscoelastic layer and that the differential stress increases with the thickness of the elastic layer. When the viscoelastic layer is thinner, the deformation observed on the surface is larger. However, the differential of stress in the strain release model on the elastic layer is smaller than that on the viscoelastic layer, which shows the transfer stress from the lower crust and upper mantle to the upper crust. Using the knowledge gained by varying the thickness and frictional strength of the lithosphere, we discuss the seismicity pattern observed along the Great Sumatran Fault