Source Parameter Sensitivity of Earthquake Simulations assisted by Machine Learning

<p>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.</p><p>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 63.9ºN-20.6ºW 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.</p><p>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.</p>

Towards physics-based PSHA using CyberShake in the South Iceland Seismic Zone

<p>Traditional Probabilistic Seismic Hazard Analysis (PSHA) estimates the level of earthquake ground shaking that is expected to be exceeded with a given recurrence time on the basis of  historical earthquake catalogues and empirical and time-independent Ground Motion Prediction Equations (GMPEs). The smooth nature of GMPEs usually disregards some well known drivers of ground motion characteristics associated with fault rupture processes, in particular in the near-fault region, complex source-site propagation of seismic waves, and sedimentary basin response. Modern physics-based earthquake simulations can consider all these effects, but require a large set of input parameters for which constraints may often be scarce. However, with the aid of high-performance computing (HPC) infrastructures  the parameter space may be sampled in an efficient and scalable manner allowing for a large suite of site-specific ground motion simulations that approach the center, body and range of expected ground motions. </p><p>CyberShake is a HPC platform designed to undertake physics-based PSHA from a large suite of earthquake simulations. These simulations are based on seismic reciprocity, rendering PSHA computationally tractable for hundreds of thousands potential earthquakes. For each site of interest, multiple kinematic rupture scenarios, derived by varying slip distributions and hypocenter location across the pre-defined fault system, are generated from an input Earthquake Forecast Model (EFM). Each event is simulated to determine ground motion intensities, which are synthesized into hazard results. CyberShake has been developed by the Southern California Earthquake Center, and used so far to assess seismic hazard in California. This work focuses on the CyberShake migration to the seismic region of South Iceland (63.5°- 64.5°N, 20°-22°W) where the largely sinistral East-West transform motion across the tectonic margin is taken up by a complex array of near-vertical and parallel North-South oriented dextral transform faults in the South Iceland Seismic Zone (SISZ) and the Reykjanes Peninsula Oblique Rift (RPOR). Here, we describe the main steps of migrating CyberShake to the SISZ and RPOR, starting by setting up a relational input database describing potential causative faults and rupture characteristics, and key sites of interest. To simulate our EFM, we use the open source code SHERIFS, a logic-tree method that converts the slip rates of complex fault systems to the corresponding annual seismicity rate. The fault slip rates are taken from a new 3D physics-based fault model for the SISZ-RPOR transform fault system. To validate model and simulation parameters, two validation steps using key CyberShake modeling tools have been carried out. First, we perform simulations of historical earthquakes and compare the synthetics with recorded ground motions and results from other forward simulations. Second, we adjust the rupture kinematics to make slip distributions more representative of SISZ-type earthquakes by comparing with static slip distributions of past significant earthquakes. Finally, we run CyberShake and compare key parameters of the synthetic ground motions with new GMPEs available for the study region. The successful migration and use of CyberShake in South Iceland is the first step of a full-scale physics-based PSHA in the region, and showcases the implementation of CyberShake in new regions.</p>

Pengukuran dan Pelatihan Kesiapsiagaan Komunitas Sekolah Dasar Muhammadiyah Banyuraden terhadap Bencana Gempa Bumi

The earthquake that occurred during school hours requires the school community to have a high level of preparedness so as not to fall victim and loss a lot. This activity aims to measure the level of preparedness against the threat of earthquake disasters and provide earthquake disaster preparedness training to the Muhammadiyah Banyuraden Elementary School community. The method used was a survey using a questionnaire, analysis of survey results based on the UNSESCO framework, namely knowledge of earthquake disasters, policies and guidelines, emergency response plans, and resource mobilization. The analysis showed that the community preparedness level of SD Muhammadiyah Banyuraden was at a moderate level with a total preparedness index of 55.8. The lowest preparedness index is in the policy and guideline parameters. The results of the survey and analysis carried out resulted in preparedness strengthening activities carried out to increase the preparedness index, namely the formation of disaster preparedness groups, compiling evacuation route maps, adapting earthquake simulations, training on first aid and evacuation, and simulating earthquake evacuation. The activities to strengthen preparedness have an impact on the preparedness index

Composite damage zones in the subsurface

SUMMARY The cumulative displacement by multiple slip events along faults may generate composite damage zones (CDZ) of increasing width, and could modify the hydraulic and mechanical properties of the faults. The internal architecture and fracture distribution within CDZs at the subsurface are analysed here by using seismic attributes of variance, curvature and dip-azimuth of the 3-D seismic reflection data from tight sandstone reservoirs in northeast Sichuan, China. The analysed faults intersect the reservoir within a depth range of 2.4–3.0 km. The damage intensity mapping revealed multiple CDZs with thicknesses approaching 1 km along faults ranging 3–15 km in length, and up to 1000 m of cumulative slip. The identification of numerous fault cores and associate damage zones led us to define three classes of CDZs: banded shape, box shape and dome shape. The mechanical strength contrasts and distortion of fault cores suggest potential weakening and strengthening (healing) mechanisms for formation of CDZs that can be extended to faulting processes and earthquake simulations.