Data-driven Accelerogram Synthesis using Deep Generative Models

• URL: http://arxiv.org/abs/2011.09038v1
• Date: Wed, 18 Nov 2020 02:12:14 GMT
• Title: Data-driven Accelerogram Synthesis using Deep Generative Models
• Authors: Manuel A. Florez, Michaelangelo Caporale, Pakpoom Buabthong, Zachary E. Ross, Domniki Asimaki and Men-Andrin Meier
• Abstract summary: We leverage recent advances in Generative Adversarial Networks (GANs) to develop a new framework for earthquake acceleration time histories. Our approach extends the Wasserstein GAN formulation to allow for the generation of ground-motions conditioned on a set of continuous physical variables. We show that the trained generator model can synthesize realistic 3-Component accelerograms conditioned on magnitude, distance, and $V_s30$.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks (GANs) to develop a new framework for synthesizing earthquake acceleration time histories. Our approach extends the Wasserstein GAN formulation to allow for the generation of ground-motions conditioned on a set of continuous physical variables. Our model is trained to approximate the intrinsic probability distribution of a massive set of strong-motion recordings from Japan. We show that the trained generator model can synthesize realistic 3-Component accelerograms conditioned on magnitude, distance, and $V_{s30}$. Our model captures the expected statistical features of the acceleration spectra and waveform envelopes. The output seismograms display clear P and S-wave arrivals with the appropriate energy content and relative onset timing. The synthesized Peak Ground Acceleration (PGA) estimates are also consistent with observations. We develop a set of metrics that allow us to assess the training process's stability and tune model hyperparameters. We further show that the trained generator network can interpolate to conditions where no earthquake ground motion recordings exist. Our approach allows the on-demand synthesis of accelerograms for engineering purposes.

Related papers

• High Resolution Seismic Waveform Generation using Denoising Diffusion [3.5046784866523932]
  This study introduces a novel, efficient, and scalable generative model for high-frequency seismic waveform generation. A spectrogram representation of seismic waveform data is reduced to a lower-dimensional submanifold via an autoencoder. A state-of-the-art diffusion model is trained to generate this latent representation, conditioned on key input parameters.
  arXiv  Detail & Related papers  (2024-10-25T07:01:48Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
  We introduce the GPT style next token motion prediction into motion prediction. Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations. We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
  arXiv  Detail & Related papers  (2024-03-20T06:22:37Z)
• A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators [46.348283638884425]
  We propose a two-step unsupervised deep learning framework named as Latent Autoregressive Recurrent Model (CLARM) for learning dynamics of charged particles in accelerators. The CLARM can generate projections at various accelerator sampling modules by capturing and decoding the latent space representation. The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T22:05:17Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• Broadband Ground Motion Synthesis via Generative Adversarial Neural Operators: Development and Validation [12.275587079383603]
  We first present the conditional ground-motion synthesis algorithm (cGM-GANO) and discuss its advantages compared to previous work. We next train cGM-GANO on simulated ground motions generated by the Southern California Earthquake Center Broadband Platform (BBP) and on recorded KiK-net data. Results specifically show that cGM-GANO produces consistent median scaling with the training data for the corresponding tectonic environments.
  arXiv  Detail & Related papers  (2023-09-07T02:08:30Z)
• Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation [69.1351513309953]
  We show that accurately simulating the low-frequency components of Room Impulse Responses (RIRs) is important to achieving good dereverberation. We demonstrate that speech dereverberation models trained on hybrid synthetic RIRs outperform models trained on RIRs generated by prior geometric ray tracing methods.
  arXiv  Detail & Related papers  (2022-12-10T20:15:23Z)
• Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network [0.0]
  Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertainties in pre-drill models. We propose a generative adversarial deep neural network (GAN) trained to reproduce geologically consistent 2D sections of fluvial successions. In our example, the method reduces uncertainty and correctly predicts most major geological features up to 500 meters ahead of drill-bit.
  arXiv  Detail & Related papers  (2022-07-04T12:52:38Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• A Spectral Energy Distance for Parallel Speech Synthesis [29.14723501889278]
  Speech synthesis is an important practical generative modeling problem. We propose a new learning method that allows us to train highly parallel models of speech.
  arXiv  Detail & Related papers  (2020-08-03T19:56:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.