PERPUSTAKAAN BIG

  • Beranda
  • Informasi
  • Berita
  • Bantuan
  • Area Pustakawan
  • Area Anggota
  • Pilih Bahasa :
    Bahasa Arab Bahasa Bengal Bahasa Brazil Portugis Bahasa Inggris Bahasa Spanyol Bahasa Jerman Bahasa Indonesia Bahasa Jepang Bahasa Melayu Bahasa Persia Bahasa Rusia Bahasa Thailand Bahasa Turki Bahasa Urdu
Image of Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo

Text

Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo

Saumik Dana - Nama Orang; Karthik Reddy Lyathakula - Nama Orang;

The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the critically stressed fault. This makes it incredibly important to construct an inversion framework that provides good estimates of the critical slip distance purely based on the observed ac-celeration at the seismogram. To eventually construct a framework that takes noisy seismogram acceleration data as input and spits out robust estimates of critical slip distance as the output, we first present the performance of the framework for synthetic data. The framework is based on Bayesian inference and Markov chain Monte Carlo methods. The synthetic data is generated by adding noise to the acceleration output of spring-slider-damper idealization of the rate and state model as the forward model.


Ketersediaan
252551Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
Penerbit
Beijing : KeAi Communications Co. Ltd.., 2021
Deskripsi Fisik
8 hlm PDF, 1.956 KB
Bahasa
Inggris
ISBN/ISSN
2666-5441
Klasifikasi
551
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.2, December 2021
Subjek
Fault friction
Rate and state model
Critical slip distance
Bayesian inference
Markov chain Monte Carlo
Info Detail Spesifik
-
Pernyataan Tanggungjawab
-
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Arriving at estimates of a rate and state fault friction model parameter using Bayesian inference and Markov chain Monte Carlo
    The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few me-ters depending on the length scale of the critically stressed fault. This makes it incredibly important to construct an inversion framework that provides good estimates of the critical slip distance purely based on the observed ac-celeration at the seismogram. To eventually construct a framework that takes noisy seismogram acceleration data as input and spits out robust estimates of critical slip distance as the output, we first present the performance of the framework for synthetic data. The framework is based on Bayesian inference and Markov chain Monte Carlo methods. The synthetic data is generated by adding noise to the acceleration output of spring-slider-damper idealization of the rate and state model as the forward model.
    Other Resource Link
Komentar

Anda harus masuk sebelum memberikan komentar

PERPUSTAKAAN BIG
  • Informasi
  • Layanan
  • Pustakawan
  • Area Anggota

Tentang Kami

Perpustakaan Badan Informasi Geospasial (BIG) adalah sebuah perpustakaan yang berada di bawah Badan Informasi Geospasial Indonesia. Perpustakaan ini memiliki koleksi yang berkaitan dengan informasi geospasial, termasuk peta, data geospasial, dan literatur terkait. Selengkapnya

Cari

masukkan satu atau lebih kata kunci dari judul, pengarang, atau subjek

Donasi untuk SLiMS Kontribusi untuk SLiMS?

© 2025 — Senayan Developer Community

Ditenagai oleh SLiMS
Pilih subjek yang menarik bagi Anda
  • Batas Wilayah
  • Ekologi
  • Fotogrametri
  • Geografi
  • Geologi
  • GIS
  • Ilmu Tanah
  • Kartografi
  • Manajemen Bencana
  • Oceanografi
  • Penginderaan Jauh
  • Peta
Icons made by Freepik from www.flaticon.com
Pencarian Spesifik