1D Stochastic Inversion of Airborne Time-domain Electromag-netic Data with Realistic Prior and Accounting for the Forward Modeling Error

09/28/2021
by   Peng Bai, et al.
0

Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

READ FULL TEXT

Authors

page 7

page 8

page 9

page 11

page 13

page 14

page 15

page 16

05/05/2021

Model reduction in acoustic inversion by artificial neural network

In ultrasound tomography, the speed of sound inside an object is estimat...
02/24/2021

Reconstruction, with tunable sparsity levels, of shear-wave velocity profiles from surface wave data

The analysis of surface wave dispersion curves is a way to infer the ver...
06/14/2018

Simultaneous model calibration and source inversion in atmospheric dispersion models

We present a cost-effective method for model calibration and solution of...
04/15/2021

A Simple Baseline for StyleGAN Inversion

This paper studies the problem of StyleGAN inversion, which plays an ess...
06/04/2021

Phase Retrieval for L^2([-π,π]) via the Provably Accurate and Noise Robust Numerical Inversion of Spectrogram Measurements

In this paper, we focus on the approximation of smooth functions f: [-π,...
06/16/2020

Two-Dimensional Non-Line-of-Sight Scene Estimation from a Single Edge Occluder

Passive non-line-of-sight imaging methods are often faster and stealthie...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.