Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Seismic Inversion by Newtonian Machine Learning   We present a wave-equation inversion method that inverts skeletonized data
for the subsurface velocity model. The skeletonized representation of the
seismic traces consists of the low-rank latent-space variables predicted by a
well-trained autoencoder neural network. The input to the autoencoder is the
recorded common shot gathers, and the implicit function theorem is used to
determine the perturbation of the skeletonized data with respect to the
velocity perturbation. The final velocity model is the one that best predicts
the observed latent-space parameters. Empirical results suggest that the
cycle-skipping problem is largely mitigated compared to the conventional full
waveform inversion (FWI) method by replacing the waveform differences by those
of the latent-space parameters. The advantage of this method over other
skeletonized data methods is that no manual picking of important features is
required because the skeletal data are automatically selected by the
autoencoder. The most significant contribution of this paper is that it
provides a general framework for using solutions to the governing PDE to invert
skeletal data generated by any type of a neural network. The governing equation
can be that for gravity, seismic waves, electromagnetic fields, and magnetic
fields. The input data can be the records from different types of data and
their skeletal features, as long as the model parameters are sensitive to their
perturbations. The skeletal data can be the latent space variables of an
autoencoder, a variational autoencoder, or a feature map from a convolutional
neural network (CNN), or principal component analysis (PCA) features. In other
words, we have combined the best features of Newtonian physics and the pattern
matching capabilities of machine learning to invert seismic data by Newtonian
machine learning.

17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc