Title: Deep learning Inversion of Seismic Data,
Abstract: In this paper, we propose a new method to tackle the mapping challenge from
time-series data to spatial image in the field of seismic exploration, i.e.,
reconstructing the velocity model directly from seismic data by deep neural
networks (DNNs). The conventional way to address this ill-posed seismic
inversion problem is through iterative algorithms, which suffer from poor
nonlinear mapping and strong non-uniqueness. Other attempts may either import
human intervention errors or underuse seismic data. The challenge for DNNs
mainly lies in the weak spatial correspondence, the uncertain
reflection-reception relationship between seismic data and velocity model as
well as the time-varying property of seismic data. To approach these
challenges, we propose an end-to-end Seismic Inversion Networks (SeisInvNet for
short) with novel components to make the best use of all seismic data.
Specifically, we start with every seismic trace and enhance it with its
neighborhood information, its observation setup and global context of its
corresponding seismic profile. Then from enhanced seismic traces, the spatially
aligned feature maps can be learned and further concatenated to reconstruct
velocity model. In general, we let every seismic trace contribute to the
reconstruction of the whole velocity model by finding spatial correspondence.
The proposed SeisInvNet consistently produces improvements over the baselines
and achieves promising performance on our proposed SeisInv dataset according to
various evaluation metrics, and the inversion results are more consistent with
the target from the aspects of velocity value, subsurface structure and
geological interface. In addition to the superior performance, the mechanism is
also carefully discussed, and some potential problems are identified for
further study.