  Seismic impedance inversion is a widely used technique for reservoir
characterization. Accurate, high-resolution seismic impedance data form the
foundation for subsequent reservoir interpretation. Deep learning methods have
demonstrated significant potential in seismic impedance inversion. Traditional
single semi-supervised networks, which directly input original seismic logging
data, struggle to capture high-frequency weak signals. This limitation leads to
low-resolution inversion results with inadequate accuracy and stability.
Moreover, seismic wavelet uncertainty further constrains the application of
these methods to real seismic data. To address these challenges, we propose
ADDIN-I: an Attention-based Dual-branch Double-Inversion Network for Impedance
prediction. ADDIN-I's dual-branch architecture overcomes the limitations of
single-branch semi-supervised networks and improves the extraction of
high-frequency weak signal features in sequence modeling. The network
incorporates an attention mechanism to further enhance its feature extraction
capabilities. To adapt the method for real seismic data applications, a deep
learning forward operator is employed to fit the wavelet adaptively. ADDIN-I
demonstrates excellent performance in both synthetic and real data
applications.
