After decades of low but continuing activity, applications of machine learning (ML) in solid
Earth geoscience have exploded in popularity. This special collection provides a snapshot …

Given the robust nonlinear regression capabilities of Artificial Intelligence (AI) technology, its
commendable performance in numerous geophysical tasks is expected. Yet, AI technology …

Mode conversion of P waves at the boundary between Earth's crust and upper mantle, when
analysed using receiver functions (RFs), allows characterization of Earth structure where …

The receiver function (RF) is a widely used crustal imaging technique. In principle, it
assumes relatively noise‐free traces that can be used to target receiver‐side structures …

We proposed a deep learning (DL) method to derive VS models from joint inversion of
Rayleigh‐wave dispersions and receiver functions, which is based on multilabel …

Receiver-function (RF) is a crustal imaging technique that entails deconvolving the radial or
transverse component with the vertical component seismogram. Analysis of the variations of …

The development of supervised deep learning technology in seismology and related fields
has been restricted due to the lack of training sets. A large amount of unlabeled data is …

The Moho discontinuity, marking the boundary between the Earth's crust and mantle, carries
abundant information about the structure and evolution of the crust-mantle system. The" …

The Moho discontinuity, marking the boundary between the Earth's crust and mantle, carries
abundant information about the structure and evolution of the crust-mantle system. The …

We propose a novel scheme that applies a multitasking convolutional neural network to
learn the back azimuthal behavior from receiver function seismograms, which can effectively …