Mantle layering greatly influences the dynamics of the Earth's interior, which is responsible for its evolution. Over the last few decades, the search for upper mantle discontinuities using various seismological techniques has gained a lot of momentum. There is a general agreement that in addition to the ubiquitous 410-and 660-km discontinuities (Shearer & Masters, 1992), there exists a weak seismic discontinuity called the X-discontinuity (Revenaugh & Jordan, 1991), in the depth range of 250-350 km, sometimes referred to as the 300 km discontinuity. The X-discontinuity shows a weak, sharp, positive (velocity increasing downward) impedance contrast and is only intermittently observed, unlike the global transition zone discontinuities. Because of its sporadic nature, the origin of X-discontinuity is controversial. However, all the proposed mechanisms are related to the chemical and thermal properties of the mantle, hence the presence of X-discontinuity has implications on mantle dynamics and geochemical signature of the mantle.Seismologically, it has been mostly detected in active subduction and hotspot regions. Figure 1 and Table S1 (Supporting Information) summarize the global disposition of X-discontinuities identified by numerous researchers along with their probing tools and plausible interpretations. Figure 1 shows that the depth of the X-phase below North America is between 290 and 320 km (

Summary The converted wave data (P-to-s or S-to-p), traditionally termed as receiver functions, are often contaminated with noise of different origin that may lead to the erroneous identification of phases and thus influence the interpretations. Here we utilize an unsupervised deep learning approach called Patchunet to de-noise the converted wave data. We divide the input data into several patches, which are input to the encoder and decoder network to extract some meaningful features. The method de-noises an image patch-by-patch and utilizes the redundant information on similar patches to obtain the final de-noised results. The method is first tested on a suite of synthetic data contaminated with various amount of Gaussian and realistic noise and then on the observed data from three permanent seismic stations: HYB (Hyderabad, India), LBTB (Lobatse, Botswana, South Africa), COR (Corvallis, Oregon, USA). The method works very well even when the signal-to-noise ratio is poor or with the presence of spike noise and deconvolution artifacts. The field data demonstrate the effectiveness of the method for attenuating the random noise especially for the mantle phases, which show significant improvements over conventional receiver function based images.

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The b value of earthquakes is very useful to forecast the occurrence of aftershocks in a given region. The b value characterizes the release of energy due to stress accumulation in the rocks through an earthquake and is a direct indicator for the prediction of aftershocks in the region. Wavelet based fractal analysis is used in this study to determine the b value by calculating the fractal dimension. This method guarantees high accuracy results through a limited dataset. The objective of this work was to demonstrate an elegant method for the determination of the b value after an earthquake and predict the occurrence of aftershocks with high accuracy. Repeated earthquakes were analyzed between 2003 and 2011 in Turkey and the b value was found for these earthquakes. The results gave an indication that the b value of the mainshock and its aftershocks are different and aftershocks occur in the region when the b value of the mainshock deviates significantly from 0.5, and aftershocks keep occurring until the b value of the earthquake approaches close to 0.5 for this region.

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