The temperature at the 410-km discontinuity is re-evaluated by comparing the depth of the discontinuity with the olivine–wadsleyite transition pressure obtained using in situ X-ray diffraction experiments by Katsura et al. (2004a) and equation of state (EoS) of MgO by Tange et al. (2009) (Tange scale) and Matsui et al. (2000) . The newly estimated temperature is 1830 ± 48 K, 70 K higher than that by our previous estimation. The EoSes of the major mantle minerals (olivine, wadsleyite, ringwoodite and perovskite) are also recalculated using the Tange scale. The adiabatic temperature gradient is calculated using the thermal expansion coefficient obtained from these EoSes. The adiabatic temperature gradient gradually decreases with increasing depth without a phase transition, and abruptly increases in association with phase transitions. The adiabatic temperature gradients are found to be 04–0.5 and 0.3 K/km in the upper and lower parts of the mantle, respectively. The temperatures at a depth of 200 km, the bottom of the mantle transition zone, the top of the lower mantle and a depth of 2700 km are found to be 1720 ± 40, 2010 ± 40, 1980 ± 40, and 2730 ± 50 K. The mantle potential temperature is found to be 1610 ± 35 K.

Adiabatic temperature profile in the mantle

With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful feature learning and expression capabilities compared with the traditional machine learning (ML) methods, which attracts worldwide researchers from different fields to its increasingly wide applications. Furthermore, in the field of geochnical engineering, DL has been widely adopted in various research topics, a comprehensive review summarizing its application is desirable. Consequently, this study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers. Four major algorithms, including feedforward neural (FNN), recurrent neural network (RNN), convolutional neural network (CNN) and generative adversarial network (GAN) along with their geotechnical applications were elaborated. In addition, a thorough summary containing pubilished literatures, the corresponding reference cases, the adopted DL algorithms as well as the related geotechnical topics was compiled. Furthermore, the challenges and perspectives of future development of DL in geotechnical engineering were presented and discussed.

Application of deep learning algorithms in geotechnical engineering: a short critical review

The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed in Elysium Planitia on Mars on 26 November 2018 and fully deployed its seismometer by the end of February 2019. The mission aims to detect, characterize and locate seismic activity on Mars, and to further constrain the internal structure, composition and dynamics of the planet. Here, we present seismometer data recorded until 30 September 2019, which reveal that Mars is seismically active. We identify 174 marsquakes, comprising two distinct populations: 150 small-magnitude, high-frequency events with waves propagating at crustal depths and 24 low-frequency, subcrustal events of magnitude Mw 3–4 with waves propagating at various depths in the mantle. These marsquakes have spectral characteristics similar to the seismicity observed on the Earth and Moon. We determine that two of the largest detected marsquakes were located near the Cerberus Fossae fracture system. From the recorded seismicity, we constrain attenuation in the crust and mantle, and find indications of a potential low-S-wave-velocity layer in the upper mantle. Mars is seismically active: 24 subcrustal magnitude 3–4 marsquakes and 150 smaller events have been identified up to 30 September 2019, by an analysis of seismometer data from the InSight lander.

/pdf/the-seismicity-of-mars-q8lwpm4gs9.pdf

The seismicity of Mars

This manuscript resulted from discussions at a workshop sponsored by the U.S. Department of Energy (DOE), Office of Science (SC), Office of Basic Energy Sciences (BES), Chemical Sciences, Geosciences, and Biosciences (CSGB) Division that was held in Leesburg, Virginia, in May 2016. We are grateful to James Rustad for his leadership, contributions to discussions at the workshop, and encouragement and support during the preparation of this review. S. E. L. appreciates support in organizing and conducting the workshop and preparing the paper from Grant DE‐FG02‐03ER15430 from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. Sandia National Laboratories (SNL) is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. DOE's National Nuclear Security Administration under Contract DENA0003525. Pacific Northwest National Laboratory (PNNL) is a multiprogram national laboratory operated by Battelle Memorial Institute for the U.S. DOE. Contributions from ORNL, SNL and PNNL are based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the U.S. Government. J. L. U. acknowledges funding by the German Science Foundation Project NE 822/34‐1|UR 64/17‐1. We also value discussions with P. Eichhubl, A. Fall, and J. F. W. Gale, contributions to workshop preparation from R. A. Schultz, and discussion and comments from R. Cygan, S.F. Forstner, Q. Wang, and journal reviewers. No data were used in the preparation of this manuscript.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019RG000671

The Role of Chemistry in Fracture Pattern Development and Opportunities to Advance Interpretations of Geological Materials

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Integral Equation Methods In Scattering Theory

We present a narrative of the eruptive events culminating in the cataclysmic January 15, 2022 eruption of Hunga Tonga-Hunga Ha'apai Volcano by synthesizing diverse preliminary seismic, volcanological, sound wave, and lightning data available within the first few weeks after the eruption occurred. The first hour of eruptive activity produced fast-propagating tsunami waves, long-period seismic waves, loud audible sound waves, infrasonic waves, exceptionally intense volcanic lightning and an unsteady volcanic plume that transiently reached—at 58 ​km—the Earth's mesosphere. Energetic seismic signals were recorded worldwide and the globally stacked seismogram showed episodic seismic events within the most intense periods of phreatoplinian activity, and they correlated well with the infrasound pressure waveform recorded in Fiji. Gravity wave signals were strong enough to be observed over the entire planet in just the first few hours, with some circling the Earth multiple times subsequently. These large-amplitude, long-wavelength atmospheric disturbances come from the Earth's atmosphere being forced by the magmatic mixture of tephra, melt and gasses emitted by the unsteady but quasi-continuous eruption from 0402±1–1800 UTC on January 15, 2022. Atmospheric forcing lasted much longer than rupturing from large earthquakes recorded on modern instruments, producing a type of shock wave that originated from the interaction between compressed air and ambient (wavy) sea surface. This scenario differs from conventional ideas of earthquake slip, landslides, or caldera collapse-generated tsunami waves because of the enormous (∼1000x) volumetric change due to the supercritical nature of volatiles associated with the hot, volatile-rich phreatoplinian plume. The time series of plume altitude can be translated to volumetric discharge and mass flow rate. For an eruption duration of ∼12 ​h, the eruptive volume and mass are estimated at 1.9 ​km 3 and ∼2 900 ​Tg, respectively, corresponding to a VEI of 5–6 for this event. The high frequency and intensity of lightning was enhanced by the production of fine ash due to magma—seawater interaction with concomitant high charge per unit mass and the high pre-eruptive concentration of dissolved volatiles. Analysis of lightning flash frequencies provides a rapid metric for plume activity and eruption magnitude. Many aspects of this eruption await further investigation by multidisciplinary teams. It represents a unique opportunity for fundamental research regarding the complex, non-linear behavior of high energetic volcanic eruptions and attendant phenomena, with critical implications for hazard mitigation, volcano forecasting, and first-response efforts in future disasters. 

https://doi.org/10.1016/j.eqrea.2022.100134

Under the Surface: Pressure-Induced Planetary-Scale Waves, Volcanic Lightning, and Gaseous Clouds Caused by the Submarine Eruption of Hunga Tonga-Hunga Ha’apai Volcano Provide an Excellent Research Opportunity

Reverse time migration: A prospect of seismic imaging methodology

Reverse time migration (RTM) using multiples generates inherent crosstalk artifacts due to the interference among multiples of different orders. We have developed a method to remove such crosstalk. This approach first separates the recorded seismic data into primary reflections and multiples using the surface-related multiples elimination algorithm and then isolates the multiples into different orders. We can take any specified, say the nth, order of multiples data as the incident wave and the next higher order multiples data, (n+1)th order, as the corresponding primary reflection data for imaging. We have applied the least-squares migration scheme to these two successive orders of multiples. Our method is denoted as least-squares RTM using controlled-order multiples (LSRTM-CM). Our numerical tests demonstrated that LSRTM-CM can significantly improve imaging quality compared with straightforward seismic imaging using multiples without multiples separation.

Least-squares reverse time migration using controlled-order multiple reflections

In the correlative least-squares reverse time migration (CLSRTM) scheme, a stacked image is updated using a gradient-based inversion algorithm. However, CLSRTM experiences the incoherent stacking of different shots during each iteration due to the use of an imperfect velocity, which leads to image smearing. To reduce the sensitivity to velocity errors, we have developed prestack correlative least-squares reverse time migration (PCLSRTM), in which a gradient descent algorithm using a newly defined initial image and an efficiently defined analytical step length is developed to separately seek the optimal image for each shot gather before the final stacking. Furthermore, a weighted objective function is also designed for PCLSRTM, so that the data-domain gradient can avoid a strong truncation effect. Numerical experiments on a three-layer model as well as a marine synthetic and a field data set reveal the merits of PCLSRTM. In the presence of velocity errors, PCLSRTM shows better convergence and provi...

Prestack correlative least-squares reverse time migration

Least-squares migration (LSM) can be effective to mitigate the limitation of finite-seismic acquisition, balance the subsurface illumination, and improve the spatial resolution of the image, but it requires iterations of migration and demigration to obtain the desired subsurface reflectivity model. The computational efficiency and accuracy of migration and demigration operators are crucial for applying the algorithm. We have developed a test of the feasibility of using the Gaussian beam as the wavefield extrapolating operator for the LSM, denoted as least-squares Gaussian beam migration. Our method combines the advantages of the LSM and the efficiency of the Gaussian beam propagator. Our numerical evaluations, including two synthetic data sets and one marine field data set, illustrate that the proposed approach could be used to obtain amplitude-balanced images and to broaden the bandwidth of the migrated images in particular for the low-wavenumber components.

Hao Hu

Papers

Under the Surface: Pressure-Induced Planetary-Scale Waves, Volcanic Lightning, and Gaseous Clouds Caused by the Submarine Eruption of Hunga Tonga-Hunga Ha’apai Volcano Provide an Excellent Research Opportunity

Reverse time migration: A prospect of seismic imaging methodology

Least-squares reverse time migration using controlled-order multiple reflections

Prestack correlative least-squares reverse time migration

Least-squares Gaussian beam migration