Ionospheric TEC and plasma anomalies possibly associated with the 14 July 2019 M_(w) 7.2 Indonesia Laiwui earthquake,from analysis of GPS and CSES data

This study presents signatures of seismo-ionospheric perturbations possibly related to the 14 July 2019 M_(w)7.2 Laiwui earthquake,detected by a cross-validation analysis of total electron content(TEC)data of the global ionospheric map(GIM)from GPS and plasma parameter data recorded by the China Seismo-Electromagnetic Satellite(CSES).After separating pre-seismic ionospheric phenomena from the ionospheric disturbances due to the magnetospheric and solar activities,we have identified three positive temporal anomalies,around the epicenter,at 1 day,3 days and 8 days before the earthquake(14 July 2019),along with a negative anomaly 6 days after the earthquake.These results agree well with the TEC spatial variations in latitude-longitude-time(LLT)maps.To confirm these anomalies further,we employed the moving mean method(MMM)to analyze ionospheric plasma parameters(electron,O^(+) and He^(+) densities)recorded by the Langmuir probe(LAP)and Plasma Analyzer Package(PAP)onboard the CSES.The analysis detected on,on Day Two,Day Four,and Day Seven before the earthquake,remarkable enhancements along the orbits around when in proximity to the epicenter.To make the investigations still more convincing,we compared the orbits on which anomalous readings were recorded to their corresponding four nearest revisiting orbits;the comparison did indeed indicate the existence of plasma parameter anomalies that appear to be associated with the Laiwui earthquake.All these results ilustrate that the unusual ionospheric perturbations detected through GPS and CSES data are possibly associated with the M_(w)7.2 Laiwui earthquake,which suggests that at least some earthquakes may be predicted by alertness to pre-seismic ionospheric anomalies over regions known to be at seismic risk.This case study also contributes additional information of value to our understanding of lithosphere-atmosphere-ionosphere coupling.

Erratum to:A review of the wave gradiometry method for seismic imaging

Earthq Sci 2023(36):254-281 Doi:10.1016/j.eqs.2023.04.002 In the original version of this article,the important funding source was inadvertently omitted in Acknowledgement section.The corrected one is as follows.

The Discovery of Deep High-Resistivity Block and Inadequately Consolidated Magma Chambers in Gaoligongshan Oblique Collisional Orogen and its Tectonic Implications

Objective The Gaoligongshan oblique collisional orogen is located in the southern section of the Hengduan Mountains, and belongs to one of the main Late Yanshanian-Himalayan oblique collisional orogens in the Sanjiang area. Many researchers have studied the geology, geochemistry and geophysics of this region, and many research achievements have been obtained from deep geophysical exploration of the region, especially using the magnetotelluric （MT） sounding technique. However,

A review of the wave gradiometry method for seismic imaging

As dense seismic arrays at different scales are deployed,the techniques to make full use of array data with low computing cost become increasingly needed.The wave gradiometry method(WGM)is a new branch in seismic tomography,which utilizes the spatial gradients of the wavefield to determine the phase velocity,wave propagation direction,geometrical spreading,and radiation pattern.Seismic wave propagation parameters obtained using the WGM can be further applied to invert 3D velocity models,Q values,and anisotropy at lithospheric(crust and/or mantle)and smaller scales(e.g.,industrial oilfield or fault zone).Herein,we review the theoretical foundation,technical development,and major applications of the WGM,and compared the WGM with other commonly used major array imaging methods.Future development of the WGM is also discussed.

Non-artifact vector P- and S-wave separation for elastic reverse time migration

Elastic reverse time migration(RTM)uses the elastic wave equation to extrapolate multicomponent seismic data to the subsurface and separate the elastic wavefield into P-and S-waves.P-and S-wave separation is a necessary step in elastic RTM to avoid crosstalk between coupled wavefields.However,the current curl-divergence operator-based separation method has a polarity reversal problem in PS imaging,and vector separation methods often have separation artifacts at the interface,which affects the quality of the imaging stack.We propose a non-artifact P-and S-wave separation method based on the first-order velocity-strain equation.This equation is used for wavefield extrapolation and separation in the first-order staggered-grid finite-difference scheme,and the storage and calculation amounts are consistent with the classical first-order velocity-stress equation.The separation equation does not calculate the partial derivatives of the elastic parameters,and thus,there is no artifact in the separated Pand S-waves.During wavefield extrapolation,the dynamic characteristics of the reflected wave undergo some changes,but the transmitted wavefield is accurate;therefore,it does not affect the dynamic characteristics of the final migration imaging.Through numerical examples of 2 D simple models,part SEAM model,BP model,and 3 D 4-layer model,different wavefield separation methods and corresponding elastic RTM imaging results are analyzed.We found that the velocity-strain based elastic RTM can image subsurface structures well,without spike artifacts caused by separation artifacts,and without polarity reversal phenomenon of the PS imaging.

Steep subduction of the Indian continental mantle lithosphere beneath the eastern Himalaya revealed by gravity anomalies

The geometry and deformation of the Indian continental mantle lithosphere(ICML)beneath the India-Eurasia collision zone are critical to understanding the accommodation of continent-continent convergence.In this paper,the distribution of residual gravity anomalies in the upper mantle of southern Tibet is estimated using the gravity data and seismic velocity models,and the heterogeneous density distribution of the upper-mantle is then recovered through three-dimensional gravity inversion.The results reveal a low-density anomaly(~300 km W-E and~100 km N-S)in the upper mantle under the eastern Himalaya,while there is no obvious density anomaly under the western Himalaya.The western boundary of the low-density anomaly coincides with the Yadong-Gulu Rift(YGR)on the surface(89°–90°E),and its southern boundary is located at~28°N,approximately 130 km southward from the Indus-Yarlung suture,probably representing the mantle suture at depth.This observation indicates that,in contrast to the western ICML which is probably underthrusting at a shallow angle,the eastern ICML be likely subducting steeply,accompanying asthenosphere upwelling.Such a laterally varying geometry suggests that a major tearing of the ICML may have taken place from the intersection of the mantle suture and the YGR in the upper mantle.The tearing and the steep subduction of the ICML might be associated with the magmatic and mineralization events in the eastern Himalaya-Gangdese and the formation of the YGR.

S-wave velocity inversion and prediction using a deep hybrid neural network

The S-wave velocity is a critical petrophysical parameter in reservoir description,prestack seismic inversion,and geomechanical analysis.However,obtaining the S-wave velocity from field measurements is difficult.When no measured Swave data are available,petrophysical modelling provides the most accurate S-wave velocity prediction.However,because of the complexity of underground geological structures and diversity of rock minerals,the prediction results of petrophysical modelling are easily affected by factors such as the cognition and experience of the modeller.Therefore,the development of novel robust and simple S-wave velocity inversion and prediction methods independent of the modeller is critical.Inspired by ensemble learning and based on the geologic sedimentation law of reservoirs and their characteristics in logging response,an Swave velocity inversion and prediction method based on deep hybrid neural network was developed by combining the classical convolution neural network(CNN)with the long short-term memory(LSTM)network.Considering the conventional logging data such as acoustic and density as the input in the proposed method,the CNN was used to establish the nonlinear mapping relationship between the input data and S-wave velocity,and the LSTM network was used to integrate the vertical variation trend of the stratum.Thus,intelligent data-driven inversion and prediction of the S-wave velocity were realised.The experimental results revealed that the proposed method exhibited a strong generalisation ability and could obtain prediction results comparable to those of petrophysical modelling with a single-well data set for training.Thus,a novel methodology for robust and convenient S-wave velocity prediction was devised.The proposed method has considerable academic and application implications.