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Pure and Applied Geophysics (PAGEOPH) is one of the leading journals in the field of geophysics. The first issue was published in 1939; thus, the journal is celebrating its 80th anniversary in 2018. The aim of this paper is to provide a complete lifetime overview of the academic structure of the journal using bibliometric indicators. This analysis includes key factors such as the most cited articles, leading authors, originating institutions and countries, publication and citation structures, and the most commonly used keywords. The bibliometric data used to conduct this analysis comes from the Scopus database. Additionally, the visualization of similarities (VOS)viewer software is used to create a graphic map of some of the bibliometric results. The graphical analysis uses co-citation, bibliographic coupling and co-occurrence of keywords. The results indicate that PAGEOPH is a leading journal in the areas in which it is indexed, with publications from a wide range of authors, institutions, and countries around the world.

The velocity is not uniformly distributed in ST area of Dongying Depression in China, which varies both horizontally and vertically. In order to obtain accurate time-depth results, it is necessary to find an appropriate velocity model for time-depth conversion according to the actual situation of the study area. First, the characteristics and main affecting factors of velocity variation in the study area were analyzed. Analysis results showed that the velocity is obviously multi-segment in the vertical direction and zoned on the plane, with a larger velocity in the northern part of Shengbei fault and a smaller velocity in the southern part. The main factors affecting the velocity distribution are the burial depth or compaction, sedimentary facies distribution and lithological composition. Then, the velocity model was established by using checkshot data of 30 wells after making synthetic records. We proposed an integrated velocity modeling method that considers the velocity distribution characteristics and matches the geological characteristics well. Above the target layer, the polynomial fitting method was used to calculate the depth of the bottom of Es3. Then, segmental V  =  v 0/ k functions were fit to calculate the thickness of each layer. Subsequently, the stripping method was used to calculate the bottom depth layer by layer. Using this method in the study area effectively reduced uncertainty, improved accuracy of time-depth conversion and accurately understood the lateral structure and stratigraphic pattern of the basin, which could facilitate a rapid search for structural traps.

Magnetic surveys are one of the most mobile and low-cost geophysical methods. However, direct searching of hydrocarbon deposits by the magnetic method was questioned for a long time because of the virtual absence of the magnetic properties of oil and gas. Last investigations indicate that physical–chemical reactions of hydrocarbon deposits with the host media often create precursors for detecting directly magnetic signals from the oil and gas deposits even in the cases of large depths. Extraction of low-signal magnetic anomalies generated by hydrocarbon deposits is demonstrated on examples of several deposits from the Middle Kur Depression (Azerbaijan) and Dnieper-Donets Depression (Ukraine). Application of the proposed magnetic data analysis will allow not only to optimize hydrocarbon deposit searching in complex geological environments but also to decrease the number of prospecting boreholes.

In view of the heterogeneity and week similarity of random noise in the desert seismic exploration, and lots of random noise focused on low frequency, the traditional bandpass filter and wavelet transform are used to separate the signal and noise. Although there are some denoising effects, the noise cannot be suppressed well, and effective signal is damaged to some extent. Because of the above shortcomings, we propose a bandpass filter denoising method based on spectral kurtosis in this paper. This method is based on the signal and the random noise’s energy distribution characteristics in the frequency domain. First, through short-time Fourier transform (STFT), the spectral kurtosis of noisy signals is obtained. Second, we design a new threshold by the obtained spectral kurtosis, the value of spectral kurtosis greater than the threshold is preserved, and the spectral kurtosis less than the threshold is set to 0. So, the method realises the adaptive choice of the filter passband, getting an adaptive bandpass filter. At the same time, the noise can be suppressed to a greater extent while the effective signal is retained very well. The noise removal results of synthetic data and actual data show that the proposed method has very good denoising performance and amplitude preserving capability.

In-seam seismic survey currently is a hot geophysical exploration technology used for the prediction of coal seam thickness in China. Many studies have investigated the relationship between the group velocity of channel wave at certain frequency and the actual thickness of exposed coal beds. But these results are based on statistics and not universally applicable to predict the thickness of coal seams. In this study, we first theoretically analyzed the relationship between the depth and energy distribution of multi-order Love-type channel waves and found that when the channel wave wavelength is smaller than the thickness of the coal seam, the energy is more concentrated, while when the wavelength is greater than the thickness, the energy reduces linearly. We then utilized the numerical simulation technology to obtain the signal of the simulated Love-type channel wave, analyzed its frequency dispersion, and calculated the theoretical dispersion curves. The results showed that the dispersion characteristics of the channel wave are closely related to the thickness of coal seam, and the shear wave velocity of the coal seam and its surrounding rocks. In addition, we for the first time realized the joint inversion of multi-order Love-type channel waves based on the genetic algorithm and inversely calculated the velocities of shear wave in both coal seam and its surrounding rocks and the thickness of the coal seam. In addition, we found the group velocity dispersion curve of the single-channel transmitted channel wave using the time–frequency analysis and obtained the phase velocity dispersion curve based on the mathematical relationship between the group and phase velocities. Moreover, we employed the phase velocity dispersion curve to complete the inversion of the above method and obtain the predicted coal seam thickness. By comparing the geological sketch of the coal mining face, we found that the predicted coal seam thickness is in good agreement with the actual thickness. Overall, adopting the channel wave inversion method that creatively uses the complete dispersion curve can obtain the shear wave velocities of the coal and its surrounding rocks, and analyzing the depth of the abruptly changed shear wave velocity can accurately obtain the thickness of the coal seam. Therefore, our study proved that this inversion method is feasible to be used in both simulation experiments and actual detection.

The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.

Small-scale geological discontinuities are not easy to detect and image in seismic data, as these features represent themselves as diffracted rather than reflected waves. However, the combined reflected and diffracted image contains full wave information and is of great value to an interpreter, for instance enabling the identification of faults, fractures, and surfaces in built-up carbonate. Although diffraction imaging has a resolution below the typical seismic wavelength, if the wavelength is much smaller than the width of the discontinuity then interference effects can be ignored, as they would not play a role in generating the seismic diffractions. In this paper, by means of synthetic examples and real data, the potential of diffraction separation for high-resolution seismic imaging is revealed and choosing the best method for preserving diffraction are discussed. We illustrate the accuracy of separating diffractions using the plane-wave destruction (PWD) and dip frequency filtering (DFF) techniques on data from the Sarawak Basin, a carbonate field. PWD is able to preserve the diffraction more intelligently than DFF, which is proven in the results by the model and real data. The final results illustrate the effectiveness of diffraction separation and possible imaging for high-resolution seismic data of small but significant geological features.

A new seismic interpolation and denoising method with a curvelet transform matching filter, employing the fast iterative shrinkage thresholding algorithm (FISTA), is proposed. The approach treats the matching filter, seismic interpolation, and denoising all as the same inverse problem using an inversion iteration algorithm. The curvelet transform has a high sparseness and is useful for separating signal from noise, meaning that it can accurately solve the matching problem using FISTA. When applying the new method to a synthetic noisy data sets and a data sets with missing traces, the optimum matching result is obtained, noise is greatly suppressed, missing seismic data are filled by interpolation, and the waveform is highly consistent. We then verified the method by applying it to real data, yielding satisfactory results. The results show that the method can reconstruct missing traces in the case of low SNR (signal-to-noise ratio). The above three problems can be simultaneously solved via FISTA algorithm, and it will not only increase the processing efficiency but also improve SNR of the seismic data.

Seismic impedance inversion is a well-known method used to obtain the image of subsurface geological structures. Utilizing the spatial coherence among seismic traces, the laterally constrained multitrace impedance inversion (LCI) is superior to trace-by-trace inversion and can produce a more realistic image of the subsurface structures. However, when the traces are numerous, it will take great computational cost and a lot of memory to solve the large-scale matrix in the multitrace inversion, which restricts the efficiency and applicability of the existing multitrace inversion algorithm. In addition, the multitrace inversion methods are not only needed to consider the lateral correlation but also should take the constraints in temporal dimension into account. As usual, these vertical constraints represent the stratigraphic characteristics of the reservoir. For instance, total-variation regularization is adopted to obtain the blocky structure. However, it still limits the magnitude of model parameter variation and therefore somewhat distorts the real image. In this paper, we propose two schemes to solve these issues. Firstly, we introduce a fast algorithm called blocky coordinate descent (BCD) to derive a new framework of laterally constrained multitrace impedance inversion. This new BCD-based inversion approach is fast and spends fewer memories. Next, we introduce a minimum gradient support regularization into the BCD-based laterally constrained inversion. This new approach can adapt to sharp layer boundaries and keep the spatial coherence. The feasibility of the proposed method is illustrated by numerical tests for both synthetic data and field seismic data.

Time-variable gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions have opened up a new avenue of opportunities for studying large-scale mass redistribution and transport in the Earth system. Over the past 19 years, GRACE/GRACE-FO time-variable gravity measurements have been widely used to study mass variations in different components of the Earth system, including the hydrosphere, ocean, cryosphere, and solid Earth, and significantly improved our understanding of long-term variability of the climate system. We carry out a comprehensive review of GRACE/GRACE-FO satellite gravimetry, time-variable gravity fields, data processing methods, and major applications in several different fields, including terrestrial water storage change, global ocean mass variation, ice sheets and glaciers mass balance, and deformation of the solid Earth. We discuss in detail several major challenges we need to face when using GRACE/GRACE-FO time-variable gravity measurements to study mass changes, and how we should address them. We also discuss the potential of satellite gravimetry in detecting gravitational changes that are believed to originate from the deep Earth. The extended record of GRACE/GRACE-FO gravity series, with expected continuous improvements in the coming years, will lead to a broader range of applications and improve our understanding of both climate change and the Earth system.