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In this study, we propose new attenuation relations for use in possible future macroseismicity-based analyses for Turkey. The most significant difference of the new relationships is that they are specifically designed for use in the full extent of Turkey. Besides, this paper supplies an extensive macroseismic database larger than ever collected for Turkey. To this end, a modifiable MATLAB program, which is fully presented in the electronic supplement to this paper, was written to analyze the collected isoseismal maps that were first geo-rectified and then gridded by dividing them into 0.02° arrays. The epicentral distances belonging to each point intersected with the pertinent isoseism were compiled in an event-based log. Then, all subsets were merged into a single dataset, which is presented in the electronic supplement. Required strong ground motion parameters were taken from an improved earthquake catalog recently given by Kadirioğlu et al. (B Earthq Eng 1:2, 2016). For modeling the relations, the early relations were mostly selected as patterns for our candidate attenuation models. Of all candidate models, the statistically significant ones were individually tested whether or not they were able to detect the best fitting model to our database. The predicted error margins of the proposed models were compared to those of early models using data-driven statistical tests. In conclusion, considering the usability limits, the estimation capabilities of proposed relationships were found to be useful to some extent than those of the early models developed for Turkey. [ABSTRACT FROM AUTHOR]

The rapid proliferation of the Internet of Things (IoT) technologies, underwater sensors and tracking systems, and advances in signal processing and information systems aid in the development of sensing technologies as well as software-defined networks, which enable effective water monitoring and modeling. The paper by Jayagopal et al. ([5], [6]) "Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning" focused on forecasting maize yield production in different climate changes in Saudi Arabia. Acta Geophys. https://doi.org/10.1007/s11600-022-00849-w 9 Mathivanan SK, Jayagopal P. Utilizing satellite and UAV data for crop yield prediction and monitoring through deep learning. [Extracted from the article]

The digital core and pore network model (PNM) are the basis of studying porous media. At present, the voxel-based maximal ball (MB) method has been widely used in the construction of PNM. However, due to the dependence on discrete data and the fuzziness of size definition, the PNM by using this method may not be accurate. The construction of PNM is essentially a geometric problem. Therefore, a computational geometry method was proposed in this paper to construct the PNM. A grid-based core surface model was constructed by using the moving cubes (MC) algorithm, the maximal inscribed ball of the grid space was extracted by using the computational geometry method, and a PNM was built by judging 12 types of dependency relationships of the master and servant spheres in the inscribed ball. Finally, combined with Berea sandstone, the physical parameters of cores obtained by the proposed method and the MB method were compared. The throat length results show that the proposed algorithm has improved the defect of small throat length when the MB method is used to partition core pore space. Meanwhile, the results of other parameters tend to be consistent, which proves the reliability of the proposed algorithm. Besides, by comparing the seepage simulation results of the two methods with the physical experiments, it was proved that the permeability calculated by the method in this paper is closer to the measured value of the physical experiment than that by the MB method. [ABSTRACT FROM AUTHOR]

The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. [ABSTRACT FROM AUTHOR]

Based on the big data GIS system, this paper first analyzes the methods and research results of the visualization of soil and water conservation level data at home and abroad. And for the proposed visual protection plan for land and water resources, a relatively excellent data architecture and the latest persistence plan have been designed. Based on this point, this paper investigates the cause of rainfall erosion and fluctuation in the mountain environment of S city and studies and determines the important technical points of water and soil conservation data images. Based on the results of this kind of research, the article further analyzes the temporal and spatial characteristics of the data and, based on the RUSLE model, conducts a directional consideration of the distribution characteristics of environmental rainfall erosion in mountainous environments: that is, comprehensively examines the impact of rainfall erosion in mountainous environments from both natural and social factors. The main factor of the pattern. Using multi-linear regression equations, combined with environmental variables and bioclimatic variables, the spatial distribution of land in Q mountainous areas is predicted under the background of global change. Finally, the article conducted an in-depth study on the people flow planning of the Leshan Giant Buddha Scenic Spot, introduced the construction of the Leshan Giant Buddha Scenic Spot Project Network, and adopted comprehensive technical solutions such as "portrait photography reconnaissance surveillance camera + online photography AI portrait recognition technology" to build the number of passengers identify and plan the design of the system and elaborate on the system's functions, such as visitor statistics, hotspot analysis, travel route analysis, video tracking, and data display. The article applies the research results of rainfall erosion in mountain environment based on big data GIS to the research on the people flow planning of Leshan Giant Buddha, which promotes the rapid development of scenic spots. [ABSTRACT FROM AUTHOR]

The main purpose of the present research is to propose a comprehensive attitude of the salinity in groundwater resources of Iran using 116 papers in the assessment of groundwater quality in different areas of the country. The conducted study contains the measurement of the values of physicochemical parameters of which electrical conductivity (EC), as the fundamental parameter for salinity determination of groundwater, was considered mostly, and by comparing it with standards, as well as employing the Wilcox diagram, the quality of water was assessed for drinking and agricultural purposes, respectively. Study of the previous papers revealed that in only the west and northwest of the country, the groundwater quality was suitable for drinking, and in the majority of provinces, EC values exceeded the recommended limits. The highest EC level was seen in central provinces which led to the aggravation of groundwater and the largest proportion of salinity, putting the quality of water in an unsuitable class for drinking. Additionally, the assessment of groundwater quality for irrigation uses using the Wilcox diagram showed high and very high salinity rates in most areas of the study area. Northwestern provinces relatively had the lowest level putting in C3–S1 class, while central areas reached the highest with C4–S4 classification. Deficiency of rainfall, high rate of groundwater exploitation, and the intrusion of saltwater from Salt Lake were the principal reasons for groundwater deterioration in central parts of the study area. [ABSTRACT FROM AUTHOR]

The structural complexities of hydrocarbon reservoirs make it difficult to correlate geological and petrophysical properties. A successful field development depends on accurately mapping the spatial distributions of reservoir key parameters. In this study, we present results on rock type analysis, estimation, and uncertainty evaluation of geological and petrophysical data of 33 wells in one of the south Iranian gas fields. This paper is divided into two parts. In the first part, we used a reservoir rock classification theme based on identifying electrofacies (EFs) and hydraulic flow units by analyzing both log and core data. In the second part of the paper, we performed estimation, uncertainty evaluation, and assessment of the porosity–thickness relationship of the high-quality EFs using geostatistical techniques. We used sequential simulation schemes to map the spatial distributions of porosity and thickness of the high-quality EFs across the field. Using probabilistic approaches, the generated multiple realizations were used to quantify the local and spatial uncertainties. Variogram analysis showed that property distributions had a higher continuity and minimum variance in the NW–SE direction. Based on spatial uncertainty analysis, we found that the indicator-based sequential simulated maps exhibited less spatial uncertainty. Furthermore, the obtained probability maps indicated that the SE part of the study area is more suitable for drilling and production scenarios. [ABSTRACT FROM AUTHOR]

Understanding the particle dynamics in flash flood gullies can help us learn how particle systems work and how river landscapes evolve. While discrete particle-based simulation techniques can accurately capture the transient forces acting on individual particles and their movement trajectories, they also suffer from the problem of excessive computation. To assess the feasibility of applying simplified discrete particle simulation techniques, this paper examines the mechanics of particle transport with loose accumulation in a generalized main and branch gully with a right angle under three different particle gradations using a coupled computational fluid dynamics and discrete element method (CFD-DEM). Numerical results indicate that the accumulation formed by the single gradation particles sliding into the main gully has the greatest degree of encroachment on the gully space and influences the lifting of the water depth of the cross section the most, and its velocity peaks at the fastest speed after being swept away by the water flow. Comparing the cumulative mass and the particle transport rate of the main gully, this paper reveals the similarity in the particle transport process. Based on the number of particle gradation (n ), the transport process of all particle gradations can be mutually converted using the time transformation scale n + 1 n . This method can effectively unify the transport process of particles with different gradations. Given the result of the work presented in this paper, it can be taken as a reference for simplifying the calculation of particle transport simulation based on particles in flash flood gullies. [ABSTRACT FROM AUTHOR]

Earth science informatics has experienced significant growth in recent decades due to advancements in computational efficiency and processing power. The results showed that the soil moisture content was minimal in the 5 cm soil layer on any given day and that the white film led to greater soil moisture content than the other treatments within a depth of 1 m with a higher outcome. Based on soil structure and soil fertility, sixteen evaluation indexes of soil quality in Mu Us sandy land were screened, and then, the evaluation index system of soil quality was constructed. To tackle these challenges, data-driven approaches such as big data analytics and artificial intelligence (AI) are being utilized with increasing frequency. [Extracted from the article]

Western Desert region between Cairo and Fayoum in Egypt is a geologically interesting area due to its active seismicity status and the continuous bidirectional urban expansions ongoing northward and southward growth. This work aims to develop a geological model of the region by combining magnetic intensity data (aeromagnetic) and potential field data (airborne gravity) with magnetotelluric (MT) data to offer decision-makers an additional evaluation tool for long-term future planning. Applying a number of filters to aerial gravity and magnetic data allowed us to calculate the subsurface density distribution and structure of the area between Cairo and Fayoum. In addition, the surface of the crust was imaged down to a depth of 6 km using a 3-D inversion of gravity data. Magnetotelluric data were gathered at eight locations over the NS-extended Cairo-Fayoum Road profile. MT data were inverted in 2-D to generate a resistivity model that defines the subsurface structure of the researched region. The subsurface geometry in the MT-derived resistivity model agrees with the results of airborne potential measurements, and no consideration was given to any faults that may have been undetected from the potential maps. Long-term seismological observations indicated that the likely active sources of earthquakes are limited to the recognized main faults and that the ongoing "1185 buildings" construction project did not and will not cause unanticipated seismicity. The paper concludes that urban growth is secure so long as the earthquake codes are rigorously considered while planning civil projects. The northern study region must be regularly monitored for induced seismicity. On the Cairo-Fayoum Road's western side, the middle sector of the study area is the safest place for future civil developments. [ABSTRACT FROM AUTHOR]

The sandy conglomerate reservoir in layer Es3 of the Liaohe Eastern Depression has good potential for oil reservoir exploration and has been identified as a key area for future exploration. The low porosity and permeability, complex lithology, and strong heterogeneity of the target layer make it difficult to predict favorable reservoirs. The objective of this study is to analyze and process conventional logging data to extract feature parameters that affect lithology by establishing a decision tree lithology classifier. Principal component analysis is used to reduce data dimensionality, and the elbow method is applied to the clustering algorithm to establish the optimal number of clusters for the automatic classification of reservoir types. Further, support vector machines are used for lithology classification based on features with higher classification capabilities. The results show that the support vector machine lithology recognition method based on feature selection achieved an accuracy of 91.8%. The processing of actual well data has verified the feasibility of the method. Based on the combination of core experiments and oil testing results, the characteristics of three types of reservoirs were presented, and potential reservoir zones were proposed for drilling wells. The comprehensive analysis and the practical application of the developed method reveal that the class I reservoir has high hydrocarbon production and could be the most favorable reservoir in the Es3 sandy conglomerate. The processing data of lithology identification and reservoir classification evaluation are consistent with core data and hydrocarbon production data, verifying the effectiveness and practicability of the method proposed in this paper. The results of this study will serve as a reference for low porosity and permeability sandy conglomerate reservoir evaluation based on machine learning in the target area. [ABSTRACT FROM AUTHOR]

The average-derivative optimal method (ADM) is widely applied in frequency-domain forward modeling for its high accuracy and simplicity. Since tuning weighted coefficients can suppress the numerical dispersion, it is extremely important to adopt a suitable optimization algorithm to determine the ADM coefficients. To date, most schemes associated with the ADM have adopted the conventional local optimization algorithms, which are sensitive to the initial value and easy to converge on local optimum. The motivation of this paper is to derive new and more accurate ADM coefficients for 2D frequency-domain elastic-wave equation by the global optimization algorithms, which can escape from the local optimum with a certain probability. We adopt simulated annealing (SA) and particle swarm optimization (PSO) algorithms for global optimization and numerical modeling. Compared with the conventional local optimization algorithm, the global optimization algorithms have smaller phase errors, especially for S-wave phase velocity. Numerical examples demonstrate that the global optimization algorithms produce more accurate results than the local optimization algorithm. [ABSTRACT FROM AUTHOR]

As Earth observation technology has advanced, the volume of remote sensing big data has grown rapidly, offering significant obstacles to efficient and effective processing and analysis. A convolutional neural network refers to a neural network that covers convolutional calculations. It is a form of deep learning, and convolutional neural networks have characterization learning characteristics, which can classify information into different data. Remote Sensing Data Processing from various sensors has been attracting with more information in Remote Sensing. Remote sensing data is generally adjusted and refined through image processing. Image processing techniques, such as filtering and feature detection, are ideal for dealing with the high-dimensionality of geographically distributed systems. The geological entity is a term in geological work which refers to the product of geological processes that occupy a certain space in the Earth's crust and are different from other materials. They are of different sizes and are divided into different types according to their size. It mainly focuses on improving classification accuracy and accurately describing scattering types. For geological entity recognition, this paper proposed a Deep Convolutional Neural Network Polarized Synthetic Aperture Radar (DCNN-PSAR). It is expected to use deep convolutional neural network technology and polarized SAR technology to explore new methods of geological entities and improve geological recognition capabilities. With the help of Multimodal Remote Sensing Data Processing, it is now possible to characterize and identify the composition of the Earth's surface from orbital and aerial platforms. This paper proposes a ground object classification algorithm for polarized SAR images based on a fully convolutional network, which realizes the geological classification function and overcomes the shortcomings of too long. The evaluation of DCNN-PSAR shows that the accuracy of the water area is showing a rising trend, and the growth rate is relatively fast in the early stage, which directly changes from 0.14 to 0.6. Still, the increase is slower in the later stage. DCNN-PSAR achieves the highest quality of remote sensing data extraction. [ABSTRACT FROM AUTHOR]

In the context of deep learning, this paper combines the arbitration mechanism to propose a GAN (Arbi-DCGAN) model based on the arbitration mechanism. First, the network structure of the proposed improved algorithm is composed of generator, discriminator and arbitrator. Then, the generator and the discriminator will conduct adversarial training according to the training plan and strengthen the ability of generating images and distinguishing the authenticity of the images according to the characteristics learned from the data set. Secondly, the arbitrator is composed of the generator, discriminator and measurement score computation module that have undergone the previous adversarial training. The arbitrator will feed back the results of the metric generator and discriminator adversarial training to the training plan. Finally, a winning limit is added to the network structure to improve the stability of model training, and the Circle loss function is used to replace the BCE loss function, which makes the model optimization process more flexible and the convergence state more clear. On the basis of geographic information system, this paper uses 325 meticulously annotated sample plans to establish a data set for deep learning, and trains the Arbi-DCGAN model to achieve the task of extracting land plots of different land types in the plan, as well as from the plane color block map to the color texture. The rendering and generation of the map complete the reconstruction task of the garden landscape. In addition, we further evaluate the results of the model's reconstruction of the garden landscape from the aspects of image quality, correct standardization and color expression. The training model has the potential to be applied to land type analysis and plane rendering in landscape architecture cases, helping designers improve the efficiency of analysis and drawing. [ABSTRACT FROM AUTHOR]

At present, the deterioration of the environment is aggravating. The study of non-pollution soil moisture materials can improve the environment to a certain extent. In this paper, the network structure and water absorption molecular scale of hydrogels are studied by means of electron microscope. In this paper, the soil moisture hydrogel was taken as the research object. Firstly, NaOH and acrylic acid were used as the main raw material to dissolve it into ethylene glycol. Then, the pH value was adjusted by different acids. After adjustment, the pH value was 7 after dialysis. Finally, acrylic acid nanoparticles hydrogel was obtained after ultrasonic treatment. Then, the network structure and molecular scale were observed by Internet of things microscope. The aged gum 8 h was injected into the hydrogel at a rate of 0.05 mL/min. When the front edge of the gel reaches the middle of the model, the formation water is injected at a speed of 0.1 mL/min, and the microscopic gel displacement mechanism of the hydrogel is analyzed by means of the Internet of things image acquisition. The results showed that nanoparticle hydrogel showed natural expansion under the electron microscope of the Internet of things. However, in dry state, the size of hydrogel is too small and the specific surface area is large. In the scanning electron microscope of the Internet of things, most hydrogels are agglomerated to form hydrogel clusters. The size of hydrogel in ethanol is the largest, and the particle size in simulated formation water is the smallest. The particle size is distributed between 150 and 450 nm. The rate of polymerization and the rate of precipitation of prepolymer are greater than that of the dispersant PVP on the surface of polymer particles. The hydrogel is cationic polymer, soluble in salt solution, but because of mutual exclusion between ions, the molecular chain cannot be completely extended; the particle size is smaller and the minimum 145 nm. It is suitable for deep migration and profile control in low-permeability rubber field. Therefore, in practice, hydrogels and their derivatives are ideal carbon-based resources. [ABSTRACT FROM AUTHOR]

The problem of urban microclimate deterioration is becoming more and more serious, and the automatic optimization of landscape space in tropical and subtropical environment needs to be solved urgently. Urban development also leads to some problems in the urban environment which are related to people's life and development. The main research content of this paper is the automatic optimization design of landscape space based on genetic algorithms and urban climate map. With the specific goal of improving the urban wind and heat environment, combined with the technical means in many fields such as architecture, urban planning, and computer technology, we try to establish a set of scientific automatic generation and optimization methods of urban design, which are suitable for different levels of work needs in the field of urban planning, from the mesoscale zoning planning to more microscopic neighborhood-scale urban design. The experimental results show that this paper uses genetic algorithms on the Rhino Grasshopper software platform to automatically find out the optimal layout of each modules with different proportions and distributions and generate the optimal layout of the environment for a given volume ratio. [ABSTRACT FROM AUTHOR]

The document titled "Acknowledgment of reviewers for 2023" expresses gratitude to the scientists and experts who reviewed papers for Acta Geophysica during the period of January 2023 to December 2023. The list includes a diverse range of individuals from various fields, representing a global perspective. The document acknowledges the valuable contributions of these reviewers without providing any additional information about their specific reviews or the papers they reviewed. [Extracted from the article]

Understanding the anomalies generated by various subsurface targets and their responses to different geophysical techniques in various subsoil types is critical to near-surface geophysical investigations. Geophysical test site (GTS) plays a vital role in near-surface geophysical investigations and related Earth sciences to adequately predict the geometries and anomalies generated by the subsurface targets. Therefore, developing a GTS on a site requires some technical efficiencies, mechanical procedures, engineering concepts and scientific approach, depending on the operating environment and the purpose of construction. This paper reviews the evolving roles of GTS in engineering, sciences, and technology via remarkable pedagogical and scientific research. The procedures for designing and installing GTS were also discussed. Every constructed GTS is unique and has its operating environment and sets of scientific requirements. As a result, the execution of GTS should be subjected to numerous factors that invariably affect the overall long time usage and performance. Comparative studies of GTS activities indicate that GTS is a vital geophysical research and academic platform to enrich the outcomes of the geophysical modelling for near-surface geophysical applications in engineering, science and technology. The evolving application of GTS has greatly impacted the field of science and engineering by enhancing the knowledge and understanding of the earth's interior, which invariably affects the engineers, geophysicists, archaeologists and geologists to be critical in the analysis, interpretation, and providing precise and accurate information of subsurface anomalies underlying the uppermost soil of the earth's crust. After a critical investigation, it was noted that the installation of GTSs is usually conceived to replicate situations often encountered in field investigation contexts. Examination shows that GTS can provide an ideal platform for young geoscientists, engineers and archaeologists to acquire the requisite skills, knowledge, technical know-how, and professional techniques for resolving near-surface challenges in real-life work situations. More also, a well-developed and equipped GTS could be a watershed in technological advancement, research development, and new scientific ideas. The GTS platform also indicates a promising pedagogical approach to geophysical educational usage, research mobilization, and development of new shallow geophysical techniques for various near-surface investigation and calibrating/testing geophysical equipment, which invariably catalyzed engineering designs, scientific concepts and technological advancement. [ABSTRACT FROM AUTHOR]

Resistivity inversion plays a significant role in recent geological exploration, which can obtain formation information through logging data. However, resistivity inversion faces various challenges in practice. Conventional inversion approaches are always time-consuming, nonlinear, non-uniqueness, and ill-posed, which can result in an inaccurate and inefficient description of subsurface structure in terms of resistivity estimation and boundary location. In this paper, a robust inversion approach is proposed to improve the efficiency of resistivity inversion. Specifically, inspired by deep neural networks (DNN) remarkable nonlinear mapping ability, the proposed inversion scheme adopts DNN architecture. Besides, the batch normalization algorithm is utilized to solve the problem of gradient disappearing in the training process, as well as the k-fold cross-validation approach is utilized to suppress overfitting. Several groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed inversion scheme. In addition, the robustness of the DNN-based inversion scheme is validated by adding different levels of noise to the synthetic measurements. Experimental results show that the proposed scheme can achieve faster convergence and higher resolution than the conventional inversion approach in the same scenario. It is very significant for geological exploration in layered formations. [ABSTRACT FROM AUTHOR]

Karst spring water dynamic characteristics and its response to atmospheric precipitation are of great significance for water resources utilization under the background of climate change. This paper selects Longzici spring area, North China, as the study area. Based on a long series of spring water flow and precipitation data, the dynamic characteristics of spring flow were analyzed and the numerical simulation of the groundwater flow model was established. The results show that the groundwater kept the sustained decline over the past decades which is in a negative equilibrium state, with a storage variable of − 2.26 million m3/year. The sensitivity of spring flow to precipitation under different precipitation scenarios shows that the water level changes in the recharge and drainage areas are similar about (3–5 cm) and slightly larger than that in the runoff area(1.5 cm) when minimum rainfall (287.24 mm) happens. When the precipitation is at its maximum (867.66 mm), the water level change in the runoff area can reach 95 cm which is much larger than those in the recharge and discharge areas. The results indicate that Longzici karst spring has a relatively good regulation water resource capacity and the runoff area is more sensitive which plays an important role in response to climate change. [ABSTRACT FROM AUTHOR]

The existence of several magnitude scales in the compiled earthquake catalogs of a specific region has made the development of regional relations connecting magnitude scales a necessity, especially for creating a homogeneous seismic catalog in which all magnitudes should be converted to a preferred magnitude scale. To consistently estimate all magnitude ranges and avoid the saturation problem for large earthquakes, the preference is always given to the moment magnitude Mw scale, wherein the most complete and reliable catalog used worldwide is the Global Centroid Moment Tensor (GCMT). However, to our knowledge, no study has yielded regional (in Algeria) relationships for converting different magnitudes to the moment magnitude (Mw,GCMT). The main reason is typically due to the lack of data pairs of different magnitude scales with Mw (GCMT). To overcome this issue, in this research paper, the moment magnitudes data used for northern Algeria (the area bounded by 32° to 39° N and 3° W to 10° E) have been taken principally from the GCMT catalog and enhanced with the European–Mediterranean Regional Centroid Moment Tensor catalogs RCMT and ZUR-CMT. Regarding this latter, it has been demonstrated in the literature that for the Mediterranean regions, a minor correction should be addressed before merging its data with the GCMT and RCMT catalogs, which are perfectly correlated. To accomplish this task, the magnitude scales tested against Mw are the surface wave magnitude, MS, and the body wave magnitude mb issued from the international seismological sources of ISC and NEIC for the same boundaries. As long as the earthquake magnitudes, in general, are affected by errors of comparable size, the best and most reliable regression method that considers the errors in both dependent and independent variables for linear conversion problems is the General Orthogonal Regression, which is adopted and applied herein to develop the regional relations. [ABSTRACT FROM AUTHOR]

Exploration of potash resources under complex geological condition is particularly important. However, it is difficult to establish characteristic equations for direct prediction, since there is no direct relation between potash content (PC) and seismic response. To solve this problem, this paper proposed a potash reservoir prediction method by a specially designed convolution neural network (CNN) structure to train the special waveform and petrophysical characteristics of potash reservoirs. Considering that the potash reservoirs and petrophysical characteristics are not a one-to-one mapping, the prediction procedure is divided into two parts. First, a CNN is constructed for potash reservoir prediction, according to the spatial waveform characteristics of potash reservoirs. The mapping between potash reservoirs and waveform characteristics is used to obtain the potash reservoir probability data by the soft-max function. Then, another CNN for PC prediction is built based on the petrophysical characteristics of potash reservoirs. Meanwhile, according to the Hadamard criterion, the petrophysical characteristics of potash reservoir are constrained by the waveform characteristics. The two CNN models are used to directly predict the PC synergistically. Consequently, the bidirectional mapping problem can be alleviated and a loss function of the PC prediction CNN constrained with the waveform is obtained. Finally, by tuning the PC prediction CNN through the loss function, PC prediction is performed. The correlation between the predicted and true PC values can reach more than 80%. [ABSTRACT FROM AUTHOR]

The present work addresses the problem of unified methodology use for vibration analysis caused by various anthropogenic sources. The experimental part of the paper contains analysis and results comparison of the vibration, induced by freight train passages and hammer percussion. Ground vibrations are important for the civil engineering objects condition since the vibration induced by train is transmitted through the ground to the foundation and structure. Ground vibration can also be a source of disturbance for the residents. However, various factors cause vibration that varies in both intensity and frequency measures. The analysis of vibration timelines, running RMS and Fourier spectra are applied. The major part of the dissertation is the analysis of RMS accelerations and velocities in one-third octave frequency bands in 1 to 80 Hz spectrum, and comparison to the background vibration noise, recorded in the same localisation. The different values of time-window observation are applied to analyse the results. The results of long lasting cargo-induced vibrations are less dependent on time-window duration than the results of impulse-induced vibrations. The acceleration and velocity analysis results are compared to the literature data concerning the ground vibration caused by railway transport and mining activity. The paper indicates the possibility to apply unified methodology for vibration analysis caused by both long-lasting sources, as trains and short-lasting ones, as industrial blasts or mine tremors. The results indicate an optimal time window for vibration measurement that would enable to automate vibration monitoring. [ABSTRACT FROM AUTHOR]

Models belonging to the ionosphere that is directly affected by factors such as solar activity, geomagnetic storm, earthquake, seasonal changes, and geographical location need to be considered altogether. In this sense, the cause of the ionospheric anomalies should be meticulously distinguished from each other. Ionospheric anomalies that occur before or (and) after an earthquake have a serious place in earthquake prediction studies. Total electron content (TEC) is one of the significant parameters to be able to discuss the anomalies of the ionosphere. This essay investigates ionospheric anomalies before and after the Mw 6.5 Samar, Philippines (12.025° N, 125.416° E and November 18, 2003, at 17:14 UT) earthquake. The paper analyzes anomalies with the aid of the TEC (TECU) map. In the paper, the time-domain TEC variables are transferred to the frequency-domain for observing some clues-peaks by short-term Fourier transformation spectral analysis. The discussion handles the effect of the solar activity with the F10.7 (sfu) index and the effect of geomagnetic storms with Bz (nT), v (km/s), P (nPa), E (mV/m), Kp (nT), and Dst (nT) parameters (index). The lower and upper boundaries of the TEC map obtained from the International Reference Ionosphere (IRI-2016) are calculated with the help of median and standard deviation. The boundary-setting process is named statistical analysis. TEC data exceeding the boundaries are marked as anomaly data. According to the paper, 11-day anomalies (9-day of which belong to pre-earthquake) are detected. Probably, the anomalies observed on November 6, 7, and 12 belong to the Samar earthquake. [ABSTRACT FROM AUTHOR]

Identification of petrophysical parameters including porosity plays an important role to evaluate hydrocarbon reservoirs. A precise prediction of porosity in oil and gas reservoirs may prevent lots of costs before drilling operations. Porosity obtained from core analysis in laboratory is the most reliable one, while they are very expensive and not always accessible. Inappropriate or missing data in under-survey locations are a key challenge for reservoir engineers. In this paper, support vector regression (SVR) is used to estimate porosity in one of the oil fields in south of Iran. SVR creates models due to structural risk minimization methods which help us to produce models with better generalization and less risk of overfitting. Definitely, measured data are always contaminated with noise. One of the common methods to reduce noise and outliers in data is to process them before using them to train the algorithm; during processing, outliers and some noisy data can be suppressed from data, while it is not always easy to distinguish real data from noise. In this paper, we modified SVR to Fuzzy SVR and Fuzzy C Means (FCM) SVR, which are used to decrease effect of noise on model, and then by adding artificial noise including random noise and outliers to data we investigated how these two methods respond to presence of noise. The results show the presence of noise and outliers in data can alter the center locations and distribution of data points in clusters in FCM SVR. Similarly, it can change the variance of Gaussian membership function we used for Fuzzy SVR, but overall, the results show Fuzzy SVR model is notably more robust against noise compared to FCM SVR. Correlation coefficient (CC) calculated between model and core data decreased from 78 to 67% after noise added to data in FCM SVR model, however, calculated CC for Fuzzy SVR remained almost steady altering from 87 to 86%. Subsequently, calculated root mean square error (RMSE) between models and core data increased from 0.0376 to 0.03827 for Fuzzy SVR, while RMSE jumped from 0.0448 to 0.0517 for FCM SVR. [ABSTRACT FROM AUTHOR]

Microseismic identification and first-arrival picking are the fundamental parts of microseismic monitoring. A deep learning model of convolution long short-term memory network (Conv-LSTM-Unet) was proposed to reduce the manual task of microseismic identification and first-arrival picking and overcome the problems of complex parameter design, low accuracy, and poor stability of traditional methods. This paper integrates microseismic identification and first-arrival picking tasks into a semantic segmentation task by using Conv-LSTM-Unet. In order to learn the spatio-temporal characteristics of microseismic, the model is based on the Unet network, and the Conv-LSTM model is formed by combining the convolution operation with LSTM, which replaces the convolution part of the original Unet network. Meanwhile, the microseismic signal is divided into three forms of single component, time–frequency map, and three-component signal to study the effect of microseismic input form on the identification model and first-arrival picking. The results show that when the signal input is three-component form, the model recognition and first-arrival picking effect are best. The Conv-LSTM-Unet model has outperformed other traditional models in first-arrival picking, with recognition accuracy up to 96.55% and maximum error of 4 ms. [ABSTRACT FROM AUTHOR]

The city of Trabzon has no site classification map or site-specific analyses representing the assessment earthquake hazard so far, although Trabzon had been influenced and damaged due to earthquakes that occurred particularly in the North Anatolian Fault zone both in the recent and distant past. This paper presents the site classification and analyzes the site-specific pseudo-spectral acceleration (PSA) for densely populated areas of Trabzon. The coastline and the east and west edges of the city demonstrate minimum Vs30 in the overall study area from 175 to 396 m/s, respectively, which falls CD to DE class in NEHRP, and ZC to ZE in TBDY. The Vs30 of the remaining areas vary between 347 and 851 m/s, representing CD to BC in NEHRP and ZD to ZB in Turkish Building Regulation. The wide range of Vs30 is mostly due to the depth of the volcanic main rock, altered and agglomerated settings of dominant formation. The estimated PSA curves along the coast side of Trabzon including the lowest Vs30 demonstrate significantly greater spectral acceleration values than the design spectrum, particularly in TA–TB range that was used for almost whole constructions in the study area. Also, PSAs of the neighborhoods mostly classified as ZC in TDBY indicate overlapping with design spectrums and peak values are pretty close to limits. As a result, the proposed paper demonstrates that the earthquake hazard should not be underestimated, in particular considering the high possibility of well-known frequent ruptures on the east side of the North Anatolian Fault. [ABSTRACT FROM AUTHOR]

This paper has proposed a new perspective of studying internal structure-based tests, the results of which will improve the present experimental methods and enrich our understanding of rock structure-based modeling without any core preparations, with low cost in a short time. Pore volume compressibility (PVC) is an important feature of rock and is related to mechanical and structural behavior of porous rock sample. An accurate evaluation of pore volume compressibility depends on experimental test which is time-consuming and costly. This paper outlines new method for evaluation of PVC of rock cores using a computed tomography (CT) scan-based finite element method (FEM). The verification studies were performed on a series of porous rock cores which were extracted from deep oil reservoirs in Iran. In order to construct a finite element model, a relationship between spatial elastic properties of samples and CT-scanned data images was derived. The samples were scanned by a conic beam computed tomography (CBCT) machine, and the scanned data were converted into a model to simulate PVC tests. The pore volumetric strains were obtained from a linear elastic analysis for each stress and pore pressure step. To validate the finite element analysis (FEA) results, a series of experimental PVC tests were conducted on the pre-scanned samples and PVC curves were extracted. As a result, the predictions calculated from the CT scan-based numerical models have shown a good correlation with the results obtained from laboratory experiments. The results revealed that it is possible to simulate PVC tests using this numerical proposed evaluation method in such a way that the cost and time of the tests were lowered. [ABSTRACT FROM AUTHOR]

Farming is an essential sustenance for the progressive population. The development of our country depends on the farmers. Plants endure by many diseases due to environmental factors. So, the farmers need to detect plant diseases at an early stage for appreciable yield. In the beginning, the observing and examining plant disease are examined physically by the expertise in the farming field, which requires a considerable measure of work/ and requires over the top handling time. Now, machine learning concepts eliminate conventional protruding and time-consuming techniques. This paper focuses on a novel method for detecting and identifying paddy leaf diseases at the early stages in Thanjavur region using radial basis function neural network (RBFNN) classifier. Further, it is optimized with salp swarm algorithm (SSA) technique. The proposed method utilizes the data from the TNAU agritech portal, IRRI knowledge bank, UCI machine learning repository databases, which have healthy and diseased images. This work illustrates four categories (Bacterial Blast, Bacterial Blight, Leaf Tungro and Brown Spot) of infected paddy images along with the normal set of images. Initially the preprocessing is performed for the acquired images then K-means segmentation algorithm segregates the image. Gray level co-occurrence matrix extracts the Texture features from the segmented image and the RBFNN classifier performs the disease classification and improves the detection accuracy by optimizing the data using SSA. The investigational results of the proposed methodology exhibit the performance in terms of accuracy of disease detection is 98.47%. However, radial basis function neural network (RBFNN) achieves the diseases detection accuracy of 97.85% and support-vector machine (SVM) classifier achieves a disease detection accuracy of 97.07%. This paper proposes a method of paddy leaf disease recognition and classification using RBFNN and salp swarm algorithm. It also suggests and identifies an image analysis by framing a set of conditions for disease affected plants. The results show that the most satisfactory outcome can be gained to verify the yield of proposed methods with least effort. [ABSTRACT FROM AUTHOR]

The purpose of entity resolution (ER) is to identify records that refer to the same real-world entity from different sources. Most traditional ER studies identify records based on string-based data, so the ER problem relies mostly on string comparison techniques. There is little research on numeric-based data. Traditional ER approaches are widely used in many domains, such as papers, gene sequencing and restaurants, but they have not been used in an earthquake disaster. In this paper, earthquake disaster event information that was collected from different websites is denoted with numeric data. To solve the problem of ER in numeric data, we use the following methods to conduct experiments. First, we treat numbers as strings and use string-based approaches. Second, we use the Euclidean distance to measure the difference between two records. Third, we combine the above two strategies and use a comprehensive approach to measure the distance between the two records. We experimentally evaluate our methods on real datasets that represent earthquake disaster event information. The experimental results show that a comprehensive approach can achieve high performance. [ABSTRACT FROM AUTHOR]

The identification of formation fluid properties of igneous rock reservoirs plays a vital role in the igneous rock oil/gas exploration and development. Due to the complex lithology, low porosity, low permeability and strong heterogeneity of igneous rock reservoirs, conventional geophysical logging evaluation methods are not as accurate as sandstone reservoirs in response to different fluid properties of igneous rock reservoirs. It is difficult to identify the fluid type of the formation. In order to solve this problem, with the rapid development of array acoustic logging technology, this paper proposes a new method for analyzing the time–frequency characteristics of full wave train data based on the large amount of information contained in the array acoustic wave train, hoping to more accurately identify the fluid properties of igneous rock reservoirs. This paper introduces the time slice and frequency slice of Choi–Williams distribution with different IMF components obtained by EMD method and analyzes the characteristics of time slice and frequency slice of reservoirs with different fluid properties, in order to be used for identification of formation fluid types. The results show that the time slices and frequency slices of different IMF components have significantly different characteristics for different formation fluid properties of igneous rocks. This new method can provide geophysical and geological researchers with some new information to more accurately identify the properties of formation fluids. [ABSTRACT FROM AUTHOR]

Through the spectrum noise logging technology, the oil field is dynamically monitored, and according to its simple logging instrument and convenient operation, the position of the outer channeling of the casing can be qualitatively judged by the abnormal noise of the measurement record, and the downhole production status of the water injection well can be accurately diagnosed. Fully grasp the problems of oil casing leakage, outer channeling and packer leakage in water injection wells, and enrich downhole operations. In this paper, the downhole noise signal data are standardized, and the K-means clustering algorithm is used to classify the downhole noise signal according to the correlation coefficient of different frequencies to obtain the low-frequency noise signal, and the low-frequency noise signal is clustered twice to obtain the channeling frequency band and the reservoir fluid frequency band. The accurate channeling frequency range is determined and conforms to the domestic and foreign research data. The channeling frequency band is processed by wavelet threshold, and the useless noise in the channeling frequency band is eliminated. The channeling noise signal curve after processing is analyzed, and the main output layers have an obvious amplitude back channeling. The K-means clustering algorithm is used to analyze the channeling frequency band, and the channeling noise is processed by wavelet threshold. It is a new noise signal curve processing method, which provides a new idea for the spectrum noise logging technology to master the problem of channeling outside the pipe in the water injection well. [ABSTRACT FROM AUTHOR]

The performance of conceptual catchment runoff models may highly depend on the specific choice of calibration methods made by the user. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are two well-known families of Evolutionary Algorithms that are widely used for calibration of hydrological and environmental models. In the present paper, five DE and five PSO optimization algorithms are compared regarding calibration of two conceptual models, namely the Swedish HBV model (Hydrologiska Byrans Vattenavdelning model) and the French GR4J model (modèle du Génie Rural à 4 paramètres Journalier) of the Kamienna catchment runoff. This catchment is located in the middle part of Poland. The main goal of the study was to find out whether DE or PSO algorithms would be better suited for calibration of conceptual rainfall-runoff models. In general, four out of five DE algorithms perform better than four out of five PSO methods, at least for the calibration data. However, one DE algorithm constantly performs very poorly, while one PSO algorithm is among the best optimizers. Large differences are observed between results obtained for calibration and validation data sets. Differences between optimization algorithms are lower for the GR4J than for the HBV model, probably because GR4J has fewer parameters to optimize than HBV. [ABSTRACT FROM AUTHOR]

Anisotropy is an inherent property of rocks. It refers to the different response of the rock properties in each direction. Understanding the anisotropy characteristics of rock failure by drilling has practical applications in improving drilling efficiency, especially for engineering applications. In this paper, a criterion is established to clarify the ductile-to-brittle transition in the drilling process of rock. Moreover, a new anisotropy index has been proposed to evaluate the effect of anisotropy on the critical state of the ductile-to-brittle transition. The digital drilling tests are conducted on six types of rock to study the drilling mechanical performance in the X, Y, and Z directions. The anisotropy characteristics of drilling parameters and mechanical specific energy (MSE) are analysed at the critical state of ductile–brittle failure. The results show that the critical state of ductile–brittle failure is manifested as an inflection point of the depth of cut. The evolution of MSE is fitted as two linear functions, which corresponds to the two stages. The order of anisotropy in MSE is obtained as: gneiss > slate > red sandstone > granite > argillaceous sandstone > sandstone. The anisotropy in the uniaxial compressive strength and MSE for different rock types has the same sequences. The advantages of the proposed method in determining rock anisotropy are illustrated. [ABSTRACT FROM AUTHOR]

Analyzing well-testing data by the type-curve matching is a modern well-testing analysis method and is widely used in the petroleum and gas industry. By improving accuracy of type curve, we can get more accurate results from analyzing well-testing data, which provide a scientific base for development of oil, gas and water resources. By solving percolation equations, we can obtain type curves. The Laplace transformation methods are often used to solve them. In this paper, we improve the accuracy of type curve by improving the numerical inverse Laplace transformation (NILT) based on infinite series. We combine the NILT based on infinite series with Levin convergence acceleration and determine necessary parameters through numerical experiments to improve accuracy and speed. To verify this method, we compare the improved method with the Stehfest method using some functions such as trigonometric function. Type curves for analysis of well-testing data for the homogeneous reservoir with elastic outer boundary and a dual porosity reservoir are plotted and compared by using the improved numerical inversion and the Stehfest numerical inversion, respectively. These results show that type curves plotted by the improved method are less in vibration and fluctuation than ones plotted by the Stehfest method. [ABSTRACT FROM AUTHOR]

Bedload transport is an important factor to describe the hydromorphological processes of fluvial systems. However, conventional bedload sampling methods have large uncertainty, making it harder to understand this notoriously complex phenomenon. In this study, a novel, image-based approach, the Video-based Bedload Tracker (VBT), is implemented to quantify gravel bedload transport by combining two different techniques: Statistical Background Model and Large-Scale Particle Image Velocimetry. For testing purposes, we use underwater videos, captured in a laboratory flume, with future field adaptation as an overall goal. VBT offers a full statistics of the individual velocity and grainsize data for the moving particles. The paper introduces the testing of the method which requires minimal preprocessing (a simple and quick 2D Gaussian filter) to retrieve and calculate bedload transport rate. A detailed sensitivity analysis is also carried out to introduce the parameters of the method, during which it was found that by simply relying on literature and the visual evaluation of the resulting segmented videos, it is simple to set them to the correct values. Practical aspects of the applicability of VBT in the field are also discussed and a statistical filter, accounting for the suspended sediment and air bubbles, is provided. [ABSTRACT FROM AUTHOR]

A spur dike is a hydraulic structure, protruding in a river or channel used for several purposes like protection of river-bank erosion and deepening of the main channel. The present paper discusses pre-existing research work on flow pattern and prediction of temporal and maximum scours depth around the spur dikes placed in different locations at 90 ∘ and 180° curved channels. The equations having approximately 2.367, 4.47, 0.17, and 0.271 (average) times with their corresponding experimental data. The parameters, influencing the scour process and flow pattern, have been identified as the ratio of flow intensity to critical velocity (V/Vc ≥ 1) is below 1 and special kind of bedding material is approximately 10 % greater than under live-bed condition and many more. The numerical value of the Froude number and the geometry of the bed surface material are also discussed in this paper. Based on these parameters, the empirical formulations and experimental studies on local scours around the straight, L-shaped, T-shaped spurs, placed at 30°, 45°, 60°, 120°, and 180° azimuthal angles have been discussed. Various numerical schemes proposed in almost seventy-five literatures have been summarized. A critical review of numerical and experimental results found in different works related to temporal and maximum scour depth, flow characteristics, and bed topography around the dike shows that the data and accompanying results are insufficient for the design of spurs used as river structures in curved channels. There are needs to carry out extensive experiments, under various flow conditions, to examine the flow behavior and scouring processes around the spurs. Due to complex flow pattern and scouring processes, taking place around the spur, it becomes difficult to understand the real physics behind these phenomenon and therefore, data-driven models are suggested to arrive at more reasonable relationships required to be used for design purposes. [ABSTRACT FROM AUTHOR]

The short-term rockburst prediction in underground engineering plays a significant role in the safety of the workers and equipment. Due to the complex link between microseismicity and the rockburst occurrence, prediction of short-term rockburst severity is always challenging. It is, therefore, necessary to develop an intelligent model that can predict rockbursts with high accuracy. Besides the predicting capability, it is essential to understand the model's interpretability regarding the decisions to ensure reliability, trust and accountability. Accordingly, this paper employs the knowledge of explainable artificial intelligences (XAI) by proposing a novel glass-box machine learning model: explainable boosting machine (EBM) to predict the short-term rockburst. Microseismic (MS) data obtained from the underground engineering projects are utilized to build the model, which is also compared with the black-box random forest (RF) model. The result shows that EBM can accurately predict the rockburst severity with high accuracy, while providing with the underlined reasoning behind the prediction from the global and local perspectives. The EBM global explanation reveals that MS energy followed by MS apparent volume and the MS events is the most contributing factor to determining the Rockburst severity. It also gives insights into the relationship between MS factors and rockburst risks, delivering how various MS parameters impact the model predictions. The local explanation extracts the understanding of wrongly predicted samples. The interpretability and transparency of the proposed method will facilitate understanding the model's decision which adds effective guidance evaluating the short-term rockburst risks. [ABSTRACT FROM AUTHOR]

This paper presents a statistical analysis of rainfall data through a practical application of statistical tests, with the aim of highlighting climatic variability in the Kébir Rhumel watershed. This analysis uses rainfall data from 1901 to 2021 collected in the Kébir Rhumel watershed (North–East Algeria). The use of the Mann–Kendall and Pettitt tests has allowed us to confirm the existence of a break in 2013 for all stations except Ain Smara and Gerrah, where it was detected in 2020. The SPI (Standardised Precipitation Index) defines five climatic periods that have alternated on the Kébir Rhumel, the wet condition being the most representative with 44% of the series analysed, and the least present is the severe drought (> 1%). The TIA (Trend Innovative Analysis) shows a positive trend for the El Milia, Tadjenant, Grarem Gouga, Guerrah and Ain Smara stations and a negative trend for the Chebabta and Tiberguent stations. Finally, the graph of the climate index of L. Emberger clearly shows the shift of the stations after the break of the sub-humid stage towards the semiarid stage. [ABSTRACT FROM AUTHOR]

This paper discusses the radiation budget and its temporal variability over Poland. The data analysis is based on the MERRA-2 reanalysis for the years 1980–2020. During the last four decades, the enhancement of climate warming has been observed, which coincides with the changes in the radiation budget. Positive and statistically significant trends at the top of the atmosphere (TOA; 0.7 ± 0.2 W/m2/10 year) and on the Earth's surface (1.5 ± 0.2 W/m2/10 year) radiation budget (net downward flux) are mainly a consequence of changes in the amount of aerosol and greenhouse gases (GHG). According to MERRA-2, the AOD during this period decreased by − 0.19 (at 550 nm), which is 87% of the long-term (1980–2021) mean value (0.22). The reduction of AOD is due mainly to the decline of non-absorbing sulfate particles, which leads to a reduction of single-scattering albedo (SSA) by − 0.008 per decade and Angstrom exponent (AE) by − 0.06 per decade (both trends statistically significant). On the other hand, the GHG concentration increased by 4.9%/10 year and 3%/10 year, respectively, for CO2 and CH4. The total column of water vapor increased (1.3%/10 year), while ozone decreased (− 1%/10 year). Despite the fact that cloud cover and cloud optical depth (COD) decreased (− 1.8%/10 year and − 1.0%/10 year), the impact of cloud on temporal variability radiation budget is small. It can be explained by nearly compensated shortwave (SW) cooling and longwave (LW) heating effects. During the analysis period, near-surface air temperature increased by 2.0 °C. The estimated increase in SW net surface radiation (7.9 W/m2) leads to climate warming by 0.8 °C, which is a consequence mainly of the reduction of aerosol (0.4 °C) and cloud cover (0.2 °C). The impact of the change of SW radiation on air temperature is more pronounced during the warm season, while during the cold, air temperature change is controlled mainly by the variability of air mass advection. [ABSTRACT FROM AUTHOR]

Some of the extreme space weather events like the geomagnetic storm of 17–18 March 2015 have produced dramatic effects on low-latitude ionosphere as reported in various studies. Similarly, some of the extreme storms are studied to emphasize the anomalous deviations in ionospheric delay; however, the effects of excess ionospheric delay on GPS-based positioning are rarely reported from India. This paper presents a robust analysis including estimation of GPS receiver position during 6 major geomagnetic storms using dual frequency GPS signals in L1 and L2 bands. Epoch-wise solution estimates are computed using precise orbit SP3 files for satellite orbit and clock corrections and receiver specific differential code biases are obtained from IGS-CODE center. A range domain Kalman filter has also been developed to smooth the 30 s sampled code pseudorange using carrier phase data and resolve the initial ambiguity. Then, the precise positioning residuals are estimated in two different cases of ionospheric corrected and ionospheric uncorrected error models. We then compare the results with ionospheric corrected position estimates to finally obtain the effect of excess daytime ionospheric variations during main phase of some extreme geomagnetic storms. Results from 5 GPS receivers located within 77°–80° E longitude sector are obtained for two 2-hour windows covering local noon during 6 storms including the St. Patrick's Day storm. The effect of severity of the storms and their impact on static precise positioning are brought out by comparing the results with performance on a quiet day. It is found that the magnitude of error in estimated altitude exhibits maximum deviations due to ionospheric variations during storms, and the dip latitude of station is important in terms of magnitude of ionospheric error in positioning over the equatorial ionization anomaly region. [ABSTRACT FROM AUTHOR]

In recent years, distributed fiber acoustic sensor (DAS) technology has been applied for high-precision acquisition of vertical seismic profile (VSP) data, which has the advantages of high-density acquisition, low cost, safety and coordination. However, coupling noise with characteristics similar to that of the spring is produced and mixed in the VSP data collected by the distributed optical fiber in the well. The energy of the coupling noise tends to be very strong, resulting in the effective VSP data being covered. In this paper, coupling noise is constructed by analyzing its morphological characteristics. The dictionaries of coupling noise and clean VSP data are constructed respectively using their different characteristics, and the morphological component analysis (MCA) algorithm is proposed to separate them. The alternating direction multiplier method (ADMM) is used to solve the objective function, for which both L1 and L2 norm regularizations are adopted in the MCA algorithm. However, the performance of the algorithm heavily relies on the coefficient selection of the threshold, which can lead to noise residue in the denoised VSP data and effective signal attenuation due to the inappropriate selection of the threshold. Therefore, the frequency-wavenumber (FK) transform is further used to extract VSP data from the separated coupling noise. The proposed MCA and FK transform (MCA–FK) algorithm is applied to the field data and has achieved good results. [ABSTRACT FROM AUTHOR]

Electromagnetic wave logging while drilling (LWD) technology is an important tool for evaluation of formation oil and gas content. It generally adopts multi-transmitter–receiver coil system structure and the symmetrical coil system arrangement with equal transmitter–receiver spacing can obtain the measurement results with borehole compensation. Here, we develop a method to realize wellbore compensation by deep learning for logging data inversion. This paper focuses on reasonable inversion of logging data through deep learning technology, which is combined with Levenberg–Marquardt (LM) algorithm, modular and fast construction of deep neural network (DNN) model. Under the condition of reducing the outermost transmitting coil, the logging data are inversed, and the inversion effect is evaluated. Our research shows that the combination of neural network and logging data can realize the measurement results with borehole compensation under the condition of reducing one transmitting coil, thereby shortening the instrument length to reduce drilling tool sticking risk and effectively reducing the LWD instrument structure complexity, high power and other problems. At the same time, the accuracy of logging data inversion is tested. The test results show that the DNN method can achieve high-precision inversion, and the average error is reduced by about 50% compared with the traditional algorithm such as linear regression. [ABSTRACT FROM AUTHOR]

To investigate the response of ionosphere to strong geomagnetic storms at different latitudes, this paper focuses on the response characteristics of global ionospheric ROTI at different latitudes during the September 2017 strong geomagnetic storms based on global ROTI maps and GPS dual-frequency observation data, and explores the PPP accuracy of GPS stations at each latitude during two different types of strong geomagnetic storms. The results show that (i) the daily mean ROTI values during the September 2017 strong geomagnetic storms show a spatial distribution of higher in the global region in the high-latitude region of North America, the northern European region and the whole Antarctic region, and lower in other regions. (ii) The highest ROTI time mean value in the high-latitude belt, with a peak value of 0.30 TECU/min, exceeds twice the peak value of the global time mean value; within the mid-latitude belt, the ROTI time mean value in the northern hemisphere is larger, with values exceeding three times those of the Southern Hemisphere. (iii) During the strong geomagnetic storm in September 2017, the PPP results of high-latitude GPS stations were affected to a greater extent, and the mean value of positioning error in the vertical direction exceeded 2.0 m. (iv) During the strong geomagnetic storm in November 2021, the changing trend of the mean value of PPP positioning error at different latitudes was consistent with that of 2017, but the mean value of positioning error and RMS at high latitudes in 2021 was higher than those in 2017. [ABSTRACT FROM AUTHOR]

This paper extracts and separates seasonal term of GPS (Global Positioning System) time series based on empirical mode decomposition and wavelet transformation. Through time series analysis of 9 GPS continuous stations in Dali, Yunnan, it is found that the vertical (U), north–south (N) and east–west (E) components of the relative motion have distinct annual and semi-annual period components. In the vertical direction, the U component has the strongest seasonal deformation characteristics, on the annual period term, between each station the correlation coefficient reaches 0.98, this is consistent with the relevant research results of many researchers; In horizontal direction, seasonal deformation is also more significant, the N component annual and semi-annual period signals are more obvious and the correlation coefficient is high, but the E component 9 stations signal are relatively scattered and poorly correlated. On the semi-annual period, the N and U directions have a very obvious and consistent semi-annual periodicity, and their two correlation coefficient numbers are 0.95 and 0.94 on average, respectively, the N and E are negatively correlated with a correlation coefficient of -0.98. In time series trend term, 9 stations show southeast movement in horizontal direction, but have great differences in vertical movement trend. Among them, YNLJ, YNYS, YNSD and YNLC are linear uplift movement with good consistency. YNYA, YNCX and YNJD show overall uplift movement, but with subsidence fluctuation, the trend of the three stations is very similar, with an average correlation coefficient reaches 0.8; XIAG station shows uplift movement as a whole, the relative motion trend in the E direction is very different from other stations in phase and amplitude, probably because it is closer to the Erhai lake and more susceptible to the influence of water level changes; YNYL also has different motion changes at different times, but the overall performance is subsidence motion. The analysis suggests that the GPS time series contains rich information on the seasonal deformation of the Earth's crust, and precipitation has an important role in influencing the seasonal deformation of continuous stations. [ABSTRACT FROM AUTHOR]

This paper studies the past and future annual droughts in the five agro-ecological zones (AEZs) of Cameroon. Station data and model outputs from the Coupled Model Intercomparison Project Phase 5 were used to compute areal datasets for each AEZ. Six statistical metrics and compromise programming method were used to evaluate and rank the models, respectively. The top three models were used to build multi-model ensemble (MME) and deduce bias-corrected MME data. They were then used to compute the Standardized Precipitation Index (SPI) used as drought indicator. As a result, the performance of the models depends on the AEZ and decreases with the increase in drought intensity. The 1980s was the most marked by severe-extreme droughts and a significant increase in drought intensity was observed in the entire domain during the past period, the years 1974, 1985 and 1988 showing the highest drought intensities. The MME tends to overestimate and underestimate the frequencies and the magnitude of these events, respectively. Bias-corrected MME data improve the results in most cases. As for the period 2071–2099, all the AEZs are likely to experience severe-extreme droughts which are expected to be more frequent before 2083 in the North (AEZs 1 and 2) and after this year in the South (AEZs 3, 4 and 5). It is also expected a slight increasing trend of the mean spatial SPIs showing a slight decrease in drought intensity. The RCPs 8.5 and 2.6 project the lowest and the highest decrease in drought intensity, respectively, while the RCP4.5 shows an average decrease. This study highlights future periods and areas at potential risk of severe-extreme droughts and can guide decision-makers in mitigation and adaptation measures. [ABSTRACT FROM AUTHOR]

The Antarctic Digital Magnetic Anomaly Project is an international research effort to construct a magnetic map of the continent based on ground, satellite, marine, and aeromagnetic surveys. This paper reports the magnetic mapping of the shelf zone in the SE part of the Wilhelm Archipelago, West Antarctica, based on magnetic surveys conducted with Zodiac boats. A spectacular feature of this area is the strong magnetic anomaly of the Antarctic Peninsula (AP) batholith, which was the product of subduction-related Mesozoic–Cenozoic arc magmatism on the former margin of Western Gondwana. We constructed and analyzed a detailed magnetic map of magnetic field anomalies using field observations of rock exposures on the islands and magnetic properties of rocks from laboratory data. The oldest volcanic rocks of Jurassic to Lower Cretaceous age relate to N-NE trending bands of negative magnetic field. The largest feature in the study area is an Upper Cretaceous/Paleogene granodiorite complex that produces a positive magnetic anomaly. Many smaller anomalies are also present over gabbroid bodies of Cretaceous age. Two-dimensional magnetic modeling shows that heterogeneities in the upper crust may have magnetic susceptibilities in the range of 0.005–0.13 SI. Magnetic field anomalies also delineate an orthogonal system of tectonic faults, including the main NE fault along the Penola Strait (sub-parallel to the AP coastline) and four intersecting faults. These fault systems may be associated with different stages of continental margin evolution along the Antarctic Peninsula. [ABSTRACT FROM AUTHOR]

Elastic full waveform inversion (EFWI) in VTI media using velocity model-based parameterization (PARM-VTIEFWI) can improve the sensitivity of anisotropic parameters, but there are still the problems of gradient's interference and Non-unique. Research has shown that logging data can constrain the Non-unique of FWI. Therefore, in this paper, we use the logging data as a constraint to limit the Non-unique of the PARM-VTIEFWI. We derive the objective function and gradient equation for the PARM-VTIEFWI based on the constraint of logging information. In addition, to reduce the interference between the gradients of parameters in VTI media, we incorporate a pseudo-Hessian matrix to precondition the gradients and derive a gradient preconditioning formula based on the pseudo-Hessian matrix in this paper. In summary, we propose a new PARM-VTIEFWI method based on logging information constraint and gradient precondition, which reduces the interference between the parameter gradients and constrains the Non-unique of the inversion. The correctness and validity of the method were demonstrated by examples. [ABSTRACT FROM AUTHOR]

The present paper aims at earthquake prediction assessment in Iran using VLF radio signal sounding and space-based ULF emission observation. VLF subionospheric data using the Denizkoy transmitter in Turkey at 26.7 kHz and a receiving station in Tehran are incorporated. Three earthquake events during 2019 located at different distances to the signal propagation path are used in this study. The short-term variations in the VLF signal for the 5 days averaged amplitude, as well as the deviation of the VLF signal against the 30 days averaged signal to monitor the alternation in the trend, are employed in this study to perform earthquake prediction assessment using VLF radio signal sounding. Several characteristic parameters of the VLF signal such fall-time, minimum 1 and minimum 2 throughout the day are used. A threshold over the standard deviation of the signal is used to determine the signal anomaly. The signal anomalies associated with three selected events and the correlation of prediction with the distance are discussed. A decision-making procedure for the detection of EQ-related anomalies based on the assessment of the proposed approach is introduced. The ULF emissions recorded by the China Seismo-Electromagnetic Satellite (CSES) are provided and associated with the two analyzed earthquakes using the VLF data due to lack of time coverage for the third case. The space-based detected ULF signals are presented and discussed. The proximity of the detected ULF emission with respect to the earthquake epicenter is discussed. [ABSTRACT FROM AUTHOR]

The inverse problem of magnetotelluric data is extremely difficult due to its nonlinear and ill-posed nature. The existing gradient-descent approaches for this task surface from the problems of falling into local minima and relying on reliable initial models, while statistical-based methods are computationally expensive. Inspired by the excellent nonlinear mapping ability of deep learning, in this study, we present a novel magnetotelluric inversion method based on fully convolutional networks. This approach directly builds an end-to-end mapping from apparent resistivity and phase data to resistivity anomaly model. The implementation of the proposed method contains two stages: training and testing. During the training stage, the weight sharing mechanism of fully convolutional network is considered, and only the single anomalous body model samples are used for training, which greatly shortens the modeling time and reduces the difficulty of network training. After that, the unknown combinatorial anomaly model can be reconstructed from the magnetotelluric data using the trained network. The proposed method is tested in both synthetic and field data. The results show that the deep learning-based inversion method proposed in this paper is computationally efficient and has high imaging accuracy. [ABSTRACT FROM AUTHOR]