Results in Geophysical Sciences最新文献

Soil is an issue material for sustainable economic and development plans; besides it considers the most valuable natural resource in all fields of citizens' life. This study is carried out to assess the impacts of heavy metals and some organic constituents on the soil, and consequently on its pollution, in a sampling point left behind the exploration activities in a site subjected to oil and gas exploration nearby Matruh Governorate, Egypt. Moreover, it is trying to evaluate the contents of some added-value rare earth in the same area. Samples were collected from ten points in a bore at different depths starting at 13,000 feet with increasing down distance every 50 feet. The study area is located within latitude 30° 48′ to 36° 889 ̋ S and longitudes 26° 59′ to 16° 406 ̋ E. Geographically, Matruh is part of the Great Western Desert.

Rare Earth Elements (REEs), Heavy Metals (HMs) and organic constituents in the soil samples were analyzed based on Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) and Fourier transform infrared spectroscopy (FT-IR). Geoaccumulation Index (Igeo), Enrichment factor (EF) and Pearson Correlation Coefficients (R) were calculated to consider the soil pollution in the studied point. Also, to evaluate the abundance of REEs and to follow the relations between those elements.

The study illustrated that: the most organic soil pollutants in the oil field sewage were aliphatic and aromatic hydrocarbon, phenols, organic sulfide aliphatic acid and others. Based on the geoaccumulation Index (Igeo) and Enrichment Factor (EF) classifications, the soil quality of the studied site can be classified, in general, as class 0, (uncontaminated) to class 6, (extremely contaminated). Moreover, The potentiality of the rare earth should be subjected to intensive studies to decide their economical mining and element processing.

A comprehensive archaeomagnetic investigation was carried out on seven ceramic fragments recovered at the "El Caracol" lava flow in the Zacapu Malpaís –Michoacan state, Mexico –. A full set of magnetic-mineralogy experiments (encompassing thermomagnetic curves, hysteresis loops, backfield curves, and isothermal remanent magnetization acquisition measurements) were carried out. The experimental methodology also considers magneto-chemical alteration detection, cooling rate, and remanence anisotropy effects to ensure reliable archaeointensity determinations. Five of the seven ceramic fragments – a pipe (potsherd I), vessel's supports (potsherd II and V), a vessel's wall-fragment (potsherd III), and a pipe's nozzle (potsherd VII) – yielded archaeointensity results within the established acceptance criteria to guarantee quality data. Thermal demagnetization of potsherds I, II, V, and VII shows between two and three magnetization components: one associated with potsherd's fabrication, the second with sample's reheating, and the third one, when present, with a viscous component. For each of the first two components, an intensity value was calculated. Potsherds I, II, III, and VII yielded archaeointensity values between 30 and 40 µT, while potsherd V had a much lower value <20 µT. Although secular variation models do not predict such low intensities, similar values have been reported for the center and south of Mexico. Archaeomagnetic dating was carried out with two regional Paleosecular Variation Curves. The "El Caracol" lava flow is an area with human occupation reported from AD 1200. The results obtained in this work, on the contrary, envision the possibility of an early occupation of the site.

Industrial vibration monitoring often requires sensors with adjustable sensitivity and suitable frequency range. In practice, most industrial studies utilize either geophones (velocimeters) or accelerometers. In some cases, where low frequency content is of interest, larger sensor will be required. In difficult installation conditions, it can be advantageous to utilize smaller, higher frequency sensor elements (geophones or accelerometers) to simplify installation and maintenance. A frequency correction of sensors or the recorded waveforms will be needed to accommodate the frequency range of interest. Most accelerometers have relatively smaller sensitivity at low frequency which can affect the calculation of vibration velocity and displacement at low frequencies. Geophones are limited by their frequency response, which drops-off significantly their sensitivity below the resonant frequency of the sensor. Structural and ground vibrations that occur under the resonant frequency could be observed at test sites, but the recorded waveforms cannot be used directly for real-time assessment, and therefore it can be beneficial to artificially expand the frequency range below the sensor's frequency cutoff. Methods for such expansion, were developed and are well established in seismological studies and exploratory geophysics. Usually, these procedures are applied in data post-processing. These methods are not applicable when an operator requires real-time feedback of the measured vibrational amplitude, for example, monitoring of machinery foundations, where excitation control is necessary to avoid infrastructure damage.

This paper presents an approach for instrument frequency extension in the necessary low-frequency range of common geophone elements in real-time applications and compares the results of the proposed technique in post-processing and in real time data collection systems.

Remote sensing technique has been used in this paper to study the effect of underground coal mining subsidence on the health condition and growth pattern of the native vegetation. The study site was an underground coal mining area of Singareni Collieries Company Limited (SCCL), India. Mining was performed in 2001, and subsidence occurred in 2001–2002. Satellite imagery of the undamaged forest before the mining subsidence was compared with the affected forest after the mining subsidence. The changes in vegetation covered areas were analyzed based on digital image classification and vegetation index. The evaluation of vegetation changes was performed for the years 2000–2005 (period 1), 2005–2010 (period 2), 2010–2018 (period 3), and the entire study period of 2000–2018 (period 4), separately. It was observed that the dense vegetation area was reduced by 16.91% during period 1 (after 3–4 years of the occurrence of subsidence), while during the consecutive later periods of 2 and 3, it increased by 24.27% and 6.59%, respectively. During the entire period 4 of the study, dense vegetation was increased by 13.95%. This would be because of natural recovery and gradual stabilization of the native soil due to the absence of human interference in the long term of time. The sparse vegetation and non-vegetated area were changed by +14.22% and +2.68% during period 1, while they were changed by -15.36%, -7.91%, and -8.91%, +1.32%, during periods 2 and 3, respectively.

Metamorphic rocks are diverse with more compositions, structures, and textures that are complex. Rock type identification and prediction from metamorphic rocks using well log data are difficult tasks. This study shows the use of cross plot technique, Pearson correlation, and factor analysis in metamorphic rocks interpretation using borehole geochemical data from the 4390–5089 m interval depth of the Chinese Continental Scientific Drilling Main hole. Lithological identification abilities, correlation between geochemical and geophysical logs, and build a factor model which link in situ chemical element to minerals were studied. The results show that Potassium and Thorium logs are the most discriminating logs in metamorphic rocks. Pearson correlation shows that Potassium and Thorium are the largest contributors to the gamma ray responses. Factor analysis results show a 2 factor model-where factor 1 (amphibole mineral) and factor 2 (K-feldspar mineral) described 76.261% of the variation in log responses. These statistical methods can be a very helpful tool in helping the task of geoscientists in the context of research drillings.

The durability of rocks is a substantial rock property that has to be considered for designing geotechnical structures. Uniaxial compressive strength (UCS) and Young's modulus (E) are key indexes for measuring rocks’ durability. Several types of artificial intelligence (AI) methods have been used for modeling these key indexes; however, surprisingly, no explainable AI (XAI) has been considered for their model developments. An XAI is a model whose assessment is not a black box, and humans could understand its problem solution approach. This study has filled this gap and presented SHAP (Shapley Additive Explanations) as one of the most recent XAI methods for modeling UCS, and E. SHAP value could successfully illustrate intercorrelations between rock properties (porosity, point load index, P-wave velocity, and Schmidt hammer rebound number) and their representative UCS and E for each individual record and also together as variables. Results indicated that P-wave velocity has the highest importance for UCS and E prediction. eXtreme gradient boosting (XGBoost) was used as a solid predictive AI system for UCS and E estimation. Outcomes (R²> 0.99) confirmed the high accuracy of the SHAP-XGBoost model comparing with other typical AI models (Random Forest and Support Vector Regression). These results indicated XAI could be considered for illustrating complicated relationships within rock mechanics and energy-resource developments.

A new method is discussed using neural network models in combination with empirical orthogonal function (EOF) analysis for the basin-scale wind-wave forecast. For the Bay of Bengal region EOF analysis has been performed separately on the significant wave height (SWH) data, zonal (U) and meridional (V) components of wind data. For basin scale forecast the dominant principal component (PC) has been subjected to univariate and multivariate neural network models for future predictions. In the univariate approach, only past values of SWH time series are used and in the multivariate approach, U and V time series are used to predict future SWH values. Efficiency in terms of accuracy and speed of four different backpropagation algorithms, namely, Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG) and Fletcher Conjugate Gradient (CGF) have been compared for 1 to 12 multistep ahead time steps and 1 to 13 neurons. After training the models using varied neurons and the PCs, representing the entire basin, the neurons are fixed at which minimum errors are obtained. Further experiments are conducted using the fixed neurons and the PCs for 1 to 12 time steps ahead SWH prediction. Finally independent datasets consisting of normal and cyclonic wind-wave parameters are tested successfully using the above fixed neurons for delays (1 to 12) corresponding to 3 days or 72 h forecast. The novelty of the study lies is the usage of the PCs which represent the entire basin rather than computations at individual locations which are expensive technically and time consuming.