pure and applied geophysics最新文献

Amid rapid economic and social development, countries worldwide are heightening their focus on addressing climate change induced by greenhouse gas emissions.CO₂ geological storage has become an increasingly mature technology and is currently recognized as one of the most effective ways to reduce CO₂ emissions on a large scale globally. The shale reservoir CO₂ geological storage combined with enhanced shale gas recovery technology (CO₂-ESGR) is a new type of CO₂ geological storage and shale gas development technology. This technology uses supercritical or liquid phase CO₂ instead of hydraulic fracturing of shale, utilizing the stronger adsorption capacity of CO₂ on shale than CH4 to displace CH4, thereby increasing the yield and production rate of shale gas while achieving geological storage of CO₂. To study the impact of different fracture parameters on CO₂ displacement of CH4, this research selected shale from the southern slope of a certain basin as the target reservoir and established a homogeneous dual-porosity and dual-permeability model. GEM was used to simulate eight scenarios for CO₂-enhanced methane extraction from shale layers, analyzing the effects of three influencing factors: half-length of fractures, fracture spacing, and number of fractures on CO₂ displacement of CH₄. Additionally, six different fracture patterns were simulated to analyze the influence of fracture patterns on CO₂ displacement of CH₄. The study found that increases in the number of fractures, fracture spacing, and half-length of fractures all increase the amount of CO₂ displaced by CH4, but the degree of influence decreases gradually. Furthermore, the average fracture pattern yields better results for both CH₄ production and CO₂ sequestration compared to unevenly distributed fractures, providing strong evidence for improving shale gas production rates and achieving geological storage of CO₂.

Joint inversion of different geophysical data sets has demonstrated its effectiveness in mitigating the limitations associated with individual methods and maximizing their respective advantages. This study investigates the groundwater aquifers in the western area of Ismailia city, Egypt, which faces water scarcity and relies heavily on groundwater. Vertical Electrical Sounding (VES) and Transient Electromagnetic (TEM) data were collected at 35 points along five parallel profiles to assess the primary aquifers in this region. These data sets, gathered at nearly identical locations, were inverted separately before performing joint inversion to enable objective comparison. The joint inversion, conducted using the Curupira Program, revealed two aquifer systems in the Pleistocene and Miocene units. The Pleistocene aquifer, identified as the primary aquifer, consists of gravel, sand, and clay lenses with depths ranging from 10 to 83 m and thicknesses from 129 to 236 m. Its resistivity ranges from 4 to 95 Ωm. The Miocene aquifer is underlying the Pleistocene aquifer at depths between 130 and 291 m and primarily consists of marine marly sandstone and limestone, with resistivity values of 6 to 22 Ωm, indicating predominantly saline groundwater. Overall, this study provides valuable insights into the hydrogeological characterization of the study area and highlights the potential and limitations of joint inversion techniques in the accurate assessment of aquifer systems.

This research examines how significant atmospheric fluctuations affect drought conditions, in the Kızılırmak Basin in Türkiye. We studied the impact of climate indices like NAO, Niño, AMO, PDO, ONI, and SOI by using the Standardized Precipitation Evapotranspiration Index (SPEI) as an indicator of drought. The findings reveal an increase in both the frequency and severity of droughts after 2015. In the 2000s, short-term droughts lasted from 1 to 3 months. However, after 2020, longer-term droughts lasting between 6 and 24 months have become more severe. Correlation and lead-time analyses reveal ENSO indices, particularly Niño 3.4 and ONI, as primary drivers of drought, with a positive impact. The SOI emerged as a significant predictor of future drought conditions. While PDO and AMO influence drought, their effects are less pronounced. Understanding these complex relationships is crucial for developing effective regional drought management strategies.

In late February 2024, a swarm–like seismic activity took place north of Kefalonia Island, in the area of central Ionian Islands. Following a machine-learning aided workflow, we compiled an enhanced, relocated seismic catalog of 2495 low- to moderate magnitude earthquakes during a 2–month period. Spatiotemporal analysis reveals a narrow epicentral distribution of nearly E-W alignment, approximately 5 km long, much longer than the length anticipated by common scaling laws for the aftershock area extension of the stronger earthquakes that did not exceed M4. Seismic activity decays at a rate slower than mainshock-aftershock sequences, providing evidence of swarm-like behavior. Fluid diffusion appears to be the critical driving force behind this sequence, effectively reproducing the spatiotemporal diffusion of the analyzed activity, whereas cascade triggering due to stress changes and transfer by the combined effect of the two relatively strongest earthquakes promote the triggering of most of the weaker earthquakes that follow in the sequence. Our ML-enhanced spatiotemporal analysis, along with the computation of 17 focal mechanisms of the stronger earthquakes using waveform modeling, support the presence of a population of smaller faults that strike obliquely in respect to the Kefalonia Transform Fault Zone (KTFZ) forming a strike slip duplex in the area between them.

Gravity changes are often used in the study of hydrological environmental changes, but the gravity changes caused by earthquakes have also long been noticed. Significant anomalies of gravity changes have been observed before several strong earthquakes that occurred around the Tibetan Plateau. However, there are few studies on the complete gravity change processes before and after different earthquakes in the same area, and the characteristics of long-term background gravitational changes are not clear. Between 1998 and 2008, an extensive dataset of repeated gravity observation data was systematically collected across the Hexi Corridor in Gansu Province, China. This unique dataset, together with three earthquakes (M ≥ 5) within the monitoring network during this period, provides an opportunity to study the complete gravity change process before and after the earthquakes. The gravity change data over a 10-year period indicate that the long-term background gravity changes in the region is negative within the Hexi corridor, possibly related to extraction of groundwater in the region. The M_S 6.1 earthquake in 2003 and the M_S 5.0 earthquake in 2008 were preceded by localized predominantly positive changes within 6 ~ 8 months prior to rupture. The earthquakes occurred during a process of gravity reversal recovery. This suggests that there may be an observational time window for the significant gravity positive changes prior to the earthquakes.

Fractures play an essential role in the study of reservoir permeability and to improve reservoir modeling, and optimized hydraulic fracturing. This work focuses on a comprehensive study of the fractures identification and fracture properties estimation using well log data in the hydrocarbon reservoir and gas hydrates reservoir of the Krishna-Godavari basin. The natural fractures and breakouts are easily identified from advanced log Formation Micro Imager (FMI). But this tool is costly and not available in many wells and also, the low signatures of fractures in well-log data make it difficult to derive accurate information. Therefore, we adopted an integrated approach such FMI, conventional log responses, semi-automated machine learning (ML) and dual porosity model to identify the fractures. First, the prominent fracture zones have been identified from image of FMI and as well as conventional well log responses. Then, we evaluated the model’s performance of various classifier ML on training and test data, and found that the Random Forest is most suitable techniques for our data set. The model achieved an accuracy of 83.5% on test data and an accuracy of 87.81% and 91% on two validation well data. Lastly, the dual porosity model which is a very effective method was developed from raw resistivity data of FMI log. In this model, bimodal & complex porosity distribution appears near fracture zones and unimodal represents near non-fractured zones. We estimated the various fracture properties fracture density, trace length, aperture, and secondary porosity respectively. The results show that natural fractures have dip angles ranging from 22.53° to 66.55°, with fracture aperture ranging from 2.2 to 11.06 mm with mean value of 4.68 mm and standard deviation of ± 3.46 mm and the fracture porosity ranged from 0.5 to 9% with mean 2.92% and standard deviation of ± 2.82% respectively. This framework provides valuable insights into fractured zone identification, characterization and improves reservoir modeling especially for tight reservoir and gas hydrate formation.

The extent of research indicates that global atmospheric oscillations exert a significant influence on climate parameters. This study aims to investigate the relationship between major atmospheric oscillations and annual maximum rainfall intensities across the Black Sea region of Türkiye between 1966 and 2015. To this end, correlation coefficients were computed—assessed for statistical significance at the α = 0.05 level using the Student’s t-test. Additionally, temporal trends in rainfall intensities were examined using the innovative trend analysis (ITA) method. The results suggest that the Arctic Oscillation and the North Atlantic Oscillation have a more pronounced effect in the eastern parts of the region. However, the ITA findings revealed no statistically significant trends in rainfall intensities across the study period.

The Chinese Loess Plateau (CLP) is prone to various geological hazards because the loess is characterized by loose sediment, water sensitivity, and extremely thick coverage. Thus, seeking a convenient technique to reveal near-surface geological structures effectively is of great significance for mitigating the loess geohazards in the CLP. However, given the complex structures and surface conditions in the loess area, traditional geophysical methods often face numerous limitations. In contrast, we employed a method with lower terrain requirements and stronger anti-interference capabilities. We selected the Heifangtai Terrace as the test site of the loess platform, which contains complete stratigraphic units of the CLP and is also a typical region with different loess landslides. We used the Extended Spatial Autocorrelation (ESPAC) and Horizontal-to-Vertical Spectral Ratio (HVSR) methods to detect the stratigraphic structure of the study area. The accuracy of these methods was verified using in-situ borehole, outcrop, and geological profile information. Our results show that the ESPAC method provides precise stratigraphic division, while the HVSR method enables rapid identification of the boundary between loose sediment and bedrock. Additionally, high-resolution ESPAC can serve as a substitute for boreholes to assist HVSR observations in constructing the resonance frequency-sediment thickness relationship for the area. Therefore, the integrated microtremor survey technology provides a rapid and cost-effective approach to acquire reliable stratigraphic information, highlighting its utility in geological hazard mitigation.

This study evaluates the possible indicators for the estimation of urban heat island (UHI) sensitivity based on spatial, social and economic data clusters covering 20 statistical parameters which were confined from the related literature. Their partial contributions to UHI formation were determined by weighting their impacts in the sample of two city centres, Erzurum and Erzincan, Turkey. Depending on their variable characteristics, all three data clusters are seen to be effective in the cities. UHI-sensitivity level was calculated to be 59.59% and 48.26% for Erzurum and Erzincan cities, respectively depending on the spatial, social and economic characteristics of the cities. The city of Erzincan is less sensitive to UHI formation since it has a gridded urban morphology, less built environment and impervious surface, population and urban density and more green space and environmental awareness level (23%) while Erzurum has an organically developed urban morphology, higher covered surface rate in the horizontal and vertical dimensions and building heights and densities and lower environmental awareness level (19%). Spatial and social advantages of Erzincan city surpassed the effect of economic indicators in the city, where the industry sector stands out, per capita car ownership and electricity consumption indicators are higher and make the city economically more sensitive to UHI formation. It is stated that spatial distribution of surface characteristics, urban geometry, organic and grid-like urban structures, shares of economic sectors and level of awareness among society are significant factors to affect UHI sensitivity between the cities. The proposed approach to determine the UHI sensitivity of areas with different urban characteristics has valuable contributions to urban planning practices.

Surface-wave analysis can be performed according to a multitude of approaches. In fact, both the determination of the dispersive properties and data inversion can be accomplished according to several possible techniques, which should be chosen on a case-by-case basis, depending on the goals and site characteristics. The Miniature Array Analysis of Microtremors (MAAM) is an interesting methodology aimed at extracting the dispersive properties of the vertical (Z) component of Rayleigh waves from passive data recorded according to a circular-symmetry array. Compared to other techniques, MAAM can investigate larger wavelengths and provide phase velocities in a wider frequency range (a triangular array with a radius of a couple of meters can provide the dispersion curve in the 2–20 Hz frequency range, approximatively). Despite its remarkable potential, MAAM can be affected by industrial noise that alters the natural microtremor field. The comparative analysis of the Z-component dispersion retrieved via Extended Spatial AutoCorrelation (ESAC) and MAAM is initially conducted to validate the adopted procedures and verify the paradigms to adopt to identify the frequency range properly investigated. Following this, analysing a second time-lapse dataset, the effects of industrial components on both the dispersion curve obtained via MAAM and the Horizontal-to-Vertical Spectral Ratio (HVSR) are investigated. The experimental evidences show that non-monochromatic industrial components can have a malicious effect on both MAAM and HVSR. In this context, analysing the coherence functions of the multi-component data recorded by a 3-component geophone, together with the spectral ratio and noise-to-signal ratio obtained during MAAM processing, is crucial for assessing the possible presence of industrial components that may affect the observables ultimately used in the joint inversion.