Acta Geophysica最新文献

The paper addresses the issue of estimating the coefficients of the sotolongo-costa and posadas (SCP) model in the presence of uncertain earthquake magnitude data. The SCP model offers a more accurate representation of regional seismicity compared to the traditional Gutenberg–Richter (G-R) law and has been integrated into the probabilistic seismic hazard analysis (PSHA) framework as NEPSHA. The study aims to develop a method to calculate the SCP coefficients in the presence of uncertain magnitude data, implement the process in R programming language, and validate its effectiveness through a case study. The methodology involves developing the mathematical relationship for estimating the SCP parameters using maximum likelihood estimation (MLE) and modifying the MLE approach to account for magnitude uncertainty. The method is tested using simulated earthquake catalogs with varying degrees of magnitude uncertainty. The results demonstrate that the proposed method can alter the estimated values of the SCP parameters, particularly the a value, by approximately 50% when magnitude uncertainty is considered. The q variable is found to be less affected by the estimation method.

Underground carbonate deposits are widespread worldwide and have considerable hydrocarbon potential. They are generally characterized by a complex microscopic structure that affects the properties of the macroscopic fluid flow and the relevant petrophysical behavior. In recent years, advances in digital technology have helped reveal the microstructures (i.e., pore connections, cracks, pore size and radius, etc.) of rocks in the subsurface. In this work, drill cores (cylinder) are taken from a deep carbonate deposit in the Sichuan Basin in western China to perform computed tomography (CT) scans, thin sections and mineral analysis. The characteristics of lithology and pore structure are investigated. Ultrasonic experiments with different fluid types and pressures are conducted to determine rock samples’ wave velocities, attenuation and crack porosity. The experimental data show that the rocks have low porosity/permeability and a complex pore/crack system, leading to significant pressure, crack and fluid type effects on the velocities, dispersion and attenuation. We develop a model of multiple pore-crack structures for carbonates by considering the complex structure and fluid properties. Digital cores are reconstructed based on CT scans, image processing and threshold segmentation. The aspect ratios of pores and cracks are extracted with their volume fractions to simulate the rock skeleton with the differential effective medium theory. The Biot–Rayleigh wave propagation equations are applied to simulate the effects of different pore and fluid types on the velocity and attenuation of P-waves. The agreement between the modeling results and the ultrasonic and log data confirms that the model can validly reproduce the wave responses.

The Tuolaishan–Lenglongling fault (TLSF–LLLF) is located in the middle-western segment of the Qilian–Haiyuan fault zone. The 2022 Menyuan Mw 6.7 earthquake that occurred in the TLSF–LLLF highlights the urgent need for understanding the mechanical property and seismicity over this fault segment. In this study, Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was used to process Sentinel-1 acquisitions covering the TLSF–LLLF fault from 2016 to 2022 to determine the interseismic velocity field along the satellite line-of-sight. The interseismic deformation field confirmed the absence of surface creep behavior across the whole TLSF–LLLF segment. Then, we utilized both the screw dislocation and block modeling strategies to invert the comprehensive spatial distribution of fault slip rate and locking depth across the TLSF–LLLF fault. The new fault locking model, constrained by all GNSS and InSAR measurements, suggests comparable fault slip rates between 4.7 and 5.6 mm/yr in the TLSF–LLLF segment, which is generally consistent with long-term geological slip rates. The locking depth increases gradually from 8 km in the western segment of the TLSF to 18 km in the eastern segment, while the locking depth for most sections of the LLLF is relatively deep (15–18 km), indicating existence of asperities on the locking along the TLSF–LLLF fault zone. In particular, a fault segment with obvious shallow locking depth was identified in the stepover region where the TLSF and LLLF intersect. The shallow locking section shows a good spatial correlation with the coseismic rupture of the 2022 Menyuan earthquake. The calculated moment rate deficit suggests that the TLSF is capable of producing an Mw 7.3 earthquake given the high seismic moment accumulation rate and a lack of small-to-moderate earthquakes.

This study presents a novel geophysical approach for estimating the level of thermal maturity (LOM) in unconventional hydrocarbon reservoirs using well log data. LOM is a crucial parameter for assessing the hydrocarbon generation potential of source rocks, but it traditionally relies on laboratory measurements of core samples, which can be time-consuming and costly. The proposed method combines two techniques: interval inversion for estimating total organic carbon (TOC) content from well logs and simulated annealing (SA) optimization for deriving LOM from the estimated TOC. The interval inversion method enables accurate TOC estimation by jointly interpreting multiple well logs over depth intervals, overcoming limitations of conventional point-by-point inversion. Using the estimated TOC, the SA algorithm optimizes an energy function related to Passey's empirical TOC-LOM relationship, iteratively finding the optimal LOM value that best fits the well log data. This approach provides a continuous in situ LOM profile along the borehole without requiring core measurements. The effectiveness of the method is demonstrated through case studies on datasets from the North Sea (Norway), the Pannonian Basin (Hungary), and the Kingak Formation (Alaska). The LOM estimates show good agreement with reported maturity levels and allow reliable reservoir characterization. Statistical analysis confirms the robustness and accuracy of the results. By reducing dependence on core data, this integrated inversion-optimization workflow streamlines the reservoir prospecting phase, enhancing operational efficiency. The method holds promising applications across diverse geological settings for cost-effective evaluation of unconventional hydrocarbon plays.

Time-lapse seismic imaging, used to detect changes in strata and physical properties beneath the seafloor, plays a crucial role in traditional resource development for reservoir monitoring. It can also be used for carbon capture and storage (CCS) monitoring in the field of carbon reduction. Continuous research and development are underway in this domain. However, the application of time-lapse seismic imaging techniques to shallow strata in coastal waters near the land remains underexplored. Despite its potential in various fields, there is a lack of sufficient demonstrations and reviews of monitoring technology using downsized data acquisition techniques. This paper introduces a portable ultra-high-resolution (UHR) 3D seismic survey system designed to monitor shallow strata in coastal waters. The field applicability of this system is examined, particularly in terms of its seismic repeatability. In this study, we developed a 3D seismic survey system suitable for the operation of ships weighing 40 tons or less. The survey was conducted with a one-year time lag in waters near Pohang, Korea, close to the shore (minimum distance 1.3 km) and with low water depths (9.5 to 25.2 m). This study employed traditional time-domain processing workflows and 4D processing techniques to generate baseline and 4D processed monitoring cube. Repeatability analyses are conducted from various perspectives. Our findings demonstrate the efficient application of the proposed UHR 3D seismic survey technique for monitoring shallow media beneath the seafloor in coastal areas where diverse engineering activities and marine geology research are conducted.

The creation of rainfall maps often relies on preliminary spatial interpolation. Significant deviations from the real distribution of rainfall are likely to occur due to the wide variety of interpolation methods, the effectiveness of which may vary. The main objective of this study is to find the best interpolation method to estimate the spatial distribution of rainfall in the Chott El Hodna basin (25,834 km²), typical of endorheic basins in Algeria. The rainfall database consisted of 42 years of monthly observations from 52 stations (1975–2017). Eight spatial interpolation models were compared, six of which were deterministic and two stochastic. Deterministic models include nearest neighbor, inverse distance weighting, local polynomial, minimum curvature, thin plate spline, and natural neighbor. The stochastic models are ordinary kriging and regression-kriging (RK). RK uniquely incorporates additional information about the geotopographical environment of the basin. The forecasting performance of each method was evaluated using statistical cross-validation indicators, as well as visual analysis and comparison with previously published isohyet maps. The evaluation concluded that the RK model is the most appropriate for producing a map of annual mean rainfall in the Chott El Hodna basin. In addition, this map covering the period from 1975 to 2017 revealed a significant average drop in rainfall: 31% compared to the periods 1913–1938 and 1913–1963, and 24% relative to the period 1922–1960/1969–1989. Further research is needed to determine the causes of these trends, assess their long-term impact, and develop effective adaptation strategies.

In this paper, a method is presented to classify volcano activity into three classes, namely quiet, strombolian, and paroxysm. The method is based on training a six-layered deep neural network (DNN) model using these signals as inputs (features): time series of the number of distances of infrasonic events, radar backscatter power, RMS of tremor in five stations close to craters of the volcano, tilt derivative, and seismic tremor source depth. The method was tested on the data related to a period of five years, and the results were concluded using indexes of precision, recall, F₁ score, and Cohen's Kappa coefficient were calculated to evaluate the qualification of the classification. Also, the results were compared to Bayesian network (BN), K-nearest neighbors (KNN), and decision tree (DT) methods. Decision learning trees and KNN are popular machine learning algorithms belonging to the class of supervised learning algorithms. They mimic the human level thinking and, differing from neural networks, are not black box models. The comparisons reveal the proposed method, especially in classifying both strombolian and paroxysm classes. This advantage makes the presented method a more reliable tool for practical use in the volcano monitoring control rooms.

The characterization of the various rock types through petrophysical data analysis is essential for comprehending geological processes and enhancing the efficacy of geophysical approaches aimed at mineralization zones. In the present study, a Fuzzy C-Means (FCM) clustering algorithm was employed to automatically classify lithounits within the western sector of the North Singhbhum Mobile Belt based on the petrophysical properties. Laboratory measurements of 326 rock samples from the study area show a wide range of density (~2350–3150 kg/m³) and magnetic susceptibility (10⁻⁵ SI to 10⁻¹ SI) values. Further FCM analysis reveals three distinct clusters: (i) cluster 1 displays high density and low magnetic susceptibility responses and comprises majorly metabasic, phyllite, and mica schist rocks, (ii) cluster 2 shows low density and low magnetic susceptibility characteristics and contains mainly metasedimentary rocks (phyllite, quartzite, and mica schist) and (iii) cluster 3 also primarily encompasses metasedimentary rocks, but it displays the low density and high magnetic susceptibility characteristics. Overlap of rock types in different clusters probably indicates the influence of secondary geological processes on the petrophysical measurements such as metamorphism, alteration, and weathering, which is also supported by the petrographical studies. Overall, the present study demonstrates the potential utility of the FCM algorithm for automatic lithology classification and inferring the associated geological processes from the petrophysical measurements. Furthermore, the correlation between the geophysical and petrophysical clusters highlights the role of petrophysical information in the automatic geological/mineral mapping. However, the complexity in cluster attributes on a detailed scale suggests that future studies in the NSMB should focus on comprehensive multi-parameter petrophysical and geochemical measurements. This approach will help in developing better strategies for 3D geophysical data inversion and resolve the complexities in petrophysical data interpretation.

Normal moveout correction is an essential part of seismic data processing. The accuracy of the result of traditional normal moveout correction methods depends largely on the accuracy of the picked normal moveout correction velocity, which has severe stretching at shallow layers and far-offset distances. However, the problem is usually solved by “mute,” leading to a low stacking number at far offset and a short illumination aperture for exploration. Therefore, a non-stretching normal moveout correction method based on extrapolation interferometry is proposed in this paper. While solving the problem of stretching, it further increases the effective extension length of seismic exploration and improves the coverage number of far-offset reflection points through the conversion between primary and multiple waves. Meanwhile, the introduction of high-order accumulation improves the application range of the method and overcomes the influence of coherent Gaussian noise. In this paper, the method is tested on synthetic data with different noise and applied to two field data. These applications in different data show that the proposed method is a purely data-driven method. The proposed method in this paper does not depend on the accuracy of the velocity picking. It not only achieves non-stretching moveout correction, but also effectively suppresses the effects of random and coherent Gaussian noise.