Acta Geophysica最新文献

Throughout history, several large-magnitude earthquakes have caused damage to the Himalayan region and humanity. To understand the present-day strain rate distribution and associated seismic moment budget, a high-resolution velocity field is an essential component. The present study estimates the contemporary seismic moment budget along three spatial sections over the Nepal Himalaya using the state-of-the-art high-resolution velocity field. For this, (1) we integrate 5 years of InSAR data with 77 available GPS observations over the Nepal Himalaya; (2) we then calculate strain rate distribution (dilatational and maximum shear strain rates) from this integrated velocity field, and (3) at last, we compare the geodetic moment accumulation rate estimated from strain rate tensors with the seismic moment release rate based on an earthquake database of 500 years. The results reveal that: (1) the geodetic strain rate is not homogeneous over the Nepal Himalaya, rather along the main central thrust, a relatively higher strain rate is observed; (2) the geodetic moment rate from west to east across three sections ranges from (23.39times 10^{18}) to (16.59times 10^{18}) Nm/yr, with the minimum of (8.05times 10^{18}) Nm/yr in central Nepal, whereas the seismic moment rate varies between (5.02times 10^{18}) and (11.41times 10^{18}) Nm/yr, with the minimum of (3.69times 10^{18}) Nm/yr in central Nepal; (3) the difference between geodetic and seismic moment rates from west to east provides a moment deficit rate of (18.37times 10^{18}) to (5.18times 10^{18}) Nm/yr, with the minimum of (4.36times 10^{18}) Nm/yr in central Nepal, and more importantly, (4) the inferred moment deficit rate suggests that the western and eastern Nepal have an earthquake potential of magnitude (M_w) 8.5 and (M_w) 8.1, respectively, whereas the central Nepal has energy budget equivalent to an (M_w) 7.9 event. In summary, the present study provides spatial distribution of earthquake potential in Nepal Himalaya using the most updated high-resolution InSAR and GPS velocity field, and the findings inevitably contribute to the time-dependent earthquake hazard analysis of the study region.

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.

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.

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.

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.