Pattern Of Anomalies Research Articles

Wintertime relationship between Antarctic sea ice and mid-high latitude atmosphere from an eddy activity perspective

The impact of Antarctic changes on the atmosphere in mid-high latitudes is a controversial topic, with little consensus on the processes and mechanisms involved. From the perspective of eddy energy, this study reveals that the interannual variability of Antarctic sea ice has a substantial impact on atmospheric eddy activities during the cold season, which in turn modulates the mean flow via eddy feedback. Specifically, the leading mode of Antarctic sea ice, characterized by a dipolar anomaly pattern, can affect eddy activities on both synoptic (2–8 days) and low-frequency (10–90 days) time scales. By comparing the total responses of eddy kinetic energy, results indicate that synoptic eddies contribute mainly to changes in Indian Ocean (58%) and Atlantic (38%) sectors, while low-frequency eddies contribute mainly to changes in Pacific (90%) and Atlantic (51%) sectors. This is further identified by diagnosing the eddy energy propagation through the Eliassen-Palm flux and wave activity flux. Moreover, the physical linkage between sea ice and transient eddy activities is established based on the anomalous surface turbulent heat flux and corresponding baroclinic processes. Subsequently, the anomalous geopotential height tendency dominated by eddy vorticity forcing generates and maintains the equivalent barotropic structure of atmospheric circulation. The results indicate that Antarctic sea ice could be a source of predictability for both weather and climate forecasts.

Pattern of Congenital Anomalies at Birth and their Associated Maternal Characteristics in Combined Military Hospital, Dhaka and Chattogram

Background: Congenital fetal anomalies are an important causes of neonatal morbidity and mortality in developed and developing countries that affect health care system. Knowledge of maternal sociodemographic and antenatal characteristics could identify a pattern of population at risk in order to target preventive interventions. The study aimed to determine the pattern of congenital anomalies in newborns, as well as the associated maternal characteristics at a tertiary level hospital in Dhaka and Chattogram, Bangladesh. Materials and methods: A descriptive cross-sectional study was conducted at Combined Military Hospital, Dhaka and Chattogram between February 2023 to September 2023, February 2021 to September 2021. A total of 50 pregnant women with fetal congenital anomalies admitted to the Department of Obstetrics and Gynaecology were included in this study. Pattern of anomalies and maternal sociodemographic and antenatal characteristics were recorded in a proforma. Results: The mean age of the mother was 27.3±6.4 years. Twenty seven (54%) of the baby were male and 23 were female (gender ratio 1.2:1). More common congenital anomalies were hydrocephalus (26%), anencephaly (22%), cleft lip (6%), cleft lip & palate (6%), polydactyly (6%), and hydrops fetalis (6%). Associated materanl characteristics were high maternal age (34%), consanguinity of marriage (14%), maternal under-nutrition (22%), maternal obesity (6%), exposure to drugs (10%), associated medical conditions (34%), no history of folic acid intake in antenatal period (62%), high grade fever in 1st trimester and history of previous birth defect (8%). Conclusion: Congenital anomalies of the CNS and musculoskeletal or gastrointestinal defects were seen most frequently. More research is required to identify the factors responsible for the different types of congenital anomalies. Chatt Maa Shi Hosp Med Coll J; Vol.23 (1); January 2024; Page 47-51

Using roof borehole electrical resistivity tomography to monitor roof water infiltration in a mine work face

Roof water inrush in coal mining is a significant type of water-related disaster that usually results from the interconnection of water-bearing geological formations formed by cracks during and after work face mining. Therefore, monitoring roof water infiltration is of paramount importance in preventing or mitigating water inrush in the mine work face. This study employed the roof borehole electrical resistivity tomography method to conduct physical experiments for monitoring water seepage in roof cracks generated during coal model mining. Additionally, roof water infiltration monitoring was performed in the 7130 work face of the Qidong Coal Mine. The results of both physical experiments and field tests demonstrate that roof borehole electrical resistivity tomography (ERT) is well suited for monitoring roof water infiltration in mine work faces, enabling the determination of the temporal and spatial distributions of water seepage. By analyzing the variation patterns of low-resistivity anomalies in the resistivity profile, the water-conducting channels and water outflow points can be identified. Experiments and field tests suggest that the resistivity and its changes are related to the amount of water inflow and water channel. With the increase of water inflow, the relatively low resistivity anomalies region increases, the resistivity value decreases and the water channel appears and expands. With the amount of water inflow decreasing, the relatively low resistivity area becomes smaller, the resistivity value increases, and the water channel narrows or even disappears.Furthermore, by combining the areas of low-resistivity anomalies, a qualitative assessment of the water content can be achieved. Finally, The results of the periodic weighting analysis of the field tests indicate that roof water infiltration is related to periodic weighting. The greater the periodic weighting is, the more severe the roof water infiltration.

Incidence of idiopathic syrinx in pediatric patients diagnosed with VACTERL association.

While the association of a syrinx with a tethered spinal cord in the context of VACTERL (vertebral defects [V], imperforate anus or anal atresia [A], cardiac malformations [C], tracheoesophageal defects [T] with or without esophageal atresia [E], renal anomalies [R], and limb defects [L]) association is known, the incidence of idiopathic syrinxes among these patients has not previously been reported. The authors aimed to characterize the incidence of syrinxes and the pattern of congenital anomalies in pediatric patients with VACTERL association, with a specific focus on the presence of idiopathic syrinxes in this population. An institutional database was retrospectively queried for all pediatric patients with VACTERL association. Patients were assessed for the presence of a syrinx. Those with no accompanying lesion to which the syrinx could be ascribed were designated idiopathic. Descriptive statistics and qualitative analyses characterized the clinical presentation and outcomes of this population. The authors retrospectively identified 186 patients between 1993 and 2023 with VACTERL association. Of these 186 patients, 141 (75.8%) had a tethered spinal cord and 44 (23.7%) had a syrinx. Most syrinxes could be ascribed to the presence of a tethered spinal cord and/or Chiari malformation; however, 4 (9.1%) of the 44 appeared idiopathic, suggesting the incidence of idiopathic syrinxes in this patient population may be as high as 2.2% (4/186). Most patients remained asymptomatic aside from a single patient who presented with mild gait dysfunction that resolved over time. All syrinxes were managed conservatively, and all but one decreased or remained stable in size on follow-up imaging. Although limited, current estimates suggest the general incidence of an idiopathic syrinx is between 5.6 and 8.4 per 100,000 people; these findings in a pediatric cohort with VACTERL association suggest an incidence of 2200 per 100,000 (i.e., 2.2%). Thus, an idiopathic syrinx may be 200-400 times as prevalent in the pediatric VACTERL population.

Microstructural mapping of neural pathways in Alzheimer's disease using macrostructure-informed normative tractometry.

Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry. We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compared MINT-derived metrics with univariate diffusion tensor imaging (DTI) metrics to examine how fiber geometry may impact the interpretation of microstructure. In two multisite cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia. We show that MINT, by jointly modeling tract shape and microstructure, has the potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways. Changes in diffusion tensor imaging metrics may be due to macroscopic changes. Normative models encode normal variability of diffusion metrics in healthy controls. Variational autoencoder applied on tractography can learn patterns of fiber geometry. WM microstructure and macrostructure are modeled with multivariate methods. Transfer learning uses pretraining and fine-tuning for increased efficiency.

Air Temperature Variations Analysis of the Hualian M6.9 Earthquake

This study examines the three-dimensional layered air temperature variations associated with the Hualian M6.9 earthquake, which occurred on 18 September 2022, using data from the National Center for Environmental Prediction (NCEP) and extracting background information for air temperature through tidal forces. Changes in air temperature stratification revealed that near the epicenter, temperature anomalies began on 12 September and peaked on 13 September, with pronounced increases in magnitude and area. These anomalies followed a seismic thermal anomaly pattern, i.e., one with greater amplitude and a wider range near the land surface, decreasing with altitude until they dissipated, while the other exhibited high atmospheric temperature anomalies, i.e., the upper atmospheric warming exceeded that near the land surface, likely due to meteorological factors. Stable weather conditions and a low geomagnetic Kp index and Dst index during the research period supported the reliability of these findings. The deformation field recorded by InSAR reflects crustal deformation directly caused by fault slip and stress accumulation; moreover, the area with significantly higher air temperature coincides with the areas with the most concentrated deformation.

Magnetic resonance imaging findings of cerebral venous malformations.

Our study aimed to evaluate the incidence and anatomical locations of cerebral venous malformations (CVMs), their alterations over time, and magnetic resonance imaging (MRI) findings of their relationships with each other in patients who underwent magnetic susceptibility-weighted imaging (SWI) and contrast-enhanced MRI. The drainage pattern of developmental venous anomalies (DVAs), the number of collecting veins, DVA thrombosis, signal-intensity abnormalities, and hemosiderin deposits (nodular, diffuse, or both) related to the DVA, were examined. The alterations over time in cavernomas (type and size) were assessed. The study evaluated 7,826 patients who had 8,957 magnetic SWIand contrast-enhanced brain MRIs. A total of 643 CVMs were detected in 520 patients. The frequencies of DVAs, cavernomas, and capillary telangiectasias were 5.26%, 1.28%, and 0.62%, respectively. Isolated DVAs were detected in 430 (71.5%), cavernomas in 77 (12.8%), and capillary telangiectasias in 52 (8.7%) cases of CVMs. The coexistence of DVA with cavernoma and DVA with capillary telangiectasia was found in 38 (6.3%) and 4 (0.7%) cases, respectively. DVA drainage was largely provided by a single collecting vein. Signal-intensity abnormalities associated with the DVA were observed in 7, and two patients had DVA thrombosis. Hemosiderin deposits were nodular in 14 cases, diffuse in three, and both nodular and diffuse in two. In a follow-up of 11 (7.8%) cavernomas, alterations in the type and size were detected. It is not uncommon for cavernomas to become symptomatic owing to alterations in their nature, size, and type over time, while DVAs rarely become symptomatic. Capillary telangiectasias are vascular malformations that tend to remain stable in terms of clinical and imaging features, and those with supratentorial localization are detected more frequently in the frontal lobe. On magnetic susceptibility sequences, hemosiderin deposits can be seen in the GVA region, not rarely but more frequently in nodular form, as well as in diffuse or nodular or diffuse and nodular forms.

Geochemical Anomaly Detection and Pattern Recognition: A Combined Study of the Apriori Algorithm, Principal Component Analysis, and Spectral Clustering

This study demonstrates the effectiveness of combining Principal Component Analysis (PCA) and the Apriori algorithm for feature selection, alongside Spectral clustering, to detect geochemical anomalies in Mississippi Valley-Type (MVT) Pb-Zn deposits in western Iran. First, PCA and Apriori enabled the identification of both syngenetic and epigenetic components, which helped in recognizing elements associated with mineralization. These elements were then modeled using Spectral clustering to detect geochemical anomalies. Unlike traditional methods like k-means, Spectral clustering does not require spherical clusters and is adept at identifying clusters of arbitrary shapes. This made it particularly suitable for analyzing the irregular shapes of geochemical anomalies in the study area. By incorporating Spectral clustering, the method effectively separated geochemical groups, revealing the underlying structure of the data. This was crucial for identifying anomalous geochemical zones and delineating areas with a high potential for Pb-Zn mineralization. The performance of the Spectral clustering algorithm was thoroughly evaluated using the Silhouette Score, the Davies–Bouldin Index, and Dunn Index. Subsampling was employed to assess the algorithm’s stability, providing a comprehensive evaluation of its effectiveness in identifying geochemical anomalies and mapping mineralization potential.

An expert ensemble for detecting anomalous scenes, interactions, and behaviors in autonomous driving

As automated vehicles enter public roads, safety in a near-infinite number of driving scenarios becomes one of the major concerns for the widespread adoption of fully autonomous driving. The ability to detect anomalous situations outside of the operational design domain is a key component in self-driving cars, enabling us to mitigate the impact of abnormal ego behaviors and to realize trustworthy driving systems. On-road anomaly detection in egocentric videos remains a challenging problem due to the difficulties introduced by complex and interactive scenarios. We conduct a holistic analysis of common on-road anomaly patterns, from which we propose three unsupervised anomaly detection experts: a scene expert that focuses on frame-level appearances to detect abnormal scenes and unexpected scene motions; an interaction expert that models normal relative motions between two road participants and raises alarms whenever anomalous interactions emerge; and a behavior expert which monitors abnormal behaviors of individual objects by future trajectory prediction. To combine the strengths of all the modules, we propose an expert ensemble (Xen) using a Kalman filter, in which the final anomaly score is absorbed as one of the states and the observations are generated by the experts. Our experiments employ a novel evaluation protocol for realistic model performance, demonstrate superior anomaly detection performance than previous methods, and show that our framework has potential in classifying anomaly types using unsupervised learning on a large-scale on-road anomaly dataset.

Anomaly detection for bridge health monitoring data based on multiple encoded images and convolutional neural network

In bridge health monitoring systems, abnormal monitoring data inevitably appear due to the complex operational conditions, and these anomalous data will seriously affect the accuracy and reliability of the results of bridge health assessment. Hence, it is vital to detect the abnormal data before further processing. However, the pattern diversity and sample scarcity usually make the accuracy of data anomaly detection unsatisfactory. A novel anomaly detection method for bridge health monitoring data based on encoded images and convolutional neural network (CNN) is proposed in this paper. First, the raw time series are encoded into images to highlight the intrinsic features in the anomaly data, and multiple encoding techniques are adopted to represent the data in different perspectives. Then, the CNN is utilized to establish the mapping between the encoded images and the anomaly patterns for achieving the data anomaly detection. At last, the proposed method is verified with the acceleration data measured from an actual large span cable-stayed bridge. It shows that the proposed method can significantly improve the performance of data anomaly detection while the results are hardly affected by the small sample sizes, and a detection accuracy of over 95% is achieved based on the multiple encoded images.

Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method

Abstract Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.

Distinct Radiative and Chemical Impacts Between the Equatorial and Northern Extratropical Volcanic Injections

AbstractStratospheric volcanic aerosols can affect the global radiative balance and stratospheric composition. In this study, we analyze ensemble experiments with an interactive stratospheric aerosol microphysical general circulation model, designed to assess the climate forcing from large‐magnitude explosive eruptions in the tropics and northern extratropics. Previous studies have generally identified a lower radiative forcing from extratropical eruptions from the shorter stratospheric lifetime of volcanic sulfate aerosols. However, our study finds that both the shorter lifetime and lower effective radiative forcing (ERF) efficacy contribute to the lower ERF in the northern extratropical eruptions. The simulated 2‐year averaged ERF efficacy in northern extratropical eruptions is 22% lower than that in the equatorial eruptions due to the seasonal mismatch of peak stratospheric aerosol optical depth and solar radiation in the first year after a northern extratropical volcanic eruption. Additionally, equatorial eruptions accelerate the Brewer‐Dobson circulation (BDC), while northern hemispheric (NH) extratropical eruptions decelerate the BDC branch in NH and accelerate the BDC branch in southern hemisphere (SH), leading to different spatio‐temporal pattern of ozone anomalies. Consequently, dominated by the dynamical processes, equatorial eruption leads to ozone loss in tropics and increase in midlatitudes, while both northern extratropical summer and winter eruptions trigger ozone decrease in NH and increase in SH.

Contrasting Rainfall Anomaly Patterns Over South America Related to Central and Eastern Atlantic Niño Modes

ABSTRACTThe present study examines the effects of the central Atlantic Niño (CAN) and eastern Atlantic Niño (EAN) events on the seasonal precipitation in South America (SA) during the 1951–2020 period. For the CAN during the summer and autumn, an interhemispheric sea surface temperature (SST) dipole mode induces an anomalous thermally direct circulation in the 10° N–10° S band and is the main factor causing precipitation anomaly patterns with a dipole structure between northern (negative) and northeastern (positive) SA. For winter and spring, the SST pattern featuring a South Atlantic dipole induces meridional and zonal anomalous circulations, which are the mechanisms causing positive precipitation anomalies in tropical SA to the north of 20° S. In contrast, for the EAN, the precipitation anomaly patterns show large areas with anomalous dryness, particularly during summer, autumn, and spring. For summer and autumn, the east–west SST anomaly gradient in the equatorial Atlantic and the associated sea level pressure (SLP) anomalies induce equatorial westerlies and a regional Walker cell with descending motions in most tropical SA, where large areas with anomalous dryness are noted. During spring, a northward SST gradient in the tropical Atlantic induces a meridional cell with descending motions in the 0°–10° S band; meanwhile, the westward SST gradient in the equatorial Atlantic and tropical South Atlantic induces a zonal circulation with descending motions over northeastern and eastern Brazil. These descending motions extend the anomalous dryness over a large area. For the EAN events, the east–west SST gradient and the associated east–west circulation in the South American/Atlantic region are crucial elements to modulate precipitation variability in SA. Therefore, the CAN and EAN events induce distinct precipitation anomaly patterns in SA due to distinct associated regional circulation patterns. The results presented here have not been discussed before and might have relevant implications for climate monitoring and modelling studies.

At the border of peri-Gondwana and Baltica – the structure of the eastern termination of the Variscan belt

The termination of the eastern Variscan belt has been a matter of long-standing debates since it is deeply concealed beneath younger sediments. Its geometry has been addressed by two competing hypotheses of the Variscan orocline and right-lateral strike-slip tectonics. Therefore, a set of new gravity and magnetic maps was compiled for Czechia, Poland and eastern Germany to track Variscan structures in the subsurface. These maps clearly show that the Rheno-Hercynian Suture turns almost 90° east the of the Harz Mts. and continues into Poland toward the ESE. Therefore, our study favours a concept of semi-orocline that terminates in the east against the Brunovistulian Block. The pattern of magnetic and gravity anomalies combined with geological evidence indicates two stages of accretion superimposed in the eastern Variscan belt. The initial W-E convergence led to the formation of NNE-SSW-trending structures that dominate the southern Bohemian Massif. These structures were overprinted by an important N-S shortening event that shaped WNW-ESE oriented features. The latter run parallel to the Baltica margin and dominate the area NE of the Elbe Fault. This is consistent with seismic data showing large-scale underthrusting of the Baltica crust beneath the Variscan belt at a distance of at least 100 km.

Detecting Urban Traffic Anomalies Using Traffic-Monitoring Data

Traffic anomaly detection is crucial for urban management, yet current research is often confined to small-scale endeavors. This study collected 9 months of real-time Wuhan traffic-monitoring data from Amap. We propose Traffic-ConvLSTM, a multi-scale spatial-temporal technique based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to effectively achieve long-term anomaly detection at the city level. First, we converted traffic track points into an image representation, which enables spatial correlation between traffic flow and roads and correlations between traffic flow and roads, as well as the surrounding environment, to be captured. Second, the model utilizes convolution kernels of different sizes to extract spatial features at road-, regional-, and city-level scales while incorporating the temporal features of different time steps to capture hourly, daily, and weekly dynamics. Additionally, varying weights are assigned to the convolution kernels and temporal features of varying spatio-temporal scales to capture the heterogeneous strengths of spatio-temporal correlations within patterns of traffic anomalies. The proposed Traffic-ConvLSTM model exhibits improved performance over existing techniques in the task of identifying long-term and large-scale traffic anomaly occurrences. Furthermore, the analysis reveals significant traffic anomalies during holidays and urban sporting events. The diverse travel patterns observed in response to various activities offer insights for large-scale urban traffic anomaly management, providing recommendations for city-level traffic-control strategies.

Granular computing-based time series anomaly pattern detection with semantic interpretation

Time series analysis may suffer from the “curse of dimensionality” due to its high-dimensionality characteristics. In terms of this issue, information granulation offers an effective vehicle to process time series at a higher level of abstraction. To take this advantage, this study conducts an interpretable time series anomaly pattern detection under the framework of granular computing, where each time series is granulated into a series of semantics according to its patterns and the anomaly ones are identified based on the obtained semantics. First, the time series is partitioned into a predefined number of segments and trend-based information granules are formed for the data points in each time interval. Guided by the principle of justifiable granularity, the granular results maintaining the main features of the original time series realize informative feature extraction and meaningful dimensionality reduction. Then, to realize semantic anomaly pattern detection, the Axiomatic Fuzzy Set (AFS) theory is generalized to construct and compute with semantic time series, and an AFS-based anomaly score is proposed to discover anomaly patterns. In the experiments, the proposed method is conducted on both UCR data sets and real-world time series, where the detected anomaly patterns are equipped with well-defined semantics.

The 3D Density Structure of the South China Sea Based on Wavelet Multi-Scale Analysis of Gravity Data and Its Tectonic Implications

Due to its unique geographical location and complex geological evolution processes, the South China Sea has been a focus of extensive research. Previous studies on the density structure of the South China Sea mostly focused on 2D density structures, with relatively limited research on 3D density structures. A comprehensive study is still needed to refine the expansion mechanism and tectonic evolution of the South China Sea. In this study, we utilized wavelet multi-scale analysis of gravity data to obtain a 3D density model of the South China Sea and discussed its tectonic evolution from the pattern of density anomalies. The inversion results show that (1) the expansion of the South China Sea caused the typical thin oceanic crust and parts of the continent–ocean transition zone may fracture due to the expansion; (2) the low-density anomaly in the upper mantle of Luzon Island may indicate partial melting or the upwelling of asthenosphere materials; and (3) the expansion of the South China Sea is influenced by multiple plate forces and uneven forces affect the distribution of high-density anomalies in the upper mantle.

Determination of orthodontic anomaly patterns in patients in the Thrace region and assessment of the relationship between orthodontic anomalies and DMFT indices: a retrospective study

OBJECTIVE: The aim of this study was to investigate the prevalence of dental and skeletal anomalies that could be a cause of malocclusion and their relationship with caries distribution in the western region of Türkiye. MATERIALS AND METHOD: A retrospective study was conducted with 1815 digital panoramic and lateral cephalometric radiographs taken from patients, age ranging between 6 and 47 years, who applied for orthodontic treatment. The lateral cephalometric radiographs, panoramic radiographs, and dental records were reviewed according to skeletal anomaly, dental malocclusion (Angle classification), and decay-missing- filling teeth (DMFT) index. Kruskal Wallis test was used in intergroup comparisons of variables that did not show normal distribution, and Dunn’s multiple comparison test was used in subgroup comparisons. The chi-square test and Yates correction were used in comparisons of qualitative data. RESULTS: The mean DMFT of the dental Class I malocclusion group was statistically and significantly lower than Class II division 1 and Class III malocclusion groups (p < 0.05). The mean DMFT index of Class II subdivision malocclusion group was statistically and significantly lower than that of Class III malocclusion group (p < 0.05). The DMFT index was significantly lower in the skeletal Class I group compared to the skeletal Class II and Class III anomaly groups (p < 0.05). CONCLUSION: There is a correlation between DMFT indices and dental malocclusions as well as skeletal anomalies. By correcting dental malocclusions and skeletal anomalies with orthodontic treatment and providing ideal occlusion, it becomes easier for patients to maintain oral hygiene and DMFT indices might decrease.

A 3‐D Seismic Tomographic Study of Spreading Structures and Smooth Seafloor Generated by Detachment Faulting—The Ultra‐Slow Spreading Southwest Indian Ridge at 64°30′E

AbstractAt ultra‐slow spreading ridges, with full spreading rates less than ∼20 mm/yr, spreading is accommodated both by highly spatially and temporally segmented magmatism, and tectonic extension along large‐scale detachment faults that exhume ultramafic material to the seafloor. In the most magma‐poor regions, detachment faulting alternates in polarity over time, producing a “flip‐flopping” effect of subsequent detachment dips. The resulting seafloor in these regions displays a morphology termed “smooth seafloor” comprising elongate, broad ridges with peridotite/serpentinite lithologies. We conducted tomographic travel‐time inversion of a 3‐D wide‐angle seismic data set acquired over a region of smooth seafloor around 64°30′E along the Southwest Indian Ridge (SISMOSMOOTH; Cruise MD199), to produce a seismic velocity volume through the crustal section and into the uppermost mantle. We observe patterns of velocity anomalies that correspond with variations in the bathymetry arising from the mode of spreading and are interpreted as changes in the degree of alteration with depth resulting from spatial and temporal variations in fluid‐rock interaction, controlled by faulting and tectonic damage processes. The detachment faults do not show simple planar structures at depth but instead mirror the shapes of the bathymetric ridges that they exhume. Magmatic input is overall highly limited, but there is one region on the lower part of an exhumed detachment footwall where a thickness of volcanic material is observed that suggests a component of syn‐tectonic volcanism, which could contribute to detachment abandonment.

Delineation of deep-seated crustal structures from magnetic data in the southeastern part of the Niger Delta basin, Nigeria

Regional magnetic data in the southeastern segment of the Nigerian Niger Delta were evaluated with the aim of mapping deep-seated tectonic elements. Enhanced filtering operations and 3D forward modelling were applied on the magnetic data. These geologic features triggered the formation of rollover anticlines and faults that serve as structural traps in the study area. The filtered residual magnetic data revealed geologic structures characterized with NE - SW, N - S, and E - W orientations. The 3-D models detected the faulted crustal blocks, gradient zones, and intra-basement compositional magnetic variations. Furthermore, some prominent horst and graben structures as well as related normal faults characterized with distinct magnetic signatures were observed. Faults of base magnetic (of various compositions) were observed to be the fabricating mechanisms of the magnetic anomalies. Collectively, these structures influenced the patterns of magnetic anomalies with direct effects on the hydrocarbon trapping systems, as well as the pathways and accumulation zones for hydrothermal minerals. On the whole, the interpreted results revealed that the basement surface is rippling. Additionally, the depth result showed sedimentary thicknesses that ranged from 4–10 km. Again, the estimated crustal thickness varied from 14 to 19 km. This study has displayed the capabilities of the magnetic method in mapping the depth and configuration of basement rocks, which are crucial in controlling the formation of structural traps. Identifying these basement structures early helps in understanding the overall geological framework and potential hydrocarbon systems.