Seismic Reflection Research Articles

Submarine canyon development controlled by slope failure and oceanographic process interactions

The southeastern Australian margin hosts a series of submarine canyons. Although the origin and evolution of canyons within the northwestern segment of the margin is relatively well studied, their quantitative morphology, interaction with longshore drift currents and slope failure remain poorly understood in the southeastern region. In this study, high-resolution bathymetry and 3D seismic reflection datasets revealed five main submarine canyons present in the central offshore Otway Basin. The canyons have V-shaped, evolving to U-shaped morphology downslope with sedimentary infill characterized by medium–high amplitudes, recognizable stratal pattern and localized chaotic seismic reflections. Analysis reveals these canyons were initiated by retrogressive slope failure on the continental slope, that once the shelf edge was reached and subsequently incised, development of the canyon heads was influenced by the shelf parallel Zeehan Current, active on the continental shelf of the region. The heads of the shelf-incised canyons preferentially migrate northwestward and were infilled by laterally accreting sedimentary packages characterized by southeast dipping seismic reflections. These indicate an early erosive phase followed by a period of deposition, a result of the prevalent eastward flowing Zeehan Current. These results have important implications for understanding of the mechanisms that control initiation and development of submarine canyons, and their morphology, both offshore southeastern Australia, and similar settings on continental margins worldwide.

East Siberian Sea Composite Tectono-Sedimentary Element, Siberian Arctic

The East Siberian Sea Composite Tectono-Sedimentary Element (ESS CTSE) occupies most of the central and southern parts of the East Siberian Sea (ESS) and extends onshore into the Chukchi Peninsula. The first multichannel seismic reflection data, acquired in this region in 1989 and 1991 revealed its structural complexity and the presence of a deep sedimentary depocentre in its central part. In the 2010s, the first long-offset seismic data provided a much better understanding of the geology of this remote segment of the Arctic shelf. It includes a large extensional basin that started to form in the Albian (?)-Cenomanian, postdating the Barremian-Aptian Chukotkan orogeny and formation of the New Siberian-Chukchi Fold-and-Thrust Belt (FTB). The latter experienced extensional collapse and developed into the tectonic basement that underlies the CTSE over its entire extent. The ESS CTSE is mainly composed of Upper Cretaceous-Holocene siliciclastic sedimentary strata with a total thickness up to 14 km. This succession has not been drilled deep enough to test its hydrocarbon potential since the beginning of exploration around 2013. However, the strata contain abundant terrestrial organic matter and are considered as potential source rocks sufficient for generating gas accumulations.

The hypertidal Santa Cruz–Chico River estuary (South Patagonia, Argentina): A hybrid ria-type system under extreme tides, arid climate and active uplift

The present study focuses on the morphosedimentary organization and sediment infilling stratigraphy of one of the largest estuaries of southern Patagonia in Argentina. With a tidal range up to 12 m, the area is subject to extreme tidal conditions, combined with moderate offshore wave climate, strong and constant westerly winds, and contrasted water and sediment discharges from the two tributaries of the estuary, the Santa Cruz and Chico rivers. The estuarine valley is entrenched in the Patagonian coastal plateau due to significant uplift. On the basis of sediment facies (sedimentary structures, grain size, geochemistry, mineralogy), meiofauna (foraminifera and testate amoebae), morphological changes and shallow geophysics (high-resolution seismic reflection, ground-penetrating radar) data, the Santa Cruz–Chico River system is defined as a hybrid system comprising a tide-influenced fluvial mouth (the Santa Cruz River) and a tide-dominated estuary (the Chico River estuary), both converging toward an elongated subtidal ria-type estuarine basin. River-supplied sands and muds by-pass the estuarine basin and are exported offshore where they settle and form an ebb-tidal delta. Sediments in the Santa Cruz–Chico River valley mainly consist of Pleistocene lowstand fluvial gravels resting on the regional Miocene substrate, and thin early Holocene transgressive deposits, deeply incised by a tidal ravinement surface that developed during the highest Holocene sea-level at ca 7500 y. BP. After the maximum stillstand, relative sea level fell and a competition occurred between erosion, promoted by water depth decrease, and deposition, favored by tidal prism reduction. At present, sediment by-passing and offshore sediment export are the dominant processes. The very large size of the ebb-tidal delta, which expands on the continental shelf, suggests that this situation has prevailed for a very long time.

Full‐waveform inversion as a tool to predict fault zone acoustic properties

AbstractUnderstanding the physical properties of fault zones is essential for various subsurface applications, including carbon capture and geologic storage, geothermal energy and seismic hazard assessment. Although three‐dimensional seismic reflection data can image the geometries of faults in the sub‐surface, it does not provide any direct information on the physical properties of fault zones. We currently cannot use seismic reflection data to infer directly which faults may be leaking or sealing and are reliant instead on shale‐gauge ratio type calculations, which are fraught with uncertainties. In this paper, we propose that full‐waveform inversion P‐wave velocity models can be used to extract information on fault zone acoustic properties directly, which may be a proxy for subsurface fault transmissibility. In this study, we use high‐quality post‐stack depth–migrated seismic reflection and full‐waveform inversion velocity data to investigate the characteristics of fault zones in the Samson Dome in the SW Barents Sea. We analyse the variance attribute of the post‐stack depth migrated and full‐waveform inversion volumes, revealing linear features that consistently appear in both datasets. These features correspond to locations of rapid velocity changes and seismic trace distortions, which we interpret as faults. These observations demonstrate the capability of full‐waveform inversion to recover fault zone velocity structures. Our findings also reveal the natural heterogeneity and complexity of fault zones, with varying P‐wave velocity anomalies within the studied fault network and along individual faults. Our results indicate a correlation between P‐wave velocity anomalies within fault zones and the modern‐day stress orientation. Faults with high P‐wave velocity are the ones that are perpendicular to the present‐day maximum horizontal stress orientation and are likely under compression. Faults with lower P‐wave velocity are the ones more parallel to the present‐day maximum horizontal stress orientation and are likely in extension. We propose that these P‐wave velocity anomalies may indicate differences in how ‘open’ and fluid filled the fault zones are (i.e. faults in extension are more open, more fluid filled and have lower VP) and therefore may provide a promising proxy for fault transmissibility.

Limited role of present-day onshore freshwater recharge in the emplacement of offshore freshened groundwater in the Canterbury Bight, New Zealand

ABSTRACT Offshore freshened groundwater (OFG) represents a significant, yet underexplored, component of the global freshwater system as it is found across various continental margins. In this study, a 3-D geological and groundwater model was developed for the Canterbury Plains and Bight (New Zealand) to assess the role of present-day onshore recharge in the emplacement of OFG. Topographic and bathymetric, seismic reflection and well data have been integrated to construct an onshore-offshore geological model. The facies property distribution and associated hydrodynamic parameters were determined using stochastic modelling techniques. The geological reconstruction was imported within a variable density groundwater model. Simulations, under transient regime, were run until equilibrium conditions between the involved aquifer portion and the adjacent ocean have been reached. Our results show that the geological model accurately captures the stratigraphic and sedimentary features of the area, and that onshore recharge at present contributes to OFG emplacement up to 15 km from the coast, which is equivalent to 25% of its maximum extent. Onshore recharge during sea-level lowstands is therefore the dominant OFG emplacement mechanism in the Canterbury Bight.

Interplay of tectonic and dynamic processes shaping multilayer extensional system in southern-central Apennines

High-hazard seismic zones can remain silent over centuries with meager seismicity rates challenging our understanding of seismic processes. We focus on the comprehensive analysis of cascading episodes of swarms and seismic sequences following the 2009 L’Aquila mainshock (MW 6.3) in the southern-central Apennine that previously experienced ~ M7 earthquakes. We enhance the seismic catalog, unmasking low-magnitude seismicity down to completeness magnitude ML ~ 0, and we unveil that the microseismicity might be secondarily triggered by the L’Aquila mainshock, influencing the frictional properties in the nearby fault zones or opening fault valves generating the intense seismic activity detected from 2009 to 2013. The diffusivity, observed in the most seismic episodes, and the high Vp/Vs values (> 1.88) indicate fluid circulation promoting multilayered extensional seismicity within 11–15 km and 16–23 km depth ranges. Mapping the 3D distribution of seismicity alongside geological data reveals an evident tectonic influence, unveiling unknown geometric aspects and providing the first evidence of a NNE-dipping deformation zone bounding at depths of 11–15 km the overlying fault system. Deeper seismicity suggests a mantellic CO2 ascending shape. These findings enrich the literature on tectonic seismic swarms in extensional domains, providing essential constraints on fluid involvement in the seismic processes and contributing to forthcoming discussions on the seismotectonic setting in high-seismic-risk areas of the Apennines of Italy.

Automatic seismic first‐break picking based on multi‐view feature fusion network

AbstractAutomatic first‐break picking is a basic step in seismic data processing, so much so that the quality of the picking largely determines the effect of subsequent processing. To a certain extent, artificial intelligence technology has solved the shortcomings of traditional first‐break picking algorithms, such as poor applicability and low efficiency. However, some problems still remain for seismic data, with a low signal‐to‐noise ratio and large first‐break change leading to inaccurate picking and poor generalization of the network. In order to improve the accuracy of the automatic first‐break picking results of the above seismic data, we propose a multi‐view automatic first‐break picking method driven by multi‐network. First, we analysed the single‐trace boundary characteristics and the two‐dimensional boundary characteristics of the first break. Based on these two characteristics of the first break, we used the Long Short‐Term Memory and the ResNet attention gate UNet (resudual attention gate UNet) networks to extract the characteristics of the first arrival and its location from the seismic data, respectively. Then, we introduced the idea of multi‐network learning in the first‐break picking work and designed a feature fusion network. Finally, the multi‐view first‐break features extracted by the Long Short‐Term Memory and resudual attention gate UNet networks are fused, which effectively improves the picking accuracy. The results obtained after applying the method to field seismic data show that the accuracy of the first break detected by a feature fusion network is higher than that given by the above two networks alone and has good applicability and resistance to noise.

Deep-seated gravity instability of the southern apron of the Ischia volcanic island (Tyrrhenian Sea, Italy)

Ischia Island is an active volcano representing the emerged sector of an E-W trending volcanic ridge largely extending undersea. Its collapsing behaviour, mainly in the form of fast-moving, terrestrial and submarine debris avalanches, has been recognized during the Holocene, but much less is known about previous gravity-driven processes. Using high-resolution multibeam bathymetric data and seismic reflection profiles, we provide evidence that the Island's southwestern flank has been involved in a slow-moving, deep-seated slope deformation that has displaced large volumes of its apron since the Late Pleistocene and until very recent or contemporary times. A long tongue of deformed seafloor, spreading up to 45 km from the Island over an area of 330 km2, between 500 and 1300 m water depths, has been detected along its southwestern slope. Different types of mass movements, genetically associated with each other, characterize this landslide: 1) a basal slump anticline, corresponding to a bulge on the bathymetry detaching at about 400 m sub-bottom depth; 2) an intermediate-mass movement chiefly consisting of debris avalanches and debris/turbiditic flows; 3) an upper mass movement consisting of hundred-metre size slumps detaching at relatively shallow depths. Conservative estimates indicate that at least 50 km3 of volcano-clastic and hemipelagic deposits have been mobilized, most of which comprise the basal slump anticline. This submarine landslide can be explained as a gravity failure of the continental slope unrelated to volcanism or rather as a process related to the dynamics of the volcanic edifice, which would imply volcano-spreading.

Study on stability evaluation of goaf based on AHP and EWM—taking the northern new district of Liaoyuan city as an example

Throughout the history of coal mining in all countries of the world, large areas of goaf have been left behind, and sudden collapses and surface subsidence of large areas of goaf may occur, especially for mining areas with long mining cycles. The northern new district of the Liaoyuan mining area has been subjected to nearly half a century of mining activities, accompanied by a gradual accumulation of disasters, which have occurred frequently in recent years. In order to assess the stability of the goaf in the study area, this paper proposes a hybrid decision-making multi-factor integrated evaluation method. The distribution of underground goafs was determined using geophysical exploration techniques (seismic survey and transient electromagnetic method) and geological drilling exploration. First, an evaluation index system was established based on the specifications of the goaf, the ecological and geological environment, and the mining conditions; the system included 14 indicators. Two weight calculation methods, AHP-EWM, were employed to determine the comprehensive weight of each indicator by combining subjective and objective weights on the basis of improved game theory. Subsequently, the fuzzy comprehensive evaluation method was utilised to complete the stability rating of each block in the study area, and MapGIS and ArcGIS were employed to complete the drawing of the stability zoning map of the northern new district goaf. The study area was divided into three zones of stability, basic stability and instability, according to the critical value. These zones accounted for 23.03%, 36.45% and 40.52% of the total area of the study area, respectively. The comprehensive on-site investigation revealed a decrease in the size and number of collapse pits and the rate of damage to the houses from the unstable zone to the stable zone. This indicates that the results of the division are consistent with the actual situation. The classification results are consistent with the actual ground disaster situation, thus verifying the rationality and validity of the evaluation method. The results indicate that the stability of the study area is generally at the lower middle level.

A fine‐tuning workflow for automatic first‐break picking with deep learning

AbstractFirst‐break picking is an essential step in seismic data processing. For reliable results, first arrivals should be picked by an expert. This is a time‐consuming procedure and subjective to a certain degree, leading to different results for different operators. In this study, we have used a U‐Net architecture with residual blocks to perform automatic first‐break picking based on deep learning. Focusing on the effects of weight initialization on first‐break picking, we conduct this research by using the weights of a pre‐trained network that is used for object detection on the ImageNet dataset. The efficiency of the proposed method is tested on two real datasets. For both datasets, we pick manually the first breaks for less than 10 of the seismic shots. The pre‐trained network is fine‐tuned on the picked shots, and the rest of the shots are automatically picked by the neural network. It is shown that this strategy allows to reduce the size of the training set, requiring fine‐tuning with only a few picked shots per survey. Using random weights and more training epochs can lead to a lower training loss, but such a strategy leads to overfitting as the test error is higher than the one of the pre‐trained network. We also assess the possibility of using a general dataset by training a network with data from three different projects that are acquired with different equipment and at different locations. This study shows that if the general dataset is created carefully it can lead to more accurate first‐break picking; otherwise, the general dataset can decrease the accuracy. Focusing on near‐surface geophysics, we perform traveltime tomography and compare the inverted velocity models based on different first‐break picking methodologies. The results of the inversion show that the first breaks obtained by the pre‐trained network lead to a velocity model that is closer to the one obtained from the inversion of expert‐picked first breaks.

Offset VSP acquired with a single-use fiber-optic probe: Gorgon CCS case study

There are many challenges associated with monitoring industrial subsurface CO2 storage. Utilizing a reliable strategy for monitoring and assurance is paramount. Numerous aspects and trade-offs need to be considered when designing a cost-effective monitoring solution that must satisfy regulatory and social licensing requirements and minimize operational complexity. Among various geophysical techniques that are included in the suite of carbon capture and storage (CCS) monitoring programs, seismic surveys provide reliable observations of the deep subsurface. However, active surface seismic techniques are expensive, resulting in significant time separation between surveys (several months or years). Downhole seismic approaches combined with fiber-optic sensing provide flexible options in designing on-demand monitoring strategies. Distributed acoustic sensing offers the opportunity to utilize injection wells for downhole time-lapse seismic imaging and microseismic detection, minimizing the impact on production operations. This paper presents the results of a multioffset vertical seismic profiling survey acquired in CO2 injection wells using bare fibers. The fibers were deployed inside the production tubing through FiberLine Intervention technology developed by Well-SENSE. The test utilized two injection wells for active seismic acquisition. The study took place on Barrow Island (Western Australia) as part of the Gorgon CCS project. The study demonstrates the technological advantages of fast deployment of the sensing fiber and the high quality of acquired seismic data. The paper highlights characteristic noise patterns in the recorded data and suggests approaches to attenuate their effects on seismic image quality. Finally, the impact of survey geometry and directional sensitivity of fiber on the recorded wavefield and the quality of seismic imaging is discussed.

Ultra-dense nodal seismic acquisition for automated geohazard analysis

In this paper, we address the problem of shallow geohazard mapping by developing an integrated workflow consisting of nodal seismic acquisition and automated data analysis. Densely spaced point-receiver land nodes and point sources are adopted as an alternative to receiver arrays or groups conventionally used in seismic exploration. The recently developed methodology of surface-consistent decomposition of the transmitted wavefield is applied to the nodal data. Ultra-resolution mapping of surface-consistent attributes in amplitude and phase is obtained, together with the generation of high-energy signal beams at the common midpoint positions, forming virtual (shot) supergathers (VSGs). The VSG preserves frequency and true-amplitude content thanks to the surface-consistent corrections applied before beam forming. Traveltimes, waveforms, and surface waves are then analyzed in the high signal-to-noise ratio VSG, leading to inversion of the subsurface parameters. Human-intensive interpretation of surface-wave dispersion curves in the f-k spectra is facilitated by the introduction of efficient machine learning algorithms for the unsupervised and supervised paradigms. The ultra-dense nodal acquisition and automated data analysis are effectively applied to the analysis of several cases of near-surface geohazards.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology, summarizes the research status of large model technology both domestically and internationally, provides an overview of the application status of large models in vertical industries, outlines the challenges and issues confronted in applying large models in the oil and gas sector, and offers prospects for the application of large models in the oil and gas industry. The existing large models can be briefly divided into three categories: large language models, visual large models, and multimodal large models. The application of large models in the oil and gas industry is still in its infancy. Based on open-source large language models, some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation. Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models. A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation, as well as core analysis. The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models, high research and development costs, and poor algorithm autonomy and control. The application of large models should be guided by the needs of oil and gas business, taking the application of large models as an opportunity to improve data lifecycle management, enhance data governance capabilities, promote the construction of computing power, strengthen the construction of “artificial intelligence + energy” composite teams, and boost the autonomy and control of large model technology.

A Detailed Understanding of Slow Self‐Arresting Rupture

AbstractRecent numerical simulation studies suggest the existence of a seismic type that is distinct from regular earthquakes—the slow self‐arresting rupture (SSAR). Unlike regular earthquakes that propagate dynamically following the initiation, The SSARs automatically arrest within the nucleation zone without interference. Additionally, numerical simulations indicate that SSARs exhibit a significantly lower energy release compared to regular earthquakes, while also exhibiting a relatively long source duration. Given these distinctive properties, comprehending the source processes of SSARs assumes great strategic importance. However, our current understanding of SSARs, particularly regarding their response to different frictional conditions and their correlation with natural phenomena, remains limited in scope. To further explore the intricacies of SSARs, we employ a three‐dimensional fully dynamic source model to simulate SSARs under various slip‐weakening frictional conditions. The findings indicate that SSARs occur in frictional environments characterized by large normalized critical slip distances, with the seismic source process being primarily influenced by this parameter. Apart from displaying significantly smaller average slip and stress drop, which are two to three orders of magnitude lower than those of regular earthquakes of comparable magnitude, SSARs also showcase a decrease in duration, seismic moment, slip rate, and stress drop as the normalized critical slip distance increases. The moment‐duration scaling law of SSARs exhibits a linear pattern. Moreover, the observation of slow earthquakes offers further implications for the presence of SSARs, indicating their potential association with a wider range of intricate seismic phenomena.

SeisRAFT: A recurrent deep learning network for 4D seismic registration and CO2 storage monitoring

The alignment or registration of seismic images is an important and extensively researched aspect in seismic data processing. This process plays a pivotal role in evaluating the evolution of CO2 plumes and assessing the potential for CO2 leakage from its reservoir storage when using 4D or time-lapse seismic methodology. The computer vision community has demonstrated considerable advantages in employing deep learning for such tasks, particularly when confronted with substantial shifts, mismatches, and noises in complex images. In this study, we leverage a cutting-edge recurrent deep learning network known as recurrent all-pairs field transforms (RAFT), incorporating tailored modifications to address seismic matching challenges. We evaluate the network's performance on both synthetic and field 4D CO2 seismic data sets, showcasing its superior accuracy and effectiveness compared to a widely used traditional method. Particularly, in some intricate and ambiguous instances, where even human vision may struggle to identify corresponding events between images, our deep learning method exhibits strong and reliable performance. Validated results confirm that this lightweight multiscale network, trained through self-supervised learning, offers a rapid, parameter-free, and end-to-end solution for 4D image matching in subsurface CO2 storage monitoring with potential applicability to many other seismic registration tasks.

Development and prospect of acoustic reflection imaging logging processing and interpretation method

Acoustic reflection imaging logging technology can detect and evaluate the development of reflection anomalies, such as fractures, caves and faults, within a range of tens of meters from the wellbore, greatly expanding the application scope of well logging technology. This article reviews the development history of the technology and focuses on introducing key methods, software, and on-site applications of acoustic reflection imaging logging technology. Based on the analyses of major challenges faced by existing technologies, and in conjunction with the practical production requirements of oilfields, the further development directions of acoustic reflection imaging logging are proposed. Following the current approach that utilizes the reflection coefficients, derived from the computation of acoustic slowness and density, to perform seismic inversion constrained by well logging, the next frontier is to directly establish the forward and inverse relationships between the downhole measured reflection waves and the surface seismic reflection waves. It is essential to advance research in imaging of fractures within shale reservoirs, the assessment of hydraulic fracturing effectiveness, the study of geosteering while drilling, and the innovation in instruments of acoustic reflection imaging logging technology.

Petroleum exploration and production in Brazil: From onshore to ultra-deepwaters

The Santos Basin in Brazil has witnessed significant oil and gas discoveries in deepwater pre-salt since the 21st century. Currently, the waters in eastern Brazil stand out as a hot area of deepwater exploration and production worldwide. Based on a review of the petroleum exploration and production history in Brazil, the challenges, researches and practices, strategic transformation, significant breakthroughs, and key theories and technologies for exploration from onshore to offshore and from shallow waters to deep–ultra-deep waters and then to pre-salt strata are systematically elaborated. Within 15 years since its establishment in 1953, Petrobras explored onshore Paleozoic cratonic and marginal rift basins, and obtained some small to medium petroleum discoveries in fault-block traps. In the 1970s, Petrobras developed seismic exploration technologies and several hydrocarbon accumulation models, for example, turbidite sandstones, allowing important discoveries in shallow waters, e.g. the Namorado Field and Enchova fields. Guided by these models/technologies, significant discoveries, e.g. the Marlim and Roncador fields, were made in deepwater post-salt in the Campos Basin. In the early 21st century, the advancements in theories and technologies for pre-salt petroleum system, carbonate reservoirs, hydrocarbon accumulation and nuclear magnetic resonance (NMR) logging stimulated a succession of valuable discoveries in the Lower Cretaceous lacustrine carbonates in the Santos Basin, including the world-class ultra-deepwater super giant fields such as Tupi (Lula), Mero and Buzios. Petroleum development in complex deep water environments is extremely challenging. By establishing the Technological Capacitation Program in Deep Waters (PROCAP), Petrobras developed and implemented key technologies including managed pressure drilling (MPD) with narrow pressure window, pressurized mud cap drilling (PMCD), multi-stage intelligent completion, development with Floating Production Storage and Offloading units (FPSO), and flow assurance, which remarkably improved the drilling, completion, field development and transportation efficiency and safety. Additionally, under the limited FPSO capacity, Petrobras promoted the world-largest CCUS-EOR project, which contributed effectively to the reduction of greenhouse gas emissions and the enhancement of oil recovery. Development and application of these technologies provide valuable reference for deep and ultra-deepwater petroleum exploration and production worldwide. The petroleum exploration in Brazil will consistently focus on ultra-deep water pre-salt carbonates and post-salt turbidites, and seek new opportunities in Paleozoic gas. Technical innovation and strategic cooperation will be held to promote the sustainable development of Brazil's oil and gas industry.

An unsupervised machine learning algorithm approach using K-Means Clustering for optimizing Surface Wave Filtering in seismic reflection data

Surface waves often cause significant noise in seismic data, complicating the interpretation of subsurface structures. Traditional filtering methods, such as FK filtering, usually struggle with non-stationary noise and require extensive manual parameter tuning. This study explores the effectiveness of using K-means clustering, incorporating attributes such as amplitude, frequency, and phase to filter surface waves from seismic data. Synthetic seismic data were first generated to test the proposed method, ensuring its robustness before application to real field data. Attributes were extracted from each seismic trace, including instantaneous amplitude, frequency, and phase. These attributes were used as input parameters for the K-means clustering algorithm. The identified clusters corresponding to surface waves were then used to filter these waves from the seismic data. The K-Means clustering effectively differentiated surface waves from reflected waves in both synthetic and real seismic datasets. The method demonstrated that by including phase as an attribute, alongside amplitude and frequency, the accuracy of surface wave detection and filtering significantly improved. The synthetic data showed a clear separation of wave types, validating the method. When applied to real field data, the approach consistently removed surface waves, clarity of seismic reflections crucial for subsurface analysis.

Construction Technology and Application of TBM in Shengli Tunnel of Tianshan Mountain under poor geological conditions

When a tunnel boring machine (TBM) excavates in poor geological conditions like fracture zones, soft rock with high ground stress, hard rock with high ground stress, and water inrush ground, severe problems such as surrounding rock collapse, convergence, rock burst and water and mud outburst are usually encountered. These problems existed in the Tianshan Shengli Tunnel project in Xinjiang, China. Several methods and techniques were adopted to reduce construction risks and improve construction efficiency. The seismic wave reflection long-distance geological forecasting method was employed to forecast and predict these adverse geological conditions. The pre-reinforcement construction method was employed on the surrounding broken rocks in front of the tunnel face to lower the risks of tunnel collapse and machine jamming. A combined steel arch was applied to prevent tunnel arch collapse. An artificial cap method was applied to prevent the jamming caused by high-ground stress soft rock. Measures such as spraying water on the tunnel face and drilling stress holes were employed to prevent rock bursts caused by high-ground stress in hard rock. Implementing the advanced pipe shed method during water influx, sudden mud, and other working conditions reduced the risk of groundwater damage to equipment and lowered risks to personnel safety. The successful application of these techniques can provide a reference for TBM tunnel construction in similar ground conditions.

U–Pb apatite geochronology shows multiple thermal overprints within the Neoarchean foreland basement of the Faroe–Shetland Terrane

The Neoarchean foreland basement of the Faroe–Shetland terrane (FST) displays abundant evidence for isotopic resetting of U–Pb systems in apatite between c. 1800 and 200 Ma, interpreted to result from episodic heating pulses associated with regional-scale tectonic events. Major apparent age peaks of c. 1800–1600 Ma broadly correspond to the timing of Nagssugtoqidian–Laxfordian orogenesis >225 km further south. These are thought to reflect widespread heating during late- to post-orogenic delamination that affected a wide area of the orogenic foreland and resulted in a low to middle greenschist-facies static overprint, affecting much of the FST basement. Late- to post-orogenic delamination might also account for major apparent age peaks at c. 1300–1100, 800 and 500–400 Ma, corresponding to, respectively, Grenvillian, Knoydartian and Caledonian orogenic events. However, east-dipping seismic reflectors in the basement west of Shetland may represent the northward extension of the Grenvillian Outer Hebrides Thrust (Zone) and/or Caledonian thrusts and so perturbation of isotherms during west-directed thrusting could therefore also account for these apparent age peaks. Minor apparent age peaks of c. 700, 600, 350 and 200 Ma are most easily interpreted as resulting from enhanced heat flux that accompanied periods of crustal extension prior to and following the Caledonian orogeny.