Multiple Basins Research Articles

Consumption Dynamics in Mixed-Income Neighborhoods with Connected Households

AbstractWe investigate the dynamics of household consumption in a setting in which households are connected across income classes. Low- and high-income households form preferences endogenously, conditional on their own and their neighbor’s past consumption. The modeling effort relies on a stochastic dynamic model of interdependent consumer choice in which the demand for commodities evolves according to a non-linear difference equation with stochastic initial states. The analysis targets a region of the parameter space that corresponds to salient features of a mixed-income neighborhood in which households are connected. Standard methods of asymptotic analysis of dynamic systems (e.g. bifurcation analysis) are combined with numerical simulation, statistical modelling of extreme events and statistical estimation techniques to investigate the dynamics. From the mathematical point of view, our analysis reveals the existence of intricate bifurcation pattern, coexistence of multiple attractors, complex basins and long transients. The essential economic finding states that key features of household consumption vary significantly in the influence the high-income households exert on the preference formation of the low-income households. In particular, we find that the prevalence of long transients, i.e. long waiting times before convergence to asymptotic states occur, is inversely related to the type of connectedness considered. We demonstrate that the dynamics of the consumption trajectory evolving over an extended time period before it settles on long-run simple consumption pattern, may not at all be captured by an asymptotic state. Thus, policies targeting the economies in mixed-income neighborhoods that are solely based on information about long-run consumption states, might trigger unwanted, unanticipated effects.

Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning

Accurate hydrological predictions are often hindered by the lack of stream gauges in data-scarce regions, where traditional transfer learning (TL) models like Long Short-Term Memory (LSTM) networks often face limitations due to reduced accuracy and adaptability. To enhance runoff prediction in such regions, we developed DAformer, a novel TL approach that integrates domain adversarial neural networks with the Informer model. Trained on comprehensive runoff data from U.S. basins, DAformer was applied to three basins in Chile and the Chaersen basin in China, demonstrating an effective transfer from data-rich to data-scarce environments. Results show that DAformer significantly outperforms LSTM-based models, improving forecast accuracy by 16.1% for 1-day lead time and by 100.5% for 5-day lead time. These improvements indicate that the DAformer model not only enhances prediction accuracy but also holds substantial practical implications for flood risk management and water resource planning in regions with limited data availability. By clustering basins based on Shuttle Radar Topography Mission (SRTM) and other geographical data, we found that relying on multiple source basins further enhances the performance. DAformer, therefore, serves as a robust and scalable method for enhancing runoff prediction for regions with limited data.

Knowledge-Based Perturbation LaF-CMA-ES for Multimodal Optimization

Multimodal optimization presents a significant challenge in optimization problems due to the existence of multiple attraction basins. Balancing exploration and exploitation is essential for the efficiency of algorithms designed to solve these problems. In this paper, we propose the KbP-LaF-CMAES algorithm to address multimodal optimization problems based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) framework. The Leaders and Followers (LaF) and Knowledge-based Perturbation (KbP) strategies are the primary components of the KbP-LaF-CMAES algorithm. The LaF strategy is utilized to extensively explore the potential local spaces, where two cooperative populations evolve in synergy. The KbP strategy is employed to enhance exploration capabilities. Improved variants of CMA-ES are used to exploit specific domains containing local optima, thereby potentially identifying the global optimum. Simulation results on the test suite demonstrate that KbP-LaF-CMAES significantly outperforms other meta-heuristic algorithms.

Poisson’s ratio-LambdaRho rock-physics templates and a study on sensitivity of different fluid indicators

Seismic fluid indicators are highly accurate when calibrated, but when the data is scarce, an interpreter must choose between different indicators. We investigate the data from multiple basins to understand the most effective fluid indicators for use. Unlike previous studies, the data comprises a large, equal, and statistically meaningful number of hydrocarbon and brine sands from different basins. We show that Poisson’s ratio-LambdaRho templates can be used to separate different facies in synthetic and real data and can be used as a useful crossplot interpretation tool. We develop a fluid indicator, as LambdaRho Poisson’s ratio multiplication [Formula: see text], which is highly sensitive to saturation and can be potentially used as a fizz-gas discriminator. We compare the sensitivity of different fluid indicators to hydrocarbon saturation by using Bhattacharyya distance, which provides a quantitative metric for how well different indicators separate hydrocarbon and brine sands, and it is a nonparametric measure unlike other fluid indicator scores developed in similar studies. Absolute properties derived from seismic by inversion are rarely available in regional studies, whereas relative elastic properties can be easily obtained and used. Following the concepts of elastic reflectivity vectors and geometry of intercept-gradient crossplots, we show how the reflectivity of different fluid indicators can be approximated from amplitude variation with offset (AVO) parameters at various chi ([Formula: see text]) angles, enabling an interpreter to use them even when inversion products are not available. Finally, we compare the effectiveness of relative elastic parameters on different AVO classes and show that no single attribute works best across all classes but for general screening purposes fluid factor and [Formula: see text] can be good choices. The findings of this study can help better characterization of fluids in exploration, appraisal, and development of hydrocarbons, and in other areas where monitoring produced and injected fluids is important like 4D seismic or carbon capture storage.

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

AbstractEnsemble‐based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process‐based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite‐dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet‐based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling‐Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics‐informed, seq‐to‐seq streamflow forecasting, which further reduces biases in the pre‐trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

Satellite‐Based Surveys Reveal Substantial Methane Point‐Source Emissions in Major Oil & Gas Basins of North America During 2022–2023

AbstractUtilizing imaging spectroscopy technology to identify methane super‐emitters plays a vital role in mitigating methane emissions in the Oil & Gas (O&G) sector. While earlier research has uncovered significant point‐source methane emissions from O&G production in the US and Canada, which are key regions with large methane emissions, a comprehensive post‐COVID‐19 survey has been notably absent. Here, we perform a detailed survey of methane super‐emitters across multiple basins of North America (Marcellus Shale, Haynesville/Bossier Shale, Permian Basin and Montney Shale) using the new Chinese Gaofen5‐01A/02 (GF5‐01A/02) satellite measurements during 2022–2023. We detect 139 individual methane plumes emanating from 122 point sources, with flux rates ranging from 519 to 16,071 kg hr−1. These emissions exhibit a highly skewed and heavy‐tailed distribution, constituting approximately 23% of the flux inversion with TROPOMI in the sample region, with a range of 13%–40%. Moreover, we observe a 66.7% reduction in methane emissions in Permian Basin during COVID‐19, followed by fluctuations until spring 2023. By summer 2023, methane emissions rebound to twice their previous magnitude (1.68 ± 0.58 Tg a−1). Using these point‐source surveys, we further quantify a regional methane emission of 2.69 ± 0.86 Tg a−1 in Permian Basin. This estimation closely aligns with top‐down inversions (2.22 ± 0.40 Tg a−1) from TROPOMI. The upscale estimation underscores the effectiveness of high‐resolution remote sensing measurements in improving bottom‐up emissions inventories and refining regional methane emission assessments. Our results highlight the potential climate benefits derived from regular monitoring and specific remediation efforts focused on relatively few strong point‐source emissions.

Magnetic particle imaging enables nonradioactive quantitative sentinel lymph node identification: feasibility proof in murine models.

Sentinel lymph node biopsy (SLNB) is an important cancer diagnostic staging procedure. Conventional SLNB procedures with 99mTc radiotracers and scintigraphy are constrained by tracer half-life and, in some cases, insufficient image resolution. Here, we explore an alternative magnetic (nonradioactive) image-guided SLNB procedure. To demonstrate that magnetic particle imaging (MPI) lymphography can sensitively, specifically, and quantitatively identify and map sentinel lymph modes (SLNs) in murine models in multiple regional lymphatic basins. Iron oxide nanoparticles were administered intradermally to healthy C57BL/6 mice (male, 12-week-old, n = 5). The nanoparticles (0.675 mg Fe/kg) were injected into the tongue, forepaw, base of tail, or hind footpad, then detected by 3-dimensional MPI at multiple timepoints between 1 hour and 4 to 6 days. In this mouse model, the SLN is represented by the first lymph node draining from the injection site. SLNs were extracted to verify the MPI signal ex vivo and processed using Perl's Prussian iron staining. Paired t-test was conducted to compare MPI signal from SLNs in vivo vs. ex vivo and considered significant if P < .05. MPI lymphography identified SLNs in multiple lymphatic pathways, including the cervical SLN draining the tongue, axillary SLN draining the forepaw, inguinal SLN draining the tail, and popliteal SLN draining the footpad. MPI signal in lymph nodes was present after 1 hour and stable for the duration of the study (4-6 days). Perl's Prussian iron staining was identified in the subcapsular space of excised SLNs. Our data support the use of MPI lymphography to specifically detect SLN(s) using a magnetic tracer for a minimum of 4 to 6 days, thereby providing information required to plan the SLN approach in cancer surgery. As clinical-scale MPI is developed, translation will benefit from a history of using iron-oxide nanoparticles in human imaging and recent regulatory-approvals for use in SLNB.

Beyond the Border: Exploring the Complex Dynamics of Water Tensions Between India and China

This paper explores the historical context and current dynamics of the transboundary water issues between India and China. Both countries share multiple transboundary river basins, with the Brahmaputra River being one of the most significant. The importance of the Brahmaputra River to both nations goes beyond just providing resources. It has implications for national security, regional stability, and human livelihoods. However, China's upstream control over the river and its extensive dam-building projects have raised concerns in downstream India and Bangladesh about potential impacts on water availability, agriculture, and livelihoods. The river system is critical for the socioeconomic well-being of millions of people, yet the competing demands for water resources have led to geopolitical tensions. The paper highlights China's strategic use of water resources, including plans for large-scale dams, which India views as a potential threat to its water security. It also underscores the importance of a nuanced understanding of hydropolitics, emphasizing the need for human security, particularly water security, in addressing these challenges. It argues that while "water wars" are unlikely, "water tensions" are significant and must be addressed through comprehensive bilateral agreements and collaborative efforts.

Quantitative analysis of the sensitivity and spatial stratified heterogeneity of extreme precipitation across river basins

Global warming is expected to lead to a continuous increase in extreme precipitation. However, the response of extreme precipitation to climate change remains not entirely clear. This study quantitatively analyzes extreme precipitation in multiple river basins of China over the past nearly 60 years using high spatial resolution data. Both extreme precipitation and mean precipitation exhibit a spatial distribution pattern of higher values in the southeast and lower values in the northwest. However, their sensitivity to temperature shows an asymmetric spatial pattern, with higher sensitivity in the northwest and lower sensitivity in the southeast. Overall, the sensitivity of extreme precipitation to temperature (9.6 %/K) is 1.1 times that of mean precipitation (8.8 %/K). Additionally, the sensitivity of extreme precipitation in relatively dry areas (Continental River Basin) is about five times that in relatively wet areas (other river basins). Vegetation has the highest explanatory power (i.e., q-value) for the spatial stratified heterogeneity of extreme precipitation, ranging from 0.61 to 0.72, with the explanatory power of natural factors exceeding that of societal factors. Furthermore, the interaction between vegetation and elevation enhances the explanatory power for the spatial stratified heterogeneity of extreme precipitation, reaching values between 0.74 and 0.82.

2023 temperatures reflect steady global warming and internal sea surface temperature variability

2023 was the warmest year on record, influenced by multiple warm ocean basins. This has prompted speculation of an acceleration in surface warming, or a stronger than expected influence from loss of aerosol induced cooling. Here we use a recent Green’s function-based method to quantify the influence of sea surface temperature patterns on the 2023 global temperature anomaly, and compare them to previous record warm years. We show that the strong deviation from recent warming trends is consistent with previously observed sea surface temperature influences, and regional forcing. This indicates that internal variability was a strong contributor to the exceptional 2023 temperature evolution, in combination with steady anthropogenic global warming.

Magmatic and sedimentological arguments for an Ediacaran active margin in the Bayankhongor Zone in western Mongolia, Central Asian Orogenic Belt

Geochronological and geochemical investigation of the magmatic and sedimentary rocks from the south-eastern tip of the Bayankhongor Zone (central Mongolia) constrains the Neoproterozoic evolution of the northern margin of the Baidrag Block. There, ultramafic to felsic igneous rocks of the Khan-Uul Massif intruded the volcano-sedimentary sequence of the Ulziit Gol Unit. U–Pb zircon ages show that both the plutonic and volcanic rocks were coeval products of the same Ediacaran (598–564 Ma) magmatism. Most of the samples have low contents of high field strength elements typical of arc-related magmas; however, some mafic rock samples display N-MORB-like chemistry. Magmatic rocks in the Khan-Uul Massif and Ulziit Gol Unit yielded exclusively positive initial εHf in zircon values (+4.9 to +13.8), implying derivation from depleted-mantle sources. Furthermore, heterogeneity of the whole-rock initial εNd values (–4.0 to +2.1) documents that fractional crystallization was accompanied by variable crustal contamination. The detrital zircon age patterns of the sandstone and tuffites in the Ulziit Gol Unit indicate that the sequence was filled dominantly by Ediacaran magmatic detritus derived from the Khan-Uul Massif and its volcanic equivalents, while the older cratonic material from the Baidrag basement represented only a subordinate component. Combined magmatic and sedimentary records imply that both the Khan-Uul Massif and the Ulziit Gol Unit may have formed in the same back-arc basin environment. This challenges the previous view that the entire Bayankhongor Zone was ophiolitic in nature. Distribution of coeval Ediacaran supra-subduction systems on the scale of the Mongolian Collage indicates the closure of multiple oceanic basins rimming the Siberian Continent. Following Rodinia break-up, this peri-Siberian realm was presumably formed by sub-parallel continental ribbons and oceanic basins. It is proposed that the amalgamation of these blocks and closure of intervening oceans reflected the Ediacaran advancing mode of the Palaeo-Pacific subduction.

Sedimentary signatures of large earthquakes along the submerged Enriquillo–Plantain Garden transpressional plate boundary, northern Caribbean

Abstract The Enriquillo–Plantain Garden fault (EPGF), the southern branch of the northern Caribbean left-lateral transpressional plate boundary, has ruptured in two devastating earthquakes along the Haiti southern peninsula: the Mw 7.0, 2010 Haiti and the Mw 7.2, 2021 Nippes earthquakes. In Jamaica, the 1692 Port Royal and 1907 Great Kingston earthquakes caused widespread damage and loss of life. No large earthquakes are known from the 200-km-long Jamaica Passage segment of this plate boundary. To address these hazards, a National Science Foundation Rapid Response survey was conducted to map the EPGF in the Jamaica Passage south of Kingston, Jamaica, and east of the island of Jamaica. From the R/V Pelican we collected &amp;gt;50 high-resolution seismic profiles and 47 gravity cores. Event deposits (EDs) were identified from lithology, physical properties, and geochemistry and were dated in 13 cores. A robust 14C chronology was obtained for the Holocene. A Bayesian age model using OxCal 4.4 calibration was applied. Out of 58 EDs that were recognized, 50 have ages that overlap within their 95% confidence ranges. This allowed for their grouping in multiple basins located as much as 150 km apart. The significant age overlap suggests that EDs along the Enriquillo–Plantain Garden plate boundary resulted from large and potentially dangerous earthquakes. Most of these earthquakes may derive from the EPGF but also from thrust faulting at this strain-partitioned transpressional boundary. The recent increase in Coulomb stress on the EPGF from the Mw 7.2 Nippes earthquake in southwestern Haiti and the discoveries reported here enhance the significance for hazard in the Jamaica Passage.

Effects of land use patterns on seasonal water quality in Chinese basins at multiple temporal and spatial scales

Diverse land use patterns exhibit varying effects on water quality across different seasons and spatial scales. However, current studies on the correlation between land use and water quality in single small-scale basins no longer meet the needs of regional coordinated development. Simultaneous comparative analysis of multiple large-scale basins can promote water environmental protection in different basins, but there is currently limited relevant research. In this study, the data from 86 sampling points across seven major river basins in China were analyzed. Multivariate statistical analysis and the redundancy analysis (RDA) were employed to investigate the influence of land use patterns on water quality at different seasons and spatial scales. The results indicated notable differences in water quality across various seasons and locations. Except for higher pH and permanganate index (COD) concentrations in the wet season in the Songhua River Basin and higher COD concentrations in the Pearl River Basin, the concentrations of all parameters in other basins are higher in the dry season. PH and COD exhibited considerable spatial variations within basins, while dissolved oxygen (DO) and ammonia nitrogen (NH4+-N) showed smaller variations. RDA showed that land use had a more pronounced effect on water quality during the dry season in the Yangtze, Liao and Pearl River Basins, while the impact was greater in the wet season in the four basins of the Yellow, Huai, Hai and Songhua River Basins. At the spatial scale, the 2000 m buffer zone had the most significant impact within the Yangtze, Hai and Liao River Basins, while the 1000 m buffer zone had the greatest impact in the Huai and Pearl River Basins. For the Yellow and Songhua River Basins, the buffer zone was 500 m during the wet season and 1000 m and 2000 m during the dry season, respectively. The research findings can offer a scientific foundation for the development of basin-specific land management policies and water quality protection measures in different basins from a multi-scale perspective.

Implications of tumor-positive sentinel lymph nodes in single vs multiple nodal basins in melanoma.

Melanoma patients' prognosis is based on the primary tumor characteristics and the tumor status of the regional lymph nodes. The advent of lymphoscintigraphy with SLN biopsy (SLNB) has shown that melanoma can drain to multiple nodal basins but the significance of multiple basins (vs. one basin) with tumor-positive sentinel lymph node(s) (+SLN) of similar tumor burden has not been shown. We examined the impact of the number of nodal basins with +SLN (+basin) in melanoma patients and its significance for patients' prognosis and survival. We identified 1,915 patients with +SLN from two randomized surgical clinical trials: Multicenter Selective Lymphadenectomy Trials I and II. Patient groups were divided based on number of +SLNs and number of +basins. Disease-free survival (DFS), distant disease-free survival (DDFS) and melanoma-specific survival (MSS) were compared with the Kaplan-Meier method and log-rank tests. Univariable and multivariable analyses were performed using Cox proportional hazard regressions. Among the 1,915 patients, 1,501 had only one +SLN (78%) in one basin and 414 (22%) had multiple +SLNs: 340 located in one basin and 74 in multiple basins. Among patients with multiple +SLNs, those with multiple +basins have a worse DFS, DDFS and MSS than those with a single basin (p ≤ 0.0001 for all comparisons). MSS was significantly different based on AJCC stages: AJCC IIIA and IIIB (p ≤ 0.001 and 0.0287, respectively). Our results suggest that the number of tumor-positive basins may be important for staging and in understanding the biology of lymph node metastases.

Real-time chemostratigraphy at wellsite; removing drilling uncertainties

Real-time chemostratigraphy is a workflow deployed at wellsites to remove stratigraphic uncertainty and support drilling operations. This study presents the chemostratigraphic wellsite workflow which, during drilling operations, support either the placement of casing points (using predefined geochemical markers to target a depth above a specific stratigraphic feature) or aid geosteering. Geosteering is typically employed during the drilling of a horizontal well and is done to keep the drill within a certain zone or horizon to maximise production. Wellsite chemostratigraphy has been effectively deployed to wellsites within multiple basins around the globe and has recently been utilised in the Perth Basin. The wellsite workflow starts before the deployment, with offset wells analysed by inductively coupled plasma (ICP) spectroscopy within a laboratory-based setting. This builds the correlative element-based framework and identifies the trends required to assist with either core placement, casing point, or geosteering. Samples are analysed by energy dispersive x-ray fluorescence units (these data are then used to confirm the zone previously defined) and can be identified at wellsite using the onsite tool, the selected elements of which are those less likely to be affected by drilling mud contamination and loss of circulation material (LCM). However, during drilling operation, there may be instances where LCM must be added to the drilling muds, which may have an effect on the data quality for certain elements. With a dedicated ICP spectrometry elemental database behind the wellsite operation, machine learning tools may be employed to ‘repair’ correlation critical elements.

Evaluating manual versus automated benthic foraminiferal δ18O alignment techniques for developing chronostratigraphies in marine sediment records

Abstract. Paleoceanographic interpretations of Plio-Pleistocene climate variability over the past 5 million years rely on the evaluation of event timing of proxy changes in sparse records across multiple ocean basins. In turn, orbital-scale chronostratigraphic controls for these records are often built from stratigraphic alignment of benthic foraminiferal stable oxygen isotope (δ18O) records to a preferred dated target stack or composite. This chronostratigraphic age model approach yields age model uncertainties associated with alignment method, target selection, the assumption that the undated record and target experienced synchronous changes in benthic foraminiferal δ18O values, and the assumption that any possible stratigraphic discontinuities within the undated record have been appropriately identified. However, these age model uncertainties and their impact on paleoceanographic interpretations are seldom reported or discussed. Here, we investigate and discuss these uncertainties for conventional manual and automated tuning techniques based on benthic foraminiferal δ18O records and evaluate their impact on sedimentary age models over the past 3.5 Myr using three sedimentary benthic foraminiferal δ18O records as case studies. In one case study, we present a new benthic foraminiferal δ18O record for International Ocean Discovery Program (IODP) Site U1541 (54°13′ S, 125°25′ W), recently recovered from the South Pacific on IODP Expedition 383. The other two case studies examine published benthic foraminiferal δ18O records of Ocean Drilling Program (ODP) Site 1090 and the ODP Site 980/981 composite. Our analysis suggests average age uncertainties of 3 to 5 kyr associated with manually derived versus automated alignment, 1 to 3 kyr associated with automated probabilistic alignment itself, and 2 to 6 kyr associated with the choice of tuning target. Age uncertainties are higher near stratigraphic segment ends and where local benthic foraminiferal δ18O stratigraphy differs from the tuning target. We conclude with recommendations for community best practices for the development and characterization of age uncertainty of sediment core chronostratigraphies based on benthic foraminiferal δ18O records.

A multi-model evaluation of probabilistic streamflow predictions via residual error modelling

Probabilistic streamflow predictions are valuable tools for predictive uncertainty estimation, hydrologic risk management, and support for decision-making in water resources. Usually, predictive uncertainty quantification is developed and assessed using only a single hydrological model, making it difficult to generalize to other model configurations. To tackle this issue, we assess changes in the model performance ranking of diverse streamflow models by applying a residual error model post-processing approach to multiple basins and multiple models. This assessment employed 141 basins from the Great Lakes watershed covering the USA and Canada, and 13 diverse streamflow models, which are evaluated using deterministic and probabilistic performance metrics. As the first study to implement probabilistic methods to diverse streamflow models applied to a multitude of basins, the analysis here examines the dependence of probabilistic streamflow estimation quality on model quality. Our findings show that streamflow model choice influences the robustness of probabilistic predictions. It was found that moving from deterministic to probabilistic predictions using a post-processing approach does not change the streamflow model performance ranking for the best and worst deterministic models, but models of intermediate rank in deterministic evaluation do not have consistent ranking when evaluated in probabilistic mode. Post-processing residual errors of long short-term memory (LSTM) network models are consistently the best-performing model in terms of deterministic and probabilistic metrics. This study highlights the significance of combining deterministic streamflow model predictions with residual error models for improving the quality and increasing the value of hydrological predictions, quantifying uncertainty, and facilitating decision-making in operational water management. It also clarifies the degree to which probabilistic predictions depend upon good model performance and can compensate for poor model performance.

Modeling of kappa factor using multivariate adaptive regression splines: application to the western Türkiye ground motion dataset

The recent seismic activity on Türkiye’s west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North–South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region–specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three–component strong–ground motion data from accelerometer sites with known VS30 values throughout western Türkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well–known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites.

An application of AHP and fuzzy entropy-TOPSIS methods to optimize upstream petroleum investment in representative African basins

The growing demand of China for petroleum heightens the complexities and prospects in worldwide investments, necessitating refined and strategic investment approaches. Evaluating the potential of different hydrocarbon-potential areas needs more comprehensive scientific evaluation models. This study aims to establish a Comprehensive Investment Potential of Petroleum (CIPP) framework for targeted sedimentary basins by using an integrated approach that combines the Analytic Hierarchy Process (AHP) and the Entropy-Weighted Fuzzy TOPSIS models. We focus particularly on representative African basins to inform strategic decision-making for the Chinese overseas petroleum enterprises. We firstly interpret the geological condition of these petroleum basins by researching multiple databases and proprietary research data. Then, we use a combined approach of ranking-classification-correlation analysis to evaluate 17 representative basins, taking into account both overall and individual key performance indicators. Our findings suggest the Illizi Basin and the Offshore Côte d'Ivoire Basin could be the most favorable for investment and development. Those like Southwest African Basin warrant cautious consideration. The new evaluation model and computational workflow offer an effective workflow for assessing multiple petroleum basins. This work provides not just practical investment strategies for companies aiming for African petroleum basins, but also a transferable methodology for optimizing investment decisions.

Deciphering the interplay between tectonic and climatic forces on hydrologic connectivity in the evolving landscapes

The intricate interplay between climate and tectonics profoundly shapes landscapes over time frames surpassing 10 million years. Active tectonic processes and climatic shifts unsettle established drainage systems, instigating fragmentation or amalgamation of watersheds. These activities yield substantial transformation in surface hydrologic connectivity, thereby underlining the profound influence of these tectonic and climatic forces on the evolution of both landscape and hydrology. Such transformations within the hydrological landscape have direct implications for the evolution of aquatic species. As connections among aquatic habitats undergo reconfiguration, they incite shifts in species distribution and adaptive responses. These findings underscore the role of tectonics and climate in not only sculpting the physical landscape but also steering the course of biological evolution within these dynamically changing aquatic ecosystems relying on hydrologic connections. Despite the significance of these interactions, scholarly literature seldom examines alterations in hydrologic connectivity over tectonic, or orogen-scale, timescales. This study aims to bridge this gap, exploring changes in hydrologic connectivity over extended periods by simulating a continental rift system akin to the Rio Grande Rift, USA, subject to various tectonoclimatic scenarios. Multiple rift basins hosting large lakes, brought into existence by active tectonic extension, are further molded by tectonic extension and post-rift climatic changes. The study focuses on phenomena such as interbasin river breakthroughs and knickpoint generation, assembling a time-series of connectivity metrics based on stream network characteristics such as flow rate, flow distance, and captured drainage areas. We anticipate that the insights gleaned from this study will enhance our comprehension of the enduring impact of tectonic and climate processes on hydrologic connectivity and the subsequent evolution of aquatic species.