Reflection Data Research Articles

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.

Monitoring and assessment of spatiotemporal soil salinization in the Lake Urmia region

Soil salinization stands as a prominent global environmental challenge, necessitating enhanced assessment methodologies. This study is dedicated to refining soil salinity assessment in the Lake Urmia region of Iran, utilizing multi-year data spanning from 2015 to 2018. To achieve this objective, soil salinity was measured at 915 sampling points during the 2015–2018 timeframe. Simultaneously, remote sensing data were derived from surface reflectance data over the same study period. Four distinct scenarios were considered such as a newly developed spectral index (Scenario I), the newly developed index combined with other salt-based spectral indices from the literature (Scenario II), indirect spectral indices based on vegetation and soil characteristics (Scenario III), and the amalgamation of both direct and indirect spectral indices (Scenario IV). Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) were employed to assess soil salinity. The measured data divided to 75% of the data as the calibration dataset, while the remaining 25% constituted the validation dataset. The findings revealed a correlation between soil salinity and spectral indices from the literature, with a range of -0.53 to 0.51, while the newly developed spectral index exhibited a stronger correlation (r = 0.59). Furthermore, RF yielded superior results when using the newly developed spectral index (Scenario I). Overall, SVM emerged as the most effective model (ME = -9.678, R2 = 0.751, and RPIQ = 1.78) when integrating direct and indirect spectral indices (Scenario IV). This study demonstrates the efficacy of combining machine learning techniques with a blend of newly developed and existing spectral indices from the literature for the monitoring of soil salinity, particularly in arid and semi-arid regions.

Development of a Background Filtering Algorithm to Improve the Accuracy of Determining Underground Cavities Using Multi-Channel Ground-Penetrating Radar and Deep Learning

In the process of using multi-channel ground-penetrating radar (GPR) for underground cavity exploration, the acquired 3D data include reflection data from underground cavities or various underground objects (structures). Reflection data from unspecified structures can interfere with the identification process of underground cavities. This study aims to identify underground cavities using a C-GAN model with an applied ResBlock technique. This deep learning model demonstrates excellent performance in the image domain and can automatically classify the presence of cavities by analyzing 3D GPR data, including reflection waveforms (A-scan), cross-sectional views (B-scan), and plan views (C-scan) measured from the ground under roads. To maximize the performance of the C-GAN model, a background filtering algorithm (BFA) was developed and applied to enhance the visibility and clarity of underground cavities. To verify the performance of the developed BFA, 3D data collected from roads in Seoul, Republic of Korea, using 3D GPR equipment were transformed, and the C-GAN model was applied. As a result, it was confirmed that the recall, an indicator of cavity prediction, improved by approximately 1.15 times compared to when the BFA was not applied. This signifies the verification of the effectiveness of the BFA. This study developed a special algorithm to distinguish underground cavities. This means that in the future, not only the advancement of separate equipment and systems but also the development of specific algorithms can contribute to the cavity exploration process.

Novel method to determine bedrock from a single sensor based seismic reflection signal using Haar wavelet transform

The determination of bedrock depth is crucial across various earth sciences and related fields. Geophysical techniques, notably the continuous wavelet transform, are increasingly employed to map subsurface bedrock structures. This method allows the transformation of 1D time domain seismic reflection signals into a 2D image, providing time–frequency representations for feature extraction. Leveraging the advantages of wavelet theory, particularly the Haar wavelet known for its simplicity and compact support, this study utilizes continuous Haar wavelet local maxima lines to enhance bedrock analysis. Seismic reflection data is acquired using an extremely sensitive geophone as a receiver along with a high dynamic range data logger. The Common Midpoint (CMP) approach, coupled with optimized offset distances, is utilized to ensure robust data quality and signal strength. Simple sources, such as a sledgehammer, generate sound waves for data collection prior to borehole drilling, correlating acquired data with lithogeological information. This research emphasizes the significance of adjusting parameters like sampling frequency, choice of wavelet, and optimal offset distances. The methodology employed, involving a single geophone, a one-channel data recorder, and a basic sledgehammer as a sound source, offers an inexpensive, straightforward, and non-invasive approach. The study demonstrates the effectiveness of Haar wavelet local maxima lines in accurately determining bedrock depth, producing results closely aligned with both predicted and actual depths.

Bio-Optical Properties and Ocean Colour Satellite Retrieval along the Coastal Waters of the Western Iberian Coast (WIC)

Essential Climate Variables (ECVs) like ocean colour provide crucial information on the Optically Active Constituents (OACs) of seawater, such as phytoplankton, non-algal particles, and coloured dissolved organic matter (CDOM). The challenge in estimating these constituents through remote sensing is in accurately distinguishing and quantifying optical and biogeochemical properties, e.g., absorption coefficients and the concentration of chlorophyll a (Chla), especially in complex waters. This study evaluated the temporal and spatial variability of bio-optical properties in the coastal waters of the Western Iberian Coast (WIC), contributing to the assessment of satellite retrievals. In situ data from three oceanographic cruises conducted in 2019–2020 across different seasons were analyzed. Field-measured biogenic light absorption coefficients were compared to satellite estimates from Ocean-Colour Climate Change Initiative (OC-CCI) reflectance data using semi-analytical approaches (QAA, GSM, GIOP). Key findings indicate substantial variability in bio-optical properties across different seasons and regions. New bio-optical coefficients improved satellite data retrieval, reducing uncertainties and providing more reliable phytoplankton absorption estimates. These results highlight the need for region-specific algorithms to accurately capture the unique optical characteristics of coastal waters. Improved comprehension of bio-optical variability and retrieval techniques offers valuable insights for future research and coastal environment monitoring using satellite ocean colour data.

Assessing Remote Sensing-Based Maize Crop Biophysical Characteristics and Evapotranspiration Estimations

The rapid growth in population, climate variability, and decreasing water resources necessitate innovative agricultural practices to ensure food security and resource conservation. This study investigates the effectiveness of various multispectral imagery from remote sensing (RS) platforms, Unmanned Aerial Systems (UAS), PlanetDove microsatellites, Sentinel-2, Landsat 8/9, and proximal MSR-5 in assessing crop biophysical characteristics (CBPCs) and actual crop evapotranspiration (ETa) for maize fields in northeastern Colorado. The research aims to evaluate the accuracy of vegetation indices (VIs) derived from these platforms in estimating key CBPCs, including leaf area index (LAI), crop height (Hc), and fractional vegetation cover (Fc), as well as ETa. Field experiments were conducted during 2022 at the USDA-ARS Limited Irrigation Research Farm in Greeley, Colorado, U.S.A., using different irrigation strategies. Surface reflectance data collected using a handled sensor and observed LAI, Hc, and Fc values, served as ground truth for validating RS estimates. The study applied various statistical analyses to compare the performance of different RS platforms and models. Results indicate that higher-resolution platforms, particularly UAS, provided higher accuracy in estimating VIs and CBPCs than satellite platforms. The study also highlights the influence of environmental conditions on the accuracy of RS models, with locally calibrated models outperforming those developed in dissimilar conditions. The findings underscore the potential of advanced RS technologies in enhancing precision agriculture practices and optimizing water resource management.

Carbon Flux Models in a Zonally Dry Tropical Forest Area (Caatinga)

Studies on energy exchange in ecosystems are critical for understanding carbon flows amid different vegetation patterns. In semi-arid areas, comprehending the variability in carbon absorption is essential for quantifying and anticipating the impacts of changes in the caatinga ecosystem. This study aims to calibrate and evaluate models for Gross Primary Production (GPP), Net Ecosystem Exchange (NEE), and Ecosystem Respiration (Reco) in the caatinga biome. NEE measurements were obtained and calculated at 30-minute intervals using EddyPro 3.6 software, from raw data measured at 10 Hz. GPP was estimated by partitioning NEE and Reco, all measured in micromoles of CO2 per square meter per second (μmol CO2m⁻²s⁻¹) using the eddy covariance (EC) tower, installed in a legal reserve at Embrapa Semiárido, in Petrolina, Pernambuco, within a section of the caatinga canopy. Following the measurement of carbon fluxes and ecosystem respiration, field measurements were conducted using a portableFieldSpec HandHeld spectroradiometer to obtain the reflectance of the canopy around the turbulent eddy covariance tower. These measurements were carried out in 2015 in a preserved caatinga area. Multiple linear regression models weredeveloped to estimate carbon fluxes from orbital images, enabling accurate estimation of GPP, NEE, and Reco, using the MODIS/Terra Daily Surface Reflectance product (MOD09GA). The primary results demonstrate the effectiveness of the developed models, particularly the GPP model, which exhibited the best statistical indices (R = 0.97; R² = 0.95; Root Mean Square Error = 0.20). Observations from the EC tower indicated that the models developed to estimate NEE, GPP, and Reco, using visible and near-infrared reflectance data, accurately represented the dry period and effectively captured the phenological aspects of the caatinga ecosystem.

Comparative study on structural, morphological, and optical properties of MS/Fe3O4 nanocomposites and M-doped Fe3O4 nanopowders (M = Mn, Zn)

In the present work, the un-doped, M-doped magnetite (Fe3O4); (M = Mn, Zn), and MS/Fe3O4 composite nanopowders with a cubic spinel-type structure and average crystallite size range from 8.30 to 12.33 nm were synthesized by co-precipitation method. The FESEM images revealed the shape of particles are spherical with a grain size in the range of 33.44–49.77 nm. Through the analysis of reflectance data using Tauc’s model, the direct band gap energies of 2.98 eV, 2.93 eV, 3.01 eV, 2.85 eV, and 2.95 eV were determined for Un-doped Fe3O4, Mn-doped Fe3O4, Zn-doped Fe3O4, MnS/Fe3O4 composite, and ZnS/Fe3O4 composite NPs respectively. The parameters such as extinction coefficient and refractive index of the nanoparticles were computed by the Kramers–Kronig (K–K) method. Non-linear optical (NLO) parameters were computed from DRS data using the Wemple–Di-Domenico (WDD) model. The calculated third-order NLO susceptibility χ(3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\chi }^{(3)}$$\\end{document} and also electrical susceptibility χe\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\chi }_{\\left(e\\right)}$$\\end{document} represented the maximum value for MnS/Fe3O4 composite NPs compared to the other samples. Considering the advanced optical parameters of MS/Fe3O4 composite samples, these particles can be suitable candidates for non-linear optical applications.

Seismic reflection data driven tectono-sedimentary analysis and hydrocarbon field characterization of the Slovak part of Transcarpathian Basin

Seismic reflection data driven tectono-sedimentary analysis and hydrocarbon field characterization of the Slovak part of Transcarpathian Basin

Hyperspectral Imaging and Machine Learning: A Promising Tool for the Early Detection of Tetranychus urticae Koch Infestation in Cotton

Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (>30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods.

REWARE: a seismic processing algorithm to retrieve geological information from the water column

SUMMARY When interpreting marine very high-resolution (VHR) single-channel seismic reflection data, the signal in the water column is generally considered as noise and is often eliminated by a water-mute application to focus on geological information under the seafloor. Alternatively, the signal in the water column can be used to study ocean currents or gas/fluid emissions. To provide images of the sedimentary formations and tectonic structures beneath the seafloor in shallow water regions, such as continental shelves and lakes, marine seismic reflection profiles are often acquired using a single-channel streamer and sparker-type source, providing VHR data, with limited penetration depth. To exploit the full potential of these single-channel data, we propose a simple algorithm, called REWARE (Recovery of Water-column Acoustic Reflectors). This algorithm allows to extract further geological information from the water-column data using open-source codes (Seismic Un*x), adding the coherent signal from the previous shots, recorded in the water column, to the previous traces. The record length becomes longer while maintaining a very high trace-to-trace consistency. To demonstrate its efficiency, we present two examples of the REWARE processing in two different geological contexts: the East Sardinia shelf (Italy) and the North Evia Gulf (Greece). This method provides deeper images than with original data for seismic data acquired across steep slopes, such as canyons or continental shelf breaks. Thus, depending on the seafloor geometry and subseafloor structures, it is possible to image or map sediment layers and tectonic structures at depth, keeping a very high structural resolution.

Envelope normalized reflection waveform inversion

AbstractThe reflection waveform inversion has the capability to reconstruct the background velocity model using only the reflection data by employing a migration/demigration process. Utilizing the waveform discrepancy to update the background velocity model, the conventional reflection waveform inversion method heavily relies on the true‐amplitude migration/demigration technique to reproduce the primary amplitude information from the observed reflections. We can reproduce the amplitude of observed reflections by performing least‐squares reverse time migration to estimate the reflectivity in each iteration. However, this strategy is quite time‐consuming. To avoid the need for the true‐amplitude migration/demigration or least‐squares reverse time migration, we develop an amplitude‐independent reflection waveform inversion method that uses an envelope‐normalized objective function. The envelope‐normalized waveform difference can extract the phase residuals accurately as a function of time. Compared with the global energy–normalized misfit, our proposed envelope‐normalized objective function is essentially a phase‐matched measurement. At the same time, due to the amplitude independence of our proposed objective function, the subsequent weak reflections contribute with a similar weight to the total value of the misfit as the strong early reflections do. This makes it possible to recover the deep subsurface velocity. Synthetic data of the Sigsbee model and marine streamer field data applications validate that our amplitude‐independent reflection waveform inversion method can further improve the resolution and accuracy by aligning the reflection events of synthetic and observed data phase to phase without the need to perform true‐amplitude migration/demigration or least‐squares reverse time migration as in conventional reflection waveform inversion.

Geomorphology and sediment mobility on sand banks: A study of Dogfish Bank, Hecate Strait, Northeast Pacific Ocean

AbstractMovement of sediment along shallow continental shelves is a natural process with wide‐ranging environmental and economic implications, making it of high importance to marine spatial planning efforts in the offshore. Development of marine renewable energy, for instance, requires detailed understanding of the morphodynamics of mobile bedforms to select foundation types and ensure safe installation of infrastructure in shallow shelf environments. This study evaluates geomorphology and sediment mobility of Dogfish Bank (< 20 mbsl) in the Hecate Strait offshore British Columbia, Canada, using hydroacoustic and airborne bathymetric data combined with seismic profiles and grain‐size information. These data reveal current‐swept features ranging from sediment‐depleted lag to sediment‐abundant sand ridges and dunes, with sand ribbons and furrows in‐between. Seismic reflection data show up to 15 m of surficial sand concentrated beneath north‐aligned sand ridges that dominate the bathymetry of northwest Hecate Strait. Sand ribbons (typically understood sediment‐limited features in shallow marine environments) are notably maintained over seabed with comparable sand thickness to adjacent dunes (i.e. sediment‐abundant features), suggesting local spatial variability in hydrodynamics and sediment characteristics (principally grain size) influence expression of mobile bedforms. Repeat mapping between 2008 and 2019 shows dunes and ribbons both migrate northwards, with largest seafloor changes along northeast‐facing lee sides of dunes, matching closely with published models of sediment mobility which suggest northward bedform migration is largely driven by storms. Median total migration distance is 164 m (northward) for dunes (time‐averaged rate of 14.9 m/year). Sand ribbons show less migration (median northward distance of 73 m) and migrate in a depth‐dependent manner. Because sand ribbons are typically flow‐parallel features, their lateral migration likely results from varying current directions and flow acceleration over shallower seabed. Sand ribbon migration should therefore a consideration in studies examining seabed change, particularly when they are formed over unconsolidated sediment.

Sediment flux variation as a record of climate change in the Late Quaternary deep‐water active Corinth Rift, Greece

AbstractThe value of deep‐water sedimentary successions as reliable records of environmental change has been questioned due to their long response times and sediment pathways leading to complex responses to climatic change and tectonic signals over differing timescales. We studied the Gulf of Corinth, Greece, to test the value of deep‐water stratigraphic successions as records of external controls on sediment flux in a setting with short response times and transport distances. The confinement of the rift basin allows for a near‐complete accounting of clastic sediment volumes. The recent acquisition of high‐resolution seismic reflection data, utilisation of International Ocean Discovery Programme Expedition 381 cores and a robust chronological framework, enable evaluation of the stratigraphy at a high temporal resolution. Combining borehole and high‐resolution seismic reflection data, distinct seismic units can be correlated to multiple paleoenvironmental proxies, permitting quantification of sediment flux variation across successive glacial–interglacial cycles at ca. 10 kyr temporal resolution. Trends in average sediment flux since ca. 242 ka show ca. 2–9 times greater sediment flux in cooler glacials compared to warmer interglacial conditions. The Holocene is an exception to low sediment flux for the interglacials, with ca. 5 times higher rates than previous interglacials. The short and steep configuration of the Sythas canyon and its fan at the base of an active submarine normal fault results in deep‐sea deposition at all sea‐level stands. In contrast, adjacent canyon systems shut down during warm intervals. When combined with palynology, results show that periods of distinct vegetation re‐organisation correlate to sediment flux changes. The temporal correlation of sediment flux to palynology in the Gulf of Corinth over the last ca. 242 kyr is evidence that variability of sediment supply is largely governed by climate‐related changes in hinterland catchments, with sea‐level and tectonics being second‐order controls on sediment flux variability.

Tectonic Quiescence in Actively Extending Back‐Arc Regions

AbstractWe analyzed refraction and reflection seismic data covering the Central Lau Spreading Center (CLSC) and the Tonga volcanic arc at 18°S in the Pacific Ocean to investigate tectonic inactivity in actively extending back‐arc systems. Our P‐wave travel time tomography shows a characteristic 6–8 km thick back‐arc crust beneath and around the CLSC and a ∼13 km thick arc‐crust at the eastern end of our profile, which corresponds to the Tonga volcanic arc. Lower crustal velocities increase to 7.5 km/s toward the volcanic arc, marking the transition from back‐arc to arc crust. These high P‐wave velocities can be explained by a high pyroxene content in the lower crust originating from depleted melts. Our seismic reflection data show steep normal faults below the CLSC and volcanic structures closer to the volcanic arc, without a tectonically inactive zone (also knows as the “diffuse plate boundary”) in between. Based on our results, we suggest that the Niuafo'ou and Tonga microplates should be treated as one tectonic plate with local zones of intra‐plate deformation that are separated by zones that are tectonically quiet.

SPECTROKINETIC INVESTIGATION OF THE PHOTOCHROMIC SYSTEM UNDER CONTINUOUS UV IRRADIANCE USING REFLECTANCE VS. TIME CURVES

The photochromic properties of the dye 5-chloro-1,3,3-trimethylspiro[indoline-2,3'-[3H] naphtho[2,1-b] [1,4]- oxazine] were investigated by exposing it to continuous monochromatic irradiation using 360 nm ultraviolet irradiation using photochromic prints at 293.15  2 K; it undergoes a photocoloration and photodecoloration state, thus following the dynamics of a photochromic cycle of growth and decay phase, respectively. Even if the isolated photo isomeric form of the photochromic dye structure is unavailable, one can quantitatively analyse the photo-induced kinetics of photochromic systems using the raw reflectance data. The quantum colour yield is represented by colour span values [K/S] obtained by converting reflectance data to Kubelka- Munk values. The dynamics of the photoreaction in a photochromic system can be examined by analysing the constants for the rate of colouration and decolouration caused by light. By employing first-order kinetics, the dependent dominant wavelength [max] can be determined by fitting the raw data obtained as a change in reflectance values from the FOTOCHROM3 spectrophotometer. The relationship between the colour span values and the concentration of the photochromic dye employed in the photochromic prints is linear.

Peningkatan Hasil Belajar Siswa Kelas 2 pada Mata Pelajaran PKN Materi Pancasila dengan Metode Demonstrasi

The aim of this research is to determine the improvement in student learning outcomes in PKN learning Pancasila material using the demonstration method for class II at SD DAQU Internasional Tangerang. This research is classroom action research (PTK). The subjects of this research were 25 class II students. Data collection techniques were carried out using observation, test and documentation methods. The PTK model uses 2 cycles, each cycle consisting of 4 stages, namely, 1) Action Plan 2) Implementation 3) Observation 4) Reflection. Data analysis techniques use student data, namely planning data, implementation data, observation data and reflection data. The research results showed that the formative test results obtained by students in the pre-cycle of 25 students only 14 students (56%) completed. In Cycle I the results were 18 students (72%) who completed. In cycle II, students' formative test results increased to 23 students (92%) who completed it. Meanwhile, 2 students who got a score of less than 70 were declared incomplete and needed to carry out follow-up action in the form of giving assignments. So that the use of the demonstration method can improve student learning outcomes in civics subjects with Pancasila material using the demonstration method for class II elementary school.

Time Dependent Spectral Ratio Technique for Micro-seismic Exploration in the Niger Delta Region of Nigeria

Abstract: Since the discovery of the hydrocarbon micro-tremors, there have been numerous efforts to understand the causes of these microseisms related to oil and gas. A spectral ratio (SR) time-dependent equation was derived based on geological principles. The equation showed tremendous success in analyzing multi-layered geological terrain and estimating the spectral ratio in the Niger Delta region of Nigeria. This study seeks to develop a reliable model using standard seismic reflection data to analyze microseismic datasets from hydrocarbon reservoirs. Type A-SR acts over a longer duration and can be used to determine hydrocarbon reservoirs. Type B-SR acts in a short time with maximum impact. It can be used to determine gas condensate flow in the hydrocarbon reservoir. It is recommended that this technique be applied in real time to ascertain its accuracy. Keywords: Niger Delta, Microseismic, Amplitude, Special ratio, Geology.

The Derim Derim Dolerite, greater McArthur Basin, Australia: Using subsurface data to characterise a mesoproterozoic magma plumbing system

The application of high-resolution seismic reflection data has spurred major advances in knowledge of the emplacement of sub-volcanic mafic magma plumbing systems in sedimentary basins, highlighting the importance of interconnected sheet intrusions in facilitating lateral magma transport, the links between host rock mechanical properties and emplacement processes, and providing insights into how intrusive activity in basins impacts their resource potential. However, most studies have focused on Mesozoic-Cenozoic mafic magma plumbing systems situated along offshore rifted margins characterised by extensive subsurface datasets. The extensive Mesoproterozoic (c. 1300 Ma) Derim Derim Dolerite, which intrude the greater McArthur Basin in northern Australia, provide a unique opportunity to study the emplacement of a Proterozoic magma plumbing system due to its penetration by numerous hydrocarbon and mineral drillholes, in addition to seismic reflection coverage. Understanding the emplacement of this system is important because of its interactions with prospective unconventional shale reservoirs in the Velkerri and Kyalla Formations, which represent one of the world's oldest known petroleum systems. This paper focuses on characterising the intrusion emplacement and magma plumbing system using an array of subsurface data, to constrain the distribution, morphology, and emplacement mechanisms of the Derim Derim Dolerite.The morphology of the large, strata-concordant intrusions encountered at shallow present-day depths (<2 km) in the greater McArthur Basin is indicative of greater original emplacement depths of >3–4 km, suggesting a significant extent of uplift and erosion. Density-derived porosity values for the Velkerri and Kyalla Formations are anomalously low for their present-day depths, corroborating a greater palaeo-depth at time of emplacement. The extent of alteration of the host rock surrounding the Derim Derim Dolerite is highly variable, with a small number of occurrences of graphitisation of the organic matter adjacent to intrusions. However, this appears to be highly localised and the detrimental impact of the Derim Derim Dolerite on potential reservoir and/or source rocks appears generally minimal.

River Ice Mapping from Landsat-8 OLI Top of Atmosphere Reflectance Data by Addressing Atmospheric Influences with Random Forest: A Case Study on the Han River in South Korea

River Ice Mapping from Landsat-8 OLI Top of Atmosphere Reflectance Data by Addressing Atmospheric Influences with Random Forest: A Case Study on the Han River in South Korea