Diagnostic and therapeutic intelligent probe: Photoacoustic/ fluorescent dual-modality of thiols detection in tumor and ROS-potentiated cancer photodynamic therapy.

New technique for visualizing individual components in model bi-disperse liquid–solid mixed batches with Electrical Resistance Tomography (ERT)

Long-term monitoring of rock mass using electrical resistivity tomography and high-frequency active seismic methods at the Bukov Underground Research Facility

Sustainable agriculture in tropical regions relies on precise understanding of soil water dynamics under varying topographic, textural, and climatic conditions. This study integrates Electrical Resistivity Tomography (ERT) and Electromagnetic Induction (EMI) to characterize soil moisture distribution and subsurface textural heterogeneity across three contrasting agricultural landscapes in West Java, Indonesia—Subang (coastal lowlands), Bandung (uplands), and Sumedang (terraced highlands). ERT provided high-resolution vertical profiles to 5 m depth, revealing resistivity ranges that correspond to lithological and hydrological properties. In Subang, low resistivity (1.7–30 Ω·m) showed high-salinity clay loam with a shallow water table. Conversely, high resistivity in Bandung (70–300 Ω·m) reflected well-drained sandy layers with limited retention. Intermediate values in Sumedang suggested deep moisture storage within clay-rich layers beneath drier topsoil, influenced by terrace morphology. EMI mapping complemented ERT by capturing lateral resistivity variations at fixed depths, offering spatial continuity across the surveyed areas. The combined approach revealed that slope gradient, soil texture, and drainage conditions jointly govern water retention and availability. The integration of ERT and EMI provides complementary information on vertical and lateral variability of soil moisture distribution. Field observations and soil profile analysis confirm the reliability of the geophysical interpretation. These findings demonstrate that integrated geophysical imaging provides an effective non-invasive tool for mapping soil moisture variability and supporting precision agriculture strategies in tropical agricultural environments.

Although conventional imaging techniques excel at capturing structural changes, they frequently overlook functional degradations that lack morphological signatures. Here, we demonstrate machine-learning-assisted electrical resistance tomography (ML-ERT) as a robust modality for the rapid, nondestructive localization of "subvisual" defects in porous laser-induced graphene (LIG). By employing masked O2 plasma irradiation, we introduced localized defects that exhibit a dramatic resistance surge up to 4 orders of magnitude while remaining indistinguishable under visual and electron microscopy. Our ML-ERT framework, powered by a one-dimensional convolutional neural network inverse solver, successfully pinpointed these hidden failures once the resistance contrast reached a threshold of R/R 0 ≥ 6.71. Furthermore, 3D finite element analysis revealed that the tomographic contrast is driven by an effective conductive volume loss exceeding 30%, identifying the degradation of internal conductive pathways as the primary mechanism. These results establish ML-ERT as a high-sensitivity diagnostic tool capable of visualizing electrically critical but optically invisible failures, providing a definitive solution for the quality control of large-area carbon electronics.

Geophysical-based assessment of hydrological sustenance in inhabited relict rock glaciers of the semi-arid Western Himalaya, India.

Polymer injection for soil and groundwater remediation: Mechanisms, applications, and lessons learned.

This paper proposes a new method for mud-effect correction and inversion of approximate apparent resistivity for oil-based mud microresistivity imaging logging. To correct the interference of the mud layer with electrode measurement signals, the Open-Short Calibration method is combined with a three-layer impedance model, and the open- and short-circuit states of the tool are simulated by the finite element method. By treating the electrode and the pad standoff as an integrated system, the method independently computes and explicitly removes the mud impedance, thereby extracting the formation impedance signal. To rapidly estimate formation parameters, a resistivity-consistency iterative inversion method is further proposed. After the formation impedance is extracted, the consistency residual among multifrequency approximate apparent resistivities is taken as the objective function, and nonlinear optimization is used to efficiently solve for formation resistivity, relative permittivity, and pad standoff. Numerical-model validation and field-well application results show that the proposed method can separate mud-signal interference over the full measurement range and enable quantitative characterization of formation apparent resistivity and related parameters.

Complex landslides contain internally heterogeneous zones that may respond differently to reactivation, complicating tree-ring based chronologies of slope activity. This study combines dendrogeomorphological analysis with electrical resistivity tomography (ERT) to test whether geophysics-based zonation could explain the spatial variability of tree-ring disturbances within a large complex landslide. ERT profiles and geomorphological mapping delineated three mechanically distinct zones: a downslope shallow-landslide sector ( S zone ), a moisture-rich gap infilled by weakly consolidated material ( G zone ), and an adjacent compact block with tension cracks ( B zone ). In total, 200 Norway spruce ( Picea abies (L.) H. Karst) were analysed for reaction wood (RW) and abrupt growth suppression (GS). RW intensity was quantified for each affected ring and GS classified by relative ring-width reduction. RW clearly dominates across the landslide. The S zone shows the highest stem inclinations and RW intensities, indicating enhanced shallow deformation, whereas RW duration is similar among zones. GS occurs everywhere but is proportionally most frequent in the B zone . Correlation analyses show that in the S and G zones , RW intensity and duration relate significantly to stem inclination, while no significant relationships appear in the B zone . These results demonstrate that internal landslide heterogeneity, as delineate by ERT, is reflected in tree-ring responses. Geophysics-based zonation offers an effective framework for interpreting growth disturbances and improves dendrogeomorphic reconstructions of complex slope movements. • Geophysics-based zonation reveals three contrasting landslide zones. • Combining geophysics and tree rings reveals internal landslide variability. • Reaction wood dominates in all three zones. • Growth suppression is dependent on the tree position.

Electrical resistivity tomography (ERT) is a widely used technique for imaging subsurface resistivity distributions, but conventional data acquisition strategies face trade-offs between resolution, efficiency, and data quality. Comprehensive datasets that maximize subsurface information are impractical to measure directly, motivating the development of ERT data reconstruction approaches that can generate any four-electrode datasets from a limited subset of measurements. In this study, two reconstruction methods: the pseudo–Pole–Pole (pdPP) approach and a modified pseudo–Pole–Dipole (pdPD) method, were systematically evaluated. First, a linear error model is applied to quantify noise propagation in the reconstructed datasets under both absolute and relative noise conditions. We then use synthetic modelling to test the imaging performance of the reconstructed datasets. Finally, field experiments at two test sites in Sweden are analysed to validate the numerical findings and to assess practical performance in real environments. Results show that, under relative errors, large readings of base measurements lead to high errors in reconstructed data, with pdPP generally yielding lower reconstruction errors than pdPD. Under absolute errors, the number of base measurements governs error accumulation, and both methods perform similarly. Imaging results show that plausible inversion results can be achieved using reconstructed datasets with estimated errors as data weighting for inversion. Field experiments validated the numerical findings and further demonstrated that pdPP method is preferred for ERT data reconstruction given that it provides more data with small reconstruction error and is better suited for efficient data acquisition. • Systematic evaluation of ERT data reconstruction using pdPP and pdPD methods. • Linear error model analysis of noise propagation under absolute and relative errors. • Synthetic and field validation confirming reliability of reconstructed datasets.

Remote high-latitude regions remain among the least explored on Earth, yet they hold crucial records of past and ongoing climate dynamics. The Tierra del Fuego archipelago, at the southern margin of the Andes, represents a key area to investigate the extent and characteristics of periglacial and potential permafrost conditions in the southern hemisphere. This study presents the first integrated geomorphological and geophysical analysis of periglacial environments in a remote sector of the Valdivieso Sierra, westards from Darwin Cordillera, in the Yendegaia National Park. Field observations identified diverse landforms—such as patterned ground, creeping slopes, cryogenic polygons, and hummocky thúfur -like structures—indicative of active periglacial processes. Complementary geophysical investigations conducted with Electrical Resistivity Tomography and Seismic Refraction Tomography revealed subsurface signatures consistent with partially frozen ground above 750 m asl. These results suggest the presence of mountain permafrost near its southernmost distributional limit in the Southern Cone and emphasize the importance of periglacial processes in shaping the regional landscape. The findings have broader implications for paleoclimate reconstruction, ecosystem dynamics, and infrastructure planning in the context of accelerating climate change. This work provides essential baseline data from previously unstudied areas and highlights the effectiveness of integrating geomorphological mapping with non-invasive geophysical techniques in data-scarce environments. • First integrated periglacial study in remote southern Tierra del Fuego Island • Active processes and frozen ground detected above ~750 m a.s.l. • Geophysical and petrophysical data suggest mountain permafrost presence • Provides baseline for future climate and environmental monitoring

Deep-learning full-waveform inversion of snowpack GPR: joint permittivity–resistivity imaging for snow–soil hydrological mapping

The efficient developmentof deep coalbed methane (CBM) is constrainedby unclear hydraulic fracture propagation mechanisms and the limitedresolution of existing monitoring techniques. Conventional methodssuch as microseismic monitoring and static resistivity imaging areunable to provide real-time, high-resolution characterization of dynamicfracture growth and fluid migration, thereby impeding accurate evaluationof stimulation effectiveness. To address these limitations, this studyintroduces a novel dynamic fracture monitoring technique based ona time-varying electric field method. By establishing a coupled electricfield–fracturing fluid flow model and deploying a high-densitydownhole electrode array integrated with real-time data processing,the technique enables millisecond-level signal acquisition and dynamicinversion of fracture geometry. Field validation demonstrates thatthe method dynamically tracks fracture propagation with a spatialaccuracy of 0.5 m and temporal resolution better than 5 min. The monitoringresults quantitatively delineate a three-stage fracture behaviorcharacterizedby nonuniform initiation, differential extension, and dynamic reorientationamong clusterswith the maximum single-cluster fracture lengthreaching 214.95 m. The interpreted fluid distribution and fractureevolution show strong consistency with independent high-frequencypressure-wave analysis of cluster-level fluid intake, confirming theaccuracy of the method. Furthermore, comparison with fracture inversionreveals the presence of stress-extended zones not penetrated by fracturingfluid, highlighting the discrepancy between the actual stimulatedvolume and the fluid-swept region. This reflects the pronounced heterogeneityand corresponding stress-propagation characteristics inherent to deepcoal reservoirs. The method offers a robust technical approach forevaluating stimulation effectiveness and optimizing fracturing designparameters in deep coalbed methane reservoirs, further contributingto the large-scale development and production breakthroughs in deepcoal seams.

The development of water-conducting fractures induced by fully mechanized top-coal caving mining of ultra-thick coal seams in the aeolian sand area of northern Shaanxi is characterized by significant nonlinearity and hysteresis, making water inrush disasters highly concealed and difficult to predict. To overcome the challenges of high-resistance shielding from dry surface sand and the extraction of weak deep signals, this study established a surface pseudo-3D time-lapse high-density electrical resistivity tomography (ERT) monitoring system at the 122105 working face of Caojiatan Coal Mine, utilizing the "artificial wet soil + deep-buried electrode" technique. Combined with anisotropy-constrained inversion and time-lapse ratio imaging technology, the dynamic process of mining-induced overburden failure was visualized and verified by in-situ mine hydrological data. The results indicate that: (1) The center of water inrush is not located at the mining advance line but exhibits a significant "spatiotemporal lag" characteristic, with a lag distance of approximately 110m, which aligns closely with the limit breaking span of the high-position key stratum. (2) The lagged water inrush channel presents a "funnel-shaped" structure (large at the top and small at the bottom) in 3D space, revealing that its formation results from the fluid-solid coupling connection between the upper separation "catchment basin" and the lower shear "diversion pipe" at the moment of key stratum breaking. (3) The moment of channel connection captured by resistivity imaging (T2) is perfectly synchronized with the onset of the mine water inflow surge, and the water inflow rapidly reached its peak (310m³/h) within the subsequent 24-48h, verifying the significant consistency between the flow field and the geoelectric field response. (4) The water-conducting channel in the goaf possesses self-healing properties; as the lag distance exceeds 150m, the bottom of the channel closes preferentially due to compaction of the caving zone. Based on these findings, a prevention concept of shifting the monitoring field of view to 0-150m behind the working face is proposed, along with a stereoscopic control strategy of "high-level interception in the funnel zone and low-level drainage in the compacted zone."

In impedance imaging, the incompatibility and nonlinearity of the inverse problem lead to problems such as blurred boundaries and severe artifacts in the reconstructed images, making it difficult to meet the requirements for precise identification of multi-layer tissue structures in the legs. To this end, this paper proposes a post-processing algorithm for leg EIT that integrates the boundary attention mechanism, with a Wasserstein generative adversarial network as the training framework, cyclic residual U-Net as the generator, and the boundary attention module embedded in the RecurrentBlock. This leads to adaptive enhancement of the ability to extract organizational boundary features through a three-path fusion of spatial attention, channel attention, and learnable Laplacian edge enhancement. A leg anatomy prior constraint loss function was designed, integrating six constraints—pixel loss, edge loss, hierarchical tissue constraint, total variation regularization, structural similarity loss, and histogram matching—to guide the reconstruction results to conform to the multi-layered tissue structure features of the leg. A simulation dataset of leg sections containing multiple tissues such as skin, fat, muscle, bone, blood vessels, and nerves was constructed, and the pre-reconstructed images were obtained using the hybrid total variation regularization algorithm as the network input. The simulation results show that, under noise-free and different signal-to-noise ratio conditions, the proposed BAM-R2UNet algorithm achieves the best performance in RMSE, SSIM and PSNR metrics compared with HTV, DnCNN and standard U-Net algorithms, can remove artifacts, accurately restore the boundary and conductivity distribution of leg tissues, and has stronger anti-noise robustness.

Active fault reactivation poses significant hazards, and understanding their near-surface structure is crucial for mitigating seismic risk. Along the Balakot-Bagh fault (BBF), the source of the 2005 Mw 7.6 Kashmir earthquake, geomorphic evidence is gradually eroded and sedimented. Traditional Electrical Resistivity Tomography (ERT) often produces smoothly varying tomograms that obscure sharp structural boundaries. This study introduces an integrated interpretation framework that combines geological mapping, high-resolution ERT imaging, and machine learning (ML) k-means clustering to improve characterization of the BBF's shallow deformation zone at Sar Pain (S1) and Naushahra (S2), Pakistan. Geological surveys at S1 document numerous NW-SE-trending coseismic rupture strands with vertical displacements of 0.1-3m, defining an actively deforming damage zone, while at S2, no surface rupture is preserved due to thick alluvial cover. Inverted ERT models at both sites reveal low-resistivity anomalies associated with fractured, water-saturated materials and fault gouge; however, conventional inversions smooth sharp resistivity gradients, limiting structural interpretation. Applying k-means clustering as a post-inversion segmentation tool transforms continuous resistivity fields into discrete lithological and structural domains. The Elbow method is used to determine the optimal number of clusters to improve interpretability. The clustered models sharpen structural discontinuities, delineate fault cores and subsidiary strands at S1, and reveal concealed deformation at S2 where surface evidence is absent. This integrated interpretation framework significantly enhances the resolution and interpretability of near-surface fault architecture within a crustal-scale thrust system. The approach is particularly effective for imaging buried fault segments and has important implications for seismic hazard assessment and land-use planning.

Time-lapse ERT monitoring of the MPE-induced cone of depression at a low-permeability NAPL site: A field-based quantitative assessment of array-dependent geometric fidelity.

Electrical resistivity tomography for geohazard assessment in West Lombok’s alluvial plain

A BiTCN-based reliable data correction method against electrode detachment in electrical resistance tomography

Geological and hydrological study of a deep-seated toppling using time-lapse electrical resistivity tomography