Acoustic Imaging Research Articles

A comparison of the sampling effectiveness of acoustic recorder, camera trap and point count methods in sampling nocturnal birds in Afrotropical landscapes.

Conservation decisions for bird diversity in the Afrotropics are often based on ecological studies utilizing diurnal bird species likely owing to difficulties associated with sampling nocturnal birds. It is therefore important to compare the sampling effectiveness of some of the available techniques that can be used in nocturnal bird surveys to guide future long-term survey efforts. Thus, we compared the sampling effectiveness of point count, acoustic recorder and camera trap for estimating nocturnal bird species richness and also across habitat types. We surveyed 20 points that were spaced at least 500 m apart in November and December 2021 in the Amurum Forest Reserve and its surroundings in Jos-Nigeria. At each point, we used two camera traps, one at the ground and the other at 2.0 m. We also used one acoustic recorder as well as a 15-min point count during each survey at each point. We encountered 11 nocturnal bird species, primarily nightjars but also owls. While we did not encounter any species with the camera traps, all 11 species were recorded using the acoustic recorder. All species except for Ketupa lacteaus were recorded in point count. Eight species were recorded in the gallery, seven in rocky and nine in savannah. Species richness and estimation using the acoustic recorder and point count were similar across habitat types. We conclude that either point count or acoustic recorders are useful for nocturnal bird surveys in Afrotropical environments. However, the choice of methods should be based on the research questions as some questions may be better answered by a specific method or even a combination of both.

The deep abdominal ultrasound transducer (DAX) increases the success rate and diagnostic accuracy of shear wave elastography for liver fibrosis assessment in patients with obesity-A prospective biopsy-controlled study.

Obesity impacts the diagnostic accuracy of shear wave elastography (SWE). A deep abdominal ultrasound transducer (DAX) capable of point (pSWE) and two-dimensional (2D)-SWE has recently been introduced to address this issue. We performed a prospective study in a cohort of mostly patients with obesity undergoing liver biopsy with a high prevalence of metabolic dysfunction-associate steatotic liver disease (MASLD). Liver stiffness measurement (LSM) was measured using vibration-controlled transient elastography (VCTE), as well as pSWE and 2D SWE on the standard (5C1) and the DAX transducers. We included 129 patients with paired LSM and liver biopsy: median age 44.0 years, 82 (63.6%) women, median BMI: 43.2 kg/m2. Histologic fibrosis stages: F0: N = 55 (42.6%), F1: N = 14 (10.9%), F2: N = 50 (38.8%), F3: N = 2 (1.6%), F4: N = 8 (6.2%). VCTE-LSM failed (N = 13) or were unreliable (IQR/median ≤30% in ≥7.1 kPa, N = 14) in 20.9% of patients. The Pearson correlation of reliable VCTE-LSM with both pSWE and 2D SWE was strong (all >0.78). The diagnostic accuracy for all LSM techniques was poor for significant fibrosis (≥F2, AUC: 0.54-0.63); however, it was good to excellent for advanced fibrosis (≥F3, AUC: 0.87-0.99) and cirrhosis (F4, AUC: 0.86-1.00). In intention-to-diagnose analysis, pSWE on DAX was significantly superior to VCTE-LSM. pSWE- and 2D-SWE enable the non-invasive identification of advanced fibrosis and cirrhosis in patients with obese MASLD. The use of the DAX transducer for acoustic radiation force imaging (ARFI)-LSM avoids technical failures in an obese population and subsequently offers advantages over VCTE-LSM for the evaluation of fibrosis in an obese MASLD population at risk for fibrosis.

Using Acoustic Tomography to Model Wood Deterioration in Cedrelinga cateniformis Ducke in the Peruvian Amazon

Forest plantations can be established in order to restore degraded areas. Acoustic tomography, which is of increasing importance in forest management, was used in the present study to obtain information for managing plantations of Cedrelinga cateniformis Ducke in the Peruvian Amazon. The species is valuable in the timber sector of Peru, but the core wood tends to deteriorate and develop cavities. The main objective of the study was to model wood deterioration in Cedrelinga cateniformis Ducke using the data obtained through acoustic tomography. Eight plantations of varying ages were analyzed using acoustic tomography in order to obtain indicators of wood deterioration. Biometric, climatic, and edaphic data (explanatory variables) were also measured in each plantation. The indicator variables and explanatory variables were compared and evaluated using correlation and principal component analysis. Wood deterioration was modelled using stepwise regression. The indicator variables differed significantly between plantations and were mainly correlated with the biometric variables (age and diameter at breast height). The models explained 81% of the variability of pith rot. The percentage rotten area was minimal in young plantations (1%), and the opposite was observed in mature trees (21.5 to 25.6%). The study findings provide valuable information, enabling foresters to determine the optimal age and diameter for felling Cedrelinga cateniformis in plantations in the Peruvian Amazon.

Continual Monitoring of Respiratory Disorders to Enhance Therapy via Real-Time Lung Sound Imaging in Telemedicine

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference.

Compound Acoustic Radiation Force Impulse Imaging of Bovine Eye by Using Phase-Inverted Ultrasound Transducer.

In general, it is difficult to visualize internal ocular structure and detect a lesion such as a cataract or glaucoma using the current ultrasound brightness-mode (B-mode) imaging. This is because the internal structure of the eye is rich in moisture, resulting in a lack of contrast between tissues in the B-mode image, and the penetration depth is low due to the attenuation of the ultrasound wave. In this study, the entire internal ocular structure of a bovine eye was visualized in an ex vivo environment using the compound acoustic radiation force impulse (CARFI) imaging scheme based on the phase-inverted ultrasound transducer (PIUT). In the proposed method, the aperture of the PIUT is divided into four sections, and the PIUT is driven by the out-of-phase input signal capable of generating split-focusing at the same time. Subsequently, the compound imaging technique was employed to increase signal-to-noise ratio (SNR) and to reduce displacement error. The experimental results demonstrated that the proposed technique could provide an acoustic radiation force impulse (ARFI) image of the bovine eye with a broader depth-of-field (DOF) and about 80% increased SNR compared to the conventional ARFI image obtained using the in-phase input signal. Therefore, the proposed technique can be one of the useful techniques capable of providing the image of the entire ocular structure to diagnose various eye diseases.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Field Testing of an Acoustic Method for Locating Air Leakages in Building Envelopes

Maintaining the airtightness of building envelopes is critical to the energy efficiency of buildings, yet leak detection remains a significant challenge, particularly during building refurbishment. This study addresses the effectiveness of the acoustic beamforming measurement method in identifying leaks in building envelopes. For this reason, an in-field study employing the acoustic beamforming measurement method was conducted. The study involved testing over 30 rooms across three different multi-story office buildings of varying ages and heterogeneous envelope structures. Numerous leaks were located in the façades, which were subsequently visually confirmed or even verified with smoke sticks. The data, captured using an acoustic camera (a microphone ring array), revealed distinct spectra that indicate the method’s potential for further research. The basic functionality and the significant potential of this methodology for localizing leakages in large buildings were proven.

Assessment of borehole breakouts from acoustic image log and its geomechanical implications — A case study from Triassic-Ordovician interval of the Berkaoui Field, southeastern Algeria

In this study, we interpret a cumulative 600 m acoustic image log across the Triassic to Cambro-Ordovician interval in the Berkaoui oil field, Algeria. We interpret 40 distinct breakout zones that have a combined length of 210 m. These breakouts are aligned in the north-northeast–south-southwest direction, indicating a mean maximum horizontal stress (SHmax) azimuth of 110°N. The observed breakouts are ranked as “A-Quality” following the World Stress Map ranking guidelines. The angular width of each breakout has been inferred from the image log analysis and the same has been used to infer the SHmax gradient by stress polygon approach following the frictional faulting mechanism. The stress polygon across all the breakout intervals provides a practical Shmax range between 24.7 MPa/km and 31.1 MPa/km, with an average gradient of approximately 27 MPa/km. Considering the Shmin range across the studied intervals, we infer a SHmax/Shmin ratio dominantly between 1.40 and 1.65, which is a much narrower and better-constrained range when compared with the previously published ranges from nearby fields with the same stratigraphy. The relative magnitudes of the in situ stresses indicate a strike-slip faulting regime in the Berkaoui Field. This study presents the utility of image log analysis and the integration of breakout interpretation to obtain a more robust geomechanical model with reduced SHmax uncertainty.

High-resolution acoustic imaging method based on equivalent source method and reweighted l1 minimization

Sparse regularization has been successfully applied to equivalent source method (ESM) in order to improve the acoustic imaging resolution. However, the application is not always feasible, especially at low frequencies. To overcome the problem, this paper proposes a high-resolution acoustic imaging method. In this method, reweighted l1 minimization is introduced to ESM to deal with the ill-posed inverse problems. Then the obtained equivalent source strengths are used to locate the sound sources. Compared to the sparse regularization-based ESM, the proposed method can provide a low side lobe and higher spatial resolution of acoustic imaging. Meanwhile, by arranging equivalent sources in three-dimensional space, the proposed method can also realize the acoustic imaging in three-dimensional sound field with high resolution. The results of the simulation and experiment demonstrate the validations.

Flexible Piezoelectric Sensors Based on Ionic Liquid‐doped Poly(L‐Lactic Acid) for Human Coughing Recognition

AbstractCough is a common symptom of various respiratory diseases. However, recognizing a subject's cough using acoustic imaging can be challenging due to the complexity of coughing and the vulnerability of acoustic acquisition to external interference. This research aims to address these issues by utilizing a flexible piezoelectric ionic liquid‐doped poly(L‐lactic acid) (PLLA) sensor to gather the pressure signals from the human surface. 1‐butyl‐3‐methylimidazolium bis (trifluoromethylsulfonyl)imide ([BMIm]TFSI) ionic liquid (IL) can enhance the piezoelectric performance of PLLA thin film, the corresponding output voltage, and impact sensitivity are improved to three times and 105.1% for the PLLA/IL thin film with 1 wt% IL, 140 °C annealing 2 h and four times drawing ratio. The effect of IL on the crystallization behavior and piezoelectric properties of thin films during their preparation is investigated. The flexible PLLA/IL sensor is integrated into a wearable electronic system which collects and transmits piezoelectric signals from cough‐induced vibrations to the filter for denoising, then the acquired cough data are decomposed using the Empirical Mode Decomposition method and trained in a multi‐layer perception model to detect human coughing. It's evident that the flexible piezoelectric PLLA/IL sensors can be applied to human coughing recognition and show great potential applications in intelligent healthcare.

Imaging Outcomes of Emergency MR Imaging in Dizziness and Vertigo: A Retrospective Cohort Study.

Patients exhibiting acute dizziness or vertigo often represent a diagnostic challenge, and many undergo neuroimaging for stroke detection. We aimed to demonstrate the imaging outcomes of first-line emergency MR imaging among patients with acute dizziness or vertigo and to determine the clinical risk factors for stroke and other acute pathology. This retrospective study included consecutive patients with acute dizziness or vertigo referred for emergency MR imaging in a tertiary hospital during 5 years. We recorded and analyzed patient characteristics, relevant clinical information, and imaging outcomes. Risk score models were derived to predict which patients were more likely to present with positive MR imaging findings. A total of 1169 patients were included. Acute stroke was found in 17%; other clinically significant pathology, in 8% of patients. In 75% of the patients, emergency MR imaging showed no significant abnormalities. Risk factors for acute stroke included older age, male sex, and a prevalence of cardiovascular risk factors and neurologic signs. Isolated dizziness had no discriminative power on imaging outcomes, and 14% of these patients showed acute stroke. Risk scores had only moderate performance in predicting acute ischemic stroke (receiver operating characteristic area under curve = 0.75) or any significant pathology (receiver operating characteristic area under curve = 0.70). Acute dizziness and vertigo remain challenging even when emergency MR imaging is readily available. One in 4 patients had acute pathology on MR imaging. Predictors for acute pathology (older age, male sex, cardiovascular risk factors, and neurologic signs) may aid in patient selection for MR imaging, optimizing the yield and clinical impact of emergency neuroimaging. Low diagnostic yields of CT and internal acoustic canal MR imaging may offer an opportunity to reduce health care expenditures in the future.

An acoustic imaging recognition based cutting tools wear state prediction method

Online monitoring of cutting tools wear is an important component of advanced manufacturing technology, which can greatly improve the processing efficiency and reduce the production cost. In this paper, a cutting tools wear state prediction method based on acoustic imaging recognition is developed. By applying the advantages of the functional generalized inverse beamforming method in the sound field reconstruction, the acoustic signal is used as the carrier to reconstruct the three-dimensional space radiated sound field. And then, slice the reconstructed sound field image and input it into the convolutional neural network model as a sample, to process and classify the image and mines the feature information related to state from the sound field image. By incorporating amplitude and phase information of the sound field, the presented method utilizes spatial domain mapping to accurately identify the noise source and address challenges such as low recognition rate and difficult diagnosis under weak fault conditions. Furthermore, the paper also demonstrates the recognition of sound field states through a fault experiment in sound box simulation, based on these theories. And the recognition of sound field states is achieved through a simulation fault experiment conducted on the sound box, thereby validating the feasibility of the state monitoring method based on pattern recognition of sound and image. Finally, the experimental object is selected as the four-edge carbide milling cutter, and the cutting tools wear state is monitored by integrating sound field reconstruction techniques with convolution feature extraction methods to validate the robustness of the proposed approach.

Correlation Iterative Method of Acoustic Tomography with Incoherent Field Sources

Correlation Iterative Method of Acoustic Tomography with Incoherent Field Sources

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Performance study and improvement of space-folded metamaterial muffler for pipe under grazing flow

A space-folded metamaterial muffler (SMM) for pipe noise attenuation was proposed in this paper, and an improved space-folded metamaterial muffler (ISMM) was formed by arranging wire meshes at the branch inlet. Under the absence of flow and grazing flow, analytical models of SMM and ISMM were developed based on the wave expansion method and Green's function, and correspondingly, the computational fluid dynamics - linear Navier-Stokes (CFD-LNS) numerical models were established. The acoustic characteristics of responsive space (RS) were described through the complex frequency plane. The transmission coefficient of SMM was obtained and verified by sound transmission loss experiments. The results indicate that due to the thermal-viscous effects, the minimum transmission coefficient of RS cannot reach 0. Therefore, the thermal-viscous effect is one of the critical factors. By the parameter retrieving method, it is clarified that the SMM is a single-negative acoustic metamaterial. The effect of the grazing velocity on the transmission coefficient and impedance was revealed quantitatively. The grazing velocity is positively correlated with impedance at the side branch inlet and negatively correlated with total impedance. Therefore, an increase in the grazing velocity will weaken the muffler performance. Since the CFD-LNS model cannot characterize aerodynamic noise, a computational aeroacoustics (CAA) model was established for SMM and ISMM. Numerical results show that the velocity and vorticity at the side branch inlet significantly decrease due to the wire mesh. When Ma is 0.1, 0.05, and 0.01, ISMM reduces aerodynamic noise by 6.11 dB, 9.19 dB, and 2.76 dB, respectively, compared to SMM. Therefore, ISMM has better aerodynamic noise suppression capability. Finally, noise reduction experiments of the ISMM under the grazing flow were performed in an anechoic chamber, and the effectiveness of the ISMM in practical applications was verified by acoustic imaging and spectrogram results.

Arbitrary multi-directional acoustic beam emission from a cylindrical metasurface with grafted topological charge

Directional sound beam emission from compact devices is highly desirable in many practical acoustic applications. Here, we present an approach for the efficient control of arbitrary multi-directional emission through a grafted metasurface that is designed by cutting and stitching two cylindrical phase-gradient metasurfaces carrying two opposite topological charges. The grafted metasurface provides a compact and robust platform for diverse multi-directional beam emission, whose beam number and azimuthal angles are precisely controlled by the topological charge. The underlying mechanism contributes to the wave interference of two opposite partial vortex waves. This scheme was verified by simulation and experimental results. Our work provides a design methodology for directional sound beam emission devices and may be useful for applications such as acoustic imaging, beam steering, and vortex detection.

Topology Optimization Design Method for Acoustic Imaging Array of Power Equipment.

Acoustic imaging technology has the advantages of non-contact and intuitive positioning. It is suitable for the rapid positioning of defects such as the mechanical loosening, discharge, and DC bias of power equipment. However, the existing research lacks the optimization design of microphone array topology. The acoustic frequency domain characteristics of typical power equipment are elaborately sorted out. After that, the cut-off frequencies of acoustic imaging instruments are determined, to meet the needs of the full bandwidth test requirements. Through a simulation calculation, the circular array is demonstrated to be the optimal shape. And the design parameters affect the imaging performance of the array to varying degrees, indicating that it is difficult to obtain the optimal array topology by an exhaustive method. Aimed at the complex working conditions of power equipment, a topology optimization design method of an acoustic imaging array for power equipment is proposed, and the global optimal solution of microphone array topology is obtained. Compared with the original array, the imaging performance of the improved LF and HF array is promoted by 54% and 49%, respectively. Combined with the simulation analysis and laboratory test, it is verified that the improved array can not only accurately locate the single sound source but also accurately identify the main sound source from the interference of the contiguous sound source.

Monitoring Thermal Exchange of Hot Water Mass via Underwater Acoustic Tomography with Inversion and Optimization Method

Thermal exchange of underwater water mass caused by marine heat wave is a hot point of research recently. In particular, because the water temperature observation along hot water mass transportation is hard work. Acoustic tomography is an advanced method to measure water temperature variations via sound signal transmission with multi-station network sensing. The 5 kHz frequency acoustic tomography used for observing water temperature variations caused by ocean heat waves is interesting work. In this paper, the numerical simulation of hot water mass is completed first, then floatation and diffusion of hot water mass in a simulation are monitored by acoustic tomography. A new inversion optimization method is proposed to obtain hot water mass transportation variations at two-dimensional temperature vertical profile. The proposed inversion method adds a regularized mode matrix and the optimization method adds the model correlation matrix to improve the results quality. The accuracy of inversion optimization results is compared and discussed, where the mean temperature error is less than 0.4 °C. Sensing water temperature variation of marine heat waves is verified via acoustic signal transmission and improved inversion optimization method. The water dynamical process observation is an application of acoustic tomography, which can be further used observe underwater environmental characteristics.

Measured Regional Division Optimization for Acoustic Tomography Velocity Field Reconstruction in a Circular Area.

The acoustic tomography (AT) velocity field reconstruction technique has become a research hotspot in recent years due to its noninvasive nature, high accuracy, and real-time measurement advantages. However, most of the existing studies are limited to the reconstruction of the velocity field in a rectangular area, and there are very few studies on a circular area, mainly because the layout of acoustic transducers, selection of acoustic paths, and division of measured regions are more difficult in a circular area than in a rectangular area. Therefore, based on AT and using the reconstruction algorithm of the Markov function and singular value decomposition (MK-SVD), this paper proposes a measured regional division optimization algorithm for velocity field reconstruction in a circular area. First, an acoustic path distribution based on the multipath effect is designed to solve the problem of the limited emission angle of the acoustic transducer. On this basis, this paper proposes an adaptive optimization algorithm for measurement area division based on multiple sub-objectives. The steps are as follows: first, two optimization objectives, the condition number of coefficient matrix and the uniformity of acoustic path distribution, were designed. Then, the weights of each sub-objective are calculated using the coefficient of variation (CV). Finally, the measured regional division is optimized based on particle swarm optimization (PSO). The reconstruction effect of the algorithm and the anti-interference ability are verified through the reconstruction experiments of the model velocity field and the simulated velocity field.

Application of coastal acoustic tomography: calibration of open boundary conditions on a numerical ocean model for tidal currents

Coastal acoustic tomography (CAT), which measures path-averaged currents from reciprocal acoustic transmission experiments and reconstructs velocity fields from the multiple path-averaged current data, is useful for monitoring tidal currents in coastal shallow water, especially if data assimilation is employed. Previous CAT data assimilation studies have focused on state estimation problems, i.e., the reconstruction of tidal currents and following dynamical discussion. In this study, we investigate the use of path-averaged currents in a boundary control problem. Specifically, we aim to use the observed path-averaged currents to determine the parameters of a numerical ocean model, which were tidal amplitudes and phases as the open boundary conditions in this study. We investigate two methods: using the ensemble Kalman filter (EnKF) results and a linearization approach called model Green’s function method. Both calibration methods decreased the amplitudes of tidal constituents at the open boundaries. We compare the model performance between the model predictions with and without the calibration of the open boundary conditions. The model predictions with the calibrated open boundary conditions improved the agreement with the observed path-averaged current. We also implemented the sequential updates of EnKF with the two calibrated open boundary conditions. The EnKF results with the independently calibrated two open boundary conditions improved the agreement with the comparison data obtained by acoustic Doppler current profiler measurement compared with the original EnKF result with the initial open boundary conditions.