Measurement and Analysis of Acoustic Backscatter Value for Bottom Classification of waters Tidung Island

Sediment in a water has an important role for organisms, namely as a habitat, a place for foraging for food, and a place for spawning. These sediment can affect the composition of organisms in the water. The purpose of this study is to calculate the value of acoustic backscatter for the classification of the bottom of the water and to see the effect of sediment grain size on the backscatter value obtained from a single beam acoustic instrument. Data collection was carried out from 10 to 12 June 2021 in the water of Tidung Island, Seribu Islands, using the SIMRAD EK-15 single beam, single frequency 200 kHz instrument. Sediment sampling was carried out at 13 stations. The results showed that the waters of Tidung Island were dominated by muddy substrate which was classified based on the Surface Backscattering Strength (SS) value. Meanwhile, the grain size of the sediment affects the SVb value, where the large the grain size of the bottom sediment, the SVb value will be higher. The higher SVb value the SS value will be higher. Keywords: Bottom Classification, Acoustic Backscatter, Tidung Island

Distributions of environmental parameters and fish at Humbold Bay, Jayapura

Research on the distribution of fish and plankton in waters that are equipped with environmental parameters is needed to obtain maximum results and increase accuracy also it provides comprehensive information. The research, which was conducted in Humbold Bay, aimed to map the fish and plankton distribution data both vertically and horizontally and combine it with environmental parameters in the bay. Fish and plankton’s data was the volume backscattering strength (Sv) value obtained using the SIMRAD EK-15 device while environmental parameter data, such as temperature, salinity, and chlorophyll obtained from marine.copernicus.eu which processed in the 5-80 m depth range. The results showed that Humbold Bay had the highest average surface temperature distribution was 30 °C, with the highest average salinity from 35.89 ppt and the highest average chlorophyll value from 0.3859 mg/m3. The horizontal distribution of plankton had an average SV value of -76.63 dB, while the fish was -56.00 dB that evenly distributed. Vertically, the Sv of plankton decreased with increasing depth as well as the Sv of fish which its’ also did not have a distribution pattern in certain environmental parameters.

Measurement of Seafloor Acoustic Backscatter Angular Dependence at 150 kHz Using a Multibeam Echosounder

Acoustic seafloor measurements with multibeam echosounders (MBESs) are currently often used for submarine habitat mapping, but the MBESs are usually not acoustically calibrated for backscattering strength (BBS) and cannot be used to infer absolute seafloor angular dependence. We present a study outlining the calibration and showing absolute backscattering strength values measured at a frequency of 150 kHz at around 10–20 m water depth. After recording bathymetry, the co-registered backscattering strength was corrected for true incidence and footprint reverberation area on a rough and tilted seafloor. Finally, absolute backscattering strength angular response curves (ARCs) for several seafloor types were constructed after applying sonar backscattering strength calibration and specific water column absorption for 150 kHz correction. Thus, we inferred specific 150 kHz angular backscattering responses that can discriminate among very fine sand, sandy gravel, and gravelly sand, as well as between bare boulders and boulders partially overgrown by red algae, which was validated by video ground-truthing. In addition, we provide backscatter mosaics using our algorithm (BBS-Coder) to correct the angle varying gain (AVG). The results of the work are compared and discussed with the published results of BBS measurements in the 100–400 kHz frequency range. The presented results are valuable in extending the very sparse angular response curves gathered so far and could contribute to a better understanding of the dependence of backscattering on the type of bottom habitat and improve their acoustic classification.

Proposed Methodology to Quantify the Amount of Methane Seepage by Understanding the Correlation Between Methane Plumes and Originating Seeps

In recent years, discoveries of methane plumes (also called methane flares) have been reported in various sea areas around the world. Clusters of naturally seeping methane bubbles rising from the seafloor are visualized as methane plumes on the echograms of quantitative echo sounders and multibeam sonars. In order to determine if seeping methane can be used as energy resources and its environmental impact, it is necessary to estimate the amount of naturally seeping methane. From April, 2020, a 3-year project is being conducted in Japan to evaluate the amount of methane seepage from methane plumes. The authors propose the following steps to quantify the amount of methane seepage accurately. First of all, methane plumes in the Exclusive Economic Zone (EEZ) of Japan are mapped out using acoustic devices such as quantitative echo sounders and multibeam sonars. Secondly, methane bubbles of a few millimeters in diameter from methane seeps at seafloor are collected and sampled using a cone-shaped collector with 20 cm in diameter, operated by Remotely Operated Vehicle (ROV). If we can identify the number of seep mouths that form into one single plume, we will be able to quantify the methane seepage from one plume. Based on this result, calibration of the mean backscattering strength and the amount of seeping methane from methane plumes becomes possible and will be applied to the mapped plumes in order to estimate the methane seepage in the EEZ of Japan. Once this calibration is established, it can be applied to the methane plumes observed worldwide, and methane seepage can be quantified simply by acoustic observations of methane plumes. In this study, a method to verify the correlation between methane plumes and methane seeps is introduced, as well as a method to locate methane seeps effectively using the Target Position function of a quantitative echo sounder. The authors intend to use this as the basic data for establishing a method to estimate the amount of methane released from a methane plume by observing the methane plume acoustically.

Application of artificial neural network to estimate bedload transport rates and bedload granulometry using outputs of stationary ADCP measurements

<p>Measuring and assessing the bedload data is a crucial for successful and efficient river management. Hence, the information about the bedload transport and characteristics helps to describe the dynamics of the river morphology and to evaluate the impacts on boat navigation, hydropower production, ecological systems and aquatic habitat.</p><p>Although the acoustic Doppler current profilers are designed to measure water velocities and discharges, they have been successfully used to measure some bedload characteristics, such as the apparent bedload velocity. The correlation between the apparent bedload velocity and the bedload transport rates measured by physical bedload samplers (e.g. pressure difference) has been examined and relatively high correlations have been reported. Moreover, laboratory experiments have proven that there is a strong correlation between the bedload concentration and particle size distribution and corrected backscattering strength obtained from the ADCPs.</p><p>The bedload transport rates yielded from the ADCPs outputs are usually derived as regression model-fitting of the measured apparent velocity and the physically collected bedload samples at the same time and position.  Alternatively, a semi-empirical kinematical approach is used, where the apparent bedload velocity is the main component and the bedload concentration is empirically estimated. However, the heterogeneous and sporadic motion of the bedload particles is often followed by high uncertainty and weak performance of these approaches.</p><p>Machine learning offers a relatively simple and robust method that has the potential to describe the nonlinearity of the complex bedload motion and so far, it has not been previously exploited for predicting transport rates. This study implements artificial neural network techniques to develop a model for predicting bedload transport rates by using only ADCP data outputs as training data. Data processing techniques are used to extract relevant features from the corrected backscattering strength and apparent velocity obtained from the ADCPs. More than 60 features were derived in the ADCPs dataset, and the most relevant features are selected through neighborhood component analysis. These features are used as inputs in conventional supervised neural network architecture which consists of two hidden layers and 35 neurons. This model is used to capture the distribution of the ADCP features for each output (e.g., physically measured transport rates and grain size from bedload samples) in the training sample. The back-propagation algorithm (BPA) is still one of the most widely used learning algorithms in the training process and thus herein applied. The learning rate, number of neurons and hidden layers were optimized by using Bayesian optimization techniques. The network was trained with more than 60 bedload samples and corresponding 5 - 10 min time series of ADCP preprocessed data. The rest of the samples were used for validation of the model. The validation resulted in correlation coefficients higher than 0.9 and the, which is significantly higher value than the corresponding values for the methodologies developed before. Aiming to develop a more robust and stable ANN model, further testing of different training algorithms must be performed, different ANN architecture should be tested, and more data shall be included.</p>

DISTRIBUTION AND DENSITY OF DEMERSAL FISHES IN YOUTEFA BAY, PAPUA, INDONESIA: A STUDY USING HYDROACOUSTIC TECHNOLOGY

A study was conducted to estimate the distribution and density of demersal fish in Youtefa Bay, Papua, Indonesia, using hydroacoustic technology. The hydroacoustic survey was carried out using a single beam echosounder SIMRAD EK-15 which operates at a frequency of 200 kHz. The hydroacoustic data was processed using Echoview software with a threshold between -70.00 to -34.00 dB. Schooling fish were detected at a maximum distance of 3 m from the seabed, with average volume backscattering strength ranging between -60.13 and -42.01 dB. The demersal fish density in the Youtefa Bay ranged from 0.09 to 42.19 fish m-3 with an average density of 12.62 fish m-3. The schools of demersal fish were dominantly detected in the coastal waters of Enggros Village to Abe Pantai Village. The condition of substrate and water depth seems to influence the spatial and vertical distribution of demersal fish in the Youtefa Bay.

Integrating Multiple-Try DREAM(ZS) to Model-Based Bayesian Geoacoustic Inversion Applied to Seabed Backscattering Strength Measurements

The key to model-based Bayesian geoacoustic inversion is to solve the posterior probability distributions (PPDs) of parameters. In order to obtain PPDs more efficiently and accurately, the state-of-the-art Markov chain Monte Carlo (MCMC) method, multiple-try differential evolution adaptive Metropolis(ZS) (MT-DREAM(ZS)), is integrated to the inverse problem because of its excellent ability to fully explore the posterior space of parameters. The effective density fluid model (EDFM), which is derived from Biot–Stoll theory to approximate the poroelastic model, and the published field measurements of backscattering strength are adopted to implement the inversion. The results show that part of the parameters can be estimated close to the measured values, and the PPDs obtained by dual-frequency inversion are more concentrated than those of single-frequency inversion because of the use of more measured backscattering strength data. Otherwise, the comparison between the predicted backscattering strength of dual-frequency inversion results and Jackson’s prediction shows that the solutions of the inverse problem are not unique and may have multiple optimal values. Indeed, the difference between the two predictions is essentially the difference in the estimation of the contribution of volume scattering to the total scattering. Nevertheless, both results are reasonable due to the lack of measurement of volume scattering parameters, and the inversion results given by the posterior probabilities based on the limited measurements and the adopted model are still considered to be reliable.

Variation of Zooplankton Mean Volume Backscattering Strength from Moored and Mobile ADCP Instruments for Diel Vertical Migration Observation

Zooplankton can be detected by using acoustic Doppler current profiler (ADCP) instruments through acquiring the mean volume backscattering strength (MVBS) data. However, the precision of the backscattered signal measured by single ADCP measurement has a limitation in the MVBS variation of zooplankton. The objectives of this study were to analyze the MVBS and vertical velocity from ADCPs at the same time and location for zooplankton’s daily vertical migration (DVM) observation. Measurements were conducted in Lembeh Strait, North Sulawesi, Indonesia. Instruments used included a moored ADCP 750 kHz and a mobile ADCP 307.2 kHz. High MVBS value was found at 11.5–16 m depths and was identified as the sound scattering layer (SSL). The DVM patterns in the SSL displayed significant differences over time and had good relationships with the diurnal cycle. Theoretical target strength (TS) from the scattering models based on a distorted-wave Born approximation (DWBA) was estimated for Oithona sp. and Paracalanus sp.; the two dominant species found in the observed area. However, ΔMVBS and ΔTS proved that the dominant zooplankton species were not the main scatterers. The strong signal in SSL was instead caused by the schools of various zooplankton species.