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DOE OSTI2020

Detection and classification capabilities of two multibeam sonars

Multibeam sonars can be used to detect and classify marine fauna under conditions when optical sensors are ineffective. In this work, we compare the detection and classification capabilities of two multibeam sonars with different operating frequencies, the Tritech Gemini 720is (720 kHz) and the Teledyne BlueView M900-2250 (2250 kHz). The two sonars were deployed with overlapping swaths in a narrow tidal channel with peak currents of approximately 2 m/s where seals, schools of fish, and diving birds were intermittently present. In comparing data concurrently acquired by both sonars, we observe differences in the appearance of detected targets and detection capabilities. The detected target tracks are classified using a random forest model as either individual biological targets (flora and fauna, including fish, diving birds, seals, and plant matter), fish schools, or nonbiological targets (including entrained air and sonar artifacts). Despite the observed differences in detection capabilities for the two sonars, automatic classification distinguishes between target classes with precision and recall above 0.7 and 0.9, respectively. Furthermore, these results suggest that while similar methodologies can be used for data analysis, some outcomes from environmental studies using multibeam sonars may be instrument-specific.

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗

DOE OSTI2019

Deployment of a 3D Sonar System at Savannah River: Results, Implications and Next Steps - 19718

Waste solids mapping is required at SRS to support the closure of high level radioactive waste tanks. The current mapping methodologies utilize a combination of video inspections and still camera photos to record the solids location and height in the tank with interpretation by site Engineering personnel. Tank liquid level is recorded during capture of the images to determine the height of solids. If no liquid is present, the images are compared to known waste tank features (e.g., cooling coils, support columns) to determine the height of the solids. In December of 2017, NuVision Engineering and Fortis Engineering conducted a demonstration of Sonar technology in SRS F-Tank Farm (FTF) Tank 7 using three risers (Risers 1, 5, and Center) for the purposes of mapping waste solids in the tank. Tank 7 was not the ideal choice for a 'first-of-a-kind' deployment given its congested internals but was selected based on the priority of tank farm operations. Savannah River Remediation (SRR) evaluated the results of this demonstration against the current solids mapping methodologies to determine potential future application of Sonar technology to support upcoming sludge or salt waste removal, decisions to cease waste removal and perform sampling and analysis, final residuals volume determinations, and applicability for use in H-Tank Farm (HTF) Type IV tanks. The efficiency of the Sonar mapping technology against the current mapping techniques and the relative costs of each were also evaluated. The Sonar demonstration performed in the three risers of Tank 7 resulted in a tank floor coverage of approximately 26%. This limited coverage was primarily due to shadow effects and image distortions created by vertical cooling coils and support columns within the waste tank. A comparison of the images produced during the Sonar demonstration and the historical Tank 7 mapping, performed in 2011 upon completion of Bulk Waste Removal Efforts (BWRE), showed physical similarities between the mounds located near the Sonar deployment risers. The technology performed well in accurately mapping solids in those areas accessible to coverage. The Sonar mapping technology may achieve more benefits if configured to work around internal obstructions, or in a Type IV tank, where the lack of vertical cooling coils may allow for a larger coverage area. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm

Eels (Anguilla spp.), including American eels (Anguilla rostrata), European eels (Anguilla anguilla), and Japanese eels (Anguilla japonica), are species of critical management and regulatory concern due to their vulnerability to various stressors during downstream migrations. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions, thereby facilitating the design, operation, and optimization of more effective downstream passage facilities from both biological and economic perspectives. However, a real-time, automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the You Only Look Once Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving F1-scores and mAP@0.50 exceeding 0.84. Additionally, the incorporation of wavelet denoising during preprocessing slightly improve detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like populations.

Deep learning↗

DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗

DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗

DOE OSTI2015

3D Sonar for tank waste quantification - 15430

An innovative remote 3D Sonar technology adapted from the oil and gas industry has been successfully applied to the mapping of radioactive sludge deposits during tank, fuel pool and silo remediation operations at the Sellafield site in the UK. The technology can: -Measure the disposition and calculate the volume of settled solids in a waste tank thereby supporting tank closure objectives - Produce accurate 3-D images of solids disposition, structure and tank internals to help identify where sludge deposits and debris are located -Capture real time 'Sonar video' of movement of solids in the tank in real time to aid retrieval - Detect and monitor gas release and bubble generation Aside from direct safety, cost and schedule benefits, improved accuracy and confidence in residual waste volume determinations can lead to increased level of regulator confidence in meeting closure objectives. This technology has potential to bring such benefits to challenges in the DOE estate, such as Savannah River Site (SRS) and Hanford Tank Farms remediation, and operations at the Hanford Waste Treatment Plant. This paper describes the 3D Sonar technology and its applications, and reviews a 3D Sonar demonstration project specifically aimed at enhancing retrieval and closure operations at SRS. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

13 HYDRO ENERGY↗

DOE OSTI2020

Hanging String Cuts in SPR Caverns: Modeling Investigation and Comparison with Sonar Data

Investigation of leaching for oil sales includes looking closely at cavern geometries. Anomalous cavern "features" have been observed near the foot of some caverns subsequent to partial drawdowns. One potential mitigation approach to reducing further growth of preexisting features is based on the hypothesis that reducing the brine string length via a "string cue' would serve to move the zone associated with additional leaching to a location higher up in the cavern and thus away from the preexisting feature. Cutting of the hanging string is expected to provide a control of leaching depth that could be used to "smooth" existing features and thus reduce geomechanical instability in that region of the cavern. The SANSMIC code has been used to predict cavern geometry changes (i.e., the extent of cavern growth with depth) based on variable input parameters for four caverns: West Hackberry 11 (WH11), West Hackberry 113 (WH113), Big Hill 104 (BH104), Big Hill 114 (BH114). By comparing the initial sonar geometry with resultant geometries calculated by the SANSMIC code, conclusions may be drawn about the potential impact of these variables on future cavern growth. Ultimately, these conclusions can be used to assess possible mitigation strategies such as the potential advantage of cutting versus not cutting a brine string. This work has resulted in a recommendation that a hanging string cut of 80 ft in WH11 would be beneficial to future cavern geometry, while there would be little to no benefit to string cuts in the other three caverns investigated here. The WH11 recommendation was followed in 2019, resulting in an operational string cut. A sonar performed after the string cut showed no adverse leaching in the area of the preexisting flare, as expected from the results of the preliminary SANSMIC runs described in this report. Additional SANSMIC modeling of the actual amount of injected raw water resulted in good agreement with the post-cut sonar.

02 PETROLEUM↗

DOE OSTI2016

Observing Ocean Ecosystems with Sonar

We present a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) is built to connect to an instrumentation network, where it consumes a real-time stream of sonar data and archives tracking and biomass data.

ocean energy↗

DOE OSTI2017

Underwater Mapping Results for Seabotix vLBV300 Vehicle with Tritech Gemini 720i Imaging Sonar near Newport, OR

This document presents results from tests to demonstrate underwater mapping capabilities of an underwater vehicle in conditions typically found in marine renewable energy arrays. These tests were performed with a tethered Seabotix vLBV300 underwater vehicle. The vehicle is equipped with an inertial navigation system (INS) based on a Gladiator Landmark 40 IMU and Teledyne Explorer Doppler Velocity Log, as well as a Gemini 720i scanning sonar acquired from Tritech. The results presented include both indoor pool and offshore deployments. The indoor pool deployments were performed on October 7, 2016 and February 3, 2017 in Corvallis, OR. The offshore deployment was performed on April 20, 2016 off the coast of Newport, OR (44.678 degrees N, 124.109 degrees W). During the mission period, the sea state varied between 3 and 4, with an average significant wave height of 1.6 m. Data was recorded from both the INS and the sonar.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

ALFA Field Testing of a Dual Sonar System for Detecting Woody Debris

Field testing of a dual sonar system for detecting woody debris in natural settings was conducted at the Tanana River Test Site (TRTS) in Nenana, AK between 8/26 and 9/23, 2015. The TRTS is approximately 65 miles south of Fairbanks and is well suited for testing hydrokinetic energy generation technologies and environmental monitoring technologies such as the dual sonar system used here in realistic settings.

16 TIDAL AND WAVE POWER↗

DOE OSTI2015

Testing of a 3-D Sonar for Future Deployment to Image Solids on the Floor of Hanford, HLW Conditioning Tanks - 15646

Florida International University (FIU) has tested the capability of 2-D and 3-D sonar systems to image solids settled on the floor of tanks, on the bottom of streams and small rivers, and in 55 gallon drums. FIU developed and successfully tested the Solid-Liquid Interface Monitor (SLIM) for mapping the interface between supernatant and settled solids in several test tanks. SLIM is comprised of: a sonar, a mechanical system for deploying the system into large high-level radioactive waste (HLW) tanks, and imaging programs and algorithms developed at and by FIU faculty and staff. Early testing had the objective to demonstrate the ability of SLIM to image and quantify the height and volume of settled solids on the floor of 1-million-gallon, HLW tanks at Hanford. Testing showed that SLIM: (1) functions accurately in liquid radioactive HLW tank conditions [i.e., caustic (pH>14), warm (up to 45 degrees Centigrade), and highly radioactive]; (2) provides accurate imaging of solids surfaces (1-2% relative error in the height of interfaces); and (3) provides an excellent volume estimation of the solids, thereby allowing engineers to fill tanks closer to the maximum allowed level and optimize solids retrieval; and (4) enables better placement of pumps inside tanks during retrieval to minimize the potential for plugging. The New Hanford technology need is for rapid imaging (even during vigorous mixing) of solids on the floor of conditioning (mixing) tanks. Results of recent successful testing show imaging times under 1 minute, solids volume estimates with <5% error and effective filtering and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Mapping Lake Sturgeon Spawning Habitat in the Upper Tennessee River using Side-Scan Sonar

Abstract The Lake Sturgeon Acipenser fulvescens is a fish species that was once dispersed widely throughout the Mississippi River drainage but was largely extirpated from the southern portions of its range by overfishing and habitat degradation. There is an ongoing restoration effort to reestablish the Lake Sturgeon to rivers of the southeastern United States. Reintroduced juvenile Lake Sturgeon now occupy several reservoirs separated from each other by hydroelectric dams along the upper Tennessee River. To complete their life history, Lake Sturgeon will migrate upriver from reservoir habitats to more lotic habitats and spawn over coarse rocky substrate, even in the tailwaters of impassable dams. Using low-cost, consumer-grade, side-scan sonar and a GIS, we mapped the substrate of four tailwaters that may be future spawning locations for Lake Sturgeon. We used video imagery collected from random locations within the mapped areas to validate our digitization of sonar imagery. We calculated the area of four substrate classes displayed in the maps to evaluate that aspect of the suitability of each of the tailwaters for Lake Sturgeon spawning. The revised maps showed that the best spawning substrate (unembedded, coarse, rocky substrate, 6–25 cm in diameter) comprised 17.0–30.5% of the total area mapped at each tailwater, while the least suitable substrate class (fine sediment, <0.2 cm in diameter) comprised 6.2–30.7% of the mapped areas. Our results suggest that any future spawning events by Lake Sturgeon below each of these dams are likely to encounter some suitable spawning substrate patches, while management opportunities exist to supplement tailwater areas with suitable spawning substrate. Received March 11, 2016; accepted May 30, 2016 Published online August 31, 2016

Walker, Daniel J.↗

DOE OSTI2018

Shallow Water Imaging Sonar System for Environmental Surveying Final Report CRADA No. TC-1130-95

The scope of this research is to develop a shallow water sonar system designed to detect and map the location of objects such as hazardous wastes or discarded ordnance in coastal waters. The system will use high frequency wide-bandwidth imaging sonar, mounted on a moving platform towed behind a boat, to detect and identify objects on the sea bottom. Resolved images can be obtained even if the targets are buried in an overlayer of silt. Reference 1 ( also attached) summarized the statement of work and the scope of collaboration.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Treatment of Tilted Sonar Data for Salt Cavern Analysis

Structural modeling and visualization of salt caverns requires three-dimensional representations. These representations are typically produced from sonar surveys conducted by companies that then produce a report of depths, distances, and volumes. There are multiple formats that are vendor dependent, and, as technology improves, there have been changes from only horizontal surveys to inclined shots for ceilings and floors to mid-cavern inclined shots. For geomechanical modeling, leaching predictions, and cavern stability visualizations, Sandia has previously written in-house software, called SONAR8, that created a consistent geometry format from the processed sonar reports. However, the increase in the need for mid-cavern inclined surveys led to the discovery of certain limitations in that code. This report describes methods used to process the multiple different formats to handle inclined shots in a consistent and accurate manner in our modeling efforts. A set of file formats and a database schema that was developed for this work is also documented in the appendices.

02 PETROLEUM↗

DOE OSTI2024

Deep Learning for Fish Identification from Sonar Data (CRADA 481 Final Report)

In eastern regions of the United States, the American eel is a species of management and regulatory concern because of significant population declines, despite the species’ previous abundance in all tributaries of rivers flowing into the Atlantic Ocean. The American eel is also a candidate for listing under the U.S. Endangered Species Act. While hydropower construction and operation are only one of several factors contributing to this population decline, such a listing could impose additional regulatory challenges for a large number of hydropower projects. In this CRADA project, we improved technologies for identifying migrating eels with the goal of reducing the cost and time required for future American eel hydropower impact assessment and mitigation studies, while maintaining accuracy. We built on results from a previous FOA project (FOA# DE-FOA-0001662), led by the Electric Power Research Institute (EPRI), which developed a highly accurate, deep-learning method for identifying migrating eels from imaging sonar data. The current study aimed to further optimize this deep-learning model, originally designed for image classification, and to develop an object detection software capable of identifying fish from sonar videos in real time, enabling the detection of events like fish migrations and specific species, such as the American eel, at hydropower dams. The data conversion algorithms were packaged as software with a graphical user interface, and the software is evaluated by external collaborators. We focused on the American eel in this project and explored the transferability of the developed deep learning models to the sea lamprey, given the similar body shape and swimming behavior between the two species.

13 HYDRO ENERGY↗

DOE OSTI2016

Mapping Lake Sturgeon ( Acipenser Fluvescens ) Spawning habitat in the Upper Tennessee River Using Side Scan Sonar

The Lake Sturgeon Acipenser fulvescens is a fish species that was once dispersed widely throughout the Mississippi River drainage but was largely extirpated from the southern portions of its range by overfishing and habitat degradation. There is an ongoing restoration effort to reestablish the Lake Sturgeon to rivers of the Southeastern United States. Reintroduced juvenile Lake Sturgeon now occupy several reservoirs along the Upper Tennessee River that are separated from each other by hydroelectric dams. To complete their life history, Lake Sturgeon will migrate upriver from reservoir habitats to more lotic habitats and spawn over coarse rocky substrate, even in the tailwaters of impassable dams. We mapped the substrate of four tailwaters that may be future Lake Sturgeon spawning locations using low-cost, consumer-grade side scan sonar and a GIS. We used video imagery collected from random locations within the mapped areas to validate our digitization of sonar imagery. We calculated the area of four substrate classes displayed in the maps to assess the suitability of each of the tailwaters for Lake Sturgeon spawning. The best spawning substrate (unembedded, coarse, rocky substrate 6 – 25 cm in diameter) comprised 17.0 – 30.5% of the total area mapped at each dam, while the least suitable substrate class (fine sediment <0.2 cm in diameter) comprised 6.2 – 30.7% of the mapped areas. Our results suggest any future spawning events by Lake Sturgeon below each of these dams are likely to encounter some suitable spawning substrate patches, while management opportunities exist to supplement other tailwater areas with suitable spawning substrate.

13 HYDRO ENERGY↗