[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119578-en":3,"doc-seo-119578-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119578,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Advanced machine learning methods for multifrequency acoustic data - PhD thesis","The dissertation investigates advanced machine learning approaches for multifrequency underwater acoustic data within marine science applications. Acoustic wave propagation supports communication, navigation, and imaging, while active acoustics enable estimating the distribution and abundance of aquatic organisms for fisheries management. The work targets data collected with hull-mounted echo sounders at different frequencies, using backscatter strengths as the primary observations, and develops methods aligned with research objectives in the CRIMAC project under the Applied Mathematics PhD framework.","Advanced machine learning methods for multifrequency acoustic data  \nAhmet Pala  \nThesis for the degree of Philosophiae Doctor (PhD) University of Bergen, Norway  \n2025  \nAdvanced machine learning methods for multifrequency acoustic data  \nAhmet Pala  \nThesis for the degree of Philosophiae Doctor (PhD) at the University of Bergen  \nDate of defense: 12.05.2025  \n© Copyright Ahmet Pala  \nThe material in this publication is covered by the provisions of the Copyright Act.  \nYear: 2025  \nTitle: Advanced machine learning methods for multifrequency acoustic data  \nName: Ahmet Pala  \nPrint: Skipnes AS / University of Bergen  \nPreface  \nThis dissertation is submitted as a partial fulfillment of the requirements for the degree of Doctor of Philosophy (Ph.D.) in Applied Mathematics at the Department of Mathematics, University of Bergen. The scientific work presented in this thesis was conducted under the supervision of Anna Oleynik and Guttorm Alendal from the Department of Mathematics at the University of Bergen, and Nils Olav Handegard from the Institute of Marine Research (IMR) .  \nThe research was carried out in close collaboration with IMR, which provided both the acoustic datasets and domain expertise. Research activities were closely aligned with the Machine Learning Work Package (WP4) in the Centre for Research-based Innovation in Marine Acoustic Abundance Estimation and Backscatter Classification (CRIMAC) Project at IMR, where methodologies were developed and applied to underwater acoustic data.  \nii Preface  \nAcknowledgements  \nThere are many people I want to thank, but let’s start with Anna, my main supervisor. She has been incredibly kind and supportive. I have learned so much from her, not just about mathematics, but about thinking critically and solving problems methodically. Her attention to detail is unbelievable. I have spent days formulating a problem, coding, double-checking everything, convinced I finally got it right   only for her to take one look and instantly spot my mistake. Every time, I am amazed. Her feedback has been beneficial—in more ways than one (and yes, I had to use that word at least once) . Most importantly, she created an environment where I felt free to make mistakes, explore, and improve. But now that I think about it, this is the only part of my PhD where she can’t correct me. So for once, I can relax and write exactly what I want: I am truly lucky to have had her as my supervisor!  \nThen there is Nils Olav, my co-supervisor, who has been truly incredible, and I mean that in every sense. PhD life is not easy, but at every single moment, I have felt his support. Whether it was research, career advice, or just life in general, he was always there, making even the most overwhelming situations feel manageable. I have lost count of how many times his guidance helped me regain focus when things seemed impossible. His patience, wisdom, and unwavering encouragement made this PhD not just bearable, but actually enjoyable.  \nTo both Anna and Nils Olav: this thesis would not exist without you. But more than that, working with you has been one of the greatest privileges of my life. I am beyond grateful for your guidance, patience, and kindness. Thank you for everything.  \nTo Carolina, my love—thank you for always being there through every high and low of this journey. You have been my voice of reason, and the person who reminded me to take breaks when I forgot they even existed. You somehow managed to put up my endless ramblings about data imbalance. And though I made many mistakes along the way, I can confidently say that the biggest blunder mistake of all would have been not having her by my side through this journey.  \nTo my family—my mother, father, brother Volkan, and Rabia-who have been with mein every step of this long journey. They waited until this moment, always believing in me, supporting me, and never judging. My mother’s kıymalı ıspanak and dolma remain unmatched. Rabia, whose phone calls are nev","cbCaiomjnbRIhtfC","https://ap.wps.com/l/cbCaiomjnbRIhtfC","pdf",11608132,1,132,"English","en",105,"# Preface\n## Acknowledgements\n# Abstract\n## Acoustic wave propagation and applications","[{\"question\":\"What real-world problem does the thesis target?\",\"answer\":\"The thesis targets underwater acoustic applications such as communication, navigation, imaging, and acoustic surveys used to support fisheries management.\"},{\"question\":\"What data type and measurement does the thesis focus on?\",\"answer\":\"It focuses on multifrequency acoustic data gathered by hull-mounted echo sounders, using received echo strength (backscatter) as the key measurement.\"},{\"question\":\"Who supervised the research?\",\"answer\":\"The research work was conducted under supervision of Anna Oleynik and Guttorm Alendal from the Department of Mathematics at the University of Bergen, and Nils Olav Handegard from the Institute of Marine Research (IMR).\"}]","Advanced machine learning methods for multifrequency acoustic data - 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