人工知能学会（以下、JSAI）と日本地球惑星科学連合（以下、JpGU）は、2024年度に続き、地球惑星科学分野データを用いたAIコンペティションGeoSciAI2025を開催した。本論文では、GeoSciAI2025の開催趣旨と地震分野・宇宙天気分野の課題を報告する。

本手法では，教師データを必要としないデノイズ手法による前処理と軽量な深層学習手法を組み合わせたデノイズ手法を提案する．前処理として，観測波形を行列として扱い，ランダム行列理論に基づく Marchenko-Pastur 分布から導出される特異値閾値により，信号成分と雑音成分を自動分離する枠組みを構築した．この時，波形データをHankel 行列化し，対角平均によって局所的構造を維持しつつ低ランク近似を適用し，時間的連続性を保ったノイズ除去を実現した．そして，残ったノイズ成分に対して，Encoder–Decoder 構造の軽量な深層学習モデルによるノイズ除去を行った．その結果， テストデータに対して本コンペティションで提示された評価指標において 65.24 を達成し，ベースラインの学習済みモデルを上回る性能を示した．

We propose a deep-learning-based denoising model optimized for Japanese seismic waveform data. Unlike the conventional DeepDenoiser [1], our model uses MeSO-net observations, which contain strong anthropogenic and urban noise, and adopts a U-Net architecture with a Convolutional Block Attention Module (CBAM) [4] for enhanced feature extraction. The input consists of a two-channel spectrogram representing the real and imaginary parts of the complex STFT. The target is an amplitude ratio mask derived from the magnitude spectra of noisy and clean signals. The loss function combines mean squared error (MSE) with signal-to-noise ratio (SNR) and cross-correlation (CC) terms to preserve waveform similarity. The model converged after 27 epochs and achieved an evaluation score of 299.71, far exceeding DeepDenoiser (15.19). Average SNRs reached ~170, and CC values exceeded 0.9 across all components. These results demonstrate that incorporating SNR and CC terms improves denoising performance while maintaining signal fidelity for Japanese seismic data.

I propose a novel loss function, Spike-Aware Weighted MSE (SAW-MSE), which emphasizes prediction accuracy during geomagnetic storm periods by adaptively weighting errors. While traditional LSTM models using mean squared error (MSE) struggle with limitations in capturing extreme Dst variations, our storm-weighted MSE incorporates a dynamic weighting mechanism governed by parameters α and β, where α controls the degree of emphasis on severe geomagnetic storms, and β determines the steepness of the penalty increase in response to more negative Dst value. The experimental result demonstrates that the proposed method improves accuracy during intense storm events, reducing RMSE from 57.88 (LSTM with MSE) to 45.94(LSTM with SAW-MSE). This result suggests that domain-specific loss function, SAW-MSE, can effectively enhance robustness in space weather forecasting.

A deep learning model to predict the geomagnetic storm index (Dst index) 24 hours in advance using solar wind data was proposed. The model employs a two-step prediction process: the preliminary prediction is performed using a Transformer-based model, and the second prediction utilizes multiple models selected according to the preliminary prediction values. The optimal combination of models was explored to minimize RMSE for the contest, resulting in a best RMSE of 28.381 on the test data.