Magnetic anomaly detection (MAD) is used for detecting moving ferromagnetic targets. In this study, we present an end-to-end deep-learning model for magnetic anomaly detection on data recorded by a single static three-axis magnetometer. We incorporate an attention mechanism into our network to improve the detection capability of long time-series signals. Our model has good performance under the Gaussian colored noise with the power spectral density of $1/f^{\alpha }$, which is similar to the field magnetic noise. Our method does not require another magnetometer to eliminate the effects of the Earth’s magnetic field or external interferences. We evaluate the network’s performance through computer simulations and real-world experiments. The high detection performance and the single magnetometer implementation show great potential for real-time detection and edge computing.

Submarine active faults and earthquakes, which contain crucial information to seafloor tectonics and submarine geohazards, can be effectively characterized by precise submarine earthquake catalogs. However, the precise and rapid building of submarine earthquake catalogs is challenging due to the following facts: (i) intense noise in ocean seismic data; (ii) the sparse seismic network; (iii) the lack of historical near-field observations. In this paper, we built a deep-learning-based automatic workflow named ESPRH for automatically building submarine earthquake catalogs from continuous seismograms. The ESPRH workflow integrates Earthquake Transformer (EqT) and Siamese Earthquake Transformer (S-EqT) for initial earthquake detection and phase picking, PickNet for phase refinement, REAL for earthquake association and rough location, and HypoInverse, HypoDD for precise earthquake relocation. We apply ESPRH to the continuous data recorded by an array of 12 broadband Ocean Bottom Seismographs (OBS) near the Challenger Deep at the southern-most Mariana subduction zone from Dec. 2016 to Jun. 2017. In this study, we acquire a high-resolution local earthquakes catalog that provides new insights into the geometry of shallow fault zones. We report the active submarine faults by seismicity in Challenger Deep which is the deepest place on Earth. These faults are a significant reference for submarine geological hazards and evidence for serpentinization. Hence, the ESPRH is qualified to construct comprehensive local submarine earthquake catalogs automatically, rapidly, and precisely from raw OBS seismic data.

The Bayan Obo deposit in Inner Mongolia is a crucial source of rare earth elements-niobium-iron, with significant economic importance for China. The ore-bearing dolomite is believed to have originated from mantle-derived magma, indicating high prospectivity in the deeper sections. However, the deep distribution of the ore-bearing dolomite remains poorly understood. Here, we develop a 3D S-wave velocity (VS) structure down to 2.5 km depth beneath the Bayan Obo using ambient noise tomography techniques, which used 25 days of continuous waveform data recorded by 312 short-period seismometers. Combined with previous geological, geochemical, and geophysical studies, our results reveal the possible 3D spatial distribution of the ore-bearing dolomite beneath the Bayan Obo. The results indicate that the VS structures beneath the main, east, and west pits change from relatively high-velocity (high-V) zones (with VS of approximately 2.8–3.2 km/s) at shallow depths (depth to approximately 1.5–1.8 km) to significant low-velocity (low-V) zones (with VS < 2.6 km/s) at deeper depths (depth below approximately 1.8 km). We suggest that the content of ore-bearing dolomite may decrease significantly at deeper depths (depth below approximately 1.8 km) beneath these pits, which possibly indicates a diminished prospecting potential there. Our results also indicate a significant high-V zone (with VS > 3.4 km/s) at depths of approximately 1–2.5 km between the main and west pits. Its lithology remains unclear and is proposed to be a key scientific issue in the future to improve our understanding of the ore-controlling structure associated with the tectonic events in the Bayan Obo deposit.

Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.