Gravity gradient data are sensitive to local density anomalies in regional geological structures. Compared with the inversion of one component of the gravity field, joint inversion of the gravity gradient tensor data can provide more constraint information, reduce the non-uniqueness, and improve the reliability of inversions. This study developed a joint inversion of gravity gradient tensor data based on the coordinate descent algorithm. A model function based on mixed L1 and L2 norms is used to constrain the joint inversion. The synthetic model tests show that the proposed method can effectively image the location of density anomalies with proper amplitude. Finally, this joint inversion method is applied to the gravity gradient tensor data observed over the Vinton Dome in Louisiana, USA. The results reveal a high-density caprock consistent with the geological information, proving the ability of this method to process the actual data.

Salt domes are favorable geological structures associated with hydrocarbon reservoirs and critical mineral resources. Currently, imaging salt diapir subsurface structures has received widespread attention. The gravity method has been widely used to interpret salt structures due to the significant density contrast between salts and surrounding sediments. However, complex mass sources at different depths challenge the interpretation of the gravity data over salt domes. In this study, we put forward the mixed L1 and L2 norm regularized inversion of the gravity data observed on the surface to image the 3-D subsurface structure of salt domes. The synthetic tests demonstrate that the mixed norm regularization inversion of gravity data can effectively recover subsurface complex density anomalies similar to salt dome structures. The practical application in the Vinton Salt Dome reveals a shallow high-density caprock and a deep low-density salt core beneath Vinton Dome, greatly enriching the underground structure information of the study area. It demonstrates that the mixed norm regularized inversion is a valuable interpretation technique for detecting and delineating salt structures.

Broadband seismometers are widely used in seismology and geophysics research. Low self-noise and large dynamic range are required in seismic detection but are limited by the optomechanical design for the newly developed fiber optic seismometers. A fiber optic broadband interferometric inverted pendulum seismometer (iIPS) is proposed. It is designed with a cross-spring inverted pendulum and a hybrid Michelson interferometer and provides a simple solution for recording seismic waves. By developing the pendulum and interferometer, iIPS achieves a long-period response of 35,043 rad/g within DC~4 Hz, a self-noise better than the Earth’s new low noise mode within 0.1 Hz~10 Hz, and a dynamic range of 160 dB~200 dB. Earthquake recordings at the Baijiatuan seismic station show that the data quality of iIPS is comparable to that of Streckeisen STS-2.5, one of the best seismometers in the world.

The orientation of the horizontal components of borehole strainmeters is crucial for many seismic analysis. However, most methods of orientation measurement require vertical component observation. In this study, we try to search best-estimated orientations of a location-known station-pair by minimizing the energy of cross-correlation functions between tangential and radial components of seismic coda data. Finally, we estimated the orientation of two horizontal four-components fiber strainmeters in a 60-m deep borehole in Lu'an, China. The result shows that this method can provide possible orientations without vertical component observation.

Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based on ambient noise cross-correlation, similar to conventional geophones. However, this array-dependent cross-correlation method is not suitable for fiber borehole strainmeters. Here, we developed a Moho imaging scheme for the characteristics of fiber borehole strainmeters based on ambient noise autocorrelation. S-wave reflection signals were extracted from the ambient noise through a series of processing steps, including phase autocorrelation (PAC), phase-weighted stacking (PWS), etc. Subsequently, the time-to-depth conversion crustal thickness beneath the station was calculated. We applied our scheme to continuous four-component recordings from four fiber borehole strainmeters in Lu’an, Anhui Province, China. The obtained Moho depth was consistent with the previous research results. Our work shows that this method is suitable for Moho imaging with fiber borehole strainmeters without relying on the number of stations.