The outgoing Editor-in-Chief of JGR: Solid Earth reflects on their tenure and expresses appreciation to all those who contributed to the success of the journal over recent years.

The surge in data across diverse fields presents an essential need for advanced techniques to merge and interpret this information. With a special emphasis on compiling geospatial data, this integration is crucial for unlocking new insights from geographic data, enhancing our ability to map and analyze trends that span across different locations and environments with more authenticity and reliability. Existing techniques have made progress in addressing data fusion; however, challenges persist in fusing and harmonizing data from different sources, scales, and modalities. This research presents a comprehensive investigation into the challenges and solutions in vector map alignment, focusing on developing methods that enhance the precision and usability of geospatial data. We explored and developed three distinct methodologies for polygonal vector map alignment: ProximityAlign, which excels in precision within urban layouts but faces computational challenges; the Optical Flow Deep Learning-Based Alignment, noted for its efficiency and adaptability; and the Epipolar Geometry-Based Alignment, effective in data-rich contexts but sensitive to data quality. In practice, the proposed approaches serve as tools to benefit from as much as possible from existing datasets while respecting a spatial reference source. It also serves as a paramount step for the data fusion task to reduce its complexity.

The entire editorial board of the Journal of Geophysical Research-Solid Earth would like to sincerely thank all our colleagues who reviewed manuscripts for us in 2023. The hours they spent reading in order to provide insightful comments on manuscripts not only help improve the quality of these manuscripts but also ensure the scientific rigor of our reviewing process and eventually, of the research published in the field of Solid Earth Geophysics by our journal. With the advent of open science and AGU's data policy, the reviewing process now also encompasses checking the accessibility and availability of data and developed software. This is a key objective of AGU's FAIR (Findable, Accessible, Interoperable and Reusable) policy, for which many reviewers have provided suggestions that helped to improve the data presentation and availability, and which also fed the editorial board's reflection on the matter. Of course, we particularly appreciate timely reviews, particularly in light of the growing demands imposed by the increase of manuscripts submitted to Journal of Geophysical Research-Solid Earth. We received 1,869 submissions in 2023, and 1,472 reviewers contributed to their evaluation by providing 2,237 reviews in total. We are deeply thankful for all of their contributions. The editorial board of Journal of Geophysical Research-Solid Earth: Rachel Abercrombie, Yves Bernab & eacute;, Michael Bostock (former editor), Mark Dekkers, Anke Friedrich, Shin-Chan Han, Satoshi Ide, Isabelle Manighetti (former EIC), Fenglin Niu, Douglas R. Schmitt, Alexandre Schubnel (EIC), Jun Tsuchiya, and all the associate editors of JGR-SE.

Editors of JGR‐Solid Earth express their appreciation to those who served as peer reviewers for the journal in 2022.

Editors of JGR-Solid Earth express their appreciation to those who served as peer reviewers for the journal in 2021.

Identifying and mapping fractures and faults are important in geosciences, especially in earthquake hazard and geological reservoir studies. This mapping can be done manually in optical images of the earth surface, yet it is time consuming and it requires an expertise that may not be available. Building upon a recent prior study, we develop a deep learning approach, based on a variant of a U-Net neural network, and apply it to automate fracture and fault mapping in optical images and topographic data. We show that training the model with a realistic knowledge of fracture and fault uneven distributions and trends, and using a loss function that operates at both pixel and larger scales through the combined use of weighted Binary Cross Entropy and Intersection over Union, greatly improves the predictions, both qualitatively and quantitatively. As we apply the model to a site differing from those used for training, we demonstrate its enhanced generalization capacity.

Recent technologies have enabled a significant growth of geographic datasets with different levels of detail and specifications. Subsequent analysis and mapping tasks may require to get the best out of the diversity of proposed sources. One solution is to integrate such maps through conflation. The purpose of this merging technique is to combine data that represent the same features from multiple datasets, into a new, richer dataset. Vector data conflation was intensively applied on linear networks like roads, streets and waterways, however combining polygonal building shapes has been relatively overlooked by the literature. In this paper, we propose an idea to aggregate a set of vector building maps to obtain a single fused representation. The proposed method takes as input two or more vector maps (more inputs lead to much more reliable maps), and decomposes the 2D space into a polygonal partition. A binary labelling procedure is applied using maps reliability weights, yielding contours of sets of connected buildings. Finally, a slicing algorithm decomposes contours into separate building instances. We show that our pipeline generates more accurate maps in terms of both IoU and F1 scores than any of the maps used as an input.

The Finite-Fault Rupture Detector (FinDer) algorithm computes rapid line-source rupture models from high-frequency seismic acceleration amplitudes (PGA). In this paper, we propose two extensions to FinDer, called FinDerS and FinDerS+, which have the advantage of taking into account a geological property of the source fault, its structural maturity, as well as its relation to the earthquake slip distribution. These two new algorithms calculate real-time earthquake slip profiles by backprojecting seismic and/or geodetic displacement amplitudes onto the FinDer line-source. This backprojection is based on a general empirical equation established in previous work that relates dynamic peak ground displacement (PGD) at the stations to on-fault coseismic slip. While FinDerS projects PGD onto the current FinDer line-source, FinDerS+ allows the rupture to grow beyond the current model extent to predict future rupture evolution. For an informed interpolation and smoothing of the estimated slip values, FinDerS and FinDerS+ both employ a generic empirical function that has been shown to relate the along-strike gradient of structural maturity of the ruptured fault, the earthquake slip distribution, and the rupture length. Therefore, while FinDer derives magnitudes from a relatively uncertain and general empirical rupture length-magnitude relations, FinDerS and FinDerS+ provide alternate and better informed magnitude estimates using the mean slip of the profiles derived from the integration of fault source maturity. The two new algorithms can incorporate both seismic strong-motion and geodetic displacement data. In order to recover PGD from strong-motion instruments, we double-integrate and high-pass filter ( > 0.075 Hz) the seismic acceleration records. Together, the three algorithms exploit the full spectrum of ground-motions, including high frequencies to derive a source fault model (FinDer) and low frequencies to determine the static offsets along this model (FinDerS and FinDerS+). We test the three algorithms for the 2019 M W 7.1 Ridgecrest (California), 2016 M W 7.0 Kumamoto (Japan), and 2008 M W 7.9 Wenchuan (China) earthquakes. Conclusively, low-frequency PGD data and integration of the fault maturity gradient do not speed-up calculations for these events, but provide additional information on slip distribution and final rupture length, as well as alternative estimates of magnitudes that can be useful to check for consistency across the algorithm suite. The FinDer algorithms systematically outperform previously established real-time PGD-based magnitude estimates in terms of speed and accuracy. The resulting slip distributions can be useful for improved ground-motion prediction given the observed relationship between seismic radiation and fault maturity.

The editors of JGR: Solid Earth announce that Plain Language Summaries will be required for all manuscripts.

Based on the review of the available stratigraphic, tectonic, morphological, geodetic, and seismological data, along with new structural observations, we present a reappraisal of the potential seismogenic faults and fault systems in the inner northwest Apennines, Italy, which was the site, one century ago, of the devastating Mw ~6.5, 1920 Fivizzano earthquake. Our updated fault catalog provides the fault locations, as well as the description of their architecture, large-scale segmentation, cumulative displacements, evidence for recent to present activity, and long-term slip rates. Our work documents that a dense network of active faults, and thus potential earthquake fault sources, exists in the region. We discuss the seismogenic potential of these faults, and propose a general tectonic scenario that might account for their development.

As faults grow over time and become more “mature,” some of their geometrical and mechanical properties evolve, and these changes modify earthquake behavior. It is thus of prime importance to know the degree of structural maturity of a fault that is likely to produce large earthquakes. Although this concept is extensively used, there is no common definition or metric to measure the structural maturity of a fault. We analyzed the heterogeneity of the surface traces of 13 large seismogenic faults whose maturity is known qualitatively. We measured the corrugations and step‐over segmentation of the traces from ∼100 m to the fault length scale. Corrugations and some properties of the segmentation are found to vary with fault structural maturity. We provide scaling relationships that quantify the structural maturity of a fault based on its surface trace. These results should help in parameterizing source faults in earthquake models.

Faults form dense, complex multi‐scale networks generally featuring a master fault and myriads of smaller‐scale faults and fractures off its trace, often referred to as damage. Quantification of the architecture of these complex networks is critical to understanding fault and earthquake mechanics. Commonly, faults are mapped manually in the field or from optical images and topographic data through the recognition of the specific curvilinear traces they form at the ground surface. However, manual mapping is time‐consuming, which limits our capacity to produce complete representations and measurements of the fault networks. To overcome this problem, we have adopted a machine learning approach, namely a U‐Net Convolutional Neural Network (CNN), to automate the identification and mapping of fractures and faults in optical images and topographic data. Intentionally, we trained the CNN with a moderate amount of manually created fracture and fault maps of low resolution and basic quality, extracted from one type of optical images (standard camera photographs of the ground surface). Based on a number of performance tests, we select the best performing model, M Ref , and demonstrate its capacity to predict fractures and faults accurately in image data of various types and resolutions (ground photographs, drone and satellite images and topographic data). M Ref exhibits good generalization capacities, making it a viable tool for fast and accurate mapping of fracture and fault networks in image and topographic data. The M Ref model can thus be used to analyze fault organization, geometry, and statistics at various scales, key information to understand fault and earthquake mechanics.

Fault trace data and supplementary information to the paper "Fault trace corrugation and segmentation as a measure of fault structural maturity" by Manighetti I., Mercier A., and de Barros L., Sept. 2021

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AbstractObservations of fault geometry and cumulative slip distribution serve as critical constraints on fault behavior over temporal scales ranging from a single earthquake to a fault’s complete history. The increasing availability of high-resolution topography (at least one observation per square meter) from air- and spaceborne platforms facilitates measuring geometric properties along faults over a range of spatial scales. However, manually mapping faults and measuring slip or scarp height is time-intensive, limiting the use of rich topography datasets. To substantially decrease the time required to analyze fault systems, we developed a novel approach for systematically mapping dip-slip faults and measuring scarp height. Our MATLAB algorithm detects fault scarps from topography by identifying regions of steep relief given length and slope parameters calibrated from a manually drawn fault map. We applied our algorithm to well-preserved normal faults in the Volcanic Tablelands of eastern California using four datasets: (1) structure-from-motion topography from a small uncrewed aerial system (sUAS; 20 cm resolution), (2) airborne laser scanning (25 cm), (3) Pléiades stereosatellite imagery (50 cm), and SRTM (30 m) topography. The algorithm and manually mapped fault trace architectures are consistent for primary faults, although can differ for secondary faults. On average, the scarp height profiles are asymmetric, suggesting fault lateral propagation and along-strike variations in the fault’s mechanical properties. We applied our algorithm to Arizona and Utah with a specific focus on the normal Hurricane fault where the algorithm mapped faults and other prominent topographic features well. This analysis demonstrates that the algorithm can be applied in a variety of geomorphic and tectonic settings.

AbstractEarthquake early warning would be improved if the final size of an ongoing earthquake could be predicted early in the rupture process. Previous research relies largely on parameters derived from seismic waveforms, resulting in widely varied estimates of how much of the rupture must be complete before final sizes can be predicted. We demonstrate here that incorporating prior information on along‐strike variation in source fault structural maturity helps improve constraints on the earthquake's final size. Using surface slip profiles from 26 large continental earthquakes worldwide, we derive a generic empirical relation between earthquake slip, rupture length, and along‐strike variability in source fault maturity. Using this equation, we fit successive slip profiles developing during each earthquake and find that, on average, we predict the final length and magnitude of an earthquake once it has reached ~20% of its total length. Our findings thus demonstrate a clear determinism in the rupture process.

The Mw 7.8 2016 Kaikoura earthquake ruptured the Kekerengu-Needle fault resulting in the loading of its eastern continuation, the Wairarapa fault. Since the most recent earthquake on Wairarapa occurred in 1855 and is one of the strongest continental earthquakes ever observed, it is critical to assess the seismic potential of the Wairarapa fault, which might be prone to break. Using Lidar data, we examine its bare-earth morphology and reveal ~650 mostly undiscovered offset geomorphic markers. Using a code we developed in earlier work, we automatically measure the lateral and vertical offsets of these markers providing more than 7000 well constrained measurements. The data document the lateral and vertical slip profiles of the 1855 earthquake for the first time and show its total slip reached ~20 m at surface. Modeling the entire offset dataset reveals 7 prior earthquakes ruptured the entire fault, each similarly producing 16.9 ± 1.4 m dextral slip and ~0.6 m vertical slip at surface in the same central bend zone of the fault. Thus, the Wairarapa fault repeatedly produced giant earthquakes and is likely able to produce a similarly strong forthcoming event. The extreme large size of the Wairarapa earthquakes questions our understanding of earthquake physics.

Supplementary dataset for Improving early estimates of large ea­­rthquake’s final fault lengths and magnitudes leveraging source fault structural maturity information. This contains individual performance test results for the algorithm discussed in this publication for each earthquake included in the study

The dataset includes: - the image and topographic data used in the study - the ground truth used in the study - the parameters of the model MRef developed in the study - the Supplementary documents and figures discussed in the paper