Büyük, Ersin; Zor, Ekrem; Tapırdamaz, Mustafa Cengiz

It is widely acknowledged that the joint inversion of magnetotelluric and seismological datasets enhances the quality of the crustal structure solution, even when the physical correlation between electrical resistivity and seismic velocity is weak or indirect. The structurally coupled joint inversion approach has received considerable attention over the past two decades for its ability to estimate such parameters by penalizing their cross-gradient vectors at similar spatial positions. Despite this interest, various structural couplings and different physical directions (incremental or decremental) have been partially overlooked. We hereby propose an approach for the joint inversion of magnetotelluric (MT) and Rayleigh wave dispersion (RWD) data to estimate uncorrelated parameters by integrating particle swarm optimization (PSO) and the Pareto optimality approach. We used the optimality framework of these methods to overcome the difficulties associated with traditional joint inversion algorithms and to obtain optimal solutions that account for both similar and contrasting physical sensitivities. The significant correlation between the inverted and synthetic models under both noise-free and noisy datasets, together with the consistent results obtained from comparison with a traditional derivative-based joint inversion algorithm, further strengthened our confidence in applying the proposed modeling approach to the field data from the southeastern Biga Peninsula, western Anatolia. The models inverted from the field data corroborate the efficacy of the presented method. A notable characteristic of the proposed methodology is its capacity to estimate uncorrelated physical parameters, such as electrical resistivity and seismic velocity, without the imposition of penalties. Therefore, the presented method not only offers advantages in joint inversion but also allows modelers to observe and analyze model parameters having different sensitivities that may indicate different physical directions.

Zor, Ekrem; Peng, Zhigang; Ergin, Mehmet; Sandvol, Eric; Sevim, Fatih; Rdamaz, Mustafa Cengiz Tap i; Mach, Phuc; Yalvac, Ouzhan; Tarancioglu, Adil; Adeboboye, Oluwaseyifunmi

The devastating 6 February 2023 Kahramanmara & scedil;earthquake sequence in southeastern T & uuml;rkiye started with a moment magnitude (Mw) 7.8 earthquake, for which the initial rupture broke the Sak4ag & ouml;z segment near Nurda & gbreve;& imath; and then jumped into a bilateral rupture along multiple segments of the Eastern Anatolian fault zone (EAFZ). This complicated rupture was followed nine hours later by an Mw 7.6 event near Ekin & ouml;z & uuml;. To better understand the spatiotemporal evolution of aftershocks, site amplification, and the structural and tectonic framework of the EAFZ in this diffuse triple junction, we deployed a dense seismometer array covering both aftershock zones for nearly four months. The main Eastern Anatolian Seismic Temporary (EAST) array includes 125 nodal, 10 broadband, and 6 strong-motion seismic stations distributed around the rupture zone. An additional linear array of 73 nodal stations was also installed across the Pazarc & imath;k segment of the EAFZ and the Sak4ag & ouml;z segment near the Mw 7.8 epicenter to record fault-zone waves for similar to 30 days. This article shows example recordings and the EAST array geometry, preliminary research results, and the metadata related to all of the stations in this array. A deep-learning-based phase picking for one month of continuous recording yielded millions of seismic phase readings and tens of thousands of aftershock locations after phase associations. We also give examples of both local and teleseismic waveforms recorded by the nodal arrays, which can be used for subsequent high-resolution earthquake relocation, imaging of crustal structures, and fault-zone imaging.