Seismic Sensors Research Articles

Classification of Non-Seismic Tsunami Early Warning Level Using Decision Tree Algorithm

Background: Tsunami caused by volcanic collapse are categorized as non-seismic uncommon events, unlike tsunamis caused by earthquakes, which are common events. The traditional tsunami early warning based on the seismic sensor (e.g. earthquake detectors) may not be applicable to volcanic tsunamis because they do not generate seismic waves. Consequently, these tsunamis cannot be detected in advance, and warnings cannot be issued. New methods should be explored to address these non-seismic tsunamis caused by volcanic collapse. Objective: This study explored the potential of machine learning algorithms in supporting early warning level issuing for non-seismic tsunamis, specifically volcanic tsunamis. The Anak Krakatau volcano event in Indonesia was used as a case study. Methods: This study generated a database of 160 collapse scenarios using numerical simulation as input sequences. A classification model was constructed by defining the worst tsunami elevation and its arrival time at the coast. The database was supervised by labeling the warning levels as targets. Subsequently, a decision tree algorithm was employed to classify the warning levels. Results: The results demonstrated that the classification model performs very well for the Major Tsunami, Minor Tsunami, and Tsunami classes, achieving high precision, recall, and F1-Score with very high accuracy of 98%. However, the macro average indicates uneven performance across classes, as there are instances of ‘No Warning’ in some coastal gauges. Conclusion: To improve the model performance in the ‘No Warning’ class, it is necessary to balance the dataset by including more ‘No Warning’ scenarios, which can be achieved by simulating additional scenarios involving very small-volume collapse. Additionally, exploring additional collapse parameters such as dip angle and outlier volume could contribute to developing a more robust classification model. Keywords: Machine Learning, Classification, Volcanic Tsunamis, Early Warning, Decision Tree

First Installation of an Optical Ocean-Bottom Seismometer, Cabled Offshore Les Saintes, Lesser Antilles

Abstract The detection and analysis of offshore seismic processes worldwide often require the use of ocean-bottom seismometers (OBSs). However, most OBS deployments are done with stand-alone stations, with data recovery delayed by months. On the other hand, electrically cabled OBS, which allows for real-time monitoring, remains exceptional due to the high cost of manufacturing, installation, and maintenance. Here, we present a new perspective for cabled array of OBSs, using purely optical seismometers, plugged at the end of long fiber-optic cables, aimed at reducing their cost for observatories requesting real-time data. The optical seismometer was developed in the last decade by the École Supérieure d’Électronique de l’Ouest, based on the Fabry–Perot interferometer, tracking at high resolution the displacement of the mobile mass of a mechanical geophone (no electronics nor feedback). A prototype was successfully installed at the top of La Soufrière volcano of Guadeloupe in 2019. We replicated this sensor and installed it 5 km offshore Les Saintes islands, at 43 m depth (Guadeloupe, Lesser Antilles) to characterize the swarm-type activity persistent after the 2004 M 6.3 earthquake (Interreg Caraïbe PREST project). The installation cruise, FIBROSAINTES, was supported by the Flotte Océanographique Française. A plow designed by GEOAZUR carried the cable and was pulled on the seafloor by the vessel ANTEA. The landing cable was connected to the interrogator, with a real-time telemetry to the Institut du Physique du Globe de Paris/Observatoire Volcanologique et Sismologique de Guadeloupe. The OBS has been qualified with local land-based velocity broad band stations. The analysis of local earthquake swarms suggests transient creep on the major normal faults. This successful installation opens promising perspectives for real-time monitoring in on-land or offshore sites, presenting harsh environmental conditions, in which commercial, electrical seismic sensors are difficult and/or costly to install and maintain.

A real scale application of a novel set of spatial and similarity features for detection and classification of natural seismic sources from Distributed Acoustic Sensing data

Summary Distributed Acoustic Sensing (DAS) turns a fiber optic into a very dense network of equally-distributed seismic sensors. We focused on the high-density sampling of the seismic wavefield, expressed in strain rates, measured by DAS. Classical approaches used to identify seismic signals rely on the recorded features at one station, but it is difficult to include spatial information in case of dense seismic station networks. This work aims at introducing new spatial and similarity features for seismic event classification suitable to analyze DAS observations. We propose a processing chain based on the XGBoost algorithm and the use of specifically designed spatio-temporal and similarity features for the event classification, and Markov Random Field for the spatial clustering. The methodology is designated to be applied on a continuous stream of DAS observations. We tested our processing chain to detect earthquakes and quarry blasts recorded in the region by permanent seismic networks and included in the RENASS catalog. These events are part of a strain-rate seismic survey carried out during a 3 weeks campaign of DAS measurements along à 91 km fiber optic cable deployed in the central Pyrenees mountains (France). Despite the high anthropogenic activities along the fiber optic path, the proposed method succeeded in detecting earthquakes of magnitude >0.4 and quarry blasts of magnitude >1.0 while limiting the number of false alarms. This performance is particularly noteworthy for low-magnitude events, where detection is accomplished despite a lower signal-to-noise ratio compared to traditional seismometers. The methodology opens the door to real time detection and classification of seismic events measured with long-distance fiber optic systems.

Rotaphone-D, A New Model of Six-Degree-of-Freedom Seismic Sensor: Description and Performance

Abstract A new model of the Rotaphone-D short-period seismic sensor with six degrees of freedom is introduced. The basis of the instrument is horizontal and vertical geophones in a special paired arrangement. The instrument is designed for simultaneous and collocated measurements of rotational and translational components of ground motion. The basic principle of measurement is briefly described. Careful calibration is necessary for this type of measurement, which has two parts: a preliminary calibration based on laboratory measurements of the characteristics of the individual geophones, and a subsequent in situ calibration, which takes into account the actual physical conditions during field measurements and is performed during the processing of the measured data. The effect of the calibration is demonstrated by specific laboratory tests. The laboratory tests have confirmed the correct functioning of the instrument. The frequency range of Rotaphone-D is from 2 to 80 Hz and is therefore optimal for monitoring local seismicity. The instrument was subsequently used in field measurements during two several-month measurement campaigns in California, United States. Examples of six-component records from two sites—The Geysers and Long Valley Caldera—are presented and briefly interpreted from a seismological perspective. Basic instrument parameters are given. The instrument is lightweight, portable, easy to install, and offers several interesting applications, which are briefly discussed. Six-component measurements (translational and rotational) have become a powerful tool, especially in volcanic seismology and in monitoring induced seismicity.

Combined application of FTAN and cross-spectra analysis to ambient noise recorded by a microseismic monitoring network

A case study of seismic interferometry applied to a small microseismic monitoring network is here presented. The main objectives of this study are (i) to quantify the lateral variability of shear-wave velocities in the studied area, and (ii) to investigate the bias produced by noise directionality and non-stationarity in the velocity estimate. Despite the limited number of stations and the short-period character of the seismic sensors, the empirical Green’s functions were retrieved for all station pairs using two years of passive data. Both group and phase velocities were derived, the former using the widespread frequency-time analysis, the latter through the analysis of the real part of the cross-spectra. The main advantage of combining these two methods is a more accurate identification of higher modes, resulting in a reduction of ambiguity during picking and data interpretation. Surface wave tomography was run to obtain the spatial distribution of group and phase velocities for the same wavelengths. The low standard deviation of the results suggests that the sparse character of the network does not limit the applicability of the method, for this specific case. The obtained maps highlight the presence of a lower velocity area that extends from the centre of the network towards southeast. Group and phase velocity dispersion curves have been jointly inverted to retrieve as many shear-wave velocity profiles as selected station pairs. While the average model can be used for a more accurate location of the local natural seismicity, the associated standard deviations give us an indication of the lateral heterogeneity of seismic velocities as a function of depth. Finally, the same velocity analysis was repeated for different time windows in order to quantify the error associated to variations in the noise field. Errors as large as 4% have been found, related to the unfavorable orientation of the receiver pairs with respect to strongly directional noise sources, and to the very short time widows. It was shown that using a one-year time window these errors are reduced to 0.3%.

Modeling the trajectories of a group of unmanned aerial vehicles based on the ADRRT-Connect algorithm in the problem of placing seismic sensors

Optimal route planning is a key aspect of successfully delivering geosensors using unmanned aerial vehicles (UAVs) to geographically distributed targets. The optimality of the route depends on the time required to plan the route and execute the flight to a target or group of targets, considering traffic safety and dynamic constraints. The trajectory calculation algorithm must always find a route, if one exists, and if it is impossible to find one, inform the operator about this circumstance. This article proposes a method based on a set of algorithms that ensure efficient distribution of targets among group members based on information about the targets and characteristics of the unmanned aerial vehicles used. The method involves constructing optimal routes considering minimizing path length, avoiding sharp turns and loops, as well as avoiding obstacles and avoiding collisions with other unmanned aerial vehicles within the group. To test the proposed method, software was developed using the ROS/Gazebo en-vironment. The proposed method is a comprehensive solution to the problem of optimal route planning for UAVs with an emphasis on efficiency, safety, and efficiency in the context of geosensor delivery using unmanned aerial vehicles.

BROADBAND SEISMIC NODE BASED ON LOW-NOISE HIGH-SENSITIVITY MOLECULAR-ELECTRONIC SENSORS

The developed wideband compact digital seismic module designed for use in ground nodal seismic systems with deployment assistance from unmanned aerial vehicle (UAV) is discussed. The operation frequency band is 1–300 Hz, which is by far wider than that of the seismic nodes based on using standard (10Hz) electrodynamic geophones. Such improvement was achieved through the use of highly sensitive seismic sensors with molecular electronic transfer (MET) that allow for covering a low-frequency part of the spectrum that is needed for broadband processing and receiving information on deep subsurface formation. Low self-noise of the MET seismic sensors used for this seismic station has been proven in special tests. According to the test results, measurements of the self-noise of the developed sensor turned out to be 27-30 dB lower than the self-noise of similar sensors with additional mass. Besides of the 3 mutually orthogonal wideband MET sensors the developed module consists of a digital electronics block enabling autonomous registration and featuring wireless data transmission capability; a battery compartment providing autonomous operation of the seismic module; a device for attaching the seismic module to UAVs and lowering it to the ground. The developed device ensures data time synchronization with satellites, records data at frequencies up to 1000 samples per second with 24 bits resolution and stores the recorded information on the internal 32 Gb storage. A distinctive feature of the presented seismic node is the implementation of quality control of seismic data using a drone. This function is implemented on a Raspberry Pi single-board computer installed on board of the UAV, which has the ability to retrieve portion of information from the node for data quality control and initiate calibration procedure.

IoT based Smart Weather Monitoring with Flood and Earthquake Detection

Weather monitoring is essential for understanding the changing climatic conditions. Traditional weather monitoring system are slow, labor intensive, and outdated, often prone to error and inaccurate predictions. To overcome these challenges and provide an up-to date weather information, the IoT (Internet of Things) has been implemented in weather monitoring. This research presents an IoT-based weather monitoring system integrated with sensors to ensure accurate weather monitoring, including flood and earth quake detection. The collected data, such as temperature, humidity, atmospheric pressure, rainfall, flood levels, and seismic activity, is transmitted through IoT protocols to a centralized repository (Cloud) and visualized in the Blynk App. Flood sensors are strategically positioned in flood-prone areas, continuously monitoring water levels to detect abnormal rises indicative of potential floods. Similarly, seismic sensors detect ground vibrations and seismic activity, providing early alerts for earthquakes. The proposed system offers a comprehensive approach to monitor the weather, contributing to enhanced preparedness and mitigation strategies for natural disasters.

GajGamini: Mitigating Man–Animal Conflict by Detecting Moving Elephants Using Ground Vibration-Based Seismic Sensor

GajGamini: Mitigating Man–Animal Conflict by Detecting Moving Elephants Using Ground Vibration-Based Seismic Sensor

Fiber-optic seismic sensing of vadose zone soil moisture dynamics

Vadose zone soil moisture is often considered a pivotal intermediary water reservoir between surface and groundwater in semi-arid regions. Understanding its dynamics in response to changes in meteorologic forcing patterns is essential to enhance the climate resiliency of our ecological and agricultural system. However, the inability to observe high-resolution vadose zone soil moisture dynamics over large spatiotemporal scales hinders quantitative characterization. Here, utilizing pre-existing fiber-optic cables as seismic sensors, we demonstrate a fiber-optic seismic sensing principle to robustly capture vadose zone soil moisture dynamics. Our observations in Ridgecrest, California reveal sub-seasonal precipitation replenishments and a prolonged drought in the vadose zone, consistent with a zero-dimensional hydrological model. Our results suggest a significant water loss of 0.25 m/year through evapotranspiration at our field side, validated by nearby eddy-covariance based measurements. Yet, detailed discrepancies between our observations and modeling highlight the necessity for complementary in-situ validations. Given the escalated regional drought risk under climate change, our findings underscore the promise of fiber-optic seismic sensing to facilitate water resource management in semi-arid regions.

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring.

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.

Moho Imaging with Fiber Borehole Strainmeters Based on Ambient Noise Autocorrelation.

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.

Monitoring urban construction and quarry blasts with low-cost seismic sensors and deep learning tools in the city of Oslo, Norway

The aim of this study is to collect information about events in the city of Oslo, Norway, that produce a seismic signature. In particular, we focus on blasts from the ongoing construction of tunnels and under-ground water storage facilities under populated areas in Oslo. We use seismic data recorded simultaneously on up to 11 Raspberry Shake sensors deployed between 2021 and 2023 to quickly detect, locate, and classify urban seismic events. We present a deep learning approach to first identify rare events and then to build an automatic classifier from those templates. For the first step, we employ an outlier detection method using auto-encoders trained on continuous background noise. We detect events using an STA/LTA trigger and apply the auto-encoder to those. Badly reconstructed signals are identified as outliers and subsequently located using their surface wave (Rg) signatures on the seismic network. In a second step, we train a supervised classifier using a Convolutional Neural Network to detect events similar to the identified blast signals. Our results show that up to 87% of about 1,900 confirmed blasts are detected and locatable in challenging background noise conditions. We demonstrate that a city can be monitored automatically and continuously for explosion events, which allows implementing an alert system for future smart city solutions.

Fiber-optic gyroscope for rotational seismic ground motion monitoring of the Campi Flegrei volcanic area

The real-time monitoring of densely populated areas with high seismic and volcanic risk is of crucial importance for the safety of people and infrastructures. When an earthquake occurs, the Earth surface experiences both translational and rotational motions. The latter are usually not monitored, but their measurement and characterization are essential for a full description of the ground motion. Here we present preliminary observational data of a high-sensitivity rotational sensor based on a 2-km-long fiber-optic Sagnac gyroscope, presently under construction in the middle of the Campi Flegrei Volcanic Area (Pozzuoli, Italy). We have evaluated its performance by analyzing data continuously recorded during an acquisition campaign of five months. The experimental setup was composed of a digital nine-component seismic station equipped with both a rotational sensor and conventional seismic sensors (seismometers, accelerometers, and tiltmeters). During this experiment we detected seismic noise and ground rotations wavefield induced by small to medium local earthquakes (M D <3). The prototype gyroscope shows a very promising sensitivity in the range of 5×10−7−8×10−9rad/s/Hz over the frequency bandwidth 5 mHz–50 Hz. Future upgrades and perspectives are discussed.

Towards real-time earthquake forecasting in Chile: Integrating intelligent technologies and machine learning

This paper proposes an innovative approach towards real-time earthquake forecasting in Chile by integrating intelligent technologies and machine learning methods. Earthquakes pose significant risks to communities and infrastructure in Chile, making accurate and timely forecasting crucial for disaster preparedness and mitigation. Traditional forecasting methods have limitations in providing real-time insights into seismic activity. In contrast, intelligent technologies such as artificial intelligence (AI) and machine learning offer promising avenues for enhancing prediction accuracy and speed. A new earthquake forecasting method using a modified clustering approach LMSCAN(Local Maxima-based Spatio-Cluster Analysis Network) and enhanced neural network time series analysis LSTM-IC (Long Short Term Memory Inverse Correlation) is presented in this paper. This neural network-based technology has been utilized to forecast earthquakes in the Chile region, one of the countries with the largest seismic activity. This study explores the integration of intelligent systems with machine learning algorithms to analyze seismic data and predict earthquake occurrences in Chile. By leveraging historical seismic data, sensor networks, and advanced predictive models, our approach aims to provide timely warnings and insights into seismic events, thereby improving disaster response and resilience. The proposed framework holds the potential to revolutionize earthquake forecasting by enabling real-time monitoring and proactive measures to safeguard communities and infrastructure in Chile and beyond. The remarkable 95 % accuracy achieved by this model is a testament to its exceptional learning process, which sets it apart from other models. Its ability to learn and adapt to new data is unparalleled, allowing it to forecast incredibly precisely and produce highly reliable results.

A feasibility study of Boree Salt body mapping in the Adavale Basin using passive seismic data

Hydrogen plays a pivotal role in the global energy transition and may require underground storage. So far salt cavern storage is the only proven technology for underground hydrogen storage. The Boree Salt in the Adavale Basin, mostly at depths from 1 to 2.5 km and up to 550 m thick, consists predominantly of halite and is deemed suitable for hydrogen storage. However, current maps are inadequate. Recently passive seismic data (ambient noise) have received much interest for subsurface imaging. The main signal from passive data is surface waves (usually below 2 Hz). The capability of surface waves for the Boree Salt body mapping is examined. Parameters of seismic sensor spacing, the dominant frequencies of the surface waves, and data noise levels are all considered. It is demonstrated that surface waves from ambient noise can map the Boree Salt bodies with a survey distance of ~40 km. Between frequencies of 0.12 and 0.25 Hz, results from the latter have better resolution because of a shorter wavelength. Moving to higher frequencies of 0.5 and 1 Hz, however, the resolution becomes worse, because the depth sensitivity of surface waves moves to the shallower part of the model with increasing frequencies, rendering them incapable of effectively probing the targeted depths. For signal/noise ratio above five, station spacing can be as large as 1 km without compromising quality. Therefore, cost-effective and environmentally friendly passive seismic data can be a good alternative to the traditional active-source data for deep salt body imaging.

Comparison of Near-Surface Attenuation from Surface Array-Based Seismic Noise Data and Borehole Weak-Motion Recordings at the STIN Test Site in Northeastern Italy

Abstract Seismic wave attenuation and the related shear-wave quality factor (QS) in the near surface are crucial parameters for ground motion simulations and seismic hazard assessments. Although recent approaches developed to calculate QS from seismic noise acquired by surface arrays have been accepted for practice, additional testing and comparison of results estimated using various geophysical methods are still necessary to verify the reliability of such techniques. This work presents the results of an experiment conducted at the STIN site in northeastern Italy, which is equipped with a 100 m deep instrumented borehole. A seismic noise campaign was implemented by installing a temporary independent local surface array of seismological stations. The gathered data were used to initially estimate the shear-wave velocity (VS) profile and frequency-dependent Rayleigh-wave attenuation, and subsequently determine the QS factor via a linearized inversion method. The study compares these findings with the VS and QS values derived from analyzing weak-motion events recorded by two permanent seismic sensors positioned at the top and bottom of the well. The results confirm the potential of the inversion procedure used to obtain QS from local-scale seismic noise arrays as a promising approach for conducting attenuation studies at the local level in less geologically complex sites.

Earthquake Events Detected by Continuous Gravimeters in Indonesia

Earthquake monitoring and early warning systems play a critical role in mitigating the impact of seismic events on human lives and infrastructure. Continuous gravimeters, sensitive instruments designed to measure second changes in gravitational forces, have emerged as valuable tools for earthquake detection and monitoring. This paper reports the significance of continuous gravimeters in detecting earthquake events, highlighting their principle of operation and contributions to seismic research. In contrast to traditional seismic sensors, continuous gravimeters offer a unique perspective on seismic activity by detecting mass redistributions resulting from the movement of Earth’s tectonic plates during an earthquake. This capacity enables them to capture the initial moments of an earthquake, contributing to early warning systems that can provide critical seconds to minutes of advance notice, enhancing preparedness and response efforts. Some earthquake events were detected by the gPhoneX gravimeters (numbers #123 and #126) placed in Institut Teknologi Bandung, Ganesha Campus. Four possible location events matched with the timeframe of the seismometer recording data on June 15, 2023. This result shows that continuous gravimeters have the potential capability to advance earthquake monitoring and early warning capabilities. By offering a complementary approach to seismic sensors, these instruments broaden our understanding of earthquake dynamics and contribute to developing more robust and effective strategies for mitigating seismic hazards.

Implementation of Seismic Sensor to Detect Tank

This study aims to investigate seismic sensors in detecting ground vibrations caused by tank or military vehicle movements. Ground vibrations generated by tank movements have specific characteristics, where the produced frequency remains constant depending on the tank's weight and the area of the track's footprint. The amplitude of these vibrations provides information about the detection range, wherein the larger the amplitude, the closer the detection range to the tank. The method employed involves field experiments or the collection of quantitative data from field trials. Test results indicate that ground vibrations produced by tanks have an average frequency of 332 Hz, depending on the tank's weight and the area of the track's footprint. The average amplitude is 4.3 volts at a detection range of 1000 meters, 5.1 volts at 500 meters, 7.6 volts at 200 meters, 8.8 volts at 100 meters, and 12.1 volts at 50 meters. The implication of this research is that seismic sensors can be effectively used as tools to detect tank or military vehicle movements at different distances. These findings could be beneficial in the development of security systems or early detection in areas vulnerable to potential threats from tanks or military vehicles.

Seismic hazard identification around Surabaya City, East Java, Indonesia: a preliminary result based on the June 2023 Mojokerto earthquake

An earthquake was felt in the Mojokerto area, East Java (Indonesia) with a magnitude of M 4.4. This area has a low seismicity level according to the latest earthquake catalog. In this paper, we conduct a study to determine the direction of fault movement in the area, using data from BMKG seismic sensors with a distance of less than 250 km. The moment tensor inversion results show that the Strike/Dip/Rake values at each nodal plane are 257/88/4 and 167/86/175. To determine the direction of fault movement, we prefer a strike of 167 degrees for this earthquake, following the direction of deformation around the area from the northeast of Java Island. From the results of the moment tensor solution and the history of seismicity that has occurred in the area around Surabaya, it can be shown that the city of Surabaya is a city that is close to seismic activity, although it tends to have small activity, this needs to be considered considering that Surabaya is one of the cities with the highest population density in Indonesia. Further research needs to be done related to disaster vulnerability around the city of Surabaya. From the results of the source mechanism and seismicity in the Surabaya area it can be concluded that the Surabaya area has a disaster risk, there needs to be further research that discusses the local footprint effects of soil in the area in the Surabaya area. This research is needed for further discussion of the possibility of disasters in the Surabaya area.