The Budyko framework consists of a curvilinear relationship between the evaporative ratio (i.e., actual evaporation over precipitation) and the aridity index (potential evaporation over precipitation) and defines evaporation’s water and energy limits. A basin’s movement within the Budyko space illustrates its hydroclimatic change and can help identify the main drivers of change. Basins are expected to move along their Budyko curves when only long-term changes in the aridity index drive changes in the evaporative ratio. We hypothesize that the increasing effects of global warming on the hydrological cycle will cause basins to move along their Budyko curves. To test our hypothesis, we quantify the movement in Budyko space of 353 river basins from 1901 to 2100 based on the outputs of nine models from the Coupled Model Intercomparison Project - Phase 5 (CMIP5). We find that significant increases in potential evaporation due to global warming will lead to basins moving primarily horizontally in Budyko space accompanied by minor changes in the evaporative ratio. However, 37% of the basins will still deviate from their Budyko curve trajectories, with less evaporation than expected by the framework. We elaborate on how land-use change, vegetation changes, or shifts in precipitation or snow to rain ratios can explain these deviations.

Geothermal well log analyses consist of utilizing pressure and temperature data measured along the wellbore to predict feed zones, reservoir temperature, and reservoir pressure. Interpreting geothermal production logs can be subjective and require great expertise to achieve repeatability. In situations where there may be several log data, interpreting the logs may be time consuming for quick decision-making processes. This work discusses the implementation of a multi-layered deep learning convolutional neural network to automatically diagnose sets of temperature and pressure well logs. The algorithm achieves results similar to those of a professional engineer. This algorithm enables the interpretation of many well logs in just a few seconds. Data input for this project is synthetic well data that mimics real data. 10,000 datasets were used. The data was split as follows: 9,800 and 200 for training and validation respectively. The algorithm used takes as input three “depth-series” logs of temperature, pressure, and temperature gradient, passes the data through a convolutional neural network including a flat layer and then a fully connected layer with five output variables which are the depths of the feed zones, the reservoir temperature, the reservoir pressure and the depth at which the reservoir pressure is known. The cost function for this model was the mean squared error. The optimizer algorithm used was Adam, and the learning rate had an exponential decay. The algorithm recorded the model state that had the lowest mean absolute validation error. The architecture was implemented in Keras with a TensorFlow backend. The best model found during the process of hyper-parameter tuning was used to predict the reservoir characteristics for the validation and testing data sets. The results show a good match between the predicted data and actual data with a training error of 0.9%, validation error of 2%, and test error of 7%. Future works will involve adding more real data to the training and validation set, increasing the number of feedzones that can be identified, and performing sequential analysis using interdisciplinary data.

The study aims to investigate the relationship between dominant climate variabilities namely El Niño-Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD) versus rainfall, and vertical humidity in Chiangmai, Thailand. The Oceanic Niño Index (ONI) which represents ENSO status and Dipole Mode Index (DMI) which represent IOD indicator were collected for the period 1986-2020 (35 years). Subsequently, the 12-month lag correlations were analyzed between both indicators against rainfall and vertical moisture at cloud-base and cloud-top height levels for the same time periods. The results indicate that the impacts of both climate variabilities may not occur during the same period of the phenomenon. The effects of ENSO to weather pattern in Chiang Mai might be lagged by 0 to 12 months. Even though the correlation of the humidity versus ONI and DMI is fluctuated. The correlation results of ONI and cloud-base humidity level has moderate values and consistent with La Niña events. However, the DMI correlations against rainfall and mixing ratio are inconclusive due to insufficient data. The relationship between ONI index, rainfall, and mixing ratio can be used to predict the future rainfall and atmospheric moisture once they are validated. Keyword: ENSO, ONI, DMI, IOD, rainfall, mixing ratio, vertical humidity

Muddy sediments are abundant across aquatic ecosystems, consisting of mineral grains and biological material. Erosional characteristics of these cohesive sediments are impacted by micro-organisms providing bio-stabilisation. Deposition may be impacted by chemical and biological composition, along with turbulence properties, which in turn influence flocculation of suspended particulate matter. Flocculation processes affect settling velocity, porosity and density characteristics. Cohesive sediments absorb contaminants, as well as influencing interactive processes between sedimentary dynamics and hydrodynamics, through their bio-physical attributes. It is therefore beneficial to predict muddy sediment transport processes via numerical modelling. Accurate modelling relies on quantitative erosional and depositional data for calibration. Through collation and analysis of field- and laboratory-derived data sets, this study examined aspects of erodibility and deposition across several aquatic environments (including estuarine, intertidal and lake sediments). A range of case studies examined floc properties, sediment composition, erosion thresholds, turbulent shear stress and suspended particulate matter concentration. Investigation of floc dynamics in estuarine sediments revealed larger, faster settling flocs in muddy sediment (mean settling velocity, Wsmean = 4.1-5.2 mm.s⁻¹; mean floc effective density, ρe,mean = 317-352 kg.m⁻³). In mixed sediment, flocs were smaller and settled more slowly (Wsmean = 3.8-4.0 mm.s⁻¹; ρe,mean = 288-508 kg.m⁻³). Comparison of oil-contaminated sediments revealed the importance of floc size class and mineral type. On the addition of oil, larger, faster settling flocs were produced in pure bentonite cases, while smaller, slower settling flocs were observed in kaolinite cases. In highly organic lake sediments (organic content = 62%), settling velocity varied over increasing suspended sediment concentration and between floc size classes (macroflocs faster than microflocs by 0.95 mm.s⁻¹). Such findings may be utilised to increase the understanding of complex sedimentary and hydrodynamic interactions within aquatic environments. This study provides quantitative data, applicable to the improvement of predictive numerical model reliability.

Climate is a key factor affecting water and energy resources. To quantify its effect, researchers have developed statistical and mechanistic models that simulate the interactions of the Water-Energy-Climate Nexus (WECN). In this work, we address two limitations of current WECN models, including the need to (1) account for the impact of climate on both demand and supply of water and energy, and (2) consider the influence of multiple climate variables in addition to temperature and precipitation. For this aim, we build upon our existing model that simulates in coupled fashion the water and energy systems of the metropolitan region of Phoenix, AZ. We design multidecadal storylines of future climate using an ensemble of downscaled general circulation models from CMIP6, along with possible energy infrastructure expansion scenarios. We develop a hydrologic model to account for the climate impacts on surface water supply sources. We constrain electricity supply based on water availability and temperature-dependent water intensities for power production. In parallel, we consider the effect of climate on demand through multilinear regressions between per-capita water and energy demand and several climate variables. This work advances the simulation of WECN interactions by integrating statistical and mechanistic models of water and energy systems with climate and hydrologic models. While the modeling framework is tested in the Phoenix metropolitan region, our findings provide useful insights that support WECN modeling efforts in other regions.

There has been a significant increase in the amount and accuracy of mineral data (from resources like Mindat, MED or the GEMI) and the improvements in technological resources make it possible to explore and answer large, outstanding scientific questions, such as, understanding the mineral assemblages on Earth and how they compare to assemblages and localities on other planets. In the last couple of years, affinity analysis methods have been used to:1) Predict unreported minerals at an existing locality, 2) Predict localities for a set of known minerals[1]. We’ve chosen to call this application “Mineral Association Analysis”[2]. Affinity analysis is an unsupervised machine learning method that uses mined association rules to find interesting patterns in the data. Most of the metrics used to evaluate market basket analysis methods focus on either the ability of the model to ingest large amounts of data[3], or using a metric based comparison of various algorithms used for association rule mining[4], or on evaluating the rules mined to more efficiently generate association rules[5]. However, when patterns generated in an unsupervised method are used to predict the occurrences of entities such as minerals, there needs to be a way to evaluate the predictions made by the model. It’s in such an area that there has been very little work. In this abstract, we explore the development of a new method to evaluate the results of association rule mining algorithms specifically when used when the association rules generated are utilized in a predictive setting. [1] Prabhu et. al (2019). In AGU Fall Meeting Abstracts (EP23D-2286). [2] Morrison et al. Nat. Geo. (2021) In Prep. [3] Agrawal et al. (1993) SIGMOD’93. [4] Sharma et al. (2012) IJERT 1(06). [5] Üstündağ and Bal (2014) Proc. in Comp.

Statistical downscaling plays an important role in reducing the uncertainty in regional-scale climate change studies. A major assumption of all statistical downscaling studies is the stationarity in the relationship identified from the historical observations. This assumption is difficult to validate since the future is truly unknown. The proposed methodology tries to overcome the limitations of this assumption by considering global physics that drive the regional climate to select the predictors to develop the statistical relationship. Since the statistical model is developed with climatic processes as a background, it can be said with higher confidence that the modelled relation would remain sound for the future as well. The proposed methodology is divided into two stages. In the first stage, a Relevance Vector Machine model is developed using historical observed global predictors that are identified to have teleconnections with the regional climate predictand. The spatial downscaling of this regional predictand has been done on a monthly scale. The bias associated with the downscaled predictand is removed by separating the anomalies from the prediction and adding that to the historical mean of the observed data. This monthly series is further disaggregated into daily series using a weather generator. The non-stationarity in the climate projections is accommodated in the weather generator and more regional features of the climate is integrated into the predictand in this stage. The proposed methodology is validated by downscaling rainfall over a river basin in India and its performance is analysed. The downscaled data is seen to reproduce characteristics of daily rainfall like consecutive wet days, the number of rainy days, wet spell duration, and dry spell duration with a bias of less than 10%.

The Neoproterozoic Era represents extreme environmental conditions with three major glaciation events. Associated with these glaciation and deglaciation periods are high amplitude positive and negative δ13C excursions which have been observed in the rock records, suggesting perturbations in oceanic biogeochemical cycles. However, whether these isotopic records reflect primary depositional signature and open ocean condition are debated. The Marwar Basin of the Indian Shield preserves one such record of the Ediacaran time period. The magnitude of negative δ13C excursion from the Marwar Basin is comparable with the Shuram excursion.1 We report elemental compositions of sixty-eight carbonate samples collected from five spatially distributed sections from the Bilara carbonates of the Marwar Basin. Selected samples from two of these sections were analyzed for their radiogenic Sr (87Sr/86Sr) and stable Ca (δ44/40Ca) isotopic compositions. Elemental compositions were measured using an Inductively Coupled Plasma Mass Spectrometer (ICP-MS, X series II) while 87Sr/86Sr and δ44/40Ca (reported relative to NIST SRM 915a) values were measured using a Thermal Ionization Mass Spectrometer (TIMS, Triton Plus), both at the CEaS, IISc Bangalore, India. The Bilara Group carbonates are sub-divided into two populations based on non-redox REY anomalies and the Y/Ho ratio. Super-Chondritic Y/Ho (40-52) and positive La anomaly (1.01-2.65) of some samples suggest deposition under open ocean conditions and connectivity to the global ocean. While heterogeneity in δ13C values is evident in samples with low Y/Ho (<40), samples with high Y/Ho (> 40) preserve low δ13C values. No significant correlation has been observed between δ13C and Ce anomaly (Ce/Ce*) suggesting absence of any paleo-redox gradient. The lowest 87Sr/86Sr (~0.7079) observed in the carbonates is comparable with Ediacaran seawater confirming retention of primary depositional signatures in these samples. These carbonates show heavy δ44/40CaSRM915a compositions (1.44‰-2.21‰) typical of Neoproterozoic post-glacial successions2. Our study confirms primary origin and open ocean nature of the late Neoproterozoic δ13C excursions in the Marwar Basin. [1] Ansari et al., (2018) Precambrian Research, [2] Silva-Tamayo et al., (2010) Precambrian Research

Flash droughts are characterized by an abrupt onset and swift intensification. Global surface soil moisture (θRS) from NASA’s Soil Moisture Active Passive (SMAP) satellite can facilitate a near-real-time assessment of emerging flash droughts at 36-km footprint. However, a robust flash drought monitoring using θRS must account for the i) short observation record of SMAP, ii) non-linear geophysical controls over θRS dynamics, and, iii) emergent meteorological drivers of flash droughts. We propose a new method for near-real-time characterization of droughts using Soil Moisture Stress (SMS, drought stress) and Relative Rate of Drydown (RRD, drought stress intensification rate) ─ developed using SMAP θRS (March 2015-2019) and footprint-scale seasonal soil water retention parameters and land-atmospheric coupling strength. SMS and RRD are nonlinearly combined to develop Flash Drought Stress Index (FDSI) to characterize emerging flash droughts (FDSI ≥ 0.71 for moderate to high RRD and SMS). Globally, FDSI shows high correlation with concurrent meteorological anomalies. A retrospective evaluation of select droughts is demonstrated using FDSI, including a mechanistic evaluation of the 2017 flash drought in the Northern Great Plains. About 5.2% of earth’s landmass experienced flash droughts of varying intensity and duration during 2015-2019 (FDSI ≥ 0.71 for >30 consecutive days), majorly in global drylands. FDSI shows high skill in forecasting vegetation health with a lead of 0-2 weeks, with exceptions in irrigated croplands and mixed forests. With readily available parameters, low data latency, and no dependence on model simulations, we provide a robust tool for global near-real-time flash drought monitoring using SMAP.

The subseasonal Madden-Julian oscillation (MJO) is among the most important modes of tropical variability on the planet and is a dominant driver of subseasonal-to-seasonal (S2S) prediction globally. The past decade has seen substantial advances in MJO prediction using dynamical forecast models, which now routinely outperform traditional statistical MJO forecasts (e.g. multiple linear regression models). At the same time, an increasing body of literature has demonstrated that machine-learning methods represent a new frontier in Earth science, opening the door to more advanced statistical forecast models of the MJO. In this study, we explore whether state-of-the-art machine learning methods can be used to make real-time MJO forecasts that outperform traditional statistical models and do comparably well to dynamical models. In particular, we utilize neural networks trained on observational tropical fields to attempt to make skillful forecasts of MJO convection out to several weeks lead time. Through contrasting the machine-learning models’ behavior with simpler statistical models and dynamical forecast models, we explore the advantages and disadvantages of statistical versus dynamical forecasts. A novel aspect of our analysis is the use of cutting-edge techniques to allow us to visualize how our neural network models makes their predictions. These techniques, such as layer-wise relevance propagation, can lead to new insights into regions of MJO predictability, allowing us to better interpret sources of MJO prediction skill within the machine-learning model. We further diagnose whether our machine-learning models contain well-known aspects of MJO prediction found in dynamical models, such as an increase in prediction skill during boreal winter or during certain phases of the stratospheric quasi-biennial oscillation.

The relationship between multiple hydroclimatic variables and vegetation conditions in the upper Madeira Basin (southwestern Amazon) has been analyzed. Vegetative dynamics are characterized using NDVI dataset as an indicator of the photosynthetic capacities of vegetation. Hydroclimatic variability is analyzed using satellite-based precipitation datasets, observed river discharge and satellite measurements of terrestrial water storage (TWS). Our results show that the vegetation in the Basin varies from energy- to water-limited. During the peak of the wet season (January-February), rainfall, discharge and TWS are negatively correlated with NDVI (r=-0.48 to -0.65), suggesting that during this period the vegetation is mainly energy-dependent. Outside this period, these correlations are positive (r=0.55 to 0.9), suggesting that vegetation depends mainly on water availability. This higher water dependence is more noticeable during the vegetation dry season (VDS; June-October). Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests remain energy-limited during the beginning of the VDS, but they become water-dependent almost at the end. Savannas show a different behaviour, where water dependence occurs months before the onset of the VDS. On the other hand, unlike the other variables, the TWS better explains the NDVI in evergreen forests during the VDS (r=0.7 to 0.85). This is probably because evergreen forests are more dependent on deep soil water. A spatial analysis between hydroclimatic variables and the NDVI shows the predominance of positive correlations in most of the basin. However, specific areas do not show significant correlations. The weak relationship in these areas is explained by two factors i) very wet conditions during most of the year in the “rainfall hotspot” regions, where the vegetation is not water-limited, and ii) recent land-use changes (deforestation) that break the natural response in the hydroclimate-vegetation system. These findings provide new evidence on the impacts of the land cover changes on the natural relationship between vegetation and hydroclimatic variability, which is particularly relevant given the increasing rates of deforestation in this region during the recent years.

Remarkably, we know more about the radiation environment onboard the International Space Station than we do about radiation values at altitudes between 30-40 km in the middle stratosphere. Within this work, we provide data about the radiation dose measured during two consecutive balloon flights flown within a 4-month timeframe over New Mexico and Antarctica Data were measured with the M-42 radiation detector. On each flight, the M-42 was installed as part of a larger research payload: MARSBOx (New Mexico, 23 September 2019); and E-MIST (Antarctica, 15 December 2019-12 January 2020). The temporal proximity of the flights provided similar prevailing space weather conditions and solar activity (minimal during each mission). Against that common backdrop, the main differences between flights, including mission duration and geomagnetic shielding could be readily compared. Near identical space weather conditions provided a window of opportunity for studying the influence of altitude and geomagnetic shielding on dose and fluence rate of galactic cosmic radiation under maximum intensity conditions. Herein, we report relevant count- and dose rates for the missions, alongside Geant4 Monte Carlo calculations; this included crossings of the Regener maximum during the ascent and descent flights over New Mexico and the absence of a distinct maximum in dose rates at zero geomagnetic shielding for the polar flight. While dose rates in silicon at float altitudes (≈35 km-39 km) were a maximum of 2.5 +/- 0.4 microGy/h over New Mexico, we reached values of up to 8.4 +/- 0.3 microGy/h over Antarctica, thereby approaching dose rates similar to the surface of Mars.

Australasian (AA) tektites are distal ejecta of a cosmic impact into terrestrial sediments 788.1 ± 2.8 ka. Protracted explorations within the strewn field, as preferred by consensus opinion, have yielded neither an astrobleme nor a proximal imprint. In 3 lesser strewn fields correlated with progenitor astroblemes, tektites are strewn asymmetrically and their total masses and minimum loft distances scale with projectile kinetic energy (KE) partitioning yield. Pursuing an a priori astrobleme location within the uniquely expansive AA strewn field ignores such findings. Absent identification of proximal ejecta in the strewn field, workers are now inferring that indochinite tektites are proximal, dismissing their known devolatilization, weightless vacuum quench and their carefully derived re-entry speeds, ≥ 80% of Earth escape. A defendable guess 40 years ago, but promoting an a priori astrobleme in Indochina is now impeding progress. Ironically, a cosmic link to the Carolina bays’ genesis is considered soundly falsified by the same absence of a correlated astrobleme. We have measured ~50,000 of these shallow, oriented, ovoid basins, located around an annulus focused on Saginaw Bay, Michigan. We posit the ovoid planforms to be surficial manifestations of cavitation voids within an incomprehensible geophysical mass flow of volatiles and entrained target clastics. Unifying both missing astroblemes, we propose an incomprehensible cosmic event on a hemisphere diametrically opposed to the AA distal tektite strewn field. We invoke a highly oblique, perhaps tangential, hypervelocity projectile ricocheting off the Earth’s limb along an extended footprint. Sub-horizontal shock to thick MIS 20 ice sheet overburden triggered endogenic comminution, as stored pressure potential within the substrate was released by phase change of pore water to steam, provisioning fluidized medial ejecta outflow for Carolina bay emplacement. Shocked ice plume expansion augmented tektite velocities, and dissipated significant partitioned KE, preventing another Chicxulub-style global conflagration. The KE partitioning process conspired with intervening ice age transgressions to dislocate proximal ejecta and obfuscate the cosmic signature. AA tektite Suborbital Analysis with appropriate dynamical accounting supports a putative antipodal Saginaw impact site, as does a recent EIGEN 6C4 gravity field assessment. The hypothesis would be falsified if 26Al/10Be burial dating of terraces under Carolina bays disallows bay deposition circa 788 ka.

A document by Qian Wang. Click on the document to view its contents.

Phyllosilicate minerals, due to their sheets structure and morphology, are known to cause anisotropy in bulk rock properties and make the bulk rock more compliant. Accurately characterizing the micromechanical behavior of phyllosilicate minerals from laboratory observations, which eventually translates to the bulk rock behavior, is still challenging due to their fine-grained nature. Recent advances in atomistic simulations open the possibility of theoretically investigating such mineral mechanical behavior. We compare the elastic properties of biotites recovered by spherical nanoindentation with those predicted from density functional theory (DFT) simulations to investigate to what extent theoretical predictions reproduce actual phyllosilicate properties. Spherical nanoindentation was conducted using schist rocks from Poorman Formation, South Dakota, USA, to recover continuous indentation stress-strain curves. Loading in the layer-normal orientation shows an average indentation modulus (M) of about 35 GPa, while loading in the layer-parallel orientation gives a higher average of about 95 GPa. To facilitate comparison, the elastic stiffness constants (cij) determined from DFT were converted to indentation modulus (M) using solutions proposed in this study. The majority of the nanoindentation modulus results are below the values inferred from the simulation results representing ideal defect-free minerals. We suggest that crystal defects present at the nano-scale, potentially ripplocations, are the dominant cause of the lower indentation modulus recovered from nanoindentation compared to those inferred from DFT simulations. Results highlight the importance of acknowledging the defects that exist down to the nano-scale as it modifies the mechanical properties of phyllosilicates compared to its pure defect-free form.

We use online data assimilation to combine information from a linear inverse model of coupled atmosphere-ocean dynamics with proxy records to create a new annual-resolution reconstruction of atmosphere and ocean fields over the last millennium. Instrumental validation of reconstructed sea-surface temperature and 0-700 m ocean heat content shows broad regions of positive spatial correlations, and high correlations (~0.6-0.9) for global averages and indices of large-scale modes of atmospheric variability. Compared to previous reconstructions, the online reconstructions show global and hemispheric averages with little-to-no millennial-scale trend and global-mean temperatures ~0.25-0.5 K cooler during early periods (1000-1400 C.E.). The spatial anomaly differences of average temperature between an early (1000-1250 C.E.) and later (1400-1700 C.E.) period show warm anomalies over high-latitude Europe and cool tropical conditions in partial agreement with previous assessments. The addition of online data assimilation, which provides dynamical memory to climate proxy information, is shown to be crucial for adequately characterizing decadal-to-centennial-scale variability of 0–700 m ocean heat content. Furthermore, the climate forecasts provide model-based physical constraints for atmosphere-ocean interaction, which become increasingly important during early periods when less proxy information is available for assimilation.

This research investigated the potential use and application of radar, satellite, and other tracking data for sea ice and weather conditions in maritime-related Search and Research (SAR) operations in the Arctic. Specifically, this study analyzed a SAR event for a missing small vessel due into Utqiaġvik (formerly, Barrow), Alaska in July 2017 as well as archival records of U.S. Coast Guard SAR incidents in Arctic Alaska between 1976 and present. This study feeds into the Arctic Domain Awareness Center (ADAC) funded project - Developing sea ice and weather forecasting tools to improve situational awareness and crisis response in the Arctic - which seeks to create a prototype early warning sea ice and weather forecasting module for hazard planning in Utqiaġvik. This research found that data availability and accessibility, particularly in low bandwidth and further offshore areas, are challenges to data uptake during a SAR event. Nonetheless, the specific SAR incident in Utqiaġvik helped to illuminate there is a breadth of tools that can be applied and used in a SAR context - traditional and knowledge, modeling data, and USCG operational data. Specifically, modeling data from tools developed by the Arctic Domain Awareness Center (ADAC), the University of Alaska Fairbanks (UAF) and other research institutions was generated during this event to help support the local SAR effort. However, a level of pre- or post-processing was necessary in many cases, which can be a challenge for when data is needed immediately. This research holds implications for future use and uptake of modeling data in local SAR operations in Arctic Alaska and potentially the Arctic overall. Given that local SAR operators are predominantly the first line of response to maritime emergencies in Arctic Alaska, the ability to share and provide a set of resources to support SAR operators can be beneficial, particularly in a rapidly changing Arctic. A more targeted and systematic way to utilize and draw upon scientific research for SAR operations can potentially support the local SAR community, especially when immediate information is necessary. In particular, leveraging different products to validate, interpolate, and extrapolate information against one another, can help create more comprehensive situational awareness, especially for further offshore SAR events.

The San Gabriel, Chino, and San Bernardino sedimentary basins in Southern California amplify earthquake ground motions and prolong the duration of shaking due to the basins’ shape and low seismic velocities. In the event of a major earthquake rupture along the southern segment of the San Andreas fault, their connection and physical proximity to Los Angeles can produce a waveguide effect and amplify strong ground motions. Improved estimates of the shape and depth of the sediment-basement interface are needed for more accurate ground-shaking models. We obtain a three-dimensional basement map of the basins by integrating gravity and seismic measurements. The travel time of the sediment-basement P-to-s conversion, and the Bouguer gravity along 10 seismic lines, are combined to produce a linear relationship that is used to extend the 2D models to a 3D basin map. Basement depth is calculated using the predicted travel time constrained by gravity with an S-wave velocity model of the area. The model is further constrained by the basement depths from 17 boreholes. The basement map shows the south-central part of the San Gabriel basin is the deepest part and a significant gravity signature is associated with our interpretation of the Raymond fault. The Chino basin deepens towards the south and shallows northeastward. The San Bernardino basin, bounded by the San Jacinto fault (SJF) and San Andreas fault zone, deepens along the edge of the SJF. In addition, we demonstrate the benefit of using gravity data to aid in the interpretation of the sediment-basement interface in receiver functions.